Quantum Noise of Kramers-Kronig Receiver

Abstrac--Kramers-Kronig (KK) receiver, which is equivalent to heterodyne detection with one single photodetector, provides an efficient method to reconstruct the complex-valued optical field by means of intensity detection given a minimum-phase signal. In this paper, quantum noise of the KK receiver is derived analytically and compared with that of the balanced heterodyne detection. We show that the quantum noise of the KK receiver keeps the radical fluctuation of the measured signal the same as that of the balanced heterodyne detection, while compressing the tangential noise to 1/3 times the radical one using the information provided by the Hilbert transform. In consequence, the KK receiver has 3/2 times the signal-to-noise ratio of balanced heterodyne detection while presenting an asymmetric distribution of fluctuations, which is also different from that of the latter. More interestingly, the projected in-phase and quadrature field operators of the retrieved signal after down conversion have a time dependent quantum noise distribution depending on the time-varying phase. This property provides a feasible scheme for controlling the fluctuation distribution according to the requirements of measurement accuracy in the specific direction. Under the condition of strong carrier wave, the fluctuations of the component requiring to be measured more accurately can be compressed to 1 / 6, which is even lower than 1/4 by measuring a coherent state. Finally, we prove the analytic conclusions by simulation results

Learning Dense Correspondences between Photos and Sketches

Humans effortlessly grasp the connection between sketches and real-world objects, even when these sketches are far from realistic. Moreover, human sketch understanding goes beyond categorization -- critically, it also entails understanding how individual elements within a sketch correspond to parts of the physical world it represents. What are the computational ingredients needed to support this ability? Towards answering this question, we make two contributions: first, we introduce a new sketch-photo correspondence benchmark, $\textit{PSC6k}$, containing 150K annotations of 6250 sketch-photo pairs across 125 object categories, augmenting the existing Sketchy dataset with fine-grained correspondence metadata. Second, we propose a self-supervised method for learning dense correspondences between sketch-photo pairs, building upon recent advances in correspondence learning for pairs of photos. Our model uses a spatial transformer network to estimate the warp flow between latent representations of a sketch and photo extracted by a contrastive learning-based ConvNet backbone. We found that this approach outperformed several strong baselines and produced predictions that were quantitatively consistent with other warp-based methods. However, our benchmark also revealed systematic differences between predictions of the suite of models we tested and those of humans. Taken together, our work suggests a promising path towards developing artificial systems that achieve more human-like understanding of visual images at different levels of abstraction. Project page: https://photo-sketch-correspondence.github.ioComment: Accepted to ICML 2023. Project page: https://photo-sketch-correspondence.github.i

Identification and characterization of a novel thermostable pyrethroid-hydrolyzing enzyme isolated through metagenomic approach

<p>Abstract</p> <p>Background</p> <p>Pyrethroid pesticides are broad-spectrum pest control agents in agricultural production. Both agricultural and residential usage is continuing to grow, leading to the development of insecticide resistance in the pest and toxic effects on a number of nontarget organisms. Thus, it is necessary to hunt suitable enzymes including hydrolases for degrading pesticide residues, which is an efficient "green" solution to biodegrade polluting chemicals. Although many pyrethroid esterases have consistently been purified and characterized from various resources including metagenomes and organisms, the thermostable pyrethroid esterases have not been reported up to the present.</p> <p>Results</p> <p>In this study, we identified a novel pyrethroid-hydrolyzing enzyme Sys410 belonging to familyV esterases/lipases with activity-based functional screening from Turban Basin metagenomic library. Sys410 contained 280 amino acids with a predicted molecular mass (Mr) of 30.8 kDa and was overexpressed in <it>Escherichia coli </it>BL21 (DE3) in soluble form. The optimum pH and temperature of the recombinant Sys410 were 6.5 and 55°C, respectively. The enzyme was stable in the pH range of 4.5-8.5 and at temperatures below 50°C. The activity of Sys410 decreased a little when stored at 4°C for 10 weeks, and the residual activity reached 94.1%. Even after incubation at 25°C for 10 weeks, it kept 68.3% of its activity. The recombinant Sys410 could hydrolyze a wide range of ρ-nitrophenyl esters, but its best substrate is ρ-nitrophenyl acetate with the highest activity (772.9 U/mg). The enzyme efficiently degraded cyhalothrin, cypermethrin, sumicidin, and deltamethrin under assay conditions of 37°C for 15 min, with exceeding 95% hydrolysis rate.</p> <p>Conclusion</p> <p>This is the first report to construct metagenomic libraries from Turban Basin to obtain the thermostable pyrethroid-hydrolyzing enzyme. The recombinant Sys410 with broad substrate specificities and high activity was the most thermostable one of the pyrethroid-hydrolyzing esterases studied before, which made it an ideal candidate for the detoxification of pyrethroids.</p

BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning

Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves, i.e., disordered electrical brainwave activity in the patient's brain. Existing works have begun to employ machine learning models to detect epileptic waves via cortical electroencephalogram (EEG). However, the recently developed stereoelectrocorticography (SEEG) method provides information in stereo that is more precise than conventional EEG, and has been broadly applied in clinical practice. Therefore, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset. While offering new opportunities, SEEG also poses several challenges. In clinical practice, epileptic wave activities are considered to propagate between different regions in the brain. These propagation paths, also known as the epileptogenic network, are deemed to be a key factor in the context of epilepsy surgery. However, the question of how to extract an exact epileptogenic network for each patient remains an open problem in the field of neuroscience. To address these challenges, we propose a novel model (BrainNet) that jointly learns the dynamic diffusion graphs and models the brain wave diffusion patterns. In addition, our model effectively aids in resisting label imbalance and severe noise by employing several self-supervised learning tasks and a hierarchical framework. By experimenting with the extensive real SEEG dataset obtained from multiple patients, we find that BrainNet outperforms several latest state-of-the-art baselines derived from time-series analysis

Crack Propagation and Microstructural Transformation on The Friction Surface of a High-Speed Railway Brake Disc

While brake disc wear represents a significant problem in high-speed rail systems, the progressive development of fatigue cracks during successive braking cycles also plays a great role in braking integrity. The modified microstructure consisting of a white etching layer (WEL) containing nanosized ferrite was observed on the friction surface of worn brake discs. In order to analyze how sequential thermal and mechanical stress affected crack propagation and microstructure evolution in brake discs, successive braking cycles were simulated on a full-scale braking bench test rig. Crack initiation and propagation mechanisms were proposed based on the experimental results, i.e., (i) occurrence of heat checking caused by heating and cooling transients during braking; (ii) heat checking increasing the roughness of the friction surface which in turn caused a local stress concentration and (iii) localized friction stress and thermal stress driving the heat checking to propagate and coalesce with the radial main crack. Analysis of the thermal-mechanical conditions that exist at the friction surface during braking indicates that WEL formation can be attributed to severe plastic deformation caused by the repeated friction between the disc and pads. Mechanical testing also indicated that WEL formation is not detrimental to brake disc integrity