Abstract: A method of making an absorbent composite that includes a first fabric, a second fabric, and particles positioned between the two fabrics and absorbent articles made from the absorbent composite. The particles are secured between the two fabric using adhesive, thermal plastic or combinations thereof. The fabrics are bonded together in a manner that limits particle movement between the fabrics. The bond pattern may define pockets or other shapes. In applications in which the bond pattern forms pockets, the particles may be positioned in the pockets. The particles may be SAP particles or other particles with advantageous properties.

Abstract: The present disclosure provides methods and systems for treating affective disorders such as depression and/or anxiety and/or related disorders. Closed-loop therapy is performed with a neurostimulator that records electroencephalographic signals from neural activity associated with symptoms of a neuropsychiatric disorder and delivers electrical stimulation when pre-specified patterns of neural activity associated with a symptom are detected. Methods are also disclosed for mapping the brain of a patient to optimize placement of electrodes for detecting neural activity associated with symptoms of a neuropsychiatric disorder and to optimize placement of electrodes for delivery of electrical stimulation. Methods of monitoring and evaluating the effects of electrical stimulation on clinical symptoms and status are also provided.

Abstract: Methods, devices, and systems for assisting individuals with communication are provided. In particular, methods, devices, and systems are provided for decoding words and sentences directly from neural activity of an individual. Cortical activity from a region of the brain involved in speech processing is recorded while an individual attempts to say or spell out words. Deep learning computational models are used to detect and classify words from the recorded brain activity. Decoding of speech from brain activity is aided by use of a language model that predicts how likely certain sequences of words are to occur. In addition, decoding of attempted non-speech motor movements from neural activity can be used to further assist communication. This neurotechnology can be used to restore communication to patients who have lost the ability to speak and has the potential to improve autonomy and quality of life.

Abstract: Integrated circuits and integrated circuit dies include TSVs laid out in symmetrical patterns. Because of the symmetrical arrangement of the TSVs and associated routing patterns, an integrated circuit is able to support operation of multiple similar dies that are placed in different positions in the integrated circuit. This in turn simplifies the design and production of the multiple similar dies, thus reducing development and manufacturing costs for the corresponding integrated circuits.

Abstract: A multi-die processor semiconductor package includes a first base integrated circuit (IC) die configured to provide, based at least in part on an indication of a configuration of a first plurality of compute dies 3D stacked on top of the first base IC die, a unique power domain to each of the first plurality of compute dies. In some embodiments, the semiconductor package also includes a second base IC die including a second plurality of compute dies 3D stacked on top of the second base IC die and an interconnect communicably coupling the first base IC die to the second base IC die.

Abstract: Systems and methods for improving behavioral therapies encompassing therapies wherein a perceptual stimulus is administered to a subject or a motor behavior is performed by the subject. Such administration of perceptual stimuli or motor performance is paired with the delivery of vagus nerve stimulation to the subject. The vagus nerve stimulation is timed with the sensory stimulus administration or motor performance in a temporal alignment that maximizes neuroplasticity and performance.

Abstract: Integrated circuits and integrated circuit dies include TSVs laid out in symmetrical patterns. Because of the symmetrical arrangement of the TSVs and associated routing patterns, an integrated circuit is able to support operation of multiple similar dies that are placed in different positions in the integrated circuit. This in turn simplifies the design and production of the multiple similar dies, thus reducing development and manufacturing costs for the corresponding integrated circuits.

Abstract: A control method for a reinforcement learning system includes following operations. The reinforcement learning system obtains training data relating to an interaction system. The interaction system interacts with a reinforcement learning agent. A neural network model is utilized by the reinforcement learning agent for selecting sequential actions from a set of candidate actions. The neural network model is trained to maximize cumulative rewards collected by the reinforcement learning agent in response to the sequential actions. During training of the neural network model, auxiliary rewards of the cumulative rewards are provided to the reinforcement learning agent according to a comparison between symptom inquiry actions of the sequential actions and diagnosed symptoms in the training data.

Abstract: A multi-die processor semiconductor package includes a first base integrated circuit (IC) die configured to provide, based at least in part on an indication of a configuration of a first plurality of compute dies 3D stacked on top of the first base IC die, a unique power domain to each of the first plurality of compute dies. In some embodiments, the semiconductor package also includes a second base IC die including a second plurality of compute dies 3D stacked on top of the second base IC die and an interconnect communicably coupling the first base IC die to the second base IC die.

