arXiv subjects

Yu Yu

Publications and source records attributed to Yu Yu.

At least 91 records · Page 5Linked to original sources

arXiv2021

Weak Lensing Magnification Reconstruction with the Modified Internal Linear Combination Method

Measuring weak lensing cosmic magnification signal is very challenging due to the overwhelming intrinsic clustering in the observed galaxy distribution. In this paper, we modify the Internal Linear Combination (ILC) method to reconstruct the lensing signal with an extra constraint to suppress the intrinsic clustering. To quantify the performance, we construct a realistic galaxy catalogue for the LSST-like photometric survey, covering $20\,000\ °^2$ with mean source redshift at $z_s\sim 1$. We find that the reconstruction performance depends on the width of the photo-z bin we choose. Due to the correlation between the lensing signal and the source galaxy distribution, the derived signal has smaller systematic bias but larger statistical uncertainty for a narrower photo-z bin. We conclude that the lensing signal reconstruction with the Modified ILC method is unbiased with a statistical uncertainty $<5\%$ for bin width $Δz^P = 0.2$.

astro-ph.CO↗

arXiv2021

Numerical investigation of non-Gaussianities in the phase and modulus of density Fourier modes

We numerically investigate non-Gaussianities in the late-time cosmological density field in Fourier space. We explore various statistics, including the two-point and three-point probability distribution function (PDF) of phase and modulus, and two \& three-point correlation function of of phase and modulus. We detect significant non-Gaussianity for certain configurations. We compare the simulation results with the theoretical expansion series of \citet{2007ApJS..170....1M}. We find that the $\mathcal{O}(V^{-1/2})$ order term alone is sufficiently accurate to describe all the measured non-Gaussianities in not only the PDFs, but also the correlations. We also numerically find that the phase-modulus cross-correlation contributes $\sim 50\%$ to the bispectrum, further verifying the accuracy of the $\mathcal{O}(V^{-1/2})$ order prediction. This work demonstrates that non-Gaussianity of the cosmic density field is simpler in Fourier space, and may facilitate the data analysis in the era of precision cosmology.

astro-ph.CO↗

arXiv2021

Biased tracer reconstruction with halo mass information

Plenty of crucial information about our Universe is encoded in the cosmic large-scale structure (LSS). However, the extractions of these information are usually hindered by the nonlinearities of the LSS, which can be largely alleviated by various techniques known as the reconstruction. In realistic applications, the efficiencies of these methods are always degraded by many limiting factors, a quite important one being the shot noise induced by the finite number density of biased matter tracers (i.e., luminous galaxies or dark matter halos) in observations. In this work, we explore the gains of biased tracer reconstruction achieved from halo mass information, which can suppress shot noise component and dramatically improves the cross-correlation between tracer field and dark matter. To this end, we first closely study the clustering biases and the stochasticity properties of halo fields with various number densities under different weighting schemes, i.e., the uniform, mass and optimal weightings. Then, we apply the biased tracer reconstruction method to these different weighted halo fields and investigate how linear bias and observational mass scatter affect the reconstruction performance. Our results demonstrate that halo masses are critical information for significantly improving the performance of biased tracer reconstruction, indicating a great application potential for substantially promoting the precision of cosmological measurements [especially for baryon acoustic oscillations (BAO)] in the ambitious on-going and future galaxy surveys.

astro-ph.CO↗

arXiv2021

Using AntiPatterns to avoid MLOps Mistakes

We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications. These lessons are presented in the form of antipatterns. Just as design patterns codify best software engineering practices, antipatterns provide a vocabulary to describe defective practices and methodologies. Here we catalog and document numerous antipatterns in financial ML operations (MLOps). Some antipatterns are due to technical errors, while others are due to not having sufficient knowledge of the surrounding context in which ML results are used. By providing a common vocabulary to discuss these situations, our intent is that antipatterns will support better documentation of issues, rapid communication between stakeholders, and faster resolution of problems. In addition to cataloging antipatterns, we describe solutions, best practices, and future directions toward MLOps maturity.

cs.LG↗

arXiv2021

Authentication of Metropolitan Quantum Key Distribution Network with Post-quantum Cryptography

