Multi-Trace Superpotentials vs. Matrix Models

We consider N = 1 supersymmetric U(N) field theories in four dimensions with adjoint chiral matter and a multi-trace tree-level superpotential. We show that the computation of the effective action as a function of the glueball superfield localizes to computing matrix integrals. Unlike the single-trace case, holomorphy and symmetries do not forbid non-planar contributions. Nevertheless, only a special subset of the planar diagrams contributes to the exact result. Some of the data of this subset can be computed from the large-N limit of an associated multi-trace Matrix model. However, the prescription differs in important respects from that of Dijkgraaf and Vafa for single-trace superpotentials in that the field theory effective action is not the derivative of a multi-trace matrix model free energy. The basic subtlety involves the correct identification of the field theory glueball as a variable in the Matrix model, as we show via an auxiliary construction involving a single-trace matrix model with additional singlet fields which are integrated out to compute the multi-trace results. Along the way we also describe a general technique for computing the large-N limits of multi-trace Matrix models and raise the challenge of finding the field theories whose effective actions they may compute. Since our models can be treated as N = 1 deformations of pure N =2 gauge theory, we show that the effective superpotential that we compute also follows from the N = 2 Seiberg-Witten solution. Finally, we observe an interesting connection between multi-trace local theories and non-local field theory.Comment: 35 pages, LaTeX, 6 EPS figures. v2: typos fixed, v3: typos fixed, references added, Sec. 5 added explaining how multi-trace theories can be linearized in traces by addition of singlet fields and the relation of this approach to matrix model

Implicit Sequence Learning of Background and Goal Information Under Double Dimensions

AbstractDouble-dimension serial reaction task in which color and alphabet were set as background and operating stimulus respectively was used to explore implicit learning in the present study. Subjects were instructed to react to the alphabets with or without the background colors in one of the following conditions: random sequence, regular sequence or blank (i.e., no background color).The results showed that random background color sequence interfered with the implicit learning of alphabet sequence. Participants’ implicit learning score in the random background color sequence was significantly lower than those in the regular background color sequence and no color background conditions. Moreover, when compared with no-background-color condition, regular-background-color sequence did not affect the learning of alphabet sequence. Finally, it was found that background color could be implicitly processed regardless whether it was in regular or random sequence

Learning Practically Feasible Policies for Online 3D Bin Packing

We tackle the Online 3D Bin Packing Problem, a challenging yet practically useful variant of the classical Bin Packing Problem. In this problem, the items are delivered to the agent without informing the full sequence information. Agent must directly pack these items into the target bin stably without changing their arrival order, and no further adjustment is permitted. Online 3D-BPP can be naturally formulated as Markov Decision Process (MDP). We adopt deep reinforcement learning, in particular, the on-policy actor-critic framework, to solve this MDP with constrained action space. To learn a practically feasible packing policy, we propose three critical designs. First, we propose an online analysis of packing stability based on a novel stacking tree. It attains a high analysis accuracy while reducing the computational complexity from $O(N^2)$ to $O(N \log N)$, making it especially suited for RL training. Second, we propose a decoupled packing policy learning for different dimensions of placement which enables high-resolution spatial discretization and hence high packing precision. Third, we introduce a reward function that dictates the robot to place items in a far-to-near order and therefore simplifies the collision avoidance in movement planning of the robotic arm. Furthermore, we provide a comprehensive discussion on several key implemental issues. The extensive evaluation demonstrates that our learned policy outperforms the state-of-the-art methods significantly and is practically usable for real-world applications.Comment: Science China Information Science

STUDY ON ADJUSTABLE CONNECTOR OF RECIPROCAL FRAME

In this paper, we design an adjustable connector of reciprocal frame, and a three-dimensional solid model of this connector with Circular Hollow Section has been created in the FEM software Abaqus CAE to study its mechanical properties. When the plastic hinge is formed at the end of the Circular Hollow Section, the connector is still in an elastic state. It is concluded that the adjustable connector of reciprocal frame has high strength and rigidity, realizing the goal for designing higher connector strength over Circular Hollow Section strength. Then parametric analysis is used to analyse the influence of the connector about each part on the mechanical properties, and the flexural rigidity of the connector has been derived. A three-dimensional wire model of reciprocal frames has been created in the FEM software Abaqus CAE, and a full-scale test model of the structure is designed. The numerical simulation results agree well with the test results. It is verified that the reliability of the modeling method and the accuracy of the connector mechanical model

