86 results about "IMG" patented technology

A system for matrix-based homomorphic encryption including a processor of a computing node configured to host a homomorphic encryption module and connected to at least one cloud server and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire plaintext M required to be encrypted; select a size of a matrix and modulus n; select invertible × matrix S1 over n, wherein n is a residue ring modulo n; compute an invertible × matrix S2 over n; set a secret key (S1, S2); and encode the plaintext M by an integer m over n, wherein m is encoded by an envelope matrix comprising a formV⁡(m)=(α0βm),wherein α and β are numbers chosen at random α, β∈n.

A method to forecast the result of a Clifford circuit acting on the qubits comprises: for a fault operator F acting on the qubits, precomputing a backward cumulant of the fault operator for each row u of binary matrices Ms and Ml, the backward cumulant reflecting an effect f=effm(F) on measurement outcomes of the qubits according to the Clifford circuit and fault operator; sampling the fault operator F for the qubits according to the predetermined noise distribution in the Clifford circuit; computing a syndrome s=Msf corresponding to the effect based on a commutator of the backward cumulant versus a row of the binary matrix Ms; computing a set of logical flips f=Mlf corresponding to the effect based on a commutator of the backward cumulant versus a row of the binary matrix Ml; and returning the result based on the syndrome and on the set of logical flips.

The invention is suitable for the technical field of cloud Android system development, and provides a Magisk installation method, device, storage medium and system without guiding mapping, and the method comprises the steps: based on a virtualization interface of the system, injecting and constructing a virtualization environment of Magisk; after a command for starting the system is obtained, dynamically loading a kernel module of the Magisk; creating a virtual mounting layer in the dynamic loading to obtain a virtualized partition; the modification of the Magisk is redirected to the virtualization partition, so that the modification of the Magisk is all limited in the virtualization environment; and configuring an automatic mounting script to regularly and automatically check the mounting state of the virtualized partition, and immediately remounting the virtualized partition when the mounting fails. Through a virtualization technology and a dynamic loading mechanism, the function integration of Magisk is realized on the premise of not modifying boot.img, the installation process is simplified, and the system security and stability are improved.

Example computer-implemented methods and systems for secure random noise generation are disclosed. One example method includes generating, by a first party, n random first bits and n -bit strings. The first party generates, based on the n -bit strings and the n random first bits, n pairs of -bit input messages. The first party performs n 1-out-of-2 oblivious transfers (OTs) of the n pairs of -bit input messages from the first party to a second party. The first party generates, based on the n -bit strings, a first random number. The first party performs, based on the first random number, secure multiparty computation (MPC) that involves the first party and the second party.

The invention discloses a memristor circuit-based implication logic synthesis and optimization method, which comprises the following steps of: acquiring a gate-level netlist for representing a Boolean function by using an ABC and a simulated annealing algorithm based on a node sensing cost function, and converting a circuit topology connection relationship in the gate-level netlist into an IMG; calculating the optimal IIG of all the four-input NPN Boolean functions by using an SAT-based precise synthesis algorithm and NPN equivalent classification, establishing an IMG off-line database by using IMG logic to represent an inverter in the IIG, and converting an input circuit into an IMG structure by using a logic rewriting algorithm and the IMG off-line database; based on a heuristic algorithm of logic reconstruction, all structures with a fan-out problem in an IMG structure are solved, and an implication network without the fan-out problem and with the number of memristors and the number of pulses reduced is obtained. According to the method, the number of operation pulses in the memristor circuit is reduced, the number of memristors is reduced, and the design cost is reduced.

A computer-implemented method for processing, on a non-quantum computer, quantum data that represent one or more tensor products of a Pauli operator basis n of size n∈, wherein comprises all the possible tensor products of n matrices of the set S={I, X, Y, Z}, wherein I is a 2×2 identity matrix, and X, Y, Z are the following Pauli matrices:X=[0110],Y=[0-ii0],and⁢ Z=[100-1],is proposed, which comprises: generating a data tree as a data structure that represents the one or more tensor products of the Pauli operator basis n, wherein the one or more tensor products respectively correspond to one or more paths of the data tree each running from a root node of the data tree to a leaf of the data tree, and determining the one or more tensor products of the Pauli operator basis n by performing a tree exploration of the generated data tree.

