arXiv subjects

Jia Liu

Publications and source records attributed to Jia Liu.

At least 289 records · Page 16Linked to original sources

arXiv2022

Heavy long-lived coannihilation partner from inelastic Dark Matter model and its signatures at the LHC

The coannihilation mechanism is a well-motivated alternative to the simple thermal freeze-out mechanism, where the dark matter relic density can be obtained through the coannihilation with a partner particle of similar mass with dark matter. When the partner particle is neutral, the inelastic nature of dark matter can help it to escape the direct detection limits. In this work, we focus on the coannihilation scenario in which the annihilation cross section is dominated by the partner-partner pair annihilation. We pay special interest on the parameter space where the coannihilation partner is long-lived, which leads to displaced signatures at the collider. In such case, it opens the heavy mass parameter space for the coannihilation dark matter, comparing with those dominated by the partner-dark matter annihilation. Specifically, we propose an inelastic scalar dark matter model with a broken symmetry, which realizes the domination of partner-partner pair annihilation. Then, we study two different realizations of the coannihilation partner decay and the existing constraints from the relic abundance, direct and indirect dark matter detection and the collider searches. We focus on the channel that the long-lived coannihilation partner decays to dark matter plus leptons. The high-luminosity LHC can reach good sensitivities for such heavy dark matter and coannihilation partner around 100--700 GeV.

hep-ph↗

arXiv2022

Scalar-mediated dark matter model at colliders and gravitational wave detectors -- A White paper for Snowmass 2021

The weakly interacting massive particles (WIMPs) have been the most popular particle dark matter (DM) candidate for the last several decades, and it is well known that WIMP can be probed via the direct, indirect and collider experiments. However, the direct and indirect signals are highly suppressed in some scalar-mediated DM models, e.g. the lepton portal model with a Majorana DM candidate. As a result, collider searches are considered as the only hope to probe such models. In this white paper, we propose that the gravitational wave (GW) astronomy also serves as a powerful tool to probe such scalar mediated WIMP models via the potential first-order phase transition GW signals. An example for the lepton portal dark matter is provided, showing the complementarity between collider and GW probes.

hep-ph↗

arXiv2022

A $ν$ scalar in the early universe and $(g-2)_μ$

We investigate a concrete scenario of a light scalar with a mass around 1 MeV which can be connected to the origin of neutrino masses and simultaneously survive current bounds on relativistic degrees of freedom in the early universe. In particular we show that a feeble coupling to the Standard Model neutrinos can relax the stringent bounds on the decays to photons inferred from the measured value of $N_{\rm eff}$. Interestingly, we find that such a scalar whose diphoton coupling is generated by a tree-level coupling to the muon of similar strength as that of the Standard Model Higgs boson can simultaneously explain the long-standing discrepancy in the measured value of the muon magnetic moment. We present a possible ultraviolet completion of this scenario providing a link between new physics in the early universe and the generation of neutrino masses.

hep-ph↗

arXiv2022

Dark matter transient annihilations in the early Universe

The cosmological evolution can modify the dark matter (DM) properties in the early Universe to be vastly different from the properties today. Therefore, the relation between the relic abundance and the DM constraints today needs to be revisited. We propose novel \textit{transient} annihilations of DM which helps to alleviate the pressure from DM null detection results. As a concrete example, we consider the vector portal DM and focus on the mass evolution of the dark photon. When the Universe cools down, the gauge boson mass can increase monotonically and go across several important thresholds; opening new transient annihilation channels in the early Universe. Those channels are either forbidden or weakened at the late Universe which helps to evade the indirect searches. In particular, the transient resonant channel can survive direct detection (DD) without tuning the DM to be half of the dark photon mass and can be soon tested by future DD or collider experiments. A feature of the scenario is the existence of a light dark scalar.

hep-ph↗

arXiv2022

A global convergence theory for deep ReLU implicit networks via over-parameterization

Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rules of many commonly used neural network architectures. Its prediction rule is provided implicitly based on the solution of an equilibrium equation. Although a line of recent empirical studies has demonstrated its superior performances, the theoretical understanding of implicit neural networks is limited. In general, the equilibrium equation may not be well-posed during the training. As a result, there is no guarantee that a vanilla (stochastic) gradient descent (SGD) training nonlinear implicit neural networks can converge. This paper fills the gap by analyzing the gradient flow of Rectified Linear Unit (ReLU) activated implicit neural networks. For an $m$-width implicit neural network with ReLU activation and $n$ training samples, we show that a randomly initialized gradient descent converges to a global minimum at a linear rate for the square loss function if the implicit neural network is \textit{over-parameterized}. It is worth noting that, unlike existing works on the convergence of (S)GD on finite-layer over-parameterized neural networks, our convergence results hold for implicit neural networks, where the number of layers is \textit{infinite}.

