arXiv subjects

Jie Xu

Publications and source records attributed to Jie Xu.

At least 415 records · Page 23Linked to original sources

arXiv2022

Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

Both the design and control of a robot play equally important roles in its task performance. However, while optimal control is well studied in the machine learning and robotics community, less attention is placed on finding the optimal robot design. This is mainly because co-optimizing design and control in robotics is characterized as a challenging problem, and more importantly, a comprehensive evaluation benchmark for co-optimization does not exist. In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. In our benchmark, each robot is composed of different types of voxels (e.g., soft, rigid, actuators), resulting in a modular and expressive robot design space. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. Furthermore, we develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques. Evaluating the algorithms on our benchmark platform, we observe robots exhibiting increasingly complex behaviors as evolution progresses, with the best evolved designs solving many of our proposed tasks. Additionally, even though robot designs are evolved autonomously from scratch without prior knowledge, they often grow to resemble existing natural creatures while outperforming hand-designed robots. Nevertheless, all tested algorithms fail to find robots that succeed in our hardest environments. This suggests that more advanced algorithms are required to explore the high-dimensional design space and evolve increasingly intelligent robots -- an area of research in which we hope Evolution Gym will accelerate progress. Our website with code, environments, documentation, and tutorials is available at http://evogym.csail.mit.edu.

cs.RO↗

arXiv2022

Amplify-and-Forward Relaying for Hierarchical Over-the-Air Computation

This paper studies a hierarchical over-the-air computation (AirComp) network over a large area, in which multiple relays are exploited to facilitate data aggregation from massive WDs. We present a two-phase amplify-and-forward (AF) relaying protocol. In the first phase, the WDs simultaneously send their data to the relays, while in the second phase, the relays amplify the respectively received signals and concurrently forward them to the fusion center (FC) for aggregation. Our objective is to minimize the computational mean squared error (MSE) at the FC, by jointly optimizing the WD transmit coefficients, the relay AF coefficients, and the FC de-noising factor, subject to their individual transmit power constraints. First, we consider the centralized design with global channel state information (CSI), in which the inter-relay signals can be exploited beneficially for data aggregation. In this case, we develop an alternating-optimization-based algorithm to obtain a high-quality solution to the computational MSE minimization problem. Next, to reduce the signaling overhead caused by the centralized design, we consider an alternative decentralized design with partial CSI, in which the relays and the FC make their own decisions by only requiring the channel power gain information across different relays. In this case, the relays and FC need to treat the inter-relay signals as harmful interference or noise. Accordingly, we optimize the transmit coefficients of the WDs associated with each relay, and the relay AF coefficients (together with the FC de-noising factor) in an iterative manner, which can be implemented efficiently in a decentralized way.

cs.IT↗

arXiv2021

A Generalization of Cyclic Code and Applications to Public Key Cryptosystems

In this paper, we define and discuss ϕ-cyclic code, which may be regarded as a general form of the ordinary cyclic code. As applications, we explain how to extend two public key encryption schemes, one is McEliece and Niederriter's cryptosystem of which is based on error-correcting code theory. Another one is NTRU public key cryptosystem of which is based on polynomial ring theory. The main purpose of this paper is to give a more general construction of NTRU based on ideal matrices and q-ary lattice theory.

cs.IT↗

arXiv2021

K-Core Decomposition on Super Large Graphs with Limited Resources

K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our industrial partner runs popular social applications with billions of users and is able to gather a rich set of user data. As a result, applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry. A simple but effective method to deal with large graphs is to train them in the distributed settings, and some distributed K-core decomposition algorithms are also proposed. Despite their effectiveness, we experimentally and theoretically observe that these algorithms consume too many resources and become unstable on super-large-scale graphs, especially when the given resources are limited. In this paper, we deal with those super-large-scale graphs and propose a divide-and-conquer strategy on top of the distributed K-core decomposition algorithm. We evaluate our approach on three large graphs. The experimental results show that the consumption of resources can be significantly reduced, and the calculation on large-scale graphs becomes more stable than the existing methods. For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.

