Search

Getting it Right from the Start (GIRFTS) is a research study that aims to implement a response to intervention (RTI) framework in early primary school (Foundation and Grade 1) to improve oral language and reading. RTI is a multi-tiered conceptual framework that supports student learning through delivery of high-quality classroom instruction, early identification of students with additional learning needs and early intervention. This research snapshot highlights some real-life examples from the GIRFTS study and outlines strategies schools can use to increase their readiness for change. School staff (teachers and leaders) and implementation support partners (ISPs) provided examples of strategies that increased school readiness for change during focus groups and interviews conducted 12 to 15 months after the GIRFTS project commenced in their school communities. This snapshot focuses on 3 key strategies that school staff and ISPs reported focusing on to increase readiness during the initial stages of implementation of GIRFTS. They are: (1) ensuring buy-in from the school community and external stakeholders; (2) investing in training programs and support systems; and (3) accepting that change takes time, and that readiness for change is dynamic. [This report was prepared by the Centre for Community Child Health, a research group of Murdoch Children's Research Institute (MCRI) with funding contributions from Eureka Benevolent Fund.]

REST (Representational State Transfer) APIs have become integral for data communication and exchange due to their simplicity, scalability, and compatibility with web standards. However, ensuring REST APIs' reliability through rigorous testing poses significant challenges, given the complexities of operations, parameters, inputs, dependencies, and call sequences. In this paper, we introduce MUCOREST, a novel Reinforcement Learning (RL)-based API testing approach that leverages Q-learning to maximize code coverage and output coverage, thereby improving bug discovery. By focusing on these proximate objectives rather than the abstract goal of maximizing failures, MUCOREST effectively discovers critical code areas and diverse API behaviors. The experimental results on a benchmark of 10 services show that MUCOREST significantly outperforms state-of-the-art API testing approaches by 11.6-261.1% in the number of discovered API bugs. MUCOREST can generate much fewer API calls to discover the same number of bugs compared to the other approaches. Furthermore, 12.17%-64.09% of the bugs discovered by the other techniques can also be found by MUCOREST., Comment: Accepted by KSE'24

End-to-end learning directly maps sensory inputs to actions, creating highly integrated and efficient policies for complex robotics tasks. However, such models are tricky to efficiently train and often struggle to generalize beyond their training scenarios, limiting adaptability to new environments, tasks, and concepts. In this work, we investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies under unseen text instructions and visual distribution shifts. To this end, we design datasets with various levels of data representation richness, refine feature extraction protocols by leveraging multi-modal foundation model encoders, and assess the suitability of different policy network heads. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors, generating spatially aware embeddings that integrate semantic and visual information. These rich features form the basis for training highly robust downstream policies capable of generalizing across platforms, environments, and text-specified tasks. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning on a small simulated dataset successfully generalize to real-world scenes, handling diverse novel goals and command formulations.

Service robots that work alongside humans in a shared environment need a navigation system that takes into account not only physical safety but also social norms for mutual cooperation. In this paper, we introduce a motion planning system that includes human states such as positions and velocities and their personal space for social-aware navigation. The system first extracts human positions from the LiDAR and the RGB-D camera. It then uses the Kalman filter to fuse that information for human state estimation. An asymmetric Gaussian function is then employed to model human personal space based on their states. This model is used as the input to the dynamic window approach algorithm to generate trajectories for the robot. Experiments show that the robot is able to navigate alongside humans in a dynamic environment while respecting their physical and psychological comfort., Comment: In Proceedings of 2024, the 7th International Conference on Control, Robotics and Informatics (ICCRI 2024)

