Abstract: Systems and methods for autonomous waste and/or recycling collection by a waste services provider during performance of a waste service activity are provided. In a waste services environment, a waste service vehicle is configured to provide services to customers. The system and method can include, without limitation: a collection station or “hub” at a centralized location in a residential neighborhood; a plurality of autonomous collection vehicles capable of traveling between the collection station and the residences in the neighborhood to collect waste/items and/or containers at the residences and deliver the waste/items and/or containers to the collection station; and a waste service vehicle capable of collecting waste/items from the collection station and transporting the waste/items away from the neighborhood to a landfill or other collection, disposal or processing site.

Abstract: Systems and methods for determining safe and unsafe zones in a workspace—where safe actions are calculated in real time based on all relevant objects (e.g., some observed by sensors and others computationally generated based on analysis of the sensed workspace) and on the current state of the machinery (e.g., a robot) in the workspace—may utilize a variety of workspace-monitoring approaches as well as dynamic modeling of the robot geometry. The future trajectory of the robot(s) and/or the human(s) may be forecast using, e.g., a model of human movement and other forms of control. Modeling and forecasting of the robot may, in some embodiments, make use of data provided by the robot controller that may or may not include safety guarantees.

Abstract: A lithium and zinc ion bi-polar battery includes, in one example, a plurality of carbon or titanium bi-polar current collectors arranged with cells to form a stack of bi-polar configuration such that each of the bi-polar current collectors is between and in direct contact with a zinc electrode and lithium-ion intercalation electrode of an adjacent pair of the cells.

Abstract: A test fixture, for a heatsink, may include a probe assembly with a thermocouple probe configured to removably contact a bottom surface of a pedestal of the heatsink, and measure a surface temperature of the heatsink. The test fixture may include an insulator housing configured to house the probe assembly and a heater block, and to insulate the probe assembly from the heater block. The heater block may be provided within the insulator housing and may be configured to provide heat to the heatsink via the bottom surface of the pedestal of the heatsink. The test fixture may include a mounting block connected to the insulator housing and configured to connect to the heatsink.

Abstract: A data management system (DMS) may support the backing up of scalable computing objects, such as hierarchical computing objects. For example, the DMS may backup a computing system and facilitate that capture and storage of snapshots of the computing system. The DMS may determine to capture a first snapshot of a computing object within the computing system. The DMS may identify components corresponding to respective portions of the computing object and cause the computing system to generate respective snapshots of the components. That is, the DMS may cause the computing system to generate multiple individual snapshots that together may constitute snapshot of the computing object. The DMS may generate the first snapshot that includes metadata for the snapshots of the components. The metadata may include a first identifier for the first snapshot and respective identifiers for the respective snapshots of the components.

Abstract: This invention provides methods and systems for enhancement of protein production from mammalian cell lines in a drug inducible manner. The methods described herein can be used to generate a protein production cell line wherein the gene coding the protein product of interest is inserted into specific safe harbor loci (SHL) within the cell's genome and the gene copy number is induced to amplify by the use of an antibiotic inducer. The method enables for the conditional activation of the drug inducible transposase. The drug inducible gene amplification method described herein effectively functions as a molecular dial: combining drug-inducible homologous recombination and conditional gene activation to fine-tune gene amplification in mammalian systems.

Abstract: A document conveying apparatus includes a correction roller, a correction portion, a biasing portion, and a regulating portion. The correction roller has a rotating shaft, and a pair of contact portions arranged spaced apart on the rotating shaft, each including contact pieces extending in radial directions from the same circumferential positions, and each capable of coming in contact with a leading edge of a document conveyed on a conveying path. The correction portion allows the correction roller to rotate only when the leading edge of the document comes in contact with both of the pair of contact portions and the skew of the document is corrected. The biasing portion biases the correction roller in a rotational direction of the correction roller. The regulating portion regulates rotation of the correction roller biased by the biasing portion so as to retract the contact pieces of the contact portions from the conveying path.

Abstract: A method for multi-object tracking from video. The method includes the following steps: (1) Capturing frames from the streaming source and preprocess the data; (2) Extract video features with three choices: a 3D-CNN backbone followed by a Transformer Encoder, a Video Transformer Encoder, a 2D-CNN Encoder with a stack of frames as input followed by a Transformer Encoder; (3) Multi-object tracking using a new end-to-end multi-task deep learning model named JDAT (Joint Detection Association Transformer), then post-processing and updating tracking state with Temporal Aggregation Module (TAM). The deep learning models in step 2 and step 3 are trained simultaneously end-to-end with a loss function that is accumulated over multiple timesteps (Collective Average Loss—CAL). Also, the model can be pretrained with weakly labeled image dataset in a self-supervised learning manner first, then finetuned on supervised video datasets with full tracking labels.

Abstract: This invention proposes a method of crowd abnormal behavior detection from video using artificial intelligence, includes three steps: step 1: Data-preprocessing; step 2: Feature extraction and abnormal prediction using a three-dimensional convolution neural network (3D CNN), step 3: Post-processing and synthesizing information to issue warning.

Abstract: A method includes accessing document including sentences, document being associated with configuration flag indicating whether ABSA, SLSA, or both are to be performed; inputting the document into language model that generates chunks of token embeddings for the document; and, based on the configuration flag, performing at least one from among the ABSA and the SLSA by inputting the chunks of token embeddings into a multi-task model. When performing the SLSA, a part of token embeddings in each of the chunks is masked, and the masked token embeddings do not belong to a particular sentence on which the SLSA is performed.

