Abstract: Biosensor devices and methods of forming the same are provided. A cavity is formed in a substrate and is configured to receive one or more charged molecules. A transistor is formed in the substrate and includes a source region, a drain region, and a channel region that are spatially separated from the cavity in a lateral direction. A gate of the transistor is disposed below the cavity and extends between the cavity and the source, drain, and channel regions. A voltage potential of the gate is based on a number of the charged molecules in the cavity.

Abstract: A bicycle wheel assembly structure includes an annular rim, a hub, nipples, and spokes. The annular rim includes multiple rim penetration sections, each of which includes a rim-side limiting part on a circumference. The hub is arranged at the center of the annular rim. The hub includes multiple hub penetration sections. The rim-side limiting part and the nipple limiting part, and also the hub-side limiting part and the spoke limiting part, are all made in a hexagonal pyramid form with apex truncated to form a planar surface and including bevel facets so as to provide an anti-sliding effect resulting from geometric constraint and high static friction at both the annular rim-side and the hub-side. The angle of the hexagonal pyramid of the nipple limiting part is 60°, 90°, or 120°, and an optimum arrangement can be made based on the material elongation of the spokes and the desired tension.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating an image corresponding to a text input. In an embodiment, operations may include accessing a text description and inputting the text description into a text encoder. The operations may include receiving, from the text encoder, a text embedding, and inputting at least one of the text description or the text embedding into a first sub-model configured to generate, based on at least one of the text description or the text embedding, a corresponding image embedding. The operations may include inputting at least one of the text description or the corresponding image embedding, generated by the first sub-model, into a second sub-model configured to generate, based on at least one of the text description or the corresponding image embedding, an output image. The operations may include making the output image, generated by the first second sub-model, accessible to a device.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating natural language based on computer code input. In an embodiment, a method may comprise one or more of: accessing a docstring generation model configured to generate docstrings from computer code; receiving one or more computer code samples; generating, using the docstring generation model and based on the received one or more computer code samples, one or more candidate docstrings representing natural language text, each of the one or more candidate docstrings being associated with at least a portion of the one or more computer code samples; identifying at least one of the one or more candidate docstrings that provides an intent of the at least a portion of the one or more computer code samples; and/or outputting, via a user interface, the at least one identified docstring with the at least a portion of the one or more computer code samples.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating an image corresponding to a text input. In an embodiment, operations may include accessing a text description and inputting the text description into a text encoder. The operations may include receiving, from the text encoder, a text embedding, and inputting at least one of the text description or the text embedding into a first sub-model configured to generate, based on at least one of the text description or the text embedding, a corresponding image embedding. The operations may include inputting at least one of the text description or the corresponding image embedding, generated by the first sub-model, into a second sub-model configured to generate, based on at least one of the text description or the corresponding image embedding, an output image. The operations may include making the output image, generated by the first second sub-model, accessible to a device.

Abstract: Semiconductor package structures are provided. An interposer is bonded to a printed circuit board (PCB) or package substrate through first solder bumps disposed on a first side of the interposer. The first solder bumps have a first pitch. A plurality of semiconductor chips are formed, and each of the semiconductor chips is bonded to a second side of the interposer through second solder bumps. The second solder bumps have a second pitch that is less than the first pitch. Each of the semiconductor chips includes a substrate with one or more transistors or integrated circuits formed thereon.

Abstract: Semiconductor package structures are provided. An interposer is bonded to a printed circuit board (PCB) or package substrate through first solder bumps disposed on a first side of the interposer. The first solder bumps have a first pitch. A plurality of semiconductor chips are formed, and each of the semiconductor chips is bonded to a second side of the interposer through second solder bumps. The second solder bumps have a second pitch that is less than the first pitch. Each of the semiconductor chips includes a substrate with one or more transistors or integrated circuits formed thereon.

Abstract: An article of manufacture includes instructions that, when loaded and executed by a processor, configure the processor to, at a coordinator electronic device, initiate a round of electronic device resource requests from a plurality of electronic devices, receive a request for an electronic device resource from each of the electronic devices, provide a fair access scheme for access to the electronic device resource for each of the electronic devices, and provide a proof to each of the electronic devices that the fair access scheme was used in providing access to the electronic device during the round.

Abstract: An apparatus includes a cryptographic key for encrypting content to be written to a storage media. The apparatus includes a control circuit configured to determine that the storage media has been physically moved, and, based on the determination that the storage media has been physically moved, erase the storage media by deleting the cryptographic key.