Quantum key distribution (QKD) provides information theoretically secures key exchange requiring authentication of the classic data processing channel via pre-sharing of symmetric private keys. In previous studies, the lattice-based post-quantum digital signature algorithm Aigis-Sig, combined with public-key infrastructure (PKI) was used to achieve high-efficiency quantum security authentication of QKD, and its advantages in simplifying the MAN network structure and new user entry were demonstrated. This experiment further integrates the PQC algorithm into the commercial QKD system, the Jinan field metropolitan QKD network comprised of 14 user nodes and 5 optical switching nodes. The feasibility, effectiveness and stability of the post-quantum cryptography (PQC) algorithm and advantages of replacing trusted relays with optical switching brought by PQC authentication large-scale metropolitan area QKD network were verified. QKD with PQC authentication has potential in quantum-secure communications, specifically in metropolitan QKD networks.

quant-ph↗

arXiv2020

A hidden anti-jamming method based on deep reinforcement learning

Most of the current anti-jamming algorithms for wireless communications only consider how to avoid jamming attacks, but ignore that the communication waveform or frequency action may be obtained by the jammers. Although existing anti-jamming methods can guarantee temporary communication effects, the long-term performance of these anti-jamming methods may be depressed when intelligent jammers are capable of learning from historical communication activities. Aiming at this issue, a hidden anti-jamming method based on the idea of reducing the jammer's sense probability is proposed. Firstly, the sensing probability of the jammer is obtained by calculating the correlation between the actions of the jammer and the user. Later, a deep reinforcement learning framework is designed, which aims at not only maximizing the communication throughput but also minimizing the action's correlation between the jammer and the user. Finally, a hidden anti-jamming algorithm is proposed, which links the instantaneous return with the communication quality of users and the correlation between users and jammer. The simulation result shows that the proposed algorithm not only avoids being sensed by the jammer but also improves its anti-jamming performance compared to the current algorithm that only considers jamming avoidance.

cs.IT↗

arXiv2020

Experimental Authentication of Quantum Key Distribution with Post-quantum Cryptography

Quantum key distribution (QKD) can provide information theoretically secure key exchange even in the era of quantum computer. However, QKD requires the classical channel to be authenticated, and the current method is pre-sharing symmetric keys. For a QKD network of $n$ users, this method requires $C_n^2 = n(n-1)/2$ pairs of symmetric keys to realize pairwise interconnection. In contrast, with the help of mature public key infrastructure (PKI) and post-quantum cryptography (PQC) with quantum resistant security, each user only needs to apply for a digital certificate from certificate authority (CA) to achieve efficient and secure authentication for QKD. We only need to assume the short-term security of the PQC algorithm to achieve the long-term security of the distributed keys. Here, we experimentally verified the feasibility, efficiency and stability of the PQC algorithm in QKD authentication, and demonstrated the advantages when new users join the QKD network. Using PQC authentication we only need to believe the CA is safe, rather than all trusted relays. QKD combined with PQC authentication will greatly promote and extend the application prospects of quantum safe communication.

quant-ph↗

arXiv2020

Measuring the integrated Sachs-Wolfe effect from the low-density regions of the universe

The integrated Sachs-Wolfe (ISW) effect is caused by the decay of cosmological gravitational potential, and is therefore a unique probe of dark energy. However, its robust detection is still problematic. Various tensions between different data sets, different large scale structure (LSS) tracers, and between data and the $Λ$CDM theory prediction, exist. We propose a novel method of ISW measurement by cross correlating CMB and the LSS traced by "low-density-position" (LDP, \citet{2019ApJ...874....7D}). It isolates the ISW effect generated by low-density regions of the universe, but insensitive to selection effects associated with voids. We apply it to the DR8 galaxy catalogue of the DESI Legacy imaging surveys, and obtain the LDPs at $z\leq 0.6$ over $\sim$ 20000 $deg^2$ sky coverage. We then cross correlate with the Planck temperature map, and detect the ISW effect at $3.2σ$. We further compare the measurement with numerical simulations of the concordance $Λ$CDM cosmology, and find the ISW amplitude parameter $A_{ISW}=1.14\pm0.38$ when we adopt a LDP definition radius $R_s=3^{'}$, fully consistent with the prediction of the standard $Λ$CDM cosmology ($A_{ISW}=1$). This agreement with $Λ$CDM cosmology holds for all the galaxy samples and $R_s$ that we have investigated. Furthermore, the S/N is comparable to that of galaxy ISW measurement. These results demonstrate the LDP method as a competitive alternative to existing ISW measurement methods, and provide independent checks to existing tensions.

