High-level Constraints Research Articles

Developing a cooperative approach under normal and contingency conditions for generation expansion planning of microgrids

AbstractThe issue of generation expansion planning in microgrids has become a challenging issue in electricity industry for two reasons: load growth and uncertainties in renewables' generation. Therefore, this issue is considered here. Here, the modelling of generation expansion planning problem has been developed in a network of microgrids in a decentralized manner, considering normal and contingency conditions. On the other hand, in order to further develop the study considered, decentralized generation expansion planning model of microgrids by considering contingency conditions has been addressed in a cooperative approach to minimize total costs. In developed model, investment decisions are made at the higher level and operational constraints has been considered at lower one. Also, case studies are defined in three different scenarios: islanding operation of microgrids as first scenario and peer‐to‐peer trading of microgrids in non‐cooperative and cooperative approaches as second and third scenarios, respectively. The results of simulations have shown that by facilitating the transactions between microgrids, their total costs are reduced. The costs of the whole set of microgrids in the non‐cooperative scenario are reduced by 9.4% compared to the islanding scenario; and the costs are reduced by 7.5% in the cooperative scenario compared to the non‐cooperative scenario.

Densification mechanism, microstructure, and thermionic emission property at the low temperature of spark plasma sintered (LaBa)B6–ZrB2 composite

The development of a high-power and long-life thermionic ceramic cathode poses significant challenges, namely the need for high electron emission capacity at low temperature and excellent machinability. In this paper, the first-principles method was used to obtain the (La0.5Ba0.5)B6 with the lowest work function, and (La0.5Ba0.5)B6–ZrB2 composite with high relative density of 98.08 % was prepared by spark plasma sintering (SPS) at T = 1800 °C, under which the densification mechanism transformed from the particle rearrangement into the grain boundary diffusion, and the corresponding apparent activation energies were 282.75 kJ/mol and 466.54 kJ/mol, respectively. The maximum current density of 12.93 A/cm2 at T = 1500 °C of the composite was higher than that of 11.71A/cm2 at T = 1600 °C of LaB6, which can be attributed to the high Fermi level and weak electronic constraints of (La0.5Ba0.5)B6. The practical work function distribution (PWFD) of 0.36 eV indicated the composite had excellent emission uniformity.

Text to video generation via knowledge distillation

<p>Text-to-video generation (T2V) has recently attracted more attention due to the wide application scenarios of video media. However, compared with the substantial advances in text-to-image generation (T2I), the research on T2V remains in its early stage. The difficulty mainly lies in maintaining the text-visual semantic consistency and the video temporal coherence. In this paper, we propose a novel distillation and translation GAN (DTGAN) to address these problems. First, we leverage knowledge distillation to guarantee semantic consistency. We distill text-visual mappings from a well-performing T2I teacher model and transfer it to our DTGAN. This knowledge serves as shared abstract features and high-level constraints for each frame in the generated videos. Second, we propose a novel visual recurrent unit (VRU) to achieve video temporal coherence. The VRU can generate frame sequences as well as process the temporal information across frames. It enables our generator to act as a multi-modal variant of the language model in neural machine translation task, which iteratively predicts the next frame based on the input text and the previously generated frames. We conduct experiments on two synthetic datasets (SBMG and TBMG) and one real-world dataset (MSVD). Qualitative and quantitative comparisons with state-of-the-art methods demonstrate that our DTGAN can generate results with better text-visual semantic consistency and temporal coherence.<strong></strong></p>

Promptable Game Models: Text-guided Game Simulation via Masked Diffusion Models

Neural video game simulators emerged as powerful tools to generate and edit videos. Their idea is to represent games as the evolution of an environment’s state driven by the actions of its agents. While such a paradigm enables users to play a game action-by-action, its rigidity precludes more semantic forms of control. To overcome this limitation, we augment game models with prompts specified as a set of natural language actions and desired states . The result—a Promptable Game Model (PGM)—makes it possible for a user to play the game by prompting it with high- and low-level action sequences. Most captivatingly, our PGM unlocks the director’s mode , where the game is played by specifying goals for the agents in the form of a prompt. This requires learning “game AI,” encapsulated by our animation model, to navigate the scene using high-level constraints, play against an adversary, and devise a strategy to win a point. To render the resulting state, we use a compositional NeRF representation encapsulated in our synthesis model. To foster future research, we present newly collected, annotated and calibrated Tennis and Minecraft datasets. Our method significantly outperforms existing neural video game simulators in terms of rendering quality and unlocks applications beyond the capabilities of the current state-of-the-art. Our framework, data, and models are available at snap-research.github.io/promptable-game-models.

