20,053 research outputs found
Rich Contacts: Corpus-Based Convolution of Audio Contact Gestures for Enhanced Musical Expression
We propose ways of enriching the timbral potential of gestural sonic material captured via piezo or contact microphones, through latency-free convolution of the microphone signal with grains from a sound corpus. This creates a new way to combine the sonic richness of large sound corpora, easily accessible via navigation through a timbral descriptor space, with the intuitive gestural interaction with a surface, captured by any contact microphone. We use convolution to excite the grains from the corpus via the microphone input, capturing the contact interaction sounds, which allows articulation of the corpus by hitting, scratching, or strumming a surface with various parts of the hands or objects. We also show how changes of grains have to be carefully handled, how one can smoothly interpolate between neighbouring grains, and finally evaluate the system against previous attempts
Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Automatically extracting useful information from electronic medical records
along with conducting disease diagnoses is a promising task for both clinical
decision support(CDS) and neural language processing(NLP). Most of the existing
systems are based on artificially constructed knowledge bases, and then
auxiliary diagnosis is done by rule matching. In this study, we present a
clinical intelligent decision approach based on Convolutional Neural
Networks(CNN), which can automatically extract high-level semantic information
of electronic medical records and then perform automatic diagnosis without
artificial construction of rules or knowledge bases. We use collected 18,590
copies of the real-world clinical electronic medical records to train and test
the proposed model. Experimental results show that the proposed model can
achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using
convolutional neural network to automatically learn high-level semantic
features of electronic medical records and then conduct assist diagnosis is
feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report
Decoupling frequencies, amplitudes and phases in nonlinear optics
In linear optics, light fields do not mutually interact in a medium. However, they do mix when their field strength becomes comparable to electron binding energies in the so-called nonlinear optical regime. Such high fields are typically achieved with ultra-short laser pulses containing very broad frequency spectra where their amplitudes and phases are mutually coupled in a convolution process. Here, we describe a regime of nonlinear interactions without mixing of different frequencies. We demonstrate both in theory and experiment how frequency domain nonlinear optics overcomes the shortcomings arising from the convolution in conventional time domain interactions. We generate light fields with previously inaccessible properties by avoiding these uncontrolled couplings. Consequently, arbitrary phase functions are transferred linearly to other frequencies while preserving the general shape of the input spectrum. As a powerful application, we introduce deep UV phase control at 207 nm by using a conventional NIR pulse shaper
CryptoKnight:generating and modelling compiled cryptographic primitives
Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware applicatio
- …