arXiv subjects

Jia Liu

Publications and source records attributed to Jia Liu.

At least 325 records · Page 18Linked to original sources

arXiv2021

Constraining neutrino masses with weak-lensing multiscale peak counts

Massive neutrinos influence the background evolution of the Universe as well as the growth of structure. Being able to model this effect and constrain the sum of their masses is one of the key challenges in modern cosmology. Weak-lensing cosmological constraints will also soon reach higher levels of precision with next-generation surveys like LSST, WFIRST and Euclid. We use the MassiveNus simulations to derive constraints on the sum of neutrino masses $M_ν$, the present-day total matter density $Ω_{\rm m}$, and the primordial power spectrum normalization $A_{\rm s}$ in a tomographic setting. We measure the lensing power spectrum as second-order statistics along with peak counts as higher-order statistics on lensing convergence maps generated from the simulations. We investigate the impact of multiscale filtering approaches on cosmological parameters by employing a starlet (wavelet) filter and a concatenation of Gaussian filters. In both cases peak counts perform better than the power spectrum on the set of parameters [$M_ν$, $Ω_{\rm m}$, $A_{\rm s}$] respectively by 63$\%$, 40$\%$ and 72$\%$ when using a starlet filter and by 70$\%$, 40$\%$ and 77$\%$ when using a multiscale Gaussian. More importantly, we show that when using a multiscale approach, joining power spectrum and peaks does not add any relevant information over considering just the peaks alone. While both multiscale filters behave similarly, we find that with the starlet filter the majority of the information in the data covariance matrix is encoded in the diagonal elements; this can be an advantage when inverting the matrix, speeding up the numerical implementation.

astro-ph.CO↗

arXiv2020

Re-examining the Solar Axion Explanation for the XENON1T Excess

The XENON1T collaboration has observed an excess in electronic recoil events below $5~\mathrm{keV}$ over the known background, which could originate from beyond-the-Standard-Model physics. The solar axion is a well-motivated model that has been proposed to explain the excess, though it has tension with astrophysical observations. The axions traveled from the Sun can be absorbed by the electrons in the xenon atoms via the axion-electron coupling. Meanwhile, they can also scatter with the atoms through the inverse Primakoff process via the axion-photon coupling, which emits a photon and mimics the electronic recoil signals. We found that the latter process cannot be neglected. After including the $\rm{keV}$ photon produced via inverse Primakoff in the detection, the tension with the astrophysical constraints can be significantly reduced. We also explore scenarios involving additional new physics to further alleviate the tension with the astrophysical bounds.

hep-ph↗

arXiv2020

Directly accessing octave-spanning dissipative Kerr soliton frequency combs in an AlN microring resonator

Self-referenced dissipative Kerr solitons (DKSs) based on optical microresonators offer prominent characteristics including miniaturization, low power consumption, broad spectral range and inherent coherence for various applications such as precision measurement, communications, microwave photonics, and astronomical spectrometer calibration. To date, octave-spanning DKSs with a free spectral range (FSR) of ~1 THz have been achieved only in ultrahigh-Q silicon nitride microresonators, with elaborate wavelength control required. Here we demonstrate an octave-spanning DKS in an aluminium nitride (AlN) microresonator with moderate loaded Q (500,000) and FSR of 374 GHz. In the design, a TE00 mode and a TE10 mode are nearly degenerate and act as pump and auxiliary modes. The presence of the auxiliary resonance balances the thermal dragging effect in dissipative soliton comb formation, crucially simplifying the DKS generation with a single pump and leading to a wide single soliton access window. We experimentally demonstrate stable DKS operation with a record single soliton step (~80 pm) and octave-spanning bandwidth (1100-2300 nm) through adiabatic pump tuning and on-chip power of 340 mW. Our scheme also allows for direct creation of the DKS state with high probability and without elaborate wavelength or power schemes being required to stabilize the soliton behavior.

physics.optics↗

arXiv2020

The Scale of Superpartner Masses and Electroweakino Searches at the High-Luminosity LHC