Constraint Programming Approach to Coverage-Path Planning for Autonomous Multi-UAV Infrastructure Inspection

This article presents a constraint modeling approach to global coverage-path planning for linear-infrastructure inspection using multiple autonomous UAVs. The problem is mathematically formulated as a variant of the Min–Max K-Chinese Postman Problem (MM K-CPP) with multi-weight edges. A high-level constraint programming language is used to model the problem, which enables model execution with different third-party solvers. The optimal solutions are obtained in a reasonable time for most of the tested instances and different numbers of vehicles involved in the inspection. For some graphs with multi-weight edges, a time limit is applied, as the problem is NP-hard and the computation time increases exponentially. Despite that, the final total inspection cost proved to be lower when compared with the solution obtained for the unrestricted MM K-CPP with single-weight edges. This model can be applied to plan coverage paths for linear-infrastructure inspection, resulting in a minimal total inspection time for relatively simple graphs that resemble real transmission networks. For more extensive graphs, it is possible to obtain valid solutions in a reasonable time, but optimality cannot be guaranteed. For future improvements, further optimization could be considered, or different models could be developed, possibly involving artificial neural networks.

UEFSD: Unsupervised medical images fusion based on exclusive features and saliency detection for SPECT-MRI images of glioma

Background and ObjectiveTo address the key feature extraction problem in glioma image fusion due to blurred edge and texture features, with certain exclusive features entangled with noise, we propose an unsupervised fusion network based on exclusive feature unentanglement and saliency detection (UEFSD). Single-photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI) are taken as examples. MethodsFirst, the SPECT image in RGB space is converted to YCbCr space. Then, Y-channel SPECT and MRI images are fed into the UEFSD-based network and the attribute and object encoders are respectively used to separate the attribute vector of exclusive information for each modality and object feature mapping based on this attribute vector. Subsequently, the attribute vector and object feature maps are fused through the fusion decoder to obtain the fused image. In the training phase, first, the image is reconstructed using the attribute vector and object feature maps of the other modalities. The feature displaying the largest difference between the source and reconstructed images is identified as the exclusive feature that cannot be obtained via reconstruction. We trained the encoders and decoders in an unsupervised manner using exclusive feature loss as a high-level constraint and introducing a saliency mask as a low-level constraint. The trained attribute encoders and object encoders feed information to the fusion decoder to generate fused images. ResultUEFSD-based network was trained on Harvard Medical School's public dataset and tested by fusing MRI-T2 glioma images with their SPECT-T1 and SPECT-Tc counterparts. The experimental results demonstrate that UEFSD performs optimally in both subjective and objective indices when compared with nine other existing networks. ConclusionThe proposed UEFSD-based network can obtain clear edge and texture glioma features, which can effectively improve the rich and natural colour information of medical images and has the potential for computer-aided diagnosis system application.

Improvement of Human Pose Estimation and Processing With the Intensive Feature Consistency Network

The modeling of human body kye-points is the most significant aspect of pose estimation appropriately. Computer vision algorithm identifies human pose, body-movement, and action in many ways. Most of the previous works taken advantage for finding accuracy or efficiency in terms of speed. However, many techniques suffer for intensive computational demands with low-latency or higher proceeding speed. We have designed a unique approach for single-person pose estimation and action recognition which is well suited for fitness application and mobility activities. The proposed framework has been developed with a base network that provides an initial pose to further refinement through Intensive Feature Consistency (IFC) network. The IFC network enforces high-level constraints on the global body intensity correction and local body part adjustments. The proposed module reduces the impact of body joint movement diversity by interpreting long-term consistent view. We have illustrated the effectiveness of proposed framework through pose estimation accuracy improvement with two benchmark datasets. Which is specified state-of the- art performance of IFC network under the required real-time processing speed on the CPU platform. The IFC network has improved 99.1% of PCK body and 94.7% of PCK torso accuracy under 31 FPS, which is comparatively higher than the existing work.