Embodiments of the present application provide a system, a device, and a method for approximate homomorphic ring encryption. A message z, which is an (n / 4)×(n / 2) matrix may be encoded to a polynomial M(X, Y) in a polynomial ring =[X, Y] / Xn / 2+1, Yn / 2−(Xn / 8−X3n / 8). The polynomial M(X, Y) may then be encrypted to a ciphertext and sent to a target device. Encoding in the polynomial ring allows at least approximate homomorphic manipulation of the subsequent ciphertext for improved security.

Embodiments of the present application provide a system, a device, and a method for approximate homomorphic ring encryption. A message z, which is an (n / 4)×(n / 2) matrix may be encoded to a polynomial M(X, Y) in a polynomial ring =[X, Y] / Xn / 2+1, Yn / 2−(Xn / 8−X3n / 8). The polynomial M(X, Y) may then be encrypted to a ciphertext and sent to a target device. Encoding in the polynomial ring allows at least approximate homomorphic manipulation of the subsequent ciphertext for improved security.

The invention relates to methods and apparatuses for reconstructing phase information in a time series, the time series being derived from a time evolution of an input state acted on by a Hamiltonian, H. First, an input state, |ψ, a total time evolution time, T, and the Hamiltonian, H are received. Next a time evolution of the input state is enacted, using H to generate an absolute value of a first time series, |f1|. In addition at least one additional family of absolute values of time series is generated, each time series being a discrete or continuous function of time t, for t∈[0,T]. Finally, phase information is extracted for the time series of the input state from the absolute values of the first time series and the at least one additional family of absolute values of time series.

Generalizations of quantum amplitude amplification and amplitude estimation algorithms work with non-boolean oracles (by way of definition, the action of a non-boolean oracle Uφ on an eigenstate |x is to apply a state-dependent phase-shift φ(x); unlike boolean oracles, the eigenvalues exp(iφ(x)) of a non-boolean oracle are not restricted to be ±1). The non-boolean amplitude amplification algorithm preferentially amplifies the amplitudes of the eigenstates based on the value of φ(x). Starting from a given initial superposition state |ψ0, the basis states with lower values of cos(φ) are amplified at the expense of the basis states with higher values of cos(φ). The non-boolean quantum mean estimation algorithm uses quantum phase estimation to estimate the expectation ψ0|Uφ|ψ0 (i.e., the expected value of exp(iφ(x)) for a random x sampled by making a measurement on |ψ0). The quantum mean estimation algorithm offers a quadratic speedup over its counterpart boolean algorithm known in the art.

A quantum method, includes initializing, by a quantum circuit, four registers, and two qubits in the quantum circuit. The quantum method further includes searching, by the quantum circuit, a uniform / weighted quantum database data states in register |QDB that match criteria specified in register |qr. The quantum method further includes applying, by the quantum circuit, an S operator. The quantum method further includes applying |M|+1-qubit Toffoli gate with |M| control qubits of the register |QDB. The quantum method further includes removing an effect of the S operator. The quantum method further includes applying, by the quantum circuit, a number 2*|j| of 3-qubit Toffoli gates. The quantum method further includes updating marked data / records / basis states based on the applying a number |j| of other 3-qubit Toffoli gates. The quantum method further includes applying |M|+1 qubit Toffoli gates. The quantum method further includes applying controlled-S operator.