Abstract: A method includes obtaining sensor data associated with a target object at a host vehicle and identifying relative positions of the target object with reference to the host vehicle over time. The method also includes generating polynomials associated with the target object over time based on the relative positions, where coefficients of each polynomial are based on a magnitude of a covariance of the relative positions used to generate the polynomial. The method further includes generating a weighted combination of the polynomials for the target object, where the weighted combination is a representation of an estimated behavior of the target object. In addition, the method includes determining whether a collision between the host vehicle and the target object is possible based on the weighted combination of the polynomials and, in response to determining that the collision is possible, initiating one or more corrective actions by the host vehicle.

Abstract: A method includes obtaining sensor data associated with a target object at a host vehicle and generating at least one first polynomial associated with the host vehicle. Each first polynomial is a representation of an estimated behavior of the host vehicle. The method also includes identifying relative positions of the target object over time and generating at least one second polynomial associated with the target object based on the relative positions. Coefficients of each second polynomial are based on a magnitude of a covariance of the relative positions used to generate the second polynomial. Each second polynomial is a representation of an estimated behavior of the target object. The method further includes determining whether a collision between the host vehicle and the target object is possible based on the first and second polynomials and, in response to determining that the collision is possible, initiating one or more corrective actions.

Abstract: Techniques for using enhanced logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system and inputting the utterance into a machine-learning model including a series of network layers. A final network layer of the series of network layers can include a logit function. The machine-learning model can map a first probability for a resolvable class to a first logit value using the logit function. The machine-learning model can map a second probability for a unresolvable class to an enhanced logit value. The method can also include the chatbot system classifying the utterance as the resolvable class or the unresolvable class based on the first logit value and the enhanced logit value.

Abstract: A field programmable gate array (FPGA) utilizing resistive switching memory technology is described. The FPGA can comprise a switching block interconnect having a set of signal input lines and a set of signal output lines. Respective intersections of the signal input lines and signal output lines can have two resistive switching memory cells, a current differential latch, and a switching transistor (also referred to as a pass gate transistor) arranged in a circuit. Resistance states of the resistive switching memory cells can be programmed to control an output voltage state of the current differential latch. The output voltage state is latched into the current differential latch which can drive a gate of the switching transistor to activate or deactivate the switching transistor, which in turn activates or deactivates an intersection of the FPGA.

Abstract: Reconfigurable antenna systems with ground tuning pads are provided herein. In certain configurations, an antenna system includes an antenna element, a first tuning conductor spaced apart from the antenna element on a first side of the antenna element, a second tuning conductor spaced apart from the antenna element on a second side of the antenna element, a first ground tuning pad configured to receive a ground voltage, and a first switch electrically connected between the first tuning conductor and the first ground tuning pad. The first switch is operable to selectively connect the first tuning conductor to the first ground tuning pad to thereby tune the antenna element.

Abstract: An anomaly detection system is disclosed. In an embodiment, the anomaly detection system includes an anomaly detection module and a warning indicator module. The anomaly detection module includes one or more auto-encoders that receive sensor data from a plurality of sensors. Each of the one or more auto-encoders receives sensor data from at least three different sensors of the plurality of sensors. By receiving data output from at least three of the sensors, the auto-encoder can recognize expected inter-related patterns from the sensor output. The warning indicator module compares an output of a given auto-encoder of the plurality of auto-encoders to an input of the given auto-encoder to obtain an error value, and then compares that error value against an error threshold. In response to the error value exceeding the error threshold, the warning indicator module issues a warning signal associated with the sensor data received by the given auto-encoder.

Abstract: The indirect querying of models to determine capabilities possessed by the model. Such indirect queries take the form of model input that potentially includes a natural language input user data. Such model input is structured such that the output of the model is either not natural language at all, or else is natural language that is not semantically responsive to the natural language input. Nevertheless, the output is evaluated to estimate or determine the capability possessed by the model. Thus, models may be more fully utilized to their better potential.

Abstract: A method of manufacturing a stem cell sheets is provided which includes: (a) obtaining mesenchymal stem cells; (b) extracting programmed death ligands one (PD-L1) from the mesenchymal stem cells; (c) selecting only PD-L1 positive (PD-L1+ MSCs) from the PD-L1; (d) differentiating the PD-L1+ cells into osteoblasts and chondroblasts in a predetermined activation condition; and (e) forming the stem cell sheets by mixing the PD-L1+ MSCs with platelet rich plasma solution and CaCl2).

Abstract: Disclosed is a printing apparatus for a 3D surface, which performs printing by ejecting a droplet onto a 3D surface and controlling an electric field on an impact path of the droplet, the printing apparatus including: a ejecting environment information provider configured to provide ejecting environment information between a nozzle and an impact point; and a controller configured to predict a result of printing on an actual substrate by accumulating previous printing results according to printing conditions and the ejecting environment information into a database, and perform the printing on the 3D surface while changing the printing conditions provided by the database based on the ejecting environment information provided by the ejecting environment information provider.

Abstract: A computer-implemented method includes: accessing a plurality of datasets, where each dataset of the plurality of datasets includes training examples; selecting datasets that include the training examples in a source language and a target language; and sampling, based on a sampling weight that is determined for each of the selected datasets, the training examples from the selected datasets to generate the training batches; training an ML model for performing at least a first task using the training examples of the training batches, by interleavingly inputting the training batches to the ML model; and outputting the trained ML model configured to perform the at least the first task on input utterances provided in at least one among the source language and the target language. The sampling weight is determined for each of the selected datasets based on one or more attributes common to the training examples of the selected dataset.