Mining Classroom Observation Data for Understanding Teachers’ Technological Pedagogical Content Knowledge Structure

On the basis of teachers’ pedagogical content knowledge proposed by Shulman, Koehler and Mishra explicitly put forward technological pedagogical content knowledge (TPACK) framework. The study shows that TPACK is a necessary knowledge for teachers to use technology for carrying effective teaching (Koehler & Mishra, 2005). It has been found that technological pedagogical knowledge (TPK) has a significant influence on TPACK structure of pre-service teachers (Zhang, 2015). This paper mainly explores the teaching structure of classroom and the TPK structure presented by teachers. Based on the existing video analysis and coding system, this study adapted and revised a curriculum teaching code table. Methods of quantitative and qualitative combination and comparative analysis are used to explore four aspects: teaching links, students’ expected cognitive level, teaching media and TPK. This study uses the classroom video analysis method to make a comparative analysis of short teaching video of award-winninged teachers and non award-winninged teachers in a competition and explores the influence of teaching activities and TPK structure of teachers on teaching effect. The statistical analysis of the results showed that the teaching link, the teaching media, and the student’s expected cognitive level have no significant effect on the teaching effect, and TPK has a significant impact on the teaching effect

GPT4Battery: An LLM-driven Framework for Adaptive State of Health Estimation of Raw Li-ion Batteries

State of health (SOH) is a crucial indicator for assessing the degradation level of batteries that cannot be measured directly but requires estimation. Accurate SOH estimation enhances detection, control, and feedback for Li-ion batteries, allowing for safe and efficient energy management and guiding the development of new-generation batteries. Despite the significant progress in data-driven SOH estimation, the time and resource-consuming degradation experiments for generating lifelong training data pose a challenge in establishing one large model capable of handling diverse types of Li-ion batteries, e.g., cross-chemistry, cross-manufacturer, and cross-capacity. Hence, this paper utilizes the strong generalization capability of large language model (LLM) to proposes a novel framework for adaptable SOH estimation across diverse batteries. To match the real scenario where unlabeled data sequentially arrives in use with distribution shifts, the proposed model is modified by a test-time training technique to ensure estimation accuracy even at the battery's end of life. The validation results demonstrate that the proposed framework achieves state-of-the-art accuracy on four widely recognized datasets collected from 62 batteries. Furthermore, we analyze the theoretical challenges of cross-battery estimation and provide a quantitative explanation of the effectiveness of our method

Optimal and parameter-free gradient minimization methods for convex and nonconvex optimization

We propose novel optimal and parameter-free algorithms for computing an approximate solution with small (projected) gradient norm. Specifically, for computing an approximate solution such that the norm of its (projected) gradient does not exceed $\varepsilon$, we obtain the following results: a) for the convex case, the total number of gradient evaluations is bounded by $O(1)\sqrt{L\|x_0 - x^*\|/\varepsilon}$, where $L$ is the Lipschitz constant of the gradient and $x^*$ is any optimal solution; b) for the strongly convex case, the total number of gradient evaluations is bounded by $O(1)\sqrt{L/\mu}\log(\|\nabla f(x_0)\|/\epsilon)$, where $\mu$ is the strong convexity modulus; and c) for the nonconvex case, the total number of gradient evaluations is bounded by $O(1)\sqrt{Ll}(f(x_0) - f(x^*))/\varepsilon^2$, where $l$ is the lower curvature constant. Our complexity results match the lower complexity bounds of the convex and strongly cases, and achieve the above best-known complexity bound for the nonconvex case for the first time in the literature. Moreover, for all the convex, strongly convex, and nonconvex cases, we propose parameter-free algorithms that do not require the input of any problem parameters. To the best of our knowledge, there do not exist such parameter-free methods before especially for the strongly convex and nonconvex cases. Since most regularity conditions (e.g., strong convexity and lower curvature) are imposed over a global scope, the corresponding problem parameters are notoriously difficult to estimate. However, gradient norm minimization equips us with a convenient tool to monitor the progress of algorithms and thus the ability to estimate such parameters in-situ

