Abstract: One or more computer processors transmit a machine learning model and an associated loss function to a worker, wherein the worker isolates private data. The one or more computer processors receive a plurality of encrypted gradients computed utilizing the transmitted machine learning model, the associated loss function, and the isolated private data. The one or more computer processors generate a plurality of adversarial perturbations, wherein the plurality of adversarial perturbations includes true perturbations and false perturbations. The one or more computer processors obfuscate the generated plurality of adversarial perturbations. The one or more computer processors transmit the obfuscated adversarial perturbations to the worker. The one or more computer processors harden the machine learning model utilizing the transmitted obfuscated adversarial perturbations and the private data.

Abstract: A method, computer system, and a computer program product for text data protection is provided. The present invention may include receiving a text data. The present invention may also include identifying a portion of the received text data having a highest impact on a first confidence score associated with a target model prediction. The present invention may further include generating at least one semantically equivalent text relative to the identified portion of the received text data. The present invention may also include determining that the generated at least one semantically equivalent text produces a second confidence score associated with the target model prediction that is less than the first confidence score associated with the target model prediction. The present invention may further include generating a prompt to suggest modifying the identified portion of the received text data using the generated at least one semantically equivalent text.

Abstract: One or more hardened machine learning models are secured against adversarial attacks by adding adversarial protection to one or more previously trained machine learning models. To generate the hardened machine learning models, the previously trained machine learning models are retrained and extended using preprocessing layers or using additional network layers which test model performance on benign or adversarial samples. A rollback strategy is additionally implemented to retain intermediate model states during the retraining to provide recovery if a training collapse is detected.

Abstract: The present disclosure is directed to devices, kits, and methods for treating lumbar spinal stenosis by at least partially decompressing a compressed nerve root. The method can include identifying the compressed nerve root and percutaneously accessing a region of a lamina located adjacent to the compressed nerve root. The method can also include forming a channel through the region of the lamina, wherein the channel can be formed medial to a lateral border of the lamina. Further, the method can include expanding the channel in a lateral direction.

Abstract: An optical device comprises first, second and third elements fabricated on a common substrate. The first element comprises an active waveguide structure supporting a first optical mode, the second element, fabricated on a planarized top surface of the first element, comprises a passive waveguide structure supporting a second optical mode, and the third element, at least partly butt-coupled to the first element, comprises an intermediate waveguide structure, positioned such that a top surface of the intermediate structure underlies a bottom surface of the passive waveguide structure. If the first optical mode differs from the second optical mode by more than a predetermined amount, a tapered waveguide structure in at least one of the second and third elements facilitates efficient adiabatic transformation between the first optical mode and the second optical mode. Mutual alignments of the first, second and third elements are defined using lithographic alignment marks.

Abstract: Various embodiments are provided for automated evaluation of machine learning models in a computing environment by one or more processors in a computing system. A level of robustness of a machine learning model against adversarial whitebox operations may be evaluated and determined by applying a data set used for testing the machine learning model, one or more adversarial operation objectives, an adversarial threat model, and a selected number of hyperparameters. Results from the adversarial operation may be analyzed and a modified machine learning model may be generated while performing the evaluating and determining.

Abstract: An optical device comprises first, second and third elements fabricated on a common substrate. The first element comprises an active waveguide structure supporting a first optical mode, the second element, fabricated on a planarized top surface of the first element, comprises a passive waveguide structure supporting a second optical mode, and the third element, at least partly butt-coupled to the first element, comprises an intermediate waveguide structure, positioned such that a top surface of the intermediate structure underlies a bottom surface of the passive waveguide structure. If the first optical mode differs from the second optical mode by more than a predetermined amount, a tapered waveguide structure in at least one of the second and third elements facilitates efficient adiabatic transformation between the first optical mode and the second optical mode. Mutual alignments of the first, second and third elements are defined using lithographic alignment marks.

Abstract: Embodiments for providing adversarial protection to computing display devices by a processor. Security defenses may be provided on one or more image display devices against automated media analysis by using adversarial noise, an adversarial patch, or a combination thereof.

Abstract: A processor that includes a register file, a read shifter, a decode unit and a plurality of functional units is introduced. The register file includes a read port. The read shifter includes a plurality of shifter entries and is configured to shift out a shifter entry among the plurality of shifter entries every clock cycle. Each of the plurality of shifter entries is associated with a clock cycle and each of the plurality of shifter entries comprises a read value that indicates an availability of the read port of the register file for a read operation in the clock cycle. The decode unit is coupled to the read shifter and is configured to decode and issue an instruction based on the read values included in the plurality of shifter entries of the read shifter. The plurality of functional units is coupled to the decode unit and the register file and is configured to execute the instruction issued by the decode unit and perform the read operation to the read port of the register file.

Abstract: The present disclosure relates to method and systems for amplifying nucleic acid molecule and quantify amplification thereof, with a polymerase chain reaction (PCR) or a loop-mediated isothermal amplification (LAMP), through bulk heating a biological enzymatic reaction mixture in solution containing nucleic acid templates, polymerase enzyme, and chemically modified nanoparticles. The method and system may comprise quantify amplification by irradiating the biological enzymatic reaction mixture during an annealing and/or elongation steps with an ultraviolet (UV) light source.

Abstract: In some examples, a terminal can establish wireless communication with a base station. The terminal can determine a challenge, transmit the challenge, receive a response, and determine that the response is valid. The terminal can, in response, establish a secure network tunnel to a network node. In some examples, a terminal can determine a first communication parameter associated with communication with the base station. The terminal can receive data indicating a second communication parameter via a secure network tunnel. The terminal can determine that the communication parameters do not match, and, in response, provide an indication that an attack is under way against the network terminal. Some example terminals transmit a challenge, determine a response status associated with the challenge, and determine that an attack is under way based on the response status.