Searches for weakly interacting particles is one of the main goals of the high luminosity LHC run. In this work we study the well motivated cases of electroweakinos with mostly Wino and Bino components. We show the relevance of squark induced t-channel production in defining the production cross section and hence the LHC reach. Moreover, a realistic evaluation of the decay branching ratios show a strong dependence on the sign of $μ$ and, for negative values of $μ$, on the relative size of the ratio of $μ$ to the gaugino masses compared with tan$β$. Overall, unless it is kinematically suppressed, or specific conditions are fulfilled, the Higgs decay channel is the most significant one, and the trilepton channel becomes subdominant with respect to final states including bottom quarks. Although the properties are different than in the Higgsino-Bino case, also in this case the discovery reach extends to mass values that are significantly larger than the ones probed at current luminosities, leading to a strong motivation for the search for electroweakinos in the high luminosity LHC run.

hep-ph↗

arXiv2020

Exploring $k$ out of Top $ρ$ Fraction of Arms in Stochastic Bandits

This paper studies the problem of identifying any $k$ distinct arms among the top $ρ$ fraction (e.g., top 5\%) of arms from a finite or infinite set with a probably approximately correct (PAC) tolerance $ε$. We consider two cases: (i) when the threshold of the top arms' expected rewards is known and (ii) when it is unknown. We prove lower bounds for the four variants (finite or infinite arms, and known or unknown threshold), and propose algorithms for each. Two of these algorithms are shown to be sample complexity optimal (up to constant factors) and the other two are optimal up to a log factor. Results in this paper provide up to $ρn/k$ reductions compared with the "$k$-exploration" algorithms that focus on finding the (PAC) best $k$ arms out of $n$ arms. We also numerically show improvements over the state-of-the-art.

cs.LG↗

arXiv2020

Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree

Recently, there have been significant interests in studying the so-called "double-descent" of the generalization error of linear regression models under the overparameterized and overfitting regime, with the hope that such analysis may provide the first step towards understanding why overparameterized deep neural networks (DNN) still generalize well. However, to date most of these studies focused on the min $\ell_2$-norm solution that overfits the data. In contrast, in this paper we study the overfitting solution that minimizes the $\ell_1$-norm, which is known as Basis Pursuit (BP) in the compressed sensing literature. Under a sparse true linear regression model with $p$ i.i.d. Gaussian features, we show that for a large range of $p$ up to a limit that grows exponentially with the number of samples $n$, with high probability the model error of BP is upper bounded by a value that decreases with $p$. To the best of our knowledge, this is the first analytical result in the literature establishing the double-descent of overfitting BP for finite $n$ and $p$. Further, our results reveal significant differences between the double-descent of BP and min $\ell_2$-norm solutions. Specifically, the double-descent upper-bound of BP is independent of the signal strength, and for high SNR and sparse models the descent-floor of BP can be much lower and wider than that of min $\ell_2$-norm solutions.

cs.LG↗

arXiv2020

Probabilistic Mapping of Dark Matter by Neural Score Matching

The Dark Matter present in the Large-Scale Structure of the Universe is invisible, but its presence can be inferred through the small gravitational lensing effect it has on the images of far away galaxies. By measuring this lensing effect on a large number of galaxies it is possible to reconstruct maps of the Dark Matter distribution on the sky. This, however, represents an extremely challenging inverse problem due to missing data and noise dominated measurements. In this work, we present a novel methodology for addressing such inverse problems by combining elements of Bayesian statistics, analytic physical theory, and a recent class of Deep Generative Models based on Neural Score Matching. This approach allows to do the following: (1) make full use of analytic cosmological theory to constrain the 2pt statistics of the solution, (2) learn from cosmological simulations any differences between this analytic prior and full simulations, and (3) obtain samples from the full Bayesian posterior of the problem for robust Uncertainty Quantification. We present an application of this methodology on the first deep-learning-assisted Dark Matter map reconstruction of the Hubble Space Telescope COSMOS field.

astro-ph.CO↗

arXiv2020

Enhancing Sensitivities to Long-lived Particles with High Granularity Calorimeters at the LHC

