Abstract: During training of deep neural networks, a Copernican loss (LC) is designed to augment a primary loss function, for example, a standard Softmax loss, to explicitly minimize intra-class variation and simultaneously maximize inter-class variation. Copernican loss operates using the cosine distance and thereby affects angles leading to a cosine embedding, which removes the disconnect between training and testing.

Abstract: Disclosed herein is an improved method for identifying images containing objects-of-interest from a large set of images. The method comprises mixing two or more of the images to create a grouped image and exposing the grouped image to an object detector trained on grouped images to make an initial determination that the grouped image was formed from at least one image containing an object-of-interest. The images which formed the grouped image are then exposed to regular object detectors to determine a classification of the object-of-interest.

Abstract: Disclosed herein is a system and method for matching products detected in an image of a shelf. The match or non-match of the products is then used to make a determination that the products are correctly positioned on the shelf of if the positioning of the products represents a plug or spread situation.

Abstract: Provided is a system, method, and computer program product for segmenting vessels in an ultrasound image. The method includes detecting edges of a vessel in the ultrasound image; detecting a vessel contour of the vessel in the ultrasound image based on the detected edges and a distance regularized level set evolution; and tracking the vessel contour with a Kalman Filter.

Abstract: Disclosed herein is a method for defining a model of a trusted IoT security gateway architecture based on a microhypervisor, wherein evaluation of the model provides a guarantee that the correct security protections are applied to each IoT device's network traffic at all times, including when under attack. The models defined in accordance with the method disclosed herein are used to verify security gateway architectures that provide robust trust properties to a broad range of legacy hardware platforms utilizing existing software with a reasonable performance overhead.

Abstract: An automated inventory monitoring system includes an image capture module able to create an image of an aisle of a retail store. Images of products labels are identified in the image and classified as shelf labels or peg labels. For shelf labels, an area of the shelf is defined and associated with the shelf label. Images of products are identified in the image and products on the shelf within an area associated with a shelf label are associated with the shelf label. Products located below a peg label are associated with the peg label. Based on the association between labels and products, out-of-stock products, plugs and spread may be detected and reported to the staff of the retail store.

Abstract: The Reconfigurable Wheel-Track (RWT) is a novel mechanism that allows a wheel to transform into a track, and vice-versa. The wheel permits a vehicle to travel quickly over smooth and semi-rough terrain, then, on-the-fly, transform rapidly into a powered track for crossing extreme terrain. The reconfigurable wheel-track consists of several main components: an outer tire/tread, drive mechanisms for the wheel and track, support mechanisms for the outer tread when in either wheel mode or track mode, and a reconfiguration mechanism that facilitates the transformation from a wheel to a track and vice-versa. The reconfigurable wheel-track includes sensing, actuation, and controls to facilitate efficient and effective transition from wheel to track and vice-versa, and securely maintain each shape.

Abstract: A method of manufacturing a porous part includes controlled freeze casting of a slurry. After freezing, a solvent in the slurry is removed by sublimation and the remaining material is sintered to form the porous part. Spatial and temporal control of thermal conditions at the boundary and inside of the mold can be controlled to create parts with controlled porosity, including size, distribution, and directionality of the pores. Porous parts with near-net-shape from ceramics, metals, polymers and other materials and their combinations can be created.

Abstract: Disclosed herein is an Overmoded Bulk Acoustic Resonator (OBAR) and a solidly-mounted OBAR (SBAR), which operate in a partially transduced 2nd overtone split between piezoelectric and electrode layers using dual all metal Bragg mirrors. The devices may be deployed in a series configuration. The devices have arbitrarily thick electrodes to minimize ohmic loss and bandwidths high enough to meet filtering requirements of 5G networks. The devices provide sharp filtering which can be performed directly at each antenna element in a form factor much smaller than the half-wavelength separation between adjacent antenna elements required when using electromagnetic resonators.

Abstract: Disclosed herein is a system and method for the automatic detection of persons engaged in the open carry of firearms at a venue. The system and method comprise strategically placed cameras at the venue which are connected to edge devices which extract frames from video generated by the cameras. The video frames are sent to a server for analysis by an AI/ML model trained to detect firearms and, specifically, to detect persons carrying firearms. If a person wielding a firearm is detected in any image, an alert is raised and local authorities are automatically contacted. The system is designed to run continuously such as to be able to quickly detect a person in a venue carrying a firearm.

