The invention is notably directed to a sensor system for performing distributed sensing and classification of sensor data. The sensor system comprises a set of distributed sensor nodes for sensing the sensor data. The sensor system is configured to encode the sensor data of each sensor node of a set of distributed sensor nodes for sensing the sensor data as high-dimensional vectors and to transmit the high-dimensional vectors over a respective link between the respective sensor node and a receiver system. The sensor system is further configured to superpose the high-dimensional vectors of the sensor data from the set of sensor nodes by physical superposition, thereby generating a superposed high-dimensional vector and to classify the superposed high-dimensional vectors at the receiver system.

Disclosed herein are systems and methods executing a security server that perform various processes using alert elements containing various data fields indicating threats of fraud or attempts to penetrate an enterprise network. Using alert elements, the security server generate integrated alerts that are associated with customers of the system and assign a risk score for the integrated alerts, which the security server uses to store and sort the integrated alerts according to a priority, based on the relative risk scores. Analyst computers may query and fetch integrated alerts from an integrate alert database, and then present the integrate alerts to be addressed by an analyst according to the priority level of the respective integrated alerts. This allows to ensure that the right customer, is worked by the right analyst, at the right time, to maximize fraud prevention and minimize customer impact.

A computer-implemented method for storing information into a polynucleotide is provided including using multiple types of nucleotide fragments, wherein each of the nucleotide fragments has an individually different sequence of bases, configuring multiple composite fragments, wherein each of the composite fragments has a set of the nucleotide fragments with different ratios of the nucleotide fragments, and encoding, via an encoder, the information into the composite fragments.

Embodiments described herein provide a new approach to learning generative models of proteins based on sequence-to-sequence learning. Specifically, sequence modeling is formulated as a few-shot learning problem: a single encoder-decoder model receives an input of a protein family which is encoded into a protein representation and the protein representation is then decoded into a distribution over sequences from that family. The model is trained on tens of thousands of multiple sequence alignments representing known protein families and evaluated on unseen families heldout from training.

Methods for controlled segregation of blocks of information encoded in the sequence of a biopolymer, such as nucleic acids and polypeptides, with rapid retrieval based on multiply addressing nanostructured data have been developed. In some embodiments, sequence controlled polymer memory objects include data-encoded biopolymers of any length or form encapsulated by natural or synthetic polymers and including one or more address tags. The sequence address labels are used to associate or select memory objects for sequencing read-out, enabling organization and access of distinct memory objects or subsets of memory objects using Boolean logic. In some embodiments, a memory object is a single-stranded nucleic acid scaffold strand encoding bit stream information that is folded into a nucleic acid nanostructure of arbitrary geometry, including one or more sequence address labels. Methods for controlled degradation of biopolymer-encoded blocks of information in the memory objects are also developed.

The present invention relates to a computer-implemented method for inferring activity of a PR cellular signaling pathway in a subject based on expression levels of three or more target genes of the PR cellular signaling pathway measured in a sample of the subject. The present invention further relates to an apparatus, to a non-transitory storage medium, and to a computer program for inferring activity of a PR cellular signaling pathway in a subject. The present invention further relates to a kit for measuring expression levels of three or more target genes of the PR cellular signaling pathway in a sample of a subject, to a kit for inferring activity of a PR cellular signaling pathway in a subject, and to the use of such kits in performing the method.

Systems and methods are provided for implementing safety-aware artificial intelligence (AI) that can be used for autonomously controlling systems, such as an autonomous vehicle, in a manner that is proven to satisfy given safety constraints. Additionally, a safety-aware training technique can be applied to learned AI-based models, such as neural networks. The safety-aware training techniques can apply automated reasoning tools (ART) while the AI model is trained, in order to produce a model that is provable safe with respect to the safety constraints. The ART can integrate verification into the training process, and thereby dynamically re-train the model based on the safety verification in a feedback loop approach. The ART can be configured to either verify that the AI model is provably safety, or to provide updates to the training parameters used during to re-train the AI model in instances when the safety verification has failed.

A biological computing platform may include a multielectrode array (MEA) connected to a computing device. The MEA may include a 2D grid of excitation sites, biological neurons disposed on the MEA, a processing device to apply a plurality of impulses at excitation sites having coordinates on the 2D grid, and one or more sensors to measure electrical signals output by one or more of the biological neurons at coordinates of the 2D grid, wherein the processing device is to receive the electrical signals from the one or more sensors and generate a representation of the electrical signals. The computing device may be programmed to receive a digital input signal, convert the digital input signal into instructions for the plurality of impulses, send the instructions to the MEA, receive the representation of the electrical signals from the MEA, and process the representation of the electrical signals.

Using nucleotide architectures to very closely and precisely place chromophores on a nucleic acid template to form dye aggregates that produce quantum coherent excitons, biexcitons, and triexcitons upon excitement to create excitonic quantum wires, switching, and gates that would then form the basis of quantum computation. Creating the various excitons and controlling the timing of the excitons would be performed using light of the corresponding wavelength and polarization to stimulate the corresponding chromophores.

Aspects of the present disclosure relate to analog signal processing circuits and methods for cellular computation.