cs.DC↗

arXiv2021

Transition pathways in Cylinder-Gyroid interface

When two distinct ordered phases contact, the interface may exhibit rich and fascinating structures. Focusing on the Cylinder-Gyroid interface system, transition pathways connecting various interface morphologies are studied armed with the Landau--Brazovskii model. Specifically, minimum energy paths are obtained by computing transition states with the saddle dynamics. We present four primary transition pathways connecting different local minima, representing four different mechanisms of the formation of the Cylinder-Gyroid interface. The connection of Cylinder and Gyroid can be either direct or indirect via Fddd with three different orientations. Under different displacements, each of the four pathways may have the lowest energy.

cond-mat.soft↗

arXiv2021

Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement

The rapid uptake of intelligent applications is pushing deep learning (DL) capabilities to Internet-of-Things (IoT). Despite the emergence of new tools for embedding deep neural networks (DNNs) into IoT devices, providing satisfactory Quality of Experience (QoE) to users is still challenging due to the heterogeneity in DNN architectures, IoT devices, and user preferences. This paper studies automated customization for DL inference on IoT devices (termed as on-thing inference), and our goal is to enhance user QoE by configuring the on-thing inference with an appropriate DNN for users under different usage scenarios. The core of our method is a DNN selection module that learns user QoE patterns on-the-fly and identifies the best-fit DNN for on-thing inference with the learned knowledge. It leverages a novel online learning algorithm, NeuralUCB, that has excellent generalization ability for handling various user QoE patterns. We also embed the knowledge transfer technique in NeuralUCB to expedite the learning process. However, NeuralUCB frequently solicits QoE ratings from users, which incurs non-negligible inconvenience. To address this problem, we design feedback solicitation schemes to reduce the number of QoE solicitations while maintaining the learning efficiency of NeuralUCB. A pragmatic problem, aggregated QoE, is further investigated to improve the practicality of our framework. We conduct experiments on both synthetic and real-world data. The results indicate that our method efficiently learns the user QoE pattern with few solicitations and provides drastic QoE enhancement for IoT devices.

cs.LG↗

arXiv2021

Improving Intention Detection in Single-Trial Classification through Fusion of EEG and Eye-tracker Data

Intention decoding is an indispensable procedure in hands-free human-computer interaction (HCI). Conventional eye-tracking system using single-model fixation duration possibly issues commands ignoring users' real expectation. In the current study, an eye-brain hybrid brain-computer interface (BCI) interaction system was introduced for intention detection through fusion of multi-modal eye-track and ERP (a measurement derived from EEG) features. Eye-track and EEG data were recorded from 64 healthy participants as they performed a 40-min customized free search task of a fixed target icon among 25 icons. The corresponding fixation duration of eye-tracking and ERP were extracted. Five previously-validated LDA-based classifiers (including RLDA, SWLDA, BLDA, SKLDA, and STDA) and the widely-used CNN method were adopted to verify the efficacy of feature fusion from both offline and pseudo-online analysis, and optimal approach was evaluated through modulating the training set and system response duration. Our study demonstrated that the input of multi-modal eye-track and ERP features achieved superior performance of intention detection in the single trial classification of active search task. And compared with single-model ERP feature, this new strategy also induced congruent accuracy across different classifiers. Moreover, in comparison with other classification methods, we found that the SKLDA exhibited the superior performance when fusing feature in offline test (ACC=0.8783, AUC=0.9004) and online simulation with different sample amount and duration length. In sum, the current study revealed a novel and effective approach for intention classification using eye-brain hybrid BCI, and further supported the real-life application of hands-free HCI in a more precise and stable manner.