Abstract: A computing method, suitable for computing a transformer model, include following steps. An input matrix corresponding to an input sequence of feature vectors is projected into a query matrix according to first learnable weights. The input matrix is projected into a value matrix according to second learnable weights. A factorized matrix is generated by an incomplete Cholesky factorization according to the query matrix and a transpose of the query matrix. An intermediate matrix is calculated according to a product between a transpose of the factorized matrix and the value matrix. An output matrix is calculated according to a product between the factorized matrix (H) and the intermediate matrix.

Abstract: A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Abstract: A medical system is able to provide a symptom query interpretation and/or a disease diagnosis interpretation. The medical system includes an interface and a processor. The interface is configured for receiving an input state. The processor is coupled with the interface. The processor is configured to execute a symptom checker to select a current action, from a plurality of candidate symptom queries and a plurality of candidate disease predictions, according to the input state. In response to the current action is a first symptom query, the processor is configured to execute an interpretable module interacted with the symptom checker to generate a diagnostic tree for simulating possible diagnosis paths, and generate a symptom query interpretation about the first symptom query according to the diagnostic tree.

Abstract: The disclosure provides a method for training generative adversarial network (GAN), a method for generating images by using GAN, and a computer readable storage medium. The method may train the first generator of the GAN with available training samples belonging to the first type category and share the knowledge learnt by the first generator to the second generator. Accordingly, the second generator may learn to generate (fake) images belonging to the second type category even if there are no available training data during training the second generator.

Abstract: A method for adjusting a convolutional neural network includes following operations. The convolutional neural network includes convolution layers in a sequential order. Receptive field widths of the convolution layers in a first model of the convolutional neural network are determined. Channel widths of the convolution layers in the first model are reduced into reduced channel widths according to the receptive field widths of the convolution layers and an input image width. A structure of a second model of the convolutional neural network is formed according to the reduced channel widths. The second model of the convolutional neural network is trained according the structure of the second model.

Abstract: A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

Abstract: A machine learning method includes steps of: obtaining, by a processor, a model parameter from a memory, and performing, by a processor, a classification model according to the model parameter, wherein the classification model comprises a plurality of neural network structural layers; calculating, by the processor, a first loss and a second loss according to a plurality of training samples, wherein the first loss corresponds to an output layer of the plurality of neural network structural layers, and the second loss corresponds to one, which is before the output layer, of the plurality of neural network structural layers; and performing, by the processor, a plurality of updating operations for the model parameter according to the first loss and the second loss to train the classification model.

Abstract: A system and method provides for false wake abatement. For instance, an audio energy detection controller opens a keyword detection window in response to a detected audio input level reaching a threshold and a keyword detector detects a keyword in response to a detected audio input corresponding to a keyword signature. In response to both a keyword being detected and the detected audio input level indicating a near field source of the speech, a device may be triggered to awaken. In this manner, the incidence of false awakenings due to keywords spoken by proximate third parties is ameliorated.

Abstract: An image processor unit is described. The image processor unit includes a plurality of inputs to receive at least one input image. The image processor unit includes a plurality of outputs to provide at least one output image. The image processor unit includes a network coupled to the plurality of inputs and the plurality of outputs. The network is to couple at least one of the inputs to at least one of the outputs. The image processor unit includes an image processor circuit coupled to the network. The network to route an input image that is received at one of the inputs to the image processor circuit. The image processor circuit is to execute image signal processing program code to generate a processed output image from the input image. The network is to route the processed output image to at least one of the outputs.

Abstract: Systems, apparatuses, and methods for efficiently performing operations system are disclosed. A computing system uses a memory for storing data, and one or more processing units. The memory includes multiple rows for storing the data with each intersection of a row and a column being a memory bit cell. The memory processes operations. For particular operations, the two or more operands are accessed simultaneously for generating a result without being read out and stored. Two indications are generated specifying at least a first row and a second row targeted by the operation. The memory generates a result by performing the operation for each of the one or more cells in the first row a stored value with a respective stored value in the one or more cells in the second row.

Abstract: A machine learning method includes steps of: (a) obtaining initial values of hyperparameters and hypernetwork parameters; (b) generating first classification model parameters according to the hyperparameters and the hypernetwork parameters, and updating the hypernetwork parameters according to a classification result based on the first classification model parameters relative to a training sample; (c) generating second classification model parameters according to the hyperparameters and the updated hypernetwork parameters, and updating the hyperparameters according to another classification result based on the second classification model parameters relative to a verification sample; and (d) repeating the steps (b) and (c) for updating the hypernetwork parameters and the hyperparameters.