LLM-based agents have demonstrated impressive zero-shot performance in vision-language navigation (VLN) task. However, existing LLM-based methods often focus only on solving high-level task planning by selecting nodes in predefined navigation graphs for movements, overlooking low-level control in navigation scenarios. To bridge this gap, we propose AO-Planner, a novel Affordances-Oriented Planner for continuous VLN task. Our AO-Planner integrates various foundation models to achieve affordances-oriented low-level motion planning and high-level decision-making, both performed in a zero-shot setting. Specifically, we employ a Visual Affordances Prompting (VAP) approach, where the visible ground is segmented by SAM to provide navigational affordances, based on which the LLM selects potential candidate waypoints and plans low-level paths towards selected waypoints. We further propose a high-level PathAgent which marks planned paths into the image input and reasons the most probable path by comprehending all environmental information. Finally, we convert the selected path into 3D coordinates using camera intrinsic parameters and depth information, avoiding challenging 3D predictions for LLMs. Experiments on the challenging R2R-CE and RxR-CE datasets show that AO-Planner achieves state-of-the-art zero-shot performance (8.8% improvement on SPL). Our method can also serve as a data annotator to obtain pseudo-labels, distilling its waypoint prediction ability into a learning-based predictor. This new predictor does not require any waypoint data from the simulator and achieves 47% SR competing with supervised methods. We establish an effective connection between LLM and 3D world, presenting novel prospects for employing foundation models in low-level motion control.

The performance of local feature descriptors degrades in the presence of large rotation variations. To address this issue, we present an efficient approach to learning rotation invariant descriptors. Specifically, we propose Rotated Kernel Fusion (RKF) which imposes rotations on the convolution kernel to improve the inherent nature of CNN. Since RKF can be processed by the subsequent re-parameterization, no extra computational costs will be introduced in the inference stage. Moreover, we present Multi-oriented Feature Aggregation (MOFA) which aggregates features extracted from multiple rotated versions of the input image and can provide auxiliary knowledge for the training of RKF by leveraging the distillation strategy. We refer to the distilled RKF model as DRKF. Besides the evaluation on a rotation-augmented version of the public dataset HPatches, we also contribute a new dataset named DiverseBEV which is collected during the drone's flight and consists of bird's eye view images with large viewpoint changes and camera rotations. Extensive experiments show that our method can outperform other state-of-the-art techniques when exposed to large rotation variations.

The emerging time-critical applications pose intense demands for enabling large-scale deterministic networks. In this paper, we propose a new Programmable Cycle-Specified Queue (PCSQ) for wide-area deterministic packet scheduling. We implement the first end-to-end high-precision rotation dequeuing, which enables microsecond-level time slot resource reservation (noted as T) and especially jitter control of up to 2T. We prototype the PCSQ scheduler on an FPGA. The PCSQ-enabled switches can guarantee bounded delay and jitter transmission on a realistic testbed.

Cloud-based services have been increasingly used to provide on-demand access to a large amount of computing requests, such as data, computing, resources, and so on, in which it is vitally important to correctly select and assign the right resources to a workload or application. This paper presents a novel online reverse auction scheme based on online algorithm for allocating the cloud computing services, which can help the cloud users and providers to build workflow applications in a cloud computing environment. The online reverse auction scheme consists of three parts: online algorithm design, competitive ratio calculation, and performance valuation. The online reverse auction-based algorithm is proposed for the cloud user agent to choose the final winners based on Vickrey–Clarke–Groves (VCG) mechanism and online algorithm (OA). The competitive analysis is applied to calculate the competitive ratio of the proposed algorithm compared with the offline algorithm. This analysis method is significant to measure the performance of proposed algorithm, without the assumption of the distribution of cloud providers’ bids. The results prove that the proposed online reverse auction-based algorithm is the appropriate mechanism because it allows the cloud user agent to make purchase decisions without knowing the future bids. The difference of auction rounds and transaction cost can impressively influence and improve the performance of the proposed reverse auction algorithm.

We present a parallel robot mechanism and the constitutive laws that govern the deformation of its constituent soft actuators. Our ultimate goal is the real-time motion-correction of a patient's head deviation from a target pose where the soft actuators control the position of the patient's cranial region on a treatment machine. We describe the mechanism, derive the stress-strain constitutive laws for the individual actuators and the inverse kinematics that prescribes a given deformation, and then present simulation results that validate our mathematical formulation. Our results demonstrate deformations consistent with our radially symmetric displacement formulation under a finite elastic deformation framework.

To better understand users’ technology utilisation behaviour, a construct named technology dependence is suggested; as well a technology dependence model is proposed and empirically tested. Based on the literature on marketing, information systems’ utilisation, and psychology, a comprehensive set of constructs and hypotheses are compiled with a methodology for testing them in this paper. A questionnaire was designed and data were collected from 255 users of smartphones in Korea, mainly consisting of students and academics. Structural equation modelling was then applied to analyse the data. The result indicated that engagement is the strongest indicator of technology dependence along with user satisfaction. User satisfaction is affected by engagement and responsiveness of the technology. And all three dimensions of interactivity, namely, control, communication, and responsiveness, are revealed to be significant indicators of engagement. Based on the findings, the model develops useful insights into the factors that influence technology dependence and provides new ideas in understanding technology utilisation.