cs.LG↗

arXiv2022

GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs

Fueled by advances in distributed deep learning (DDL), recent years have witnessed a rapidly growing demand for resource-intensive distributed/parallel computing to process DDL computing jobs. To resolve network communication bottleneck and load balancing issues in distributed computing, the so-called ``ring-all-reduce'' decentralized architecture has been increasingly adopted to remove the need for dedicated parameter servers. To date, however, there remains a lack of theoretical understanding on how to design resource optimization algorithms for efficiently scheduling ring-all-reduce DDL jobs in computing clusters. This motivates us to fill this gap by proposing a series of new resource scheduling designs for ring-all-reduce DDL jobs. Our contributions in this paper are three-fold: i) We propose a new resource scheduling analytical model for ring-all-reduce deep learning, which covers a wide range of objectives in DDL performance optimization (e.g., excessive training avoidance, energy efficiency, fairness); ii) Based on the proposed performance analytical model, we develop an efficient resource scheduling algorithm called GADGET (greedy ring-all-reduce distributed graph embedding technique), which enjoys a provable strong performance guarantee; iii) We conduct extensive trace-driven experiments to demonstrate the effectiveness of the GADGET approach and its superiority over the state of the art.

cs.DC↗

arXiv2022

Comparing weak lensing peak counts in baryonic correction models to hydrodynamical simulations

Next-generation weak lensing (WL) surveys, such as by the Vera Rubin Observatory's LSST, the $\textit{Roman}$ Space Telescope, and the $\textit{Euclid}$ space mission, will supply vast amounts of data probing small, highly nonlinear scales. Extracting information from these scales requires higher-order statistics and the controlling of related systematics such as baryonic effects. To account for baryonic effects in cosmological analyses at reduced computational cost, semi-analytic baryonic correction models (BCMs) have been proposed. Here, we study the accuracy of BCMs for WL peak counts, a well studied, simple, and effective higher-order statistic. We compare WL peak counts generated from the full hydrodynamical simulation IllustrisTNG and a baryon-corrected version of the corresponding dark matter-only simulation IllustrisTNG-Dark. We apply galaxy shape noise expected at the depths reached by DES, KiDS, HSC, LSST, $\textit{Roman}$, and $\textit{Euclid}$. We find that peak counts in BCMs are (i) accurate at the percent level for peaks with $\mathrm{S/N}<4$, (ii) statistically indistinguishable from IllustrisTNG in most current and ongoing surveys, but (iii) insufficient for deep future surveys covering the largest solid angles, such as LSST and $\textit{Euclid}$. We find that BCMs match individual peaks accurately, but underpredict the amplitude of the highest peaks. We conclude that existing BCMs are a viable substitute for full hydrodynamical simulations in cosmological parameter estimation from beyond-Gaussian statistics for ongoing and future surveys with modest solid angles. For the largest surveys, BCMs need to be refined to provide a more accurate match, especially to the highest peaks.

astro-ph.CO↗

arXiv2022

Probabilistic Mass Mapping with Neural Score Estimation

Weak lensing mass-mapping is a useful tool to access the full distribution of dark matter on the sky, but because of intrinsic galaxy ellipticies and finite fields/missing data, the recovery of dark matter maps constitutes a challenging ill-posed inverse problem. We introduce a novel methodology allowing for efficient sampling of the high-dimensional Bayesian posterior of the weak lensing mass-mapping problem, and relying on simulations for defining a fully non-Gaussian prior. We aim to demonstrate the accuracy of the method on simulations, and then proceed to applying it to the mass reconstruction of the HST/ACS COSMOS field. The proposed methodology combines elements of Bayesian statistics, analytic theory, and a recent class of Deep Generative Models based on Neural Score Matching. This approach allows us 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. 3) Obtain samples from the full Bayesian posterior of the problem for robust Uncertainty Quantification. We demonstrate the method on the $κ$TNG simulations and find that the posterior mean significantly outperfoms previous methods (Kaiser-Squires, Wiener filter, Sparsity priors) both on root-mean-square error and in terms of the Pearson correlation. We further illustrate the interpretability of the recovered posterior by establishing a close correlation between posterior convergence values and SNR of clusters artificially introduced into a field. Finally, we apply the method to the reconstruction of the HST/ACS COSMOS field and yield the highest quality convergence map of this field to date.