Tensor-based Match Pursuit Algorithm for MIMO Radar Imaging

In MIMO radar, existing sparse imaging algorithms commonly vectorize the receiving data, which will destroy the multi-dimension structure of signal and cause the algorithm performance decline. In this paper, the sparsity characteristic and multi-dimension characteristic of signals are considered simultaneously and a new compressive sensing imaging algorithm named tensor-based match pursuit(TMP) is proposed. In the proposed method, MIMO radar tensor signal model is established to eliminate “dimension disaster” at first. Then, exploiting tensor decomposition to process tensor data sets, tensor-based match pursuit is formulated for multi-dimension sparse signal recovery, in which atom vectors orthogonality selection strategy and basis-signal reevaluation are used to eliminate the wrong indices and enhance resolution respectively. Simulation results validates that the proposed method can complete high-resolution imaging correctly compared with conventional greedy sparse recovery algorithms. Additionally, under fewer snapshots condition, RMSE of proposed method is far lower than other sparse recovery algorithms

Robust Adaptive Control of STATCOMs to Mitigate Inverter-Based-Resource (IBR)-Induced Oscillations

The interaction among inverter-based resources (IBRs) and power network may cause small-signal stability issues, especially in low short-circuit grids. Besides, the integration of static synchronous compensators (STATCOMs) in a multi-IBR system for voltage support can deteriorate small-signal stability. However, it is still challenging to understand the impact mechanism of STATCOMs on IBR-induced oscillation issues and to design STATCOMs' control for damping these oscillation issues in a multi-IBR system, due to complex system dynamics and varying operating conditions. To tackle these challenges, this paper proposes a novel method to reveal how STATCOMs influence IBR-induced oscillation issues in a multi-IBR system from the viewpoint of grid strength, which can consider varying operating conditions. Based on this proposed method, we find critical operating conditions, wherein the system tends to be most unstable; moreover, we demonstrate that robust small-signal stability issues of the multi-IBR system with STATCOMs can be simplified as that of multiple subsystems under critical operating conditions, which avoids traversing all operating conditions and establishing system's detailed models. On this basis, an adaptive control-parameter design method is proposed for STATCOMs to ensure system's robust small-signal stability under varying operating conditions. The efficacy of proposed methods is validated by a 39-node test system

Achieving high strength and ductility in magnesium alloys via densely hierarchical double contraction nanotwins.

Light-weight magnesium alloys with high strength are especially desirable for the applications in transportation, aerospace, electronic components, and implants owing to their high stiffness, abundant raw materials, and environmental friendliness. Unfortunately, conventional strengthening methods mainly involve the formation of internal defects, in which particles and grain boundaries prohibit dislocation motion as well as compromise ductility invariably. Herein, we report a novel strategy for simultaneously achieving high specific yield strength (∼160 kN m kg-1) and good elongation (∼23.6%) in a duplex magnesium alloy containing 8 wt % lithium at room temperature, based on the introduction of densely hierarchical {1011}-{101 1} double contraction nanotwins (DCTWs) and full-coherent hexagonal close-packed (hcp) particles in twin boundaries by ultrahigh pressure technique. These hierarchical nanoscaled DCTWs with stable interface characteristics not only bestow a large fraction of twin interface but also form interlaced continuous grids, hindering possible dislocation motions. Meanwhile, orderly aggregated particles offer supplemental pinning effect for overcoming latent softening roles of twin interface movement and detwinning process. The processes lead to a concomitant but unusual situation where double contraction twinning strengthens rather than weakens magnesium alloys. Those cutting-edge results provide underlying insights toward designing alternative and more innovative hcp-type structural materials with superior mechanical properties