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The aim of this study is to examine the correlation between reading culture and the mathematical literacy self-efficacy among pre-service teachers and assesses these variables in relation to several factors. Utilizing convenience sampling, the research sample consists of 562 pre-service teachers studying in mathematics and primary school teaching programs in seven different universities. Data was gathered using the Reading Culture and Mathematical Literacy Self-Efficacy Scales, along with a personal information form. Both descriptive and inferential statistical methods were employed to analyze the data. Results revealed a significant but low-level correlation between reading culture, its sub-dimensions, and mathematical literacy self-efficacy levels among pre-service teachers. Notably, pre-service teachers engaging with scientific books demonstrated higher mathematical literacy self-efficacy than those preferring literary books. In addition, pre-service teachers' scores of mathematical literacy self-efficacy and reading culture are not independent of the department they study in. Reading culture, gender, and department were significant predictors of mathematical literacy. [This paper was published in the "International Journal of Progressive Education" v20 n4 p1-30 2024.]

Modern quantum machine learning (QML) methods involve the variational optimization of parameterized quantum circuits on training datasets, followed by predictions on testing datasets. Most state-of-the-art QML algorithms currently lack practical advantages due to their limited learning capabilities, especially in few-shot learning tasks. In this work, we propose three new frameworks employing quantum diffusion model (QDM) as a solution for the few-shot learning: label-guided generation inference (LGGI); label-guided denoising inference (LGDI); and label-guided noise addition inference (LGNAI). Experimental results demonstrate that our proposed algorithms significantly outperform existing methods., Comment: 10 pages

We study offline off-dynamics reinforcement learning (RL) to utilize data from an easily accessible source domain to enhance policy learning in a target domain with limited data. Our approach centers on return-conditioned supervised learning (RCSL), particularly focusing on the decision transformer (DT), which can predict actions conditioned on desired return guidance and complete trajectory history. Previous works tackle the dynamics shift problem by augmenting the reward in the trajectory from the source domain to match the optimal trajectory in the target domain. However, this strategy can not be directly applicable in RCSL owing to (1) the unique form of the RCSL policy class, which explicitly depends on the return, and (2) the absence of a straightforward representation of the optimal trajectory distribution. We propose the Return Augmented Decision Transformer (RADT) method, where we augment the return in the source domain by aligning its distribution with that in the target domain. We provide the theoretical analysis demonstrating that the RCSL policy learned from RADT achieves the same level of suboptimality as would be obtained without a dynamics shift. We introduce two practical implementations RADT-DARA and RADT-MV respectively. Extensive experiments conducted on D4RL datasets reveal that our methods generally outperform dynamic programming based methods in off-dynamics RL scenarios., Comment: 26 pages, 10 tables, 10 figures

In this paper, we first show that current learning-based video codecs, specifically the SSF codec, are not suitable for real-world applications due to the mismatch between the encoder and decoder caused by floating-point round-off errors. To address this issue, we propose the static quantization of the hyper prior decoding path. The quantization parameters are determined through an exhaustive search of all possible combinations of observers and quantization schemes from PyTorch. For the SSF codec, when encoding and decoding on different machines, the proposed solution effectively mitigates the mismatch issue and enhances compression efficiency results by preventing severe image quality degradation. When encoding and decoding are performed on the same machine, it constrains the average BD-rate increase to 9.93% and 9.02% for UVG and HEVC-B sequences, respectively., Comment: 4 pages, submitted to ISCAS2025

We characterize the compactness of embedding derivatives from Hardy space $H^p$ into Lebesgue space $L^q(\mu)$. We also completely characterize the boundedness and compactness of derivative area operators from $H^p$ into $L^q(\mathbb{S}_n)$, $0

While Swarm User Interfaces (SUIs) have succeeded in enriching tangible interaction experiences, their limitations in autonomous action planning have hindered the potential for personalized and dynamic interaction generation in tabletop games. Based on the AI-Gadget Kit we developed, this paper explores how to integrate LLM-driven agents within tabletop games to enable SUIs to execute complex interaction tasks. After defining the design space of this kit, we elucidate the method for designing agents that can extend the meta-actions of SUIs to complex motion planning. Furthermore, we introduce an add-on prompt method that simplifies the design process for four interaction behaviors and four interaction relationships in tabletop games. Lastly, we present several application scenarios that illustrate the potential of AI-Gadget Kit to construct personalized interaction in SUI tabletop games. We expect to use our work as a case study to inspire research on multi-agent-driven SUI for other scenarios with complex interaction tasks.