Evolving Fault Tolerant Robotic Controllers

Fault tolerant control and evolutionary algorithms are two different research areas. However with the development of artificial intelligence, evolutionary algorithms have demonstrated competitive performance compared to traditional approaches for the optimisation task. For this reason, the combination of fault tolerant control and evolutionary algorithms has become a new research topic with the evolving of controllers so as to achieve different fault tolerant control schemes. However most of the controller evolution tasks are based on the optimisation of controller parameters so as to achieve the fault tolerant control, so structure optimisation based evolutionary algorithm approaches have not been investigated as the same level as parameter optimisation approaches. For this reason, this thesis investigates whether structure optimisation based evolutionary algorithm approaches could be implemented into a robot sensor fault tolerant control scheme based on the phototaxis task in addition to just parameter optimisation, and explores whether controller structure optimisation could demonstrate potential benefit in a greater degree than just controller parameter optimisation. This thesis presents a new multi-objective optimisation algorithm in the structure optimisation level called Multi-objective Cartesian Genetic Programming, which is created based on Cartesian Genetic Programming and Non-dominated Sorting Genetic Algorithm 2, in terms of NeuroEvolution based robotic controller optimisation. In order to solve two main problems during the algorithm development, this thesis investigates the benefit of genetic redundancy as well as preserving neutral genetic drift in order to solve the random neighbour pick problem during crowding fill for survival selection and investigates how hyper-volume indicator is employed to measure the multi-objective optimisation algorithm performance in order to assess the convergence for Multi-objective Cartesian Genetic Programming. Furthermore, this thesis compares Multi-objective Cartesian Genetic Programming with Non-dominated Sorting Genetic Algorithm 2 for their evolution performance and investigates how Multi-objective Cartesian Genetic Programming could be performing for a more difficult fault tolerant control scenario besides the basic one, which further demonstrates the benefit of utilising structure optimisation based evolutionary algorithm approach for robotic fault tolerant control

New Insights into the Charge-Transfer-to-Solvent Spectrum of Aqueous Iodide: Surface versus Bulk.

Liquid phase charge-transfer-to-solvent (CTTS) transitions are important, as they serve as photochemical routes to solvated electrons. In this work, broadband deep-ultraviolet electronic sum frequency generation (DUV-ESFG) and two-photon absorption (2PA) spectroscopic techniques were used to assign and compare the nature of the aqueous iodide CTTS excitations at the air/water interface and in bulk solution. In the one-photon absorption (1PA) spectrum, excitation to the 6s Rydberg-like orbital (5p → 6s) gives rise to a pair of spin-orbit split iodine states, 2P3/2 and 2P1/2. In the 2PA spectra, the lower-energy 2P3/2 peak is absent and the observed 2PA peak, which is ∼0.14 eV blue-shifted relative to the upper 2P1/2 CTTS peak seen in 1PA, arises from 5p → 6p electronic promotion. The band observed in the ESFG spectrum is attributed to mixing of excited states involving 5p → 6p and 5p → 6s promotions caused by both vibronic coupling and the external electric field generated by asymmetric interfacial solvation

Molecular characterization and function analysis of the vitellogenin receptor from the cotton bollworm, Helicoverpa armigera (Hubner) (Lepidoptera, Noctuidae)

Developing oocytes accumulate plentiful yolk protein during oogenesis through receptor-mediated endocytosis. The vitellogenin receptor (VgR), belonging to the low-density lipoprotein receptor (LDLR) family, regulates the absorption of yolk protein. In this work, the full-length vitellogenin receptor (HaVgR) in the cotton bollworm Helicoverpa armigera was identified, encoding a 1817 residue protein. Sequence alignment revealed that the sequence of HaVgR contained all of the conservative structural motifs of LDLR family members, and phylogenetic analysis indicated that HaVgR had a high identity among Lepidoptera and was distinct from that of other insects. Consistent with other insects, HaVgR was specifically expressed in ovarian tissue. The developmental expression pattern showed that HaVgR was first transcribed in the newly metamorphosed female adults, reached a peak in 2-day-old adults and then declined. Western blot analysis also revealed an ovarian-specific and developing expression pattern, which was consistent with the HaVgR mRNA transcription. Moreover, RNAi-mediated HaVgR knockdown strongly reduced the VgR expression in both the mRNA and protein levels, which inhibited the yolk protein deposition in the ovaries, led to the dramatic accumulation of vitellogenin and the up-regulation of HaVg expression in hemolymph, and eventually resulted in a declined fecundity. Together, all of these findings demonstrate that HaVgR is a specific receptor in uptake and transportation of yolk protein for the maturation of oocytes and that it plays a critical role in female reproduction

Federated Unlearning via Active Forgetting

The increasing concerns regarding the privacy of machine learning models have catalyzed the exploration of machine unlearning, i.e., a process that removes the influence of training data on machine learning models. This concern also arises in the realm of federated learning, prompting researchers to address the federated unlearning problem. However, federated unlearning remains challenging. Existing unlearning methods can be broadly categorized into two approaches, i.e., exact unlearning and approximate unlearning. Firstly, implementing exact unlearning, which typically relies on the partition-aggregation framework, in a distributed manner does not improve time efficiency theoretically. Secondly, existing federated (approximate) unlearning methods suffer from imprecise data influence estimation, significant computational burden, or both. To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings. Our framework differs from existing federated unlearning methods that rely on approximate retraining or data influence estimation. Instead, we leverage new memories to overwrite old ones, imitating the process of \textit{active forgetting} in neurology. Specifically, the model, intended to unlearn, serves as a student model that continuously learns from randomly initiated teacher models. To preserve catastrophic forgetting of non-target data, we utilize elastic weight consolidation to elastically constrain weight change. Extensive experiments on three benchmark datasets demonstrate the efficiency and effectiveness of our proposed method. The result of backdoor attacks demonstrates that our proposed method achieves satisfying completeness