We investigate a magnetic dipolar system influenced by Zeeman splitting, DM interaction, and KSEA exchange interaction, with an initial focus on quantum resource dynamics and a final application in modeling a quantum battery (QB). We analyze the effects of dephasing noise and thermal equilibrium on quantum resources, such as the $l_1$-norm of coherence, quantum discord, and concurrence, by solving the Lindblad master equation and evaluating the Gibbs state. Our findings indicate that increased Zeeman splitting diminishes quantum resources under dephasing and thermal equilibrium conditions. However, when we use the Hamiltonian of this system to realize our QB, Zeeman splitting boosts performance metrics such as ergotropy, instantaneous power, capacity, and quantum coherence during cyclic charging. We observe that the axial parameter improves QB performance, with coherence reaching a saturation point, beyond which ergotropy continues to rise, introducing the concept of incoherent ergotropy and highlighting the need to understand its true origin. Both KSEA interaction and the rhombic parameter consistently enhance quantum resources across the dephasing and thermal equilibrium regimes, and thus improve QB performance. The DM interaction improves QB metrics and shields quantum resources against temperature variations in the Gibbs state but remains insensitive during dephasing dynamics. Our work uncovers complex trends, including ergotropy enhancement without quantum coherence, the preferential role of QB capacity over quantum coherence, and the phenomenon of no-work extraction despite the presence of quantum coherence. These findings facilitate a robust foundation for future research on magnetic dipolar QBs, emphasizing non-unitary charging processes, environmental effects, and practical implementations. We show that the NMR platform could be a promising testbed for simulating such QBs., Comment: 17 pages, 10 figures. All comments are welcome

By introducing a Higgs-Dilaton potential, the 2T model has full triggering for a first order electroweak phase transition, namely for the mass of Dilaton between $300 $ GeV and $550$ GeV. We have also compared the transition strengths in the case with and without daisay loops, the difference being always less than $0.2$. The effective Higgs potential has given a sphaleron energy less than $8.4$ TeV. The value of $\beta/H^*$ is larger than $25$ and less than $34$ in all cases that are sufficient to trigger the first order electroweak phase transition. Gravitational wave energy density caused by this transition, may be sufficiently detected by future detectors, could indirectly confirm Dilaton., Comment: 17 Pages, 10 Figures

This study examines the performance of finite spin quantum batteries (QBs) using Heisenberg spin models with Dzyaloshinsky-Moriya (DM) and Kaplan--Shekhtman--Entin-Wohlman--Aharony (KSEA) interactions. The QBs are modeled as interacting quantum spins in local inhomogeneous magnetic fields, inducing variable Zeeman splitting. We derive analytical expressions for the maximal extractable work, ergotropy and the capacity of QBs, as recently examined by Yang et al. [Phys. Rev. Lett. 131, 030402 (2023)]. These quantities are analytically linked through certain quantum correlations, as posited in the aforementioned study. Different Heisenberg spin chain models exhibit distinct behaviors under varying conditions, emphasizing the importance of model selection for optimizing QB performance. In antiferromagnetic (AFM) systems, maximum ergotropy occurs with a Zeeman splitting field applied to either spin, while ferromagnetic (FM) systems benefit from a uniform Zeeman field. Temperature significantly impacts QB performance, with ergotropy in the AFM case being generally more robust against temperature increases compared to the FM case. Incorporating DM and KSEA couplings can significantly enhance the capacity and ergotropy extraction of QBs. However, there exists a threshold beyond which additional increases in these interactions cause a sharp decline in capacity and ergotropy. This behavior is influenced by temperature and quantum coherence, which signal the occurrence of a sudden phase transition. The resource theory of quantum coherence proposed by Baumgratz et al. [Phys. Rev. Lett. 113, 140401 (2014)] plays a crucial role in enhancing ergotropy and capacity. However, ergotropy is limited by both the system's capacity and the amount of coherence. These findings support the theoretical framework of spin-based QBs and may benefit future research on quantum energy storage devices., Comment: 14 pages, 11 figures

This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance systems (ADAS) research. Unlike existing datasets, BSD offers a unique integration of high-resolution imaging radar, lidar, and camera sensors, providing unprecedented 360-degree coverage to bridge the current gap in high-resolution radar data availability. Spanning urban, rural, and highway environments, BSD enables detailed exploration into radar-based object detection and sensor fusion techniques. The dataset is aimed at facilitating academic and research collaborations between Bosch and current and future partners. This aims to foster joint efforts in developing cutting-edge HAD and ADAS technologies. The paper describes the dataset's key attributes, including its scalability, radar resolution, and labeling methodology. Key offerings also include initial benchmarks for sensor modalities and a development kit tailored for extensive data analysis and performance evaluation, underscoring our commitment to contributing valuable resources to the HAD and ADAS research community.