Chatbots have been an interesting application of natural language generation since its inception. With novel transformer based Generative AI methods, building chatbots have become trivial. Chatbots which are targeted at specific domains for example medicine and psychology are implemented rapidly. This however, should not distract from the need to evaluate the chatbot responses. Especially because the natural language generation community does not entirely agree upon how to effectively evaluate such applications. With this work we discuss the issue further with the increasingly popular LLM based evaluations and how they correlate with human evaluations. Additionally, we introduce a comprehensive factored evaluation mechanism that can be utilized in conjunction with both human and LLM-based evaluations. We present the results of an experimental evaluation conducted using this scheme in one of our chatbot implementations which consumed educational reports, and subsequently compare automated, traditional human evaluation, factored human evaluation, and factored LLM evaluation. Results show that factor based evaluation produces better insights on which aspects need to be improved in LLM applications and further strengthens the argument to use human evaluation in critical spaces where main functionality is not direct retrieval., Comment: Accepted in The First Workshop on Large Language Models for Evaluation in Information Retrieval

An advantage of Large Language Models (LLMs) is their contextualization capability - providing different responses based on student inputs like solution strategy or prior discussion, to potentially better engage students than standard feedback. We present a design and evaluation of a proof-of-concept LLM application to offer students dynamic and contextualized feedback. Specifically, we augment an Online Programming Exercise bot for a college-level Cloud Computing course with ChatGPT, which offers students contextualized reflection triggers during a collaborative query optimization task in database design. We demonstrate that LLMs can be used to generate highly situated reflection triggers that incorporate details of the collaborative discussion happening in context. We discuss in depth the exploration of the design space of the triggers and their correspondence with the learning objectives as well as the impact on student learning in a pilot study with 34 students.

This paper introduces a new method of paper circuit fabrication that overcomes design barriers and increases flexibility in circuit design. Conventional circuit boards rely on thin traces, which limits the complexity and accuracy when applied to paper circuits. To address this issue, we propose a method that uses large conductive zones in paper circuits and performs subtractive processing during their fabrication. This approach eliminates design barriers and allows for more flexibility in circuit design. We introduce PaperCAD, a software tool that simplifies the design process by converting traditional circuit design to paper circuit design. We demonstrate our technique by creating two paper circuit boards. Our approach has the potential to promote the development of new applications for paper circuits., Comment: ACM CHI 2023 Workshop 02: Beyond Prototyping Boards: Future Paradigms for Electronics Toolkits

This paper explores the implementation of embedded magnets to enhance paper-based interactions. The integration of magnets in paper-based interactions simplifies the fabrication process, making it more accessible for building soft robotics systems. We discuss various interaction patterns achievable through this approach and highlight their potential applications., Comment: ACM DIS 2023 Workshop 02: Soft Robotics and Programmable Materials for Human-Computer Interaction

Despite the success of Quantum Neural Networks (QNNs) in decision-making systems, their fairness remains unexplored, as the focus primarily lies on accuracy. This work conducts a design space exploration, unveiling QNN unfairness, and highlighting the significant influence of QNN deployment and quantum noise on accuracy and fairness. To effectively navigate the vast QNN deployment design space, we propose JustQ, a framework for deploying fair and accurate QNNs on NISQ computers. It includes a complete NISQ error model, reinforcement learning-based deployment, and a flexible optimization objective incorporating both fairness and accuracy. Experimental results show JustQ outperforms previous methods, achieving superior accuracy and fairness. This work pioneers fair QNN design on NISQ computers, paving the way for future investigations.

We completely characterize $L^p-L^q$ boundedness of two classes of Forelli-Rudin type operators on the unit ball of $\mathbb{C}^n$ for all $(p, q)\in [1, \infty]\times [1, \infty]$. The results are not only a complement to some previous results on Forelli-Rudin type operators by Kures and Zhu in 2006 and the first author in 2015, but also a high dimension extension of some results by Cheng, Fang, Wang and Yu in 2017., Comment: 20 pages; 1 figure

Efficient and accurate product relevance assessment is critical for user experiences and business success. Training a proficient relevance assessment model requires high-quality query-product pairs, often obtained through negative sampling strategies. Unfortunately, current methods introduce pooling bias by mistakenly sampling false negatives, diminishing performance and business impact. To address this, we present Bias-mitigating Hard Negative Sampling (BHNS), a novel negative sampling strategy tailored to identify and adjust for false negatives, building upon our original False Negative Estimation algorithm. Our experiments in the Instacart search setting confirm BHNS as effective for practical e-commerce use. Furthermore, comparative analyses on public dataset showcase its domain-agnostic potential for diverse applications., Comment: Submitted to WWW'24 Industry Track