A method, system, and computer program product for implementing noise mitigation in dynamic quantum circuits. A condition in a dynamic quantum circuit is identified. A condition refers to a situation or circumstance used to identify or detect a source of noise in the dynamic quantum circuit that may result in an error occurring in the dynamic quantum circuit. For example, the condition may correspond to the idling times, spectator qubits, and idling qubits in cross-talk resistant states (quantum states and in the dynamic quantum circuit. Based on the identified condition, a context-dependent dynamical decoupling sequence is inserted in the dynamic quantum circuit. A context-dependent dynamical decoupling sequence refers to a dynamical decoupling sequence that is inserted in the dynamic quantum circuit based on the context, such as the occurrence of a particular operation (e.g., mid-circuit measurement, the occurrence of a feed-forward operation, the occurrence of qubit shuttling).

The invention relates to a U-Boot-based secondary starting system and method.The secondary starting system comprises a main control chip, an ROM, an SRAM and a special register are integrated in the main control chip, and ROMCODE is solidified in the ROM; the second-stage starting method is realized through a second-stage starting program, and the second-stage starting program comprises an SPL mirror image and a U-Boot mirror image. By executing different board-level compiling configurations, the U-boot mirror image is divided into two types, namely a u-boot .img image which is not integrated with firmware and a u-boot .itb image which is integrated with firmware, and the U-boot mirror image is divided into two types, namely a U-boot .img image which is not integrated with firmware and a U-boot .itb image which is integrated with firmware. The two-stage starting method comprises two stages of loading the U-boot by the SPL and loading the operating system by the U-boot.

Machine learning system. Some embodiments are directed to a machine learning system configured to map an input data instance to an output according to a system mapping. The system mapping may be composed of a plurality of functions. A potential input vector in a second latent space ( ) for the input data instance may be determined from a potential reference vector associated with a plurality of reference data instances identified as parents.

A method for changing a bitwidth of an FPGA configuration for an FPGA, the FPGA configuration having a plurality of at least 2n bit-containing data signals with nε and ≥3, and the method having the step: when a threshold of a current consumption and / or a temperature of the FPGA is exceeded and / or a replacement signal is present, replacing k least significant bits of the data signals in each case with a zero with kε and ≥2 during an execution of the FPGA configuration on the FPGA.

Embodiments use quantum conditional logic in the Quantum Phase Estimation Algorithm (QPEA) to compute eigenvalues prior to inversion. Embodiments estimate the eigenvalues of a unitary, U=eiÂt, generated by a N×N Hermitian matrix Â. The binary representations of the n-bit estimations of eigenvalues of  may be encoded in these states: |λi=|b1b2 . . . bn; λi is an estimation of the i-th eigenvalue, excluding degeneracy, and .b1b2 . . . bn is its binary representation. To perform the eigenvalue inversion, an n-qubit controlled Ry rotation with angle λi / 2(n−1) conditioned on seeing |b1b2 . . . bn is applied for each possible n-bit binary string b1b2 . . . bn (2n values). The overall unitary is called a “uniformly controlled Ry rotation” in literature.

Described are various embodiments of a system and method for minimizing a storage mode dephasing error caused by a reset of the auxiliary qubit. In one embodiment, the method comprises the steps of performing on the auxiliary qubit a first ηge pulse causing a |g↔|e transition in said auxiliary qubit, waiting for a designated echo time duration; and performing on the auxiliary qubit a second ηge pulse. The method may further comprise performing a f0g1 reset on the auxiliary qubit.

The application discloses a kind of based on the safe processing and transmission method of DICOM file, client and server, comprising: obtaining DICOM file, the DICOM file includes metadata info and image data img;The metadata info is de-sensitized and is handled, and sensitive data is obtained;The sensitive data is encrypted, and the sensitive data after encryption is written in image data img;The metadata info after de-sensitization processing and the image data img of writing encrypted sensitive data are generated new DICOM file according to preset format, and the new DICOM file is sent to server.The application retains the IMG data and META data of original file, and is stored in somewhere of file itself by safe encryption means, and processed DICOM cannot view METADATA and other private information by ordinary method, but using corresponding authorized reverse method can still analyze and view relevant information.Therefore, the method retains META information under encryption mode, while patient privacy is maximally protected.