Abstract: Disclosed herein is a framework for the modeling and design of materials based on differentiable programming, where the models can be trained by gradient-based optimization. Within this framework, all the components are differentiable and can be seamlessly integrated and unified with deep learning. The framework can design and optimize materials for a variety of applications.

Abstract: Disclosed herein is a system architecture that structures commodity heterogeneous interconnected computing platforms around universal object abstractions, which are a fundamental system abstraction and building block that provides practical and provable end-to-end guarantees of security, correctness, and timeliness for the platform.

Abstract: A flexible and stretchable integrated electronic device includes a substrate having a stiffness gradient, wherein a rigid electronic device is embedded within the substrate. The stiffness gradient within the substrate prevents delamination at the interface between the substrate and the embedded device. The stiffness gradient is accomplished by providing at least two distinct zones in the substrate with uniform stiffness, with each zone decreasing in stiffness as in a distance from the embedded device increases, or the gradient is accomplished by having a zone with a varying stiffness.

Abstract: A high-density storage system for goods is described in which totes carrying the goods are storage in a storage structure and stored and retrieved by robotic carriers. The carriers move laterally and/or longitudinally along the exterior of the support structure and retrieve totes from the interior of the structure by manipulating rows of coupled totes. Totes at the ends of rows are quickly removed and stored in another row until the desired tote appears at the end of the row, at which point the carrier proceeds with the tote to the exit point of the storage system. Storing totes is also a quick action by pushing them into any row. As a tote is pushed into the row, it will automatically couple with a tote inside the row that it comes into contact with.

Abstract: Disclosed herein is a system and method using an equivariant neural network for predicting quantum mechanical charge density. The equivariant neural network serves as a surrogate for the density-functional theory used to calculate a selfconsistent field and predicts the central observable charge density, which, in addition to enabling force calculations, can also accelerate DFT itself and compute a full range of chemical properties.

Abstract: A system includes an inertial sensing device having an inertial sensor and plurality of stress sensors configured to measure stress applied to the inertial sensing device, and at least one computing device configured to: receive sensor data from the inertial sensor and the plurality of stress sensors; and determine a drift compensation of the inertial sensor based on the sensor data. Other systems, methods, and devices are disclosed.

Abstract: A near-field probe (and associated method) compatible with near-infrared electromagnetic radiation and high temperature applications above 300°° C. (or 500° C. in some applications) includes an optical waveguide and a photonic thermal emitting structure comprising a near-field thermally emissive material coupled to or part of the optical waveguide. The photonic thermal emitting structure is structured and configured to emit near-field energy responsive to at least one environmental parameter of interest, and the near-field probe is structured and configured to enable extraction of the near-field energy to a far-field by coupling the near-field energy into one or more guided modes of the optical waveguide.

Abstract: A method for learning a representation of a sequence of frames includes encoding, via an encoder network, the sequence of frames to obtain a set of feature maps and extracting, a motion-guided slot learning mechanism, mid-level features from the set of feature maps. The method further includes quantizing the mid-level features via a vector quantization process to obtain a set of tokens, and decoding, via a decoder network, the tokens to obtain a reconstructed sequence of frames. The method still further includes optimizing a combination of a reconstruction loss and a motion loss to train the encoder and decoder networks.

Abstract: Provided is a system, method, and computer program product for creating a deep-learning model for processing image data. The method includes establishing dense connections between each layer of a plurality of layers of a convolutional neural network (CNN) and a plurality of preceding layers of the CNN, downsampling an input of each downsampling layer of a plurality of downsampling layers in a first branch of the CNN, and upsampling an input of each upsampling layer of a plurality of upsampling layers in a second branch of the CNN by convolving the input.

Abstract: A method for sequential point cloud forecasting is described. The method includes training a vector-quantized conditional variational autoencoder (VQ-CVAE) framework to map an output to a closest vector in a discrete latent space to obtain a future latent space. The method also includes outputting, by a trained VQ-CVAE, a categorical distribution of a probability of V vectors in a discrete latent space in response to an input previously sampled latent space and past point cloud sequences. The method further includes sampling an inferred future latent space from the categorical distribution of the probability of the V vectors in the discrete latent space. The method also includes predicting a future point cloud sequence according to the inferred future latent space and the past point cloud sequences. The method further includes denoising, by a denoising diffusion probabilistic model (DDPM), the predicted future point cloud sequences according to an added noise.