The carbon footprint associated with large language models (LLMs) is a significant concern, encompassing emissions from their training, inference, experimentation, and storage processes, including operational and embodied carbon emissions. An essential aspect is accurately estimating the carbon impact of emerging LLMs even before their training, which heavily relies on GPU usage. Existing studies have reported the carbon footprint of LLM training, but only one tool, mlco2, can predict the carbon footprint of new neural networks prior to physical training. However, mlco2 has several serious limitations. It cannot extend its estimation to dense or mixture-of-experts (MoE) LLMs, disregards critical architectural parameters, focuses solely on GPUs, and cannot model embodied carbon footprints. Addressing these gaps, we introduce \textit{\carb}, an end-to-end carbon footprint projection model designed for both dense and MoE LLMs. Compared to mlco2, \carb~significantly enhances the accuracy of carbon footprint estimations for various LLMs. The source code is released at \url{https://github.com/SotaroKaneda/MLCarbon}., Comment: 15 pages, 8 figures

Objective: COVID-19 has spread worldwide and made a huge influence across the world. Modeling the infectious spread situation of COVID-19 is essential to understand the current condition and to formulate intervention measurements. Epidemiological equations based on the SEIR model simulate disease development. The traditional parameter estimation method to solve SEIR equations could not precisely fit real-world data due to different situations, such as social distancing policies and intervention strategies. Additionally, learning-based models achieve outstanding fitting performance, but cannot visualize mechanisms. Methods: Thus, we propose a deep dynamic epidemiological (DDE) method that combines epidemiological equations and deep-learning advantages to obtain high accuracy and visualization. The DDE contains deep networks to fit the effect function to simulate the ever-changing situations based on the neural ODE method in solving variants' equations, ensuring the fitting performance of multi-level areas. Results: We introduce four SEIR variants to fit different situations in different countries and regions. We compare our DDE method with traditional parameter estimation methods (Nelder-Mead, BFGS, Powell, Truncated Newton Conjugate-Gradient, Neural ODE) in fitting the real-world data in the cases of countries (the USA, Columbia, South Africa) and regions (Wuhan in China, Piedmont in Italy). Our DDE method achieves the best Mean Square Error and Pearson coefficient in all five areas. Further, compared with the state-of-art learning-based approaches, the DDE outperforms all techniques, including LSTM, RNN, GRU, Random Forest, Extremely Random Trees, and Decision Tree. Conclusion: DDE presents outstanding predictive ability and visualized display of the changes in infection rates in different regions and countries.

Bag manipulation through robots is complex and challenging due to the deformability of the bag. Based on dynamic manipulation strategy, we propose a new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a perception module to identify the key region of the plastic bag from arbitrary initial configurations. According to the segmentation, ShakingBot iteratively executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking, and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can effectively open the bag without the need to account for the crumpled configuration.Then, we insert the items and lift the bag for transport. We perform our method on a dual-arm robot and achieve a success rate of 21/33 for inserting at least one item across various initial bag configurations. In this work, we demonstrate the performance of dynamic shaking actions compared to the quasi-static manipulation in the bagging task. We also show that our method generalizes to variations despite the bag's size, pattern, and color., Comment: Manipulating bag through robots to bagging

Fabric folding through robots is complex and challenging due to the deformability of fabric. Based on deconstruction strategy, we split the complex fabric folding task into three relatively simple sub-tasks, and propose a Deconstructed Fabric Folding Network (DeFNet), including corresponding three modules to solve them. (1) We use the Folding Planning Module (FPM), which is based on Latent Space Roadmap, to infer the most straight folding intermediate states from the start to the goal in latent space. (2) We utilize the flow-based approach, Folding Action Module (FAM), to calculate the action coordinates and execute them to reach the inferred intermediate state. (3) We introduce an Iterative Interactive Module (IIM) for fabric folding tasks, which can iteratively execute the FPM and FAM after every grasp-and-place action until the fabric reaches the goal. Experimentally, We demonstrated our method on multi-step fabric folding tasks against three baselines in simulation. We also apply the method to an existing robotic system and present its performance., Comment: 8 pages

This study linked ninth-grade student background data and school-reported high school transcript data from the national High School Longitudinal Study of 2009 (HSLS:09) to student item responses on the 2013 National Assessment of Educational Progress (NAEP) mathematics assessment to examine the relationships between high school coursework and end-of-high school mathematics achievement. In a series of marginal maximum likelihood regression analyses, we find that STEM course GPAs, credits earned in AP/IB math and science courses, higher levels of math course content, and course-taking in chemistry and physics are all positively associated with NAEP math achievement. These relationships persist even when gender, race/ethnicity, early grade 9 mathematics achievement, and socioeconomic status are included as covariates. Cluster analysis of students with high estimated achievement suggest multiple paths to high mathematics achievement for students with both high- and low-socioeconomic status backgrounds.