cs.HC↗

arXiv2021

Context-Aware Online Client Selection for Hierarchical Federated Learning

Federated Learning (FL) has been considered as an appealing framework to tackle data privacy issues of mobile devices compared to conventional Machine Learning (ML). Using Edge Servers (ESs) as intermediaries to perform model aggregation in proximity can reduce the transmission overhead, and it enables great potentials in low-latency FL, where the hierarchical architecture of FL (HFL) has been attracted more attention. Designing a proper client selection policy can significantly improve training performance, and it has been extensively used in FL studies. However, to the best of our knowledge, there are no studies focusing on HFL. In addition, client selection for HFL faces more challenges than conventional FL, e.g., the time-varying connection of client-ES pairs and the limited budget of the Network Operator (NO). In this paper, we investigate a client selection problem for HFL, where the NO learns the number of successful participating clients to improve the training performance (i.e., select as many clients in each round) as well as under the limited budget on each ES. An online policy, called Context-aware Online Client Selection (COCS), is developed based on Contextual Combinatorial Multi-Armed Bandit (CC-MAB). COCS observes the side-information (context) of local computing and transmission of client-ES pairs and makes client selection decisions to maximize NO's utility given a limited budget. Theoretically, COCS achieves a sublinear regret compared to an Oracle policy on both strongly convex and non-convex HFL. Simulation results also support the efficiency of the proposed COCS policy on real-world datasets.

cs.LG↗

arXiv2021

Transmission Power Control for Over-the-Air Federated Averaging at Network Edge

Over-the-air computation (AirComp) has emerged as a new analog power-domain non-orthogonal multiple access (NOMA) technique for low-latency model/gradient-updates aggregation in federated edge learning (FEEL). By integrating communication and computation into a joint design, AirComp can significantly enhance the communication efficiency, but at the cost of aggregation errors caused by channel fading and noise. This paper studies a particular type of FEEL with federated averaging (FedAvg) and AirComp-based model-update aggregation, namely over-the-air FedAvg (Air-FedAvg). We investigate the transmission power control to combat against the AirComp aggregation errors for enhancing the training accuracy and accelerating the training speed of Air-FedAvg. Towards this end, we first analyze the convergence behavior (in terms of the optimality gap) of Air-FedAvg with aggregation errors at different outer iterations. Then, to enhance the training accuracy, we minimize the optimality gap by jointly optimizing the transmission power control at edge devices and the denoising factors at edge server, subject to a series of power constraints at individual edge devices. Furthermore, to accelerate the training speed, we also minimize the training latency of Air-FedAvg with a given targeted optimality gap, in which learning hyper-parameters including the numbers of outer iterations and local training epochs are jointly optimized with the power control. Finally, numerical results show that the proposed transmission power control policy achieves significantly faster convergence for Air-FedAvg, as compared with benchmark policies with fixed power transmission or per-iteration mean squared error (MSE) minimization. It is also shown that the Air-FedAvg achieves an order-of-magnitude shorter training latency than the conventional FedAvg with digital orthogonal multiple access (OMA-FedAvg).

cs.IT↗

arXiv2021

Supporting and Controlling Complex Concurrency in Fault- Tolerant Distributed Systems

Distributed computing often gives rise to complex concurrent and interacting activities. In some cases several concurrent activities may be working together, i.e. cooperating, to solve a given problem; in other cases, the activities may be independent but needing to share common system resources for which they must compete. Many difficulties and limitations occur in the widely advocated objects and (trans)actions model when it is supposed to support cooperating activities. We have introduced previously the concept of coordinated atomic (CA) actions [Xu et al. 1995]; this paper analyzes and examines the derived objects and CA actions model for constructing fault-tolerant distributed systems and providing unified support for both cooperative and competitive concurrency. Our investigation reveals and clarifies several significant problems that have not previously been studied extensively, including the problem of ensuring consistent access to shared objects from a joint action as opposed to a set of independent actions. Conceptual and implementation-related solutions are proposed and illustrated.

cs.DC↗

arXiv2021

Tilt grain boundaries of hexagonal structures: a spectral viewpoint

We propose a spectral viewpoint for grain boundaries that are generally quasiperiodic. To accurately capture the spectra computationally, it is crucial to adopt the projection method for quasiperiodic functions. Armed with the Lifshitz-Petrich free energy, we take the spectral viewpoint to examine tilt grain boundaries of the hexagonal phase. Several ingredients of grain boundaries are extracted, which are not easy to obtain from real-space profiles. We find that only a few spectra substantially contribute to the formation of grain boundaries. Their linear relation to the intrinsic spectra of the bulk hexagonal phase is independent of the tilt angle. By examining the feature of the spectral intensities, we propose a definition of the interface width. The widths calculated from this definition are consistent with visual estimation.

