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Wallbounce : Push wall to navigate with Contact-Implicit MPC
No full textIn this work, we introduce a framework that enables highly maneuverable locomotion using non-periodic contacts. This task is challenging for traditional optimization and planning methods to handle due to difficulties in specifying contact mode sequences in real-time. To address this, we use a bi-level contact-implicit planner and hybrid model predictive controller to draft and execute a motion plan. We investigate how this method allows us to plan arm contact events on the shmoobot, a smaller ballbot, which uses an inverse mouse-ball drive to achieve dynamic balancing with a low number of actuators. Through multiple experiments we show how the arms allow for acceleration, deceleration and dynamic obstacle avoidance that are not achievable with the mouse-ball drive alone. This demonstrates how a holistic approach to locomotion can increase the control authority of unique robot morpohologies without additional hardware by leveraging robot arms that are typically used only for manipulation. Project website: https://cmushmoobot.github.io/Wallbounc
Sequential Charging Station Location Optimization under Uncertain Charging Behavior and User Growth
No full textCharging station availability is crucial for a thriving electric vehicle market. Due to budget constraints, locating these stations usually proceeds in phases, which calls for careful consideration of the (random) charging demand growth throughout the planning horizon. This paper integrates user choice behavior into two-stage and multi-stage stochastic programming models for intracity charging station planning under demand uncertainty. We derive a second-order conic representation for the nonlinear, nonconvex formulation by taking advantage of the binary nature of location variables and propose subgradient inequalities to accelerate computation. Numerical results demonstrate the value of employing multi-stage models, particularly in scenarios of high demand fluctuations, increased demand dispersion, and high user sensitivity to the distance-to-recharge.6 pages, 4 figures, to appear in the Proceedings of the 63rd IEEE Conference on Decision and Control (CDC 2024), Milan, Italy, Dec 202
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning
No full textMeta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in a source domain. Yet, in practical scenarios where the target task diverges from that in the source domain, meta-learning based method is susceptible to over-fitting. To overcome this, we introduce a novel framework, Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning, which is crafted to comprehensively exploit the cross-domain transferable image prior that each image can be decomposed into complementary low-frequency content details and high-frequency robust structural characteristics. Motivated by this insight, we propose to decompose each query image into its high-frequency and low-frequency components, and parallel incorporate them into the feature embedding network to enhance the final category prediction. More importantly, we introduce a feature reconstruction prior and a prediction consistency prior to separately encourage the consistency of the intermediate feature as well as the final category prediction between the original query image and its decomposed frequency components. This allows for collectively guiding the network\u27s meta-learning process with the aim of learning generalizable image feature embeddings, while not introducing any extra computational cost in the inference phase. Our framework establishes new state-of-the-art results on multiple cross-domain few-shot learning benchmarks
Improved Bohr-type Inequalities for the Cesaro Operator
No full textIn this paper, we derive the sharp improved versions of Bohr-type inequalities for the Cesáro operator acting on the class of bounded analytic functions defined on the unit disk \D=\left\{z\in\C:\left|z\right|<1\right\}. In order to achieve these results, we utilize the principle of substituting the initial coefficients of the majorant series with the absolute values of the Cesáro operator associated with a bounded analytic function defined on \D and its derivative, as well as for the Schwarz function
Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services
No full textGenerative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. In this paper, we address challenges of edge-enabled AIGC service provisioning, which remain underexplored in the literature. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We subsequently introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics. We obtain mathematical relationships of these metrics with the computational resources required by GenAI models via experimentation. Afterward, we decompose the formulation into a model caching subproblem on a long-timescale and a resource allocation subproblem on a short-timescale. Since the variables to be solved are discrete and continuous, respectively, we leverage a double deep Q-network (DDQN) algorithm to solve the former subproblem and propose a diffusion-based deep deterministic policy gradient (D3PG) algorithm to solve the latter. The proposed D3PG algorithm makes an innovative use of diffusion models as the actor network to determine optimal resource allocation decisions. Consequently, we integrate these two learning methods within the overarching two-timescale deep reinforcement learning (T2DRL) algorithm, the performance of which is studied through comparative numerical simulations.14 pages, 8 figures, 39 reference
Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP
No full textEnvironmental monitoring and predictive modeling of the Water Quality Index (WQI) through the assessment of the water quality.18 page
Observation of freezing phenomenon in high-dimensional quantum correlation dynamics
No full textQuantum information processing (QIP) based on high-dimensional quantum systems provides unique advantages and new potentials where high-dimensional quantum correlations (QCs) play vital roles. Exploring the resistance of QCs against noises is crucial as QCs are fragile due to complex and unavoidable system-environment interactions. In this study, we investigate the performance of high-dimensional QCs under local dephasing noise using a single nitrogen-vacancy center in diamond. A freezing phenomenon in the high-dimensional quantum discord dynamics was observed, showing discord is robust against local dephasing noise. Utilizing a robustness metric known as freezing index, we found that the discord of qutrits outperforms their qubits counterpart when confronted with dephasing noise. Furthermore, we developed a geometric picture to explain this intriguing freezing phenomenon phenomenon. Our findings highlight the potential of utilizing discord as a physical resource for advancing QIP in high-dimensional quantum settings
On the existence of extremal solutions for the conjugate discrete-time Riccati equation
No full textIn this paper we consider a class of conjugate discrete-time Riccati equations (CDARE), arising originally from the linear quadratic regulation problem for discrete-time antilinear systems. Recently, we have proved the existence of the maximal solution to the CDARE with a nonsingular control weighting matrix under the framework of the constructive method. Our contribution in the work is to finding another meaningful Hermitian solutions, which has received little attention in this topic. Moreover, we show that some extremal solutions cannot be attained at the same time, and almost (anti-)stabilizing solutions coincide with some extremal solutions. It is to be expected that our theoretical results presented in this paper will play an important role in the optimal control problems for discrete-time antilinear systems
An in-depth analysis of quiet-Sun IRIS Brightenings
No full textSmall-scale brightenigs are ubiquitous, dynamic and energetic phenomena found in the chromopshere. An advanced filter-detection algorithm applied to high-resolution observations from the Interface Region Imaging Spectrograph enables the detection of these brightenings close to the noise level. This algorithm also tracks the movement of these brightenings and extracts their characteristics. This work outlines the results of an in-depth analysis of a quiet-Sun dataset including a comparison of a brighter domain - associated with a super-granular boundary - to the quiescent inter-network domains. Several characteristics of brightenings from both domains are extracted and analysed, providing a range of sizes, durations, brightness values, travel distances, and speeds. The ``Active quiet-Sun events tend to travel shorter distances and at slower speeds along the plane-of-sky than their ``True quiet-Sun counterparts. These results are consistent with the magnetic field model of super-granular photospheric structures and the magnetic canopy model of the chromosphere above. Spectroscopic analyses reveal that BPs demonstrate blue-shift (as well as some bi-directionality) and that they may rise from the chromosphere into the TR. We believe these bright points to be magnetic in nature, are likely the result of magnetic reconnection, and follow current sheets between magnetic field gradients, rather than along magnetic field lines themselves.18 pages, 11 figure
Power law convergence and concavity for the Logarithmic Schrödinger equation
No full textWe study concavity properties of positive solutions to the Logarithmic Schrödinger equation in a general convex domain with Dirichlet conditions. To this aim, we analyse the auxiliary Lane-Emden problems and build, for any and , solutions such that is convex. By choosing and letting we eventually construct a solution of the Logarithmic Schrödinger equation such that is concave. This seems one of the few attempts in studying concavity properties for superlinear, sign changing sources. To get the result, we both make inspections on the constant rank theorem and develop Liouville theorems on convex epigraphs, which might be useful in other frameworks