astro-ph.CO↗

arXiv2021

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains, compiler and AI researchers do not have access to the datasets and frameworks that enable fast iteration and development of ideas, and getting started requires a significant engineering investment. What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field. We introduce CompilerGym, a set of environments for real world compiler optimization tasks, and a toolkit for exposing new optimization tasks to compiler researchers. CompilerGym enables anyone to experiment on production compiler optimization problems through an easy-to-use package, regardless of their experience with compilers. We build upon the popular OpenAI Gym interface enabling researchers to interact with compilers using Python and a familiar API. We describe the CompilerGym architecture and implementation, characterize the optimization spaces and computational efficiencies of three included compiler environments, and provide extensive empirical evaluations. Compared to prior works, CompilerGym offers larger datasets and optimization spaces, is 27x more computationally efficient, is fault-tolerant, and capable of detecting reproducibility bugs in the underlying compilers. In making it easy for anyone to experiment with compilers - irrespective of their background - we aim to accelerate progress in the AI and compiler research domains.

cs.PL↗

arXiv2021

SoK: Untangling File-based Encryption on Mobile Devices

File-based encryption (FBE) schemes have been developed by software vendors to address security concerns related to data storage. While methods of encrypting data-at-rest may seem relatively straightforward, the main proponents of these technologies in mobile devices have nonetheless created seemingly different FBE solutions. As most of the underlying design decisions are described either at a high-level in whitepapers, or are accessible at a low-level by examining the corresponding source code (Android) or through reverse-engineering (iOS), comparisons between schemes and discussions on their relative strengths are scarce. In this paper, we propose a formal framework for the study of file-based encryption systems, focusing on two prominent implementations: the FBE scheme used in Android and Linux operating systems, as well as the FBE scheme used in iOS. Our proposed formal model and our detailed description of the existing algorithms are based on documentation of diverse nature, such as whitepapers, technical reports, presentations and blog posts, among others. Using our framework we validate the security of the existing key derivation chains, as well as the security of the overall designs, under widely-known security assumptions for symmetric ciphers, such as IND-CPA or INT-CTXT security, in the random-oracle model.

cs.CR↗

arXiv2021

Distributionally robust second-order stochastic dominance constrained optimization with Wasserstein ball

We consider a distributionally robust second-order stochastic dominance constrained optimization problem. We require the dominance constraints hold with respect to all probability distributions in a Wasserstein ball centered at the empirical distribution. We adopt the sample approximation approach to develop a linear programming formulation that provides a lower bound. We propose a novel split-and-dual decomposition framework which provides an upper bound. We establish quantitative convergency for both lower and upper approximations given some constraint qualification conditions. To efficiently solve the non-convex upper bound problem, we use a sequential convex approximation algorithm. Numerical evidences on a portfolio selection problem valid the convergency and effectiveness of the proposed two approximation methods.

math.OC↗

arXiv2021

Detecting Neutrino Mass by Combining Matter Clustering, Halos, and Voids

We quantify the information content of the non-linear matter power spectrum, the halo mass function, and the void size function, using the Quijote $N$-body simulations. We find that these three statistics exhibit very different degeneracies amongst the cosmological parameters, and thus the combination of all three probes enables the breaking of degeneracies, in turn yielding remarkably tight constraints. We perform a Fisher analysis using the full covariance matrix, including all auto- and cross-correlations, finding that this increases the information content for neutrino mass compared to a correlation-free analysis. The multiplicative improvement of the constraints on the cosmological parameters obtained by combining all three probes compared to using the power spectrum alone are: 137, 5, 8, 20, 10, and 43, for $Ω_m$, $Ω_b$, $h$, $n_s$, $σ_8$, and $M_ν$, respectively. The marginalized error on the sum of the neutrino masses is $σ(M_ν)=0.018\,{\rm eV}$ for a cosmological volume of $1\,(h^{-1}{\rm Gpc})^3$, using $k_{\max}=0.5\,h{\rm Mpc}^{-1}$, and without CMB priors. We note that this error is an underestimate insomuch as we do not consider super-sample covariance, baryonic effects, and realistic survey noises and systematics. On the other hand, it is an overestimate insomuch as our cuts and binning are suboptimal due to restrictions imposed by the simulation resolution. Given upcoming galaxy surveys will observe volumes spanning $\sim 100\,(h^{-1}{\rm Gpc})^3$, this presents a promising new avenue to measure neutrino mass without being restricted by the need for accurate knowledge of the optical depth, which is required for CMB-based measurements. Furthermore, the improved constraints on other cosmological parameters, notably $Ω_m$, may also be competitive with CMB-based measurements.