The search for long-lived particles (LLP) is an exciting physics opportunity in the upcoming runs of the Large Hadron Collider. In this paper, we focus on a new search strategy of using the High Granularity Calorimeter (HGCAL), part of the upgrade of the CMS detector, in such searches. In particular, we demonstrate that the high granularity of the calorimeter with this upgrade, which allows us to see "shower tracks" in the calorimeter, can play a crucial role in identifying the signal and suppressing the background. We study the potential reach of the HGCAL using a signal model in which the Standard Model Higgs boson decays into a pair of LLPs, $h \to XX$. After carefully estimating the Standard Model QCD and the misreconstructed fake-track backgrounds, we give the projected reach for both a more conservative vector boson fusion trigger and a novel displaced-track-based trigger. Our results show that the best reach for the Higgs decay branching ratio, BR$(h \to XX)$, in the vector boson fusion channel is about $\mathcal{O}(10^{-4})$ with lifetime $cτ_X \sim 0.1$--$1$ meters, while for the gluon gluon fusion channel it is about $\mathcal{O}(10^{-5}\text{--}10^{-6})$ for similar lifetimes. Alternatively, for an LLP with $cτ_X \sim 10^3$ meters, the HGCAL based search should be able to probe BR$(h\to XX)$ down to a few $\times 10^{-4}$($10^{-2}$) in the gluon gluon fusion (vector boson fusion) channels, respectively. In comparison with these previous searches, our new search shows enhanced sensitivity in complementary regions of the LLP parameter space.

hep-ph↗

arXiv2020

Cross-correlation between Subaru Hyper Suprime-Cam Galaxy Weak Lensing and Planck Cosmic Microwave Background Lensing

Cross-correlations between galaxy weak lensing (WL) and Cosmic Microwave Background (CMB) lensing are a powerful tool to probe matter fluctuations at intermediate redshifts and to detect residual systematics in either probe. In this paper, we study the cross-correlation of galaxy WL from the Hyper Suprime-Cam Subaru Strategic Program (HSC) first data release and CMB lensing from the final Planck data release, for HSC source galaxies at 0.3< z < 1.5. HSC is the deepest Stage-III galaxy WL survey, and provides both a great opportunity to study the high-redshift universe and new challenges related to its exceptionally high source density, such as source blending. The cross-correlation signal is measured at a significance level of 3.1$σ$. The amplitude of our best-fit model with respect to the best-fit 2018 Planck cosmology is $A = 0.81\pm 0.25$, consistent with $A=1$. Our result is also consistent with previous CMB lensing and galaxy WL cross-correlation studies using different surveys. We perform tests with respect to the WL $B$-modes, the point-spread-function, photometric redshift errors, and thermal Sunyaev-Zel'dovich leakage, and find no significant evidence of residual systematics.

astro-ph.CO↗

arXiv2020

A Reversible Data hiding Scheme in Encrypted Domain for Secret Image Sharing based on Chinese Remainder Theorem

Reversible data hiding in encrypted domain (RDH-ED) schemes based on symmetric or public key encryption are mainly applied to the security of end-to-end communication. Aimed at providing reliable technical supports for multi-party security scenarios, a separable RDH-ED scheme for secret image sharing based on Chinese remainder theorem (CRT) is presented. In the application of (t, n) secret image sharing, the image is first shared into n different shares of ciphertext. Only when not less than t shares obtained, can the original image be reconstructed. In our scheme, additional data could be embedded into the image shares. To realize data extraction from the image shares and the reconstructed image separably, two data hiding methods are proposed: one is homomorphic difference expansion in encrypted domain (HDE-ED) that supports data extraction from the reconstructed image by utilizing the addition homomorphism of CRT secret sharing; the other is difference expansion in image shares (DE-IS) that supports the data extraction from the marked shares before image reconstruction. Experimental results demonstrate that the proposed scheme could not only maintain the security and the threshold function of secret sharing system, but also obtain a better reversibility and efficiency compared with most existing RDH-ED algorithms. The maximum embedding rate of HDE-ED could reach 0.5000 bits per pixel and the average embedding rate of DE-IS is 0.0545 bits per bit of ciphertext.

cs.CR↗

arXiv2020

Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning

Understanding the convergence performance of asynchronous stochastic gradient descent method (Async-SGD) has received increasing attention in recent years due to their foundational role in machine learning. To date, however, most of the existing works are restricted to either bounded gradient delays or convex settings. In this paper, we focus on Async-SGD and its variant Async-SGDI (which uses increasing batch size) for non-convex optimization problems with unbounded gradient delays. We prove $o(1/\sqrt{k})$ convergence rate for Async-SGD and $o(1/k)$ for Async-SGDI. Also, a unifying sufficient condition for Async-SGD's convergence is established, which includes two major gradient delay models in the literature as special cases and yields a new delay model not considered thus far.