Monocular 3D Pose Estimation via Pose Grammar and Data Augmentation.

In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image. Our model takes estimated 2D pose as the input and learns a generalized 2D-3D mapping function to leverage into 3D pose. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNNs) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i.e., kinematics, symmetry, motor coordination). The proposed model thus enforces high-level constraints over human poses. In learning, we develop a data augmentation algorithm to further improve model robustness against appearance variations and cross-view generalization ability. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization capability of different methods. We empirically observe that most state-of-the-art methods encounter difficulty under such setting while our method can well handle such challenges.

Effectively Incorporating Weighted Cost-to-go Heuristic in Suboptimal CBS (Extended Abstract)

Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The majority of modern MAPF solvers focus on improving CBS by reducing the size of this tree through various strategies with few methods modifying the low level planner. All low level planners in existing CBS methods use an unweighted cost-to-go heuristic, with suboptimal CBS methods also using a conflict heuristic to help the high level search. Contrary to prevailing beliefs, we show that the cost-to-go heuristic can be used significantly more effectively by weighting it in a specific manner alongside the conflict heuristic. We introduce two variants of doing so and demonstrate that this change can lead to 2-100x speedups in certain scenarios. Additionally, we show the first theoretical relation of prioritized planning and bounded suboptimal CBS and demonstrate that our methods are their natural generalization.

Multi-Level Dependent-Chance Model for Hydropower Reservoir Operations

Some hydropower reservoirs are operated under different constraint levels. For these reservoirs, a multi-level (ML) dependent-chance (DC) model is established. In the model, only when the higher-level constraints are satisfied are the lower-level constraints or system benefits considered. The multi-level dependent-chance (MLDC) model is specified by two models. One is based on existing reliability-constrained (RC) dynamic programming (DP), in which the soft constraints are addressed using reliability constraints of 1, and the priorities are reflected using the order of magnitudes of Lagrange multipliers. The other is the explicit dependent-chance reasoning in the DP recursive function, in which each soft constraint is represented as an objective function of negative expected failure time and the optimum is the solution with a larger value for all higher-level objective functions. The proposed models are applied to derive long-term operation rules for the hydropower system on the middle-lower Lancang River. The results show the feasibility and performances of the explicit graded constraint control of the proposed model and the solution methods.

Exploring the development of high-level contributions to body representation using the rubber hand illusion and the monkey hand illusion

During development our body undergoes significant changes, yet we are able to maintain a coherent experience of our body and sense of self. Bodily experience is thought to comprise integration of multisensory signals (vision, touch, and proprioception) constrained by top-down knowledge of body appearance. Evidence from developmental studies suggests that low-level multisensory integration develops throughout childhood, reaching adult levels by 10 years of age. However, how high-level cognitive knowledge changes during childhood to constrain our multisensory body experience is unknown. This study describes four experiments examining high-level contributions to the bodily experience in children compared with adults using the rubber hand illusion and a monkey hand illusion. We found that children (5–17 years of age) exhibited more flexible body representations, showing stronger illusions for small and fantastical (monkey) fake hands compared with adults. Conversely, using a task indirectly capturing changes in hand size, we found that children and adults demonstrated statistically equivalent increases and decreases in hand size following illusions over large and small hands, respectively. Interestingly, at baseline children showed a bias in reporting larger hand size judgments that decreased with age. Finally, we did not find a relationship between individual differences in fantasy proneness and illusion strength for a fantastical (monkey) hand for children or adults, suggesting that developmental changes of top-down constraints are not purely driven by more diffuse boundaries between imagination and reality. These data suggest that high-level constraints acting on our multisensory body experience change during development, allowing children a more flexible bodily experience compared with adults.

