Information thermodynamics bridges information theory and statistical physics by connecting information content and entropy production through measurement and feedback control. Maxwell's demon is a ...hypothetical character that uses information about a system to reduce its entropy. Here we realize a Maxwell's demon acting on a superconducting quantum circuit. We implement quantum non-demolition projective measurement and feedback operation of a qubit and verify the generalized integral fluctuation theorem. We also evaluate the conversion efficiency from information gain to work in the feedback protocol. Our experiment constitutes a step toward experimental studies of quantum information thermodynamics in artificially made quantum machines.
The experimental observation of Peregrine solitons in a multicomponent plasma with the critical concentration of negative ions is reported. A slowly amplitude modulated perturbation undergoes ...self-modulation and gives rise to a high amplitude localized pulse. The measured amplitude of the Peregrine soliton is 3 times the nearby carrier wave amplitude, which agrees with the theory. The numerical solution of the nonlinear Schrödinger equation is compared with the experimental results.
Summary
Background
Application of deep‐learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training ...images required for such a system is thought to be extremely large.
Objectives
To determine whether deep‐learning technology could be used to develop an efficient skin cancer classification system with a relatively small dataset of clinical images.
Methods
A deep convolutional neural network (DCNN) was trained using a dataset of 4867 clinical images obtained from 1842 patients diagnosed with skin tumours at the University of Tsukuba Hospital from 2003 to 2016. The images consisted of 14 diagnoses, including both malignant and benign conditions. Its performance was tested against 13 board‐certified dermatologists and nine dermatology trainees.
Results
The overall classification accuracy of the trained DCNN was 76·5%. The DCNN achieved 96·3% sensitivity (correctly classified malignant as malignant) and 89·5% specificity (correctly classified benign as benign). Although the accuracy of malignant or benign classification by the board‐certified dermatologists was statistically higher than that of the dermatology trainees (85·3% ± 3·7% and 74·4% ± 6·8%, P < 0·01), the DCNN achieved even greater accuracy, as high as 92·4% ± 2·1% (P < 0·001).
Conclusions
We have developed an efficient skin tumour classifier using a DCNN trained on a relatively small dataset. The DCNN classified images of skin tumours more accurately than board‐certified dermatologists. Collectively, the current system may have capabilities for screening purposes in general medical practice, particularly because it requires only a single clinical image for classification.
What's already known about this topic?
Several computer‐aided classification systems have been introduced that achieve high sensitivity for melanoma detection; however, low specificity was a trade‐off for high sensitivity.
The application of deep‐learning technology to skin cancer classification could potentially improve the sensitivity and specificity of skin cancer screening.
The number of training images required for such a system is thought to be extremely large, and compiling large datasets for rare skin conditions is difficult.
What does this study add?
Our deep convolutional neural network (DCNN), trained on only 4867 images from 1842 patients, classified images of skin tumours into 14 different diagnoses more accurately than board‐certified dermatologists.
The fluctuation range was only ± 3·2% by fivefold cross‐validation, showing the robustness of the system.
Our DCNN system requires only a single image and provides 96·3% sensitivity and 89·5% specificity in the detection of skin cancer; however, it should be validated in a prospective clinical study before use for screening purposes in general medical practice.
Linked Editorial: Janda and Soyer. Br J Dermatol 2019; 180:247–248.
Plain language summary available online
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The vacuum of quantum chromodynamics has an incredibly rich structure at the nonperturbative level, which is intimately connected with the topology of gauge fields, and put to a test by the strong CP ...problem. We investigate the long-distance properties of the theory in the presence of the topological θ term. This is done on the lattice, using the gradient flow to isolate the long-distance modes in the functional integral measure and tracing it over successive length scales. The key point is that the vacuum splits into disconnected topological sectors with markedly different physical characteristics, which gives rise to a nontrivial behavior depending on θ. We find that the color fields produced by quarks and gluons are screened, and confinement is lost, for bare vacuum angles |θ|>0, thus providing a natural solution of the strong CP problem. The renormalized vacuum angle θ is found to flow to zero in the infrared limit, leading to a self-consistent solution within QCD.
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We experimentally implement a system of cavity optomagnonics, where a sphere of ferromagnetic material supports whispering gallery modes (WGMs) for photons and the magnetostatic mode for magnons. We ...observe pronounced nonreciprocity and asymmetry in the sideband signals generated by the magnon-induced Brillouin scattering of light. The spin-orbit coupled nature of the WGM photons, their geometrical birefringence, and the time-reversal symmetry breaking in the magnon dynamics impose the angular-momentum selection rules in the scattering process and account for the observed phenomena. The unique features of the system may find interesting applications at the crossroad between quantum optics and spintronics.
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