cs.LG↗

arXiv2020

Tuning the electronic properties of hydrogen passivated C3N nanoribbons through van der Waals stacking

The two-dimensional (2D) C3N has emerged as a material with promising applications in high performance device owing to its intrinsic bandgap and tunable electronic properties. Although there are several reports about the bandgap tuning of C3N via stacking or forming nanoribbon, bandgap modulation of bilayer C3N nanoribbons (C3NNRs) with various edge structures is still far from well understood. Here, based on extensive first-principles calculations, we demonstrated the effective bandgap engineering of C3N by cutting it into hydrogen passivated C3NNRs and stacking them into bilayer heterostructures. It was found that armchair (AC) C3NNRs with three types of edge structures are all semiconductors, while only zigzag (ZZ) C3NNRs with edges composed of both C and N atoms (ZZ-CN/CN) are semiconductors. The bandgaps of all semiconducting C3NNRs are larger than that of C3N nanosheet. More interestingly, AC-C3NNRs with CN/CN edges (AC-CN/CN) possess direct bandgap while ZZ-CN/CN have indirect bandgap. Compared with the monolayer C3NNR, the bandgaps of bilayer C3NNRs can be greatly modulated via different stacking orders and edge structures, varying from 0.43 eV for ZZ-CN/CN with AB'-stacking to 0.04 eV for AC-CN/CN with AA-stacking. Particularly, transition from direct to indirect bandgap was observed in the bilayer AC-CN/CN heterostructure with AA'-stacking, and the indirect-to-direct transition was found in the bilayer ZZ-CN/CN with AB-stacking. This work provides insights into the effective bandgap engineering of C3N and offers a new opportunity for its applications in nano-electronics and optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Searching for the Higgsino-Bino Sector at the LHC

We study the search for electroweakinos at the 13 TeV LHC in the case of heavy scalar superpartners. We consider both the direct production mode and the one associated with the decay of heavy Higgs bosons, and concentrate on the case of light Higgsinos and Binos. In this case, the direct production searches becomes more challenging than in the light Wino scenario. In the direct production mode, we use the current experimental searches to set the reach for these particles at larger luminosities, and we emphasize the relevance of considering both the neutral gauge boson and the neutral Higgs decay modes of the second and third lightest neutralino. We show the complementarity of these searches with the ones induced by the decay of the heavy Higgs bosons, which are dominated by the associated production of the lightest neutralino with the second and third lightest ones, with the latter decaying into gauge bosons. We show that, depending on the value of $\tanβ$, the Higgs boson decay channel remains competitive with the direct production channel up to heavy Higgs boson masses of about 1 TeV. Moreover, this search is not limited by the same kinematic considerations as the ones in the direct production mode and can cover masses up to the kinematic threshold for the decay of the heavier electroweakinos into the lightest neutralino. This decay mode provides also an alternative way of looking for heavy Higgs bosons in this range of masses and hence should be a high priority for future LHC analyses.

hep-ph↗

arXiv2020

Peristaltic pumping in thin, non-axisymmetric, annular tubes

Two-dimensional laminar flow of a viscous fluid induced by peristalsis due to a moving wall wave has been studied previously for a rectangular channel, a circular tube, and a concentric circular annulus. Here we study peristaltic flow in a non-axisymmetric annular tube, where the flow is three dimensional, with azimuthal motions. This geometry is motivated by experimental observations of cerebrospinal fluid flow along perivascular spaces (PVSs) surrounding arteries in the brain, which is at least partially driven by peristaltic pumping. These PVSs are well matched, in cross-section, by an adjustable model consisting of an inner circle (arterial wall) and an outer ellipse (outer edge of the PVS), not necessarily concentric. We use this model, which may have other applications, as a basis for numerical simulations of peristaltic flow. We use a finite-element scheme to compute the flow driven by a propagating sinusoidal radial displacement of the inner wall. Unlike peristaltic flow in a concentric circular annulus, the flow is fully three-dimensional, with streamlines wiggling in both the radial and axial directions. We examine the dependence of the flow on the elongation of the outer elliptical wall and on the eccentricity of the configuration. We find that time-averaged volumetric flow decreases with increasing ellipticity or eccentricity. Azimuthal pressure variations, caused by the wall wave, drive an oscillatory azimuthal flow in and out of the narrower gaps. The additional shearing motion in the azimuthal direction will enhance Taylor dispersion in these flows, an effect that might have practical applications.

