Abstract: Describe is a resonator that uses ferroelectric (FE) material in a capacitive structure. The resonator includes a first plurality of metal lines extending in a first direction; an array of capacitors comprising ferroelectric material; a second plurality of metal lines extending in the first direction, wherein the array of capacitors is coupled between the first and second plurality of metal lines; and a circuitry to switch polarization of at least one capacitor of the array of capacitors. The switching of polarization regenerates acoustic waves. In some embodiments, the acoustic mode of the resonator is isolated using phononic gratings all around the resonator using metal lines above and adjacent to the FE based capacitors.

Abstract: Techniques for utilizing model and hyperparameter optimization for multi-objective machine learning are disclosed. In one example, a method comprises the following steps. One of a plurality of hyperparameter optimization operations and a plurality of model parameter optimization operations are performed to generate a first solution set. The other of the plurality of hyperparameter optimization operations and the plurality of model parameter optimization operations are performed to generate a second solution set. At least a portion of the first solution set and at least a portion of the second solution set are combined to generate a third solution set.

Abstract: A memory device, an integrated circuit component including an array of the memory devices, and an integrated device assembly including the integrated circuit component. The memory devices includes a first electrode; a second electrode including an antiferromagnetic (AFM) material; and a memory stack including: a first layer adjacent the second electrode and including a multilayer stack of adjacent layers comprising ferromagnetic materials; a second layer adjacent the first layer; and a third layer adjacent the second layer at one side thereof, and adjacent the first electrode at another side thereof, the second layer between the first layer and the third layer, the third layer including a ferromagnetic material. The memory device may correspond to a magnetic tunnel junction (MTJ) magnetic random access memory bit cell, and the memory stack may correspond to a MTJ device.

Abstract: A pbit device, in one embodiment, includes a first field-effect transistor (FET) that includes a source region, a drain region, a source electrode on the source region, a drain electrode on the drain region, a channel region between the source and drain regions, a dielectric layer on a surface over the channel region, an electrode layer above the dielectric layer, and a ferroelectric (FE) material layer between the dielectric layer and the electrode layer. The pbit device also includes a second FET comprising a source electrode, a drain electrode, and a gate electrode. The drain electrode of the second FET is connected to the drain electrode of the first FET.

Abstract: A computer-implemented machine learning model training method and resulting machine learning model. One embodiment of the method may comprise receiving at a computer memory training data; and training on a computer processor a machine learning model on the received training data using a plurality of batch sizes to produce a trained processor. The training may include calculating a plurality of activations during a forward pass of the training and discarding at least some of the calculated plurality of activations after the forward pass of the training.

Abstract: One embodiment provides a method, including: receiving at least one deep learning job for scheduling and running on a distributed system comprising a plurality of nodes; receiving a batch size range indicating a minimum batch size and a maximum batch size that can be utilized for running the at least one deep learning job; determining a plurality of runtime estimations for running the at least one deep learning job; creating a list of optimal combinations of (i) batch sizes and (ii) numbers of the plurality of nodes for running both (a) the at least one deep learning job and (b) current deep learning jobs; and scheduling the at least one deep-learning job at the distributed system, responsive to identifying, by utilizing the list, that the distributed system has necessary processing resources for running both (iii) the at least one deep learning job and (iv) the current deep learning jobs.

Abstract: A method for producing a media file with a blur effect in an electronic device is provided. The method includes segmenting an image frame into a plurality of segments. Further, the method includes determining at least one segment from the plurality of segments comprising one of a foreground Region of Interest (ROI) and a background region of the ROI and detecting whether one of the foreground region of the ROI and the background region of the ROI comprises motion information and static information. Further, the method includes automatically applying a motion type blur effect and/or a static type blur effect on one of the foreground region of the ROI, and the background region of the ROI. The method includes generating the media file based on the applied the motion type blur effect and the static type blur effect and storing the media file.

Abstract: Methods, systems, and computer program products for accelerating inference of transformer-based models are provided herein. A computer-implemented method includes obtaining a machine learning model comprising a plurality of transformer blocks, a task, and a natural language dataset; generating a compressed version of the machine learning model based on the task and the natural language dataset, wherein the generating comprises: obtaining at least one set of tokens, wherein each token in the set corresponds to one of the items in the natural language dataset, identifying and removing one or more redundant output activations of different ones of the plurality of transformer blocks for the at least one set of tokens, and adding one or more input activations corresponding to the one or more removed output activations into the machine learning model at subsequent ones of the plurality of the transformer blocks; and outputting the compressed version of the machine learning model to at least one user.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate switching a model training process from a ground truth training phase to an adversarial training phase based on performance of a model trained in the ground truth training phase are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that identifies a performance condition of a model trained in a model training process. The computer executable components can further comprise a trainer component that switches the model training process from a ground truth training process to an adversarial training process based on the identified performance condition.

Abstract: Described is an ultra-dense ferroelectric memory. The memory is fabricated using a patterning method by that applies atomic layer deposition with selective dry and/or wet etch to increase memory density at a given via opening. A ferroelectric capacitor in one example comprises: a first structure (e.g., first electrode) comprising metal; a second structure (e.g., a second electrode) comprising metal; and a third structure comprising ferroelectric material, wherein the third structure is between and adjacent to the first and second structures, wherein a portion of the third structure is interdigitated with the first and second structures to increase surface area of the third structure. The increased surface area allows for higher memory density.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform: determining one or more work orders for a service provider; determining an optimal service route for the one or more work orders; updating an available time slot in a work schedule of the service provider; and transmitting the work schedule updated with the optimal service route to be displayed on a user interface executed on a device of the service provider. Other embodiments are also provided.