Abstract: An apparatus includes a cryptographic key for decrypting content to be read from a storage media, and a control circuit. The control circuit is configured to, upon a boot of a server, dynamically generate a new boot authentication code using a prescribed method and determine a reconstituted boot authentication code. The reconstituted boot authentication code was reconstituted from an initial boot authentication code that was previously generated using the prescribed method. The control circuit is configured to compare the new and reconstituted boot authentication codes, and, based on a determination that the new and reconstituted boot authentication codes do not match, take a corrective action.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating computer code based on natural language input. In an embodiment, a method may comprise one or more of: receiving a docstring representing natural language text specifying a digital programming result; generating, using a trained machine learning model, and based on the docstring, a computer code sample configured to produce respective candidate results; causing the computer code sample to be executed; identifying, based on the executing, a computer code sample configured to produce a particular candidate result associated with the digital programming result; performing at least one of outputting, via a user interface, the identified computer code sample, compiling the identified computer code sample, transmitting the identified computer code sample to a recipient device, storing the identified computer code sample, and/or re-executing the identified computer code sample.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating an image corresponding to a text input. In an embodiment, operations may include accessing a text description and inputting the text description into a text encoder. The operations may include receiving, from the text encoder, a text embedding, and inputting at least one of the text description or the text embedding into a first sub-model configured to generate, based on at least one of the text description or the text embedding, a corresponding image embedding. The operations may include inputting at least one of the text description or the corresponding image embedding, generated by the first sub-model, into a second sub-model configured to generate, based on at least one of the text description or the corresponding image embedding, an output image. The operations may include making the output image, generated by the first second sub-model, accessible to a device.

Abstract: A bicycle wheel assembly structure includes an annular rim, a hub, nipples, and spokes. The annular rim includes rim penetration sections, each of which includes a rim-side limiting part. The hub is arranged at a center of the annular rim and includes hub penetration sections. The nipples each include a thread-connecting section and a nipple limiting part. The nipples are arranged on the rim penetration sections, and the nipple limiting parts are mounted to the rim-side limiting parts. The spokes include an intermediate section, a head section, and a thread section. The head sections are mounted to the hub penetration sections. The thread sections are connected to the thread-connecting sections of the nipples. The spokes, when set between the annular rim and the hub, induce tensions that cooperate with static frictional forces at the rim-side limiting parts and the nipple limiting parts resist sliding and prevent loosening of the spokes.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating computer code based on natural language input. In an embodiment, a method may comprise one or more of: receiving a docstring representing natural language text specifying a digital programming result; generating, using a trained machine learning model, and based on the docstring, a computer code sample configured to produce respective candidate results; causing the computer code sample to be executed; identifying, based on the executing, a computer code sample configured to produce a particular candidate result associated with the digital programming result; performing at least one of outputting, via a user interface, the identified computer code sample, compiling the identified computer code sample, transmitting the identified computer code sample to a recipient device, storing the identified computer code sample, and/or re-executing the identified computer code sample.

Abstract: An identity disambiguation data is received via a communication interface. A distributed identity disambiguation repository is used to determine whether the identity disambiguation data has been used previously to establish a digital identity. In response to a determination made using the distributed identity disambiguation repository that the identity disambiguation data has not been used previously to establish a digital identity a globally-unique digital identity is issued based at least in part on the identity disambiguation data. The identity disambiguation data comprises data that is unique to one human user.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating natural language based on computer code input. In an embodiment, a method may comprise one or more of: accessing a docstring generation model configured to generate docstrings from computer code; receiving one or more computer code samples; generating, using the docstring generation model and based on the received one or more computer code samples, one or more candidate docstrings representing natural language text, each of the one or more candidate docstrings being associated with at least a portion of the one or more computer code samples; identifying at least one of the one or more candidate docstrings that provides an intent of the at least a portion of the one or more computer code samples; and/or outputting, via a user interface, the at least one identified docstring with the at least a portion of the one or more computer code samples.

Abstract: A unified authentication system for decentralized identity platforms is disclosed. In various embodiments, a request comprising one or more identity claims and a digital address is received. The digital address is used to verify, via a verification node associated with a digital address provider, the one or more identity claims. Access to a service is provided, in response to the request, based at least in part on a response from the verification node indicating the one or more identity claims have been verified. The verification node is configured to obtain consent, in real time, from a user with which the digital address is associated, prior to providing said response indicating the one or more identity claims have been verified.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating an image corresponding to a text input. In an embodiment, operations may include accessing a text description and inputting the text description into a text encoder. The operations may include receiving, from the text encoder, a text embedding, and inputting at least one of the text description or the text embedding into a first sub-model configured to generate, based on at least one of the text description or the text embedding, a corresponding image embedding. The operations may include inputting at least one of the text description or the corresponding image embedding, generated by the first sub-model, into a second sub-model configured to generate, based on at least one of the text description or the corresponding image embedding, an output image. The operations may include making the output image, generated by the first second sub-model, accessible to a device.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating natural language based on computer code input. In an embodiment, a method may comprise one or more of: accessing a docstring generation model configured to generate docstrings from computer code; receiving one or more computer code samples; generating, using the docstring generation model and based on the received one or more computer code samples, one or more candidate docstrings representing natural language text, each of the one or more candidate docstrings being associated with at least a portion of the one or more computer code samples; identifying at least one of the one or more candidate docstrings that provides an intent of the at least a portion of the one or more computer code samples; and/or outputting, via a user interface, the at least one identified docstring with the at least a portion of the one or more computer code samples.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating computer code based on natural language input. In an embodiment, a method may comprise one or more of: receiving a docstring representing natural language text specifying a digital programming result; generating, using a trained machine learning model, and based on the docstring, a computer code sample configured to produce respective candidate results; causing the computer code sample to be executed; identifying, based on the executing, a computer code sample configured to produce a particular candidate result associated with the digital programming result; performing at least one of outputting, via a user interface, the identified computer code sample, compiling the identified computer code sample, transmitting the identified computer code sample to a recipient device, storing the identified computer code sample, and/or re-executing the identified computer code sample.