astro-ph.CO↗

arXiv2020

The copula of the cosmological matter density field is non-Gaussian

Non-Gaussianity of the cosmological matter density field can be largely reduced by a local Gaussianization transformation (and its approximations such as the logrithmic transformation). Such behavior can be recasted as the Gaussian copula hypothesis, and has been verified to very high accuracy at two-point level. On the other hand, statistically significant non-Gaussianities in the Gaussianized field have been detected in simulations. We point out that, this apparent inconsistency is caused by the very limited degrees of freedom in the copula function, which make it misleading as a diagnosis of residual non-Gaussianity in the Gaussianized field. Using the copula density, we highlight the departure from Gaussianity. We further quantify its impact in the predicted n-point correlation functions. We explore a remedy of the Gaussian copula hypothesis, which alleviates but not completely solves the above problems.

astro-ph.CO↗

arXiv2020

PrivPy: Enabling Scalable and General Privacy-Preserving Machine Learning

We introduce PrivPy, a practical privacy-preserving collaborative computation framework, especially optimized for machine learning tasks. PrivPy provides an easy-to-use and highly compatible Python programming front-end which supports high-level array operations and different secure computation engines to allow for security assumptions and performance trade-offs. With PrivPy, programmers can write modern machine learning algorithms conveniently and efficiently in Python. We also design and implement a new efficient computation engine, with which people can use competing cloud providers to efficiently perform general arithmetics over real numbers. We demonstrate the usability and scalability of PrivPy using common machine learning models (e.g. logistic regression and convolutional neural networks) and real-world datasets (including a 5000-by-1-million matrix).

cs.CR↗

arXiv2020

Multimode silicon photonics using on-chip geometrical-optics

On-chip optical interconnect has been widely accepted as a promising technology to realize future large-scale multiprocessors. Mode-division multiplexing (MDM) provides a new degree of freedom for optical interconnects to dramatically increase the link capacity. Present on-chip multimode devices are based on traditional wave-optics. Although large amount of computation and optimization are adopted to support more modes, mode-independent manipulation is still hard to be achieved due to severe mode dispersion. Here, we propose a universal solution to standardize the design of fundamental multimode building blocks, by introducing a geometrical-optics-like concept adopting waveguide width larger than the working wavelength. The proposed solution can tackle a group of modes at the same time with very simple processes, avoiding demultiplexing procedure and ensuring compact footprint. Compare to conventional schemes, it is scalable to larger mode channels without increasing the complexity and whole footprint. As a proof of concept, we demonstrate a set of multimode building blocks including crossing, bend, coupler and switches. Low losses of multimode waveguide crossing and bend are achieved, as well as ultra-low power consumption of the multimode switch is realized since it enables reconfigurable routing for a group of modes simultaneously. Our work promotes the multimode photonics research and makes the MDM technique more practical.

physics.optics↗

arXiv2020

Unsupervised Representation Learning for Gaze Estimation

Although automatic gaze estimation is very important to a large variety of application areas, it is difficult to train accurate and robust gaze models, in great part due to the difficulty in collecting large and diverse data (annotating 3D gaze is expensive and existing datasets use different setups). To address this issue, our main contribution in this paper is to propose an effective approach to learn a low dimensional gaze representation without gaze annotations, which to the best of our best knowledge, is the first work to do so. The main idea is to rely on a gaze redirection network and use the gaze representation difference of the input and target images (of the redirection network) as the redirection variable. A redirection loss in image domain allows the joint training of both the redirection network and the gaze representation network. In addition, we propose a warping field regularization which not only provides an explicit physical meaning to the gaze representations but also avoids redirection distortions. Promising results on few-shot gaze estimation (competitive results can be achieved with as few as <= 100 calibration samples), cross-dataset gaze estimation, gaze network pretraining, and another task (head pose estimation) demonstrate the validity of our framework.