cond-mat.mtrl-sci↗

arXiv2021

Optimized Power Control Design for Over-the-Air Federated Edge Learning

This paper investigates the transmission power control in over-the-air federated edge learning (Air-FEEL) system. Different from conventional power control designs (e.g., to minimize the individual mean squared error (MSE) of the over-the-air aggregation at each round), we consider a new power control design aiming at directly maximizing the convergence speed. Towards this end, we first analyze the convergence behavior of Air-FEEL (in terms of the optimality gap) subject to aggregation errors at different communication rounds. It is revealed that if the aggregation estimates are unbiased, then the training algorithm would converge exactly to the optimal point with mild conditions; while if they are biased, then the algorithm would converge with an error floor determined by the accumulated estimate bias over communication rounds. Next, building upon the convergence results, we optimize the power control to directly minimize the derived optimality gaps under both biased and unbiased aggregations, subject to a set of average and maximum power constraints at individual edge devices. We transform both problems into convex forms, and obtain their structured optimal solutions, both appearing in a form of regularized channel inversion, by using the Lagrangian duality method. Finally, numerical results show that the proposed power control policies achieve significantly faster convergence for Air-FEEL, as compared with benchmark policies with fixed power transmission or conventional MSE minimization.

cs.IT↗

arXiv2021

A System for General In-Hand Object Re-Orientation

In-hand object reorientation has been a challenging problem in robotics due to high dimensional actuation space and the frequent change in contact state between the fingers and the objects. We present a simple model-free framework that can learn to reorient objects with both the hand facing upwards and downwards. We demonstrate the capability of reorienting over 2000 geometrically different objects in both cases. The learned policies show strong zero-shot transfer performance on new objects. We provide evidence that these policies are amenable to real-world operation by distilling them to use observations easily available in the real world. The videos of the learned policies are available at: https://taochenshh.github.io/projects/in-hand-reorientation.

cs.RO↗

arXiv2021

Solving Yamabe Problem by An Iterative Method

We introduce an iterative scheme to prove the Yamabe problem $ - aΔ_{g} u + S u = λu^{p-1} $, firstly on open domain $ (Ω, g) $ with Dirichlet boundary conditions, and then on closed manifolds $ (M, g) $ by local argument. It is a new proof, which solves the Yamabe problem for $ n \geqslant 3 $ in a uniform argument, beyonds the traditional analysis with respect to the minimization of functionals.

math.AP↗

arXiv2021

Realization of broadband truly rainbow trapping in gradient-index heterostructures

Unidirectionally propagating waves (UPW) such as topologically protected edge modes and surface magnetoplasmons (SMPs) has been a research hotspot in the last decades. In the study of UPW, metals are usually treated as perfect electric conductors (PECs) which, in general, are the boundary conditions. However, it was reported that the transverse resonance condition induced by the PEC wall(s) may significantly narrow up the complete one-way propagation (COWP) band. In this paper, we propose two ways to achieve ultra-broadband one-way waveguide in terahertz regime. The first way is utilizing the epsilon negative (ENG) metamaterial (MM) and the other one is replacing the PEC boundary with perfect magnetic conductor (PMC) boundary. In both conditions, the total bandwidth of the COWP bands can be efficiently broadened by more than three times. Moreover, based on the ultra-broadband one-way configurations, gradient-index metamaterial-based one-way waveguides are proposed to achieve broadband truly rainbow trapping (TRT). By utilizing the finite element method, the realization of the broadband TRT without backward reflection is verified in gradient-index structures. Besides, giant electric field enhancement is observed in a PMC-based one-way structure with an ultra-subwavelength ($\approx 10^{-4} λ_0$, $λ_0$ is the wavelength in vaccum) terminal, and the amplitude of the electric field is enormously enhanced by five orders of magnitude. Our findings are beneficial for researches on broadband terahertz communication, energy harvesting and strong-field devices.