This invention discloses a low-dose CT image denoising method, comprising the following steps: S1: establishing a dataset, defining a set of noisy LDCT images x, and a set of high-quality NDCT images y corresponding to the LDCT image set x; S2: constructing a denoising model with parameter θ. Use templates to build Mapping; S3: Training the denoising model using hybrid loss Set the learning rate and momentum parameters, and use error backpropagation to train the denoising model. S4: Image denoising; After preprocessing the LDCT images in the test set, the images are input into the trained denoising model to obtain the corresponding denoised images. The denoising method proposed in this application takes into account the denoising difficulty of different regions of the image, and adaptively balances them by adding weights. It also promotes the generation of image texture details by losing high-frequency information, thus avoiding the denoised image from becoming overly smooth.

The present disclosure relates to error correction coding based on generalized concatenated codes with restricted error values for code-based cryptography. The error correction encoding comprises encoding the information according to a McEliece cryptosystem or according to a Niederreiter cryptosystem, in each case using an error vector containing at most t ε non-zero error values; and a combination of: a permutation operation, a scrambling operation; and a coding operation involving a p-ary generalized concatenated code, GCC, having an error correction capability t up to which it can correct all possible error patterns. The GCC comprises multiple outer codes A(1) with different dimensions n1 and 1=0, . . . ,N, and an inner code B, the inner code B being one of an error correction code over Gaussian integers, an error correction code over Eisenstein integers, or a binary BCH error correction code, wherein q is a natural number with q>2 in the case of an error correction code over Gaussian integers or Eisenstein integers and with q=2 in the case of a binary BCH error correction code. A corresponding decoding method comprises applying erase decoding such that all decoding errors, if any, are recognized during the decoding of the GCC.

It is provided an apparatus comprising interface circuitry, machine-readable instructions, and processing circuitry to execute the machine-readable instructions. The machine-readable instructions include instructions to obtain a migration history log from a first confidential computing environment executing a workload. The migration history log comprising data about one or more performed migrations of the workload. The machine-readable instructions further include instructions to obtain an attestation evidence collection associated with the workload. The attestation evidence collection associated with the workload comprising one or more attestation evidence associated with the workload. The machine-readable instructions further include instructions to obtain a migration image of the workload from the first confidential computing environment. The machine-readable instructions further include instructions to transmit at least one of the migration image, the attestation evidence collection and the migration history log to a second confidential computing environment

A quantum circuit is configured to implement a quantum gradient algorithm when executed on qubits of a quantum computing system. The quantum gradient algorithm includes a phase oracleOSfmdefined by a finite difference approximation with an order greater than zero, and a complexity of the quantum gradient algorithm scales as (√{square root over (k)} / ϵ). The quantum circuit is repeatedly executed on qubits of a quantum computing system to determine a k-dimensional gradient of a function ƒ(x) within an error ϵ at point x0, where ƒ(x) is not a Gevrey class G1 / 2 function.

This disclosure relates to a spoken language understanding (SLU) system and method for training the same. The method comprises: generating a vector ei to represent an ith word token wi based on n word tokens; generating an intent-specific matrix EI and a slot-specific matrix ES based on a sequence of vectors e1:n; generating an intent label-specific matrix VI and slot label-specific matrices VS,K based on the intent-specific matrix EI and the slot-specific matrix ES, respectively; generating a multiple intent representing matrix 1 and a slot representing matrix +2 based on the slot-specific matrix ES, the intent label-specific matrix VI and the slot label-specific matrices VS,k; generating final intent outputs based on the multiple intent representing matrix 1 and the intent label-specific matrix VI; generating final slot output based on the slot representing matrix +2 and the slot-specific matrix ES; adjusting the SLU system based on an objective loss .