cs.CV↗

arXiv2020

Neutrino effects on the morphology of cosmic large-scale structure

In this work, we propose a powerful probe of neutrino effects on the large-scale structure (LSS) of the Universe, i.e., Minkowski functionals (MFs). The morphology of LSS can be fully described by four MFs. This tool, with strong statistical power, is robust to various systematics and can comprehensively probe all orders of N-point statistics. By using a pair of high-resolution N-body simulations, for the first time, we comprehensively studied the subtle neutrino effects on the morphology of LSS. For an ideal LSS survey of volume $\sim1.73$ Gpc$^3$/$h^3$, neutrino signals are mainly detected from void regions with a significant level up to $\thicksim 10σ$ and $\thicksim 300σ$ for CDM and total matter density fields, respectively. This demonstrates its enormous potential for much improving the neutrino mass constraint in the data analysis of up-coming ambitious LSS surveys.

astro-ph.CO↗

arXiv2020

Probing Primordial Chirality with Galaxy Spins

Chiral symmetry is maximally violated in weak interactions, and such microscopic asymmetries in the early Universe might leave observable imprints on astrophysical scales without violating the cosmological principle. In this Letter, we propose a helicity measurement to detect primordial chiral violation. We point out that observations of halo-galaxy angular momentum directions (spins), which are frozen in during the galaxy formation process, provide a fossil chiral observable. From the clustering mode of large scale structure of the Universe, we construct a spin mode in Lagrangian space and show in simulations that it is a good probe of halo-galaxy spins. In standard model, a strong symmetric correlation between the left and right helical components of this spin mode and galaxy spins is expected. Measurements of these correlations will be sensitive to chiral breaking, providing a direct test of chiral symmetry breaking in the early Universe.

astro-ph.CO↗

arXiv2020

A Differential Approach for Gaze Estimation

Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as biases which are subject dependent. Therefore, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to his/her actual gaze. In this paper, we introduce a novel image differential method for gaze estimation. We propose to directly train a differential convolutional neural network to predict the gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by allowing the comparison between two eye images, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or when the latter methods are followed by subject specific gaze adaptation.

cs.CV↗

arXiv2020

The Weak Lensing Peak Statistics in the Mocks by the inverse-Gaussianization Method

We apply the inverse-Gaussianization method proposed in \citealt{arXiv:1607.05007} to fast produce weak lensing convergence maps and investigate the peak statistics, including the peak height counts and peak steepness counts, in these mocks. We find that the distribution of peak height and steepness is in good agreement with the simulation. The difference is $\lesssim 20\%$ for these peak statistics in the maps at source redshift $z_s=1$. Besides, the loss of off-diagonal elements in peak covariance motivates us to consider the super sample variance in weak lensing peak statistics. We propose correction methods to effectively recover the (anti-)correlation among different bins by adding scatters in the mean value of these mocks. Finally, as an example of the application, we adopt the improved inverse-Gaussianization method with the above improvement to fast generate 40,000 mocks to calculate precision matrices between the power spectrum and peak statistics.

astro-ph.CO↗

arXiv2019

Nonlinear Reconstruction of the Velocity Field

We propose a new velocity reconstruction method based on the displacement estimation by recently developed methods. The velocity is first reconstructed by transfer functions in Lagrangian space and then mapped into Eulerian space. High resolution simulations are used to test the performance. We find that the new reconstruction method outperforms the standard velocity reconstruction in the sense of better cross-correlation coefficient, less velocity misalignment and smaller amplitude difference. We conclude that this new method has the potential to improve the large-scale structure sciences involving a velocity reconstruction, such as kinetic Sunyaev-Zel'dovich measurement and supernova cosmology.

astro-ph.CO↗

arXiv2019

Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis

As an indicator of human attention gaze is a subtle behavioral cue which can be exploited in many applications. However, inferring 3D gaze direction is challenging even for deep neural networks given the lack of large amount of data (groundtruthing gaze is expensive and existing datasets use different setups) and the inherent presence of gaze biases due to person-specific difference. In this work, we address the problem of person-specific gaze model adaptation from only a few reference training samples. The main and novel idea is to improve gaze adaptation by generating additional training samples through the synthesis of gaze-redirected eye images from existing reference samples. In doing so, our contributions are threefold: (i) we design our gaze redirection framework from synthetic data, allowing us to benefit from aligned training sample pairs to predict accurate inverse mapping fields; (ii) we proposed a self-supervised approach for domain adaptation; (iii) we exploit the gaze redirection to improve the performance of person-specific gaze estimation. Extensive experiments on two public datasets demonstrate the validity of our gaze retargeting and gaze estimation framework.

cs.CV↗