physics.optics↗

arXiv2021

Optimal Transmit Beamforming for Secrecy Integrated Sensing and Communication

This paper studies a secrecy integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) aims to send confidential messages to a single-antenna communication user (CU), and at the same time sense several targets that may be suspicious eavesdroppers. To ensure the sensing quality while preventing the eavesdropping, we consider that the BS sends dedicated sensing signals (in addition to confidential information signals) that play a dual role of artificial noise (AN) for confusing the eavesdropping targets. Under this setup, we jointly optimize the transmit information and sensing beamforming at the BS, to minimize the matching error between the transmit beampattern and a desired beampattern for sensing, subject to the minimum secrecy rate requirement at the CU and the transmit power constraint at the BS. Although the formulated problem is non-convex, we propose an algorithm to obtain the globally optimal solution by using the semidefinite relaxation (SDR) together with a one-dimensional (1D) search. Next, to avoid the high complexity induced by the 1D search, we also present two sub-optimal solutions based on zero-forcing and separate beamforming designs, respectively. Numerical results show that the proposed designs properly adjust the information and sensing beams to balance the tradeoffs among communicating with CU, sensing targets, and confusing eavesdroppers, thus achieving desirable transmit beampattern for sensing while ensuring the CU's secrecy rate.

cs.IT↗

arXiv2021

$Q$-tensor gradient flow with quasi-entropy and discretizations preserving physical constraints

We propose and analyze numerical schemes for the gradient flow of $Q$-tensor with the quasi-entropy. The quasi-entropy is a strictly convex, rotationally invariant elementary function, giving a singular potential constraining the eigenvalues of $Q$ within the physical range $(-1/3,2/3)$. Compared with the potential derived from the Bingham distribution, the quasi-entropy has the same asymptotic behavior and underlying physics. Meanwhile, it is very easy to evaluate because of its simple expression. For the elastic energy, we include all the rotationally invariant terms. The numerical schemes for the gradient flow are built on the nice properties of the quasi-entropy. The first-order time discretization is uniquely solvable, keeping the physical constraints and energy dissipation, which are all independent of the time step. The second-order time discretization keeps the first two properties unconditionally, and the third with an $O(1)$ restriction on the time step. These results also hold when we further incorporate a second-order discretization in space. Error estimates are also established for time discretization and full discretization. Numerical examples about defect patterns are presented to validate the theoretical results.

math.NA↗

arXiv2021

Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communication

This paper studies the UAV-enabled integrated sensing and communication (ISAC), in which UAVs are dispatched as aerial dual-functional access points (APs) for efficient ISAC. In particular, we consider a scenario with one UAV-AP equipped with a vertically placed uniform linear array (ULA), which sends combined information and sensing signals to communicate with multiple users and sense potential targets at interested areas on the ground simultaneously. Our objective is to jointly design the UAV maneuver with the transmit beamforming for optimizing the communication performance while ensuring the sensing requirements. First, we consider the quasi-stationary UAV scenario, in which the UAV is deployed at an optimizable location over the whole ISAC mission period. In this case, we jointly optimize the UAV deployment location, as well as the transmit information and sensing beamforming to maximize the weighted sum-rate throughput, subject to the sensing beampattern gain requirements and transmit power constraint. Although the above problem is non-convex, we find a high-quality solution by using the techniques of SCA and SDR, together with a 2D location search. Next, we consider the fully mobile UAV scenario, in which the UAV can fly over different locations during the ISAC mission period. In this case, we optimize the UAV flight trajectory, jointly with the transmit beamforming over time, to maximize the average weighted sum-rate throughput, subject to the sensing beampattern gain requirements and transmit power constraints as well as practical flight constraints. While the joint UAV trajectory and beamforming problem is more challenging to solve, we propose an efficient algorithm by adopting the alternating optimization together with SCA. Finally, numerical results are provided to validate the superiority of our proposed designs as compared to various benchmark schemes.

cs.IT↗