Simplified-attention Enhanced Graph Convolutional Network for 3D human pose estimation

Optical motion capture systems have been used intensively to obtain human body poses. However, there still exist several problems. First is the dislocation problem caused by joints being too close together. The second is the joint lost problem. Restricted by severe self-occlusions, cameras may not capture the target joints. Given this observation, we investigate the high-level constraints over human poses to solve these two problems. In this work, we present a Simplified-attention Enhanced Graph Convolutional Network (SaEGC-Net) to extract both spatial and temporal features from monocular videos flexibly. The SaEGC-Net for 3D human pose estimation is U-shaped and involves the Cascaded Spatial-Temporal Graph Convolutional (CST-GC) blocks and the Simplified Spatial-Temporal Attention (SST-Att) blocks, allowing for drawing long-range dependencies between unconnected joints by graph topologies and attention mechanism, respectively. Specifically, the CST-GC block embeds two predefined graph structures into a convolutional network, incorporating discriminative features from distant joints. The proposed SST-Att block disregards redundant information by sharing part of the attention map, which is highly lightweight. It also considers dimension-expanded joint relationships to maintain the diversity of dependence. To evaluate the effectiveness of our method, we conduct extensive experiments on two datasets: Human3.6M and our own dataset FDU-Motion. Results demonstrate that our model achieves excellent performance and can competently handle the above two problems. Also, ablation studies show that our network’s submodules can better exploit the motion information of the human body.

Sustainable Governance and Green Innovation: A Perspective from Gender Diversity in China’s Listed Companies

Sustainable governance has become essential in corporate sustainable development. As female executives bring diversity to corporate governance, their impact on the corporate sustainability has attracted wide attention. Using the evidence from China’s listed companies in Shanghai and Shenzhen A-shares between 2010 and 2019, this paper examines the impact of gender diversity of executives on corporate green innovation. We find that the proportion of female executives has a significant negative impact on corporate green innovation. The results show: (1) Considering the heterogeneity of corporate risks, the negative impact of female executives on green innovation exists when the company is exposed to high risks, that is, in the subsample of firms with high risk-taking level and financial constraints; (2) considering the heterogeneity of corporate characteristics, female executives have a negative impact on green innovation in small non-state-owned companies with high separation of ownership and control; (3) considering the heterogeneity of industries, the effect of female executives on green innovation is significant in non-heavy pollution industries; (4) the mechanism test shows that patriarchy culture weakens the influence of female executives. In an environment where men are in power, the impact of female executives on green innovation is not significant; (5) taking the 2018 environmental fee-to-tax policy as a quasi-experiment, we find that female executives will instead promote corporate green innovation in areas where the environmental tax burden has increased significantly. The results imply that since corporate green innovation is a high-risk investment, female executives will make green innovation decisions more prudently based on corporate operating characteristics. This research provides a new perspective for understanding the role of female executives in corporate governance and corporate sustainable development.

Multi-Actuation Controller for Performance Vehicles: Optimal Torque Allocation and Active Aerodynamic

A multi-actuation model predictive controller is designed to improve the stability of the performance vehicles during high-speed maneuvers. The actuators included in this study are four electric motors for the wheels and two active aerodynamic wings at the front and rear of the vehicle. The designed controller integrates optimal corner torque allocation with an optimal active aerodynamics control system. A model predictive control scheme is used to adjust the air wings angle of attack and optimize the corner torques. A high-level constraint adjustment module is added to the controller to observe nonlinear tire behavior and optimize aerodynamic wings activation as required. Nonlinear tire behavior and actuator dynamics are considered and included in the prediction model. The controller performance is verified in simulation with MATLAB/Simulink and CarSim.