An RTOS-based, (Real-Time Operating System-based) multi-application development method and apparatus are provided. The method comprises: compiling and packaging an AWTK, (Toolkit Any Where) engine, a Jerryscript engine, and .c files through a GNU, (Gnu's Not Unix) compiler suite to generate a system.bin file; compiling and packaging xml files, js (JavaScript) files, and image files required by multiple applications through the AWTK engine to generate a fat.img containing the multiple applications; and burning the fat.img containing the multiple applications and the system.bin file into a POS (point of sale) machine with an RTOS. The Jerryscript engine and the AWTK engine support the development of JS language, then resource data (data) are generated from all images, XML (describing image elements and coordinates), and JS files used in the development of APPS, and finally, the resource data are compiled by GCC, (GNU Compiler Collection) to generate an APP.bin, thereby achieving multi-application development.

The invention discloses a time synchronization information security identification method and system based on a self-attention mechanism, and relates to a time synchronization and high-precision clock technology, and the system comprises a data collection and processing module which is used for receiving, caching and processing original time information data, converting the original time information data into variable input of a self-attention matrix calculation module, and outputting the variable input of the self-attention matrix calculation module; the variable input is a preprocessed clock error sequence; the self-attention matrix calculation module is used for receiving the preprocessed clock error sequence, calculating context correlation of clock error data and outputting a probability matrix; the security identification decision module is used for averaging all time points represented by the sequence so as to aggregate and obtain global features; global features are mapped into abnormal probabilities through weights and offsets, and sequence judgment results are divided into normal results and abnormal results through binary decision. According to the invention, illegal information embedded in a complex modulation mode can be effectively identified, and the perception and defense capability of the system to hidden attacks is significantly improved.

A processing system configured to execute one or more processing operations, the execution of the one or more processing operations involving the evaluation of at least one Boolean function. The processing system includes an evaluation device configured to evaluate each Boolean function ƒ defined from 2n over 2, in a variable x of length n over 2n. The evaluation device includes: a differential calculation unit configured to calculate differentials Δƒ(y)=ƒ(y)⊕ƒ(y−1) for each integer intermediary variable y ranging from 1 to 2n−1; an accumulation unit configured to sum, using the XOR operator, the product of the differential Δƒ(y) and cn2<sup2>n< / sup2>−y(x) for each value of y ranging from 1 to 2n−1, according to the following XOR accumulation term ⊕y=12<sup2>n< / sup2>−1(Δƒ(y))cn2<sup2>n< / sup2>−y(x), where cnz(x) is the function associating x to the outbound carry of arithmetic summation x+z; a XOR adder configured to add ƒ(0) to the result of the accumulation unit, wherein the output of the XOR adder provides the result of the evaluation of the Boolean function ƒ.

A method for processing data, comprising receiving a folder attached request at a virtual media service operating on a processor, creating a virtual image data file and lookup table in response to the folder attached request at the virtual media service, preparing content to be populated into a master boot record region in response to the folder attached request and generating a virtual USB device in response to the populated master boot record.

A computer-implemented method for generating an approximate image of a matrix A includes constructing, in a processing unit, a decomposition of C, U, and R matrices of the matrix A, where the C matrix includes k-column vectors and the R matrix includes k-row vectors retrieved from a memory holding the matrix A; and performing singular value decomposition on C, U and R matrices in a processing unit to generate an approximate matrix # imgabs0 # such that # imgabs1 # for matrix A

A system for matrix-based public key homomorphic encryption, including a processor of a computing node configured to host a homomorphic encryption module and connected to at least one cloud server and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire plaintext x required to be encrypted; select a size of a matrix and modulus n; select invertible × matrix S over n, wherein n is a residue ring modulo n; compute an invertible × matrix S−1 over n; select two random secret keys (S, S−1) and (K, K−1) of a homomorphic encryption scheme Σ(m), where S and K belong to a ring of ×-matrix Ml(m) over residue ring m with modulus m=pq, where p, q are primes; select (K, K−1) as a master key; use the master key to encrypt all elements of matrices S and S−1; obtain two ciphertexts P1=EncK(S) and P2=EncK(S−1), which both form public key of the Σ(m) including pk=(P1, P2), wherein a secret key is sk=((S, S−1), (K, K−1)); and apply the pk and the sk for encryption and decryption of the plaintext x.