Mitsubishi Software UPD Download
This software package provides the PyTorch implementation of Partial Group Convolutional Neural Networks described in the NeurIPS 2022 paper "Learning Partial Equivariances from Data". Partial G-CNNs are able to learn layer-wise levels of partial and full equivariance to discrete, continuous groups and combinations thereof, directly from data. Partial G-CNNs retain full equivariance when beneficial, but adjust it whenever it becomes harmful. The software package also provides scripts to reproduce the results in the paper.
Mitsubishi Software Download
This software package implements the CISOR reconstruction algorithm along with other benchmark algorithms that attempt to recover the distribution of refractive indices of an object in a multiple scattering regime. The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications. While the most popular formulations of the problem are based on linearizing the object-light relationship, there is an increased interest in considering nonlinear formulations that can account for multiple light scattering. Our proposed algorithm for nonlinear diffractive imaging, called Convergent Inverse Scattering using Optimization and Regularization (CISOR), is based on our new variant of fast . . .
Many sounds that humans encounter are hierarchical in nature; a piano note is one of many played during a performance, which is one of many instruments in a band, which might be playing in a bar with other noises occurring. Inspired by this, we re-frame the musical source separation problem as hierarchical, combining similar instruments together at certain levels and separating them at other levels. This allows us to deconstruct the same mixture in multiple ways, depending on the appropriate level of the hierarchy for a given application. In this software package, we present pytorch implementations of various methods for hierarchical musical instrument separation, with some methods focusing on separating specific instruments (like guitars) . . .
Quantum Annealing (QA) can be used to quickly obtain near-optimal solutions for Quadratic Unconstrained Binary Optimization (QUBO) problems. In QA hardware, each decision variable of a QUBO should be mapped to one or more adjacent qubits in such a way that pairs of variables defining a quadratic term in the objective function are mapped to some pair of adjacent qubits. However, qubits have limited connectivity in existing QA hardware. This software Python codes implementing integer linear programs to search for an embedding of the problem graph into certain classes of minors of the QA hardware, which we call template embeddings. In particular, we consider the template embedding that are minors of the Chimera graph used in D-Wave . . .
In this software release, we provide a PyTorch implementation of the adversarially-contrastive optimal transport (ACOT) algorithm. Through ACOT, we study the problem of learning compact representations for sequential data that captures its implicit spatio-temporal cues. To separate such informative cues from the data, we propose a novel contrastive learning objective via optimal transport. Specifically, our formulation seeks a low-dimensional subspace representation of the data that jointly (i) maximizes the distance of the data (embedded in this subspace) from an adversarial data distribution under a Wasserstein distance, (ii) captures the temporal order, and (iii) minimizes the data distortion. To generate the adversarial distribution, . . .
This package provides a generalized solution for planning dynamic contact-interaction trajectories. The software package leverages existing open-source code for [Contact Implicit Trajectory Optimization]( ) based on a variable smooth contact model and a successive convexification algorithm for the trajectory optimization. This software package adds a penalty loop that adjusts the penalty on the virtual forces automatically and a post-process stage that improves solutions through a forward pass by exploiting the contact information implied by the utilization of the virtual forces.Underactuated dynamics with frictional rigid-body contacts is modeled using [MuJoCo]( ). The convex . . .
This software demonstrates the use of the Functional Mockup Interface (FMI) to construct extended Kalman filters (EKF) and ensemble Kalman filters (EnKF) for state estimation in Modelica using the Dymola compiler. One of the key advantages of Modelica is that it enables users to create large-scale physical system models via the interconnection of simpler subsystem or component models, and thereby manage the complexity inherent in describing these large systems. While one candidate use for such models is in using data to estimate unmeasured variables of a complex system, the equation compilation process can make it difficult to work with the state vector directly when implementing state estimation methods. Functional mockup . . .
This software is the pytorch implementation of FoldingNet++, which is a novel end-to-end graph-based deep autoencoder to achieve compact representations of unorganized 3D point clouds in an unsupervised manner. The encoder of the proposed networks adopts similar architectures as in PointNet, which is a well-acknowledged method for supervised learning of 3D point clouds, such as recognition and segmentation. The decoder of the proposed networks involves three novel modules: folding module, graph-topology-inference module, and graph-filtering module. The folding module folds a canonical 2D lattice to the underlying surface of a 3D point cloud, achieving coarse reconstruction; the graph-topology-inference module learns a graph . . .
Non-negative data arise in a variety of important signal processing domains, such as power spectra of signals, pixels in images, and count data. We introduce a novel non-negative dynamical system model for sequences of such data. The model we propose is called non-negative dynamical system (NDS), and bridges two active fields, dynamical systems and nonnegative matrix factorization (NMF). Its formulation follows that of linear dynamical systems, but the observation and the latent variables are assumed non-negative, the linear transforms are assumed to involve non-negative coefficients, and the additive random innovations both for the observation and the latent variables are replaced by multiplicative random innovations. The software . . .
CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics Several recommended practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.
The comfortable setup software for WINDOWS based personal computers allows a perfect tuning of the MR-J2S and the connected servomotors. This software makes it easy to do monitor, diagnosis, reading and writing of parameters, and test operations from the setup via a personal computer.
The software changes gains automatically and searches out the value that ensures the shortest possible settling time with a minimum overshoot and vibration. Ability is best shown, when high-level adjustment is required.