Abstract: The invention relates to the field of special purpose robotic systems to conduct external functions such as cleaning, monitoring and inspection of structures such as tubular assets in a splash zone. The splash zone is defined as the section of a marine structure that is periodically in and out of water due to the action of waves or tides, usually falling within (+)10m to (?)20m water depth. In embodiments, splash zone inspection robot system 1 comprises station 300, submersible saddle 350, submersible robot 400, and subsea robot controller 308. A predetermined set of controllable clamps selectively secure submersible robot 400 to submersible saddle 350 or structure 2 and allow incremental traversal along submersible saddle 350 or structure 2.

Abstract: Techniques for distributed federated learning leverage a multi-layered defense strategy to provide for reduced information leakage. In lieu of aggregating model updates centrally, an aggregation function is decentralized into multiple independent and functionally-equivalent execution entities, each running within its own trusted executed environment (TEE). The TEEs enable confidential and remote-attestable federated aggregation. Preferably, each aggregator entity runs within an encrypted virtual machine that support runtime in-memory encryption. Each party remotely authenticates the TEE before participating in the training. By using multiple decentralized aggregators, parties are enabled to partition their respective model updates at model-parameter granularity, and can map single weights to a specific aggregator entity. Parties also can dynamically shuffle fragmentary model updates at each training iteration to further obfuscate the information dispatched to each aggregator execution entity.

Abstract: Techniques for distributed federated learning leverage a multi-layered defense strategy to provide for reduced information leakage. In lieu of aggregating model updates centrally, an aggregation function is decentralized into multiple independent and functionally-equivalent execution entities, each running within its own trusted executed environment (TEE). The TEEs enable confidential and remote-attestable federated aggregation. Preferably, each aggregator entity runs within an encrypted virtual machine that support runtime in-memory encryption. Each party remotely authenticates the TEE before participating in the training. By using multiple decentralized aggregators, parties are enabled to partition their respective model updates at model-parameter granularity, and can map single weights to a specific aggregator entity. Parties also can dynamically shuffle fragmentary model updates at each training iteration to further obfuscate the information dispatched to each aggregator execution entity.

Abstract: Disclosed herein is a humeral head and cup trials, a system for humeral trialing, and a method for removing a humeral head and cup trial from a humeral stem. The humeral trial may include a first portion, a second portion, and a post extending from the second portion. The first portion may define an articular surface. The post may define a first length in a first configuration and a second length in a second configuration. The first length may be greater than the second length. The post may change from the first configuration to the second configuration by moving the first portion with respect to the second portion.

Abstract: Methods, systems, and computer program products for accelerating inference of neural network models via dynamic early exits are provided herein. A computer-implemented method includes determining a plurality of candidate exit points of a neural network model; obtaining a plurality of outputs of the neural network model for data samples in a target dataset, wherein the plurality of outputs comprises early outputs of the neural network model from the plurality of candidate exit points and regular outputs of the neural network model; and a set of one or more exit points from the plurality of candidate exits points that are dependent on the target dataset based at least in part on the plurality of outputs.

Abstract: Embodiments provide methods and systems for predicting chargeback behavioral data of an account holder. The method performed by a server system includes accessing payment transaction data associated with the account holder from a transaction database. The payment transaction data includes a set of transaction indicators corresponding to payment transactions performed by the account holder within a predetermined time period. The method further includes generating a set of transaction features based on the set of transaction indicators. Furthermore, the method includes computing, via a chargeback risk prediction model, a set of chargeback risk probability scores corresponding to one or more time intervals associated with the account holder based, at least in part, on the set of transaction features. The method also includes transmitting a notification to an issuer server associated with the account holder based, at least in part, on the set of chargeback risk probability scores.

Abstract: Seal assembly 100, useful for repairing an uncontrolled surface such as pipeline 110, comprises seal 102 which is subjected to pressure and temperature and remains operable under, e.g., subsea conditions. Seal assembly 100 is capable of sealing pipelines of diameters up to 48 inches. Seal assembly 100 is actuated/energized by axially compressing/loading seal assembly 100 between one or more controlled surfaces and an uncontrolled surface and can be used for repairing and providing sealing in flowline or pipelines of either subsea or other petrochemical industries where the pipelines. Spool assembly 700 comprising seal assembly 100 may be used to repair a damaged pipeline section after the damaged section is removed to provide a sealed replacement fluid path for the pipeline.

Abstract: Seal assembly 100, useful for repairing an uncontrolled surface such as pipeline 110, comprises seal 102 which is subjected to pressure and temperature and remains operable under, e.g., subsea conditions. Seal assembly 100 is capable of sealing pipelines of diameters up to 48 inches. Seal assembly 100 is actuated/energized by axially compressing/loading seal assembly 100 between one or more controlled surfaces and an uncontrolled surface and can be used for repairing and providing sealing in flowline or pipelines of either subsea or other petrochemical industries where the pipelines. Spool assembly 700 comprising seal assembly 100 may be used to repair a damaged pipeline section after the damaged section is removed to provide a sealed replacement fluid path for the pipeline.

Abstract: The disclosure provides compositions and methods of use of formulations effective for the delivery of biologically active molecules and materials, including therapeutic and diagnostic agents, and combinations thereof, to cells, tissues, and living organisms. More particularly, the present invention is related to the incorporation of hydrophobic nanoparticles, such as hydrophobic metal nanoparticles, into the membranes of vesicular delivery vehicles. These hydrophobic nanoparticles enhance the endosomal membrane fusogenicity of the formulations through the promotion of inverted hexagonal phase formation in the lipid bilayers of the vesicular delivery vehicles. As a result, the vesicular delivery vehicles more readily fuse with the endosomal membranes, leading to enhanced endosomal escape of the vesicular delivery vehicle and its contents, thereby facilitating the delivery of the biologically active molecules and materials incorporated within the vesicular delivery vehicles to their sites of action.