Simulators are powerful tools for autonomous robot learning as they offer scalable data generation, flexible design, and optimization of trajectories. However, transferring behavior learned from simulation data into the real world proves to be difficult, usually mitigated with compute-heavy domain randomization methods or further model fine-tuning. We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks. To this end, we first build a simulator by integrating Gaussian Splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks. In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, crafty programming of expert demonstration training data, and the task understanding capabilities of Liquid networks. Through a series of quantitative flight tests, we demonstrate the robust transfer of navigation skills learned in a single simulation scene directly to the real world. We further show the ability to maintain performance beyond the training environment under drastic distribution and physical environment changes. Our learned Liquid policies, trained on single target manoeuvres curated from a photorealistic simulated indoor flight only, generalize to multi-step hikes onboard a real hardware platform outdoors.

Research on aging processes of Gas Electron Multipliers (GEMs) is important to obtain insights on how to increase the detector's longevity, stability, and performance, as highlighted in the latest developments roadmap by the European Council of Future Accelerators (ECFA). One key aspect of the aging process is the deposit formation on the electrodes surfaces. In this work, through the analysis of the molecular content on the surface of a used GEM, we provide evidence for polyimide redeposition as a source of organic material contributing to the formation of insulating layers on the electrodes, which eventually lead to sparks and detector failure. Furthermore, we show that chromium, used to promote adhesion between copper and polyimide, in the device undergoes a diffusion process, effectively blurring the layered structure. We demonstrate the significance of surface-sensitive chemical analysis to investigate the surface deposits on electrodes of gaseous detectors and our results reveal the necessity of standardization and more stringent study protocols., Comment: 19 pages and 10 figures

The widespread use of knowledge graphs in various fields has brought about a challenge in effectively integrating and updating information within them. When it comes to incorporating contexts, conventional methods often rely on rules or basic machine learning models, which may not fully grasp the complexity and fluidity of context information. This research suggests an approach based on reinforcement learning (RL), specifically utilizing Deep Q Networks (DQN) to enhance the process of integrating contexts into knowledge graphs. By considering the state of the knowledge graph as environment states defining actions as operations for integrating contexts and using a reward function to gauge the improvement in knowledge graph quality post-integration, this method aims to automatically develop strategies for optimal context integration. Our DQN model utilizes networks as function approximators, continually updating Q values to estimate the action value function, thus enabling effective integration of intricate and dynamic context information. Initial experimental findings show that our RL method outperforms techniques in achieving precise context integration across various standard knowledge graph datasets, highlighting the potential and effectiveness of reinforcement learning in enhancing and managing knowledge graphs., Comment: This paper has been accepted by the 2024 International Conference on Machine Learning and Neural Networks (MLNN 2024)

Quantum batteries, quantum systems for energy storage, have gained interest due to their potential scalable charging power density. A quantum battery proposal based on the Dicke model has been explored using organic microcavities, which enable a cavity-enhanced energy transfer process called superabsorption. However, energy storage lifetime in these devices is limited by fast radiative emission losses, worsened by superradiance. Here, we demonstrate a promising approach to extend the energy storage lifetime of Dicke quantum batteries using molecular triplet states. We examine a type of multi-layer microcavities where an active absorption layer transfers energy to the molecular triplets of a storage layer, identifying two regimes based on exciton-polariton resonances. We tested one of these mechanisms by fabricating and characterising five devices across a triplet-polariton resonance. We conclude by discussing potential optimisation outlooks for this class of devices., Comment: 9 pages, 5 figures. 6 supplementary pages, 4 supplementary figures

According to the International Energy Agency, each human uses more than 80 GJ of energy per year; this is equivalent to leaving a washing machine continuously running for one year for every person on Earth. This consumption is expected to increase by 28% by 2040 (from 2015 levels). The majority (86%) of this energy comes from fossil fuels. This dependence on fossil fuels comes with major environmental costs, with climate change arguably being the greatest challenge facing our era. Renewable energy offers a possible solution. However, renewable energy sources, like solar and wind are not continuous sources, and therefore energy storage technology or batteries, remain an urgent challenge for further worldwide adoption of renewable energy. Alongside the need for efficient batteries to store renewable energy, the portability of batteries makes them an essential component in mobile technologies, including electric vehicles. Current batteries operate on the basis of well-understood electrochemical principles which were developed two centuries ago. While there is an ongoing intense effort aimed at improving their performance through optimization of the materials and the device architecture, it is worth exploring completely novel and disruptive approaches towards energy storage. Quantum batteries are energy storage devices that utilise quantum mechanics to enhance performance or functionality. While they are still in their infancy with only proof-of-principle demonstrations achieved, their radically innovative design principles offer a potential solution to future energy challenges., Comment: 9 pages, 2 figures