Architectural layout design through deep learning and agent-based modeling: A hybrid approach

This paper presents a novel hybrid approach for generating automated 2D architectural layouts by combining agent-based modeling with deep learning algorithms. The primary goal of this research is to maintain the designers' high-level, supervisory control over the generated results and process, allowing them to manage the whole process so that the created results satisfy the desired topological and geometrical constraints. The proposed hybrid approach consists of two different methods. First, hierarchical phases of agent-based modeling are simulated to generate a bubble diagram that satisfies the topological conditions. A rule-based algorithm converts bubble diagrams into heat maps. Second, the pix2pix algorithm translates the heat maps into an architectural spatial layout as a conditional GAN and deep learning approach. In doing so, a unique dataset was manually generated, and the cGAN algorithm was trained based on this dataset. The hybrid method of these processes makes it possible to generate an architectural layout based on a particular footprint and desired high-level constraints. The findings of agent-based modeling showed complete consistency with the required topological requirements, whereas deep learning results demonstrated the ability of cGAN to satisfy geometrical constraints learned throughout the training phase. The hybrid method's results showed enhanced computational accuracy in generating synthetic architectural layouts compared to previous studies.

Automated Movement Assessment in Stroke Rehabilitation

We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e., type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e., lower extremity training for neurological accidents).

Declarative data serving

Recent advances in computer architecture and networking have ushered in a new age of edge computing, where computation is placed close to the point of data collection to facilitate low-latency decision making. As the complexity of such deployments grow into networks of interconnected edge devices, getting the necessary data to be in "the right place at the right time" can become a challenge. We envision a future of edge analytics where data flows between edge nodes are declaratively configured through high-level constraints. Using machine learning model-serving as a prototypical task, we illustrate how the heterogeneity and specialization of edge devices can lead to complex, task-specific communication patterns even in relatively simple situations. Without a declarative framework, managing this complexity will be challenging for developers and will lead to brittle systems. We conclude with a research vision for database community that brings our perspective to the emergent area of edge computing.

A new constraint programming model and solving for the cyclic hoist scheduling problem

The cyclic hoist scheduling problem (CHSP) is a well-studied optimisation problem due to its importance in industry. Despite the wide range of solving techniques applied to the CHSP and its variants, the models have remained complicated and inflexible, or have failed to scale up with larger problem instances. This article re-examines modelling of the CHSP and proposes a new simple, flexible constraint programming formulation. We compare current state-of-the-art solving technologies on this formulation, and show that modelling in a high-level constraint language, MiniZinc, leads to both a simple, generic model and to computational results that outperform the state of the art. We further demonstrate that combining integer programming and lazy clause generation, using the multiple cores of modern processors, has potential to improve over either solving approach alone.

Nutmeg: a MIP and CP Hybrid Solver Using Branch-and-Check

This paper describes the implementation of Nutmeg, a solver that hybridizes mixed integer linear programming and constraint programming using the branch-and-cut style of logic-based Benders decomposition known as branch-and-check. Given a high-level constraint programming model, Nutmeg automatically derives a mixed integer programming master problem that omits global constraints with weak linear relaxations, and a constraint programming subproblem identical to the original model. At every node in the branch-and-bound search tree, the linear relaxation computes dual bounds and proposes solutions, which are checked for feasibility of the omitted constraints in the constraint programming subproblem. In the case of infeasibility, conflict analysis generates Benders cuts, which are appended to the linear relaxation to cut off the candidate solution. Experimental results show that Nutmeg’s automatic decomposition outperforms pure constraint programming and pure mixed integer programming on problems known to have successful implementations of logic-based Benders decomposition, but performs poorly on general problems, which lack specific decomposable structure. Nonetheless, Nutmeg outperforms the standalone approaches on one problem with no known decomposable structure, providing preliminary indications that a hand-tailored decomposition for this problem could be worthwhile. On the whole, Nutmeg serves as a valuable tool for novice modelers to try hybrid solving and for expert modelers to quickly compare different logic-based Benders decompositions of their problems.

Abstract Constraints for Safe and Robust Robot Learning from Demonstration

My thesis research incorporates high-level abstract behavioral requirements, called ‘conceptual constraints’, into the modeling processes of robot Learning from Demonstration (LfD) techniques. My most recent work introduces an LfD algorithm called Concept Constrained Learning from Demonstration. This algorithm encodes motion planning constraints as temporal Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Future work will incorporate conceptual constraints into human-aware motion planning algorithms. Additionally, my research will investigate how these concept constrained algorithms and models are best incorporated into effective interfaces for end-users.