physics.flu-dyn↗

arXiv2020

Multi-Armed Bandits with Local Differential Privacy

This paper investigates the problem of regret minimization for multi-armed bandit (MAB) problems with local differential privacy (LDP) guarantee. In stochastic bandit systems, the rewards may refer to the users' activities, which may involve private information and the users may not want the agent to know. However, in many cases, the agent needs to know these activities to provide better services such as recommendations and news feeds. To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee. In this paper, we prove a lower bound and propose algorithms whose regret upper bounds match the lower bound up to constant factors. Numerical experiments also confirm our conclusions.

cs.LG↗

arXiv2020

Influence Function based Data Poisoning Attacks to Top-N Recommender Systems

Recommender system is an essential component of web services to engage users. Popular recommender systems model user preferences and item properties using a large amount of crowdsourced user-item interaction data, e.g., rating scores; then top-$N$ items that match the best with a user's preference are recommended to the user. In this work, we show that an attacker can launch a data poisoning attack to a recommender system to make recommendations as the attacker desires via injecting fake users with carefully crafted user-item interaction data. Specifically, an attacker can trick a recommender system to recommend a target item to as many normal users as possible. We focus on matrix factorization based recommender systems because they have been widely deployed in industry. Given the number of fake users the attacker can inject, we formulate the crafting of rating scores for the fake users as an optimization problem. However, this optimization problem is challenging to solve as it is a non-convex integer programming problem. To address the challenge, we develop several techniques to approximately solve the optimization problem. For instance, we leverage influence function to select a subset of normal users who are influential to the recommendations and solve our formulated optimization problem based on these influential users. Our results show that our attacks are effective and outperform existing methods.

cs.CR↗

arXiv2020

A Light Scalar Explanation of $(g-2)_μ$ and the KOTO Anomaly

The KOTO experiment has recently performed a search for neutral Kaons decaying into neutral pions and a pair of neutrinos. Three events were observed in the KOTO signal region, with an expected background of about 0.05. Since no clear signal of systematic errors have been found, the excess of events in the decay $K_{L}\rightarrowπ^0ν\barν$ is quite intriguing. One possibility to explain this anomaly would be the presence of a scalar $ϕ$ with mass of the order of the pion mass and inducing decays $K_L \to π^0 ϕ$ which mimic the observed signal.A scalar with mass of the order of the pion mass and a coupling to muons of the order of the Standard Model Higgs coupling could also explain the muon anomalous magnetic moment anomaly $(g-2)_μ$. We built on these facts to show that a light singlet scalar with couplings to the leptons and quarks as the ones induced by mixing with Higgs states in two Higgs doublet models may lead to an explanation of both anomalies. More specifically, we show that this is the case in the so-called type-X models in which leptons and quarks couple to two different Higgs doublets, and for scalar masses that are in the range between 40 and 70 MeV. Due to the relatively large coupling to leptons required to fit $(g-2)_μ$, the scalar lifetime accidentally falls into the sub-nanosecond range which is essential to evade the severe proton beam dump experiments and astrophysical constraints, though it becomes sensitive to constraints from electron beam dump experiments. The additional phenomenological properties of this model are discussed.

hep-ph↗

arXiv2020

Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach

With rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing. Toward this end, we propose a new decentralized stochastic gradient method with sparse differential Gaussian-masked stochastic gradients (SDM-DSGD) for non-convex distributed edge learning. Our main contributions are three-fold: i) We theoretically establish the privacy and communication efficiency performance guarantee of our SDM-DSGD method, which outperforms all existing works; ii) We show that SDM-DSGD improves the fundamental training-privacy trade-off by {\em two orders of magnitude} compared with the state-of-the-art. iii) We reveal theoretical insights and offer practical design guidelines for the interactions between privacy preservation and communication efficiency, two conflicting performance goals. We conduct extensive experiments with a variety of learning models on MNIST and CIFAR-10 datasets to verify our theoretical findings. Collectively, our results contribute to the theory and algorithm design for distributed edge learning.

cs.DC↗