astro-ph.CO↗

arXiv2021

Dynamic behavior of polar nanoregions in re-entrant relaxor 0.6Bi(Mg1/2Ti1/2)O3-0.4PbTiO3

The existence of polar nanoregions is the most important characteristic of ferroelectric relaxors, however, the size determination and dynamic of PNRs remains uncertain. We reveal a re-entrant relaxor behavior and ferroelectric-paraelectric transition coexists in complex perovskite oxide 0.6Bi(Mg1/2Ti1/2)O3-0.4PbTiO3. Two dielectric anomalies (i) the low-temperature re-entrant relaxor transition and (ii) the high-temperature diffuse phase transition (DPT) were described by the phenomenological statistical model. The sizes of the two kinds of polar nanoregions (PNRs) corresponding to two ferroelectric states were obtained. The dynamic of PNRs were analyzed using isothermal electrical modulus, which shows three critical temperatures associated with the diffuse phase transition, the formation and freezing of PNRs, respectively. The temperature evolution of the PNRs evolution depends on the stoichiometry of bismuth. The results provide new insights into the dynamic behavior of PNRs and the modification way of re-entrant relaxor behavior.

cond-mat.mtrl-sci↗

arXiv2021

On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons

This paper studies the problem of finding the exact ranking from noisy comparisons. A comparison over a set of $m$ items produces a noisy outcome about the most preferred item, and reveals some information about the ranking. By repeatedly and adaptively choosing items to compare, we want to fully rank the items with a certain confidence, and use as few comparisons as possible. Different from most previous works, in this paper, we have three main novelties: (i) compared to prior works, our upper bounds (algorithms) and lower bounds on the sample complexity (aka number of comparisons) require the minimal assumptions on the instances, and are not restricted to specific models; (ii) we give lower bounds and upper bounds on instances with unequal noise levels; and (iii) this paper aims at the exact ranking without knowledge on the instances, while most of the previous works either focus on approximate rankings or study exact ranking but require prior knowledge. We first derive lower bounds for pairwise ranking (i.e., compare two items each time), and then propose (nearly) optimal pairwise ranking algorithms. We further make extensions to listwise ranking (i.e., comparing multiple items each time). Numerical results also show our improvements against the state of the art.

cs.LG↗

arXiv2021

The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons

This paper studies the sample complexity (aka number of comparisons) bounds for the active best-$k$ items selection from pairwise comparisons. From a given set of items, the learner can make pairwise comparisons on every pair of items, and each comparison returns an independent noisy result about the preferred item. At any time, the learner can adaptively choose a pair of items to compare according to past observations (i.e., active learning). The learner's goal is to find the (approximately) best-$k$ items with a given confidence, while trying to use as few comparisons as possible. In this paper, we study two problems: (i) finding the probably approximately correct (PAC) best-$k$ items and (ii) finding the exact best-$k$ items, both under strong stochastic transitivity and stochastic triangle inequality. For PAC best-$k$ items selection, we first show a lower bound and then propose an algorithm whose sample complexity upper bound matches the lower bound up to a constant factor. For the exact best-$k$ items selection, we first prove a worst-instance lower bound. We then propose two algorithms based on our PAC best items selection algorithms: one works for $k=1$ and is sample complexity optimal up to a loglog factor, and the other works for all values of $k$ and is sample complexity optimal up to a log factor.

cs.LG↗

arXiv2021

Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach

In this paper, we study the problem of fair worker selection in Federated Learning systems, where fairness serves as an incentive mechanism that encourages more workers to participate in the federation. Considering the achieved training accuracy of the global model as the utility of the selected workers, which is typically a monotone submodular function, we formulate the worker selection problem as a new multi-round monotone submodular maximization problem with cardinality and fairness constraints. The objective is to maximize the time-average utility over multiple rounds subject to an additional fairness requirement that each worker must be selected for a certain fraction of time. While the traditional submodular maximization with a cardinality constraint is already a well-known NP-Hard problem, the fairness constraint in the multi-round setting adds an extra layer of difficulty. To address this novel challenge, we propose three algorithms: Fair Continuous Greedy (FairCG1 and FairCG2) and Fair Discrete Greedy (FairDG), all of which satisfy the fairness requirement whenever feasible. Moreover, we prove nontrivial lower bounds on the achieved time-average utility under FairCG1 and FairCG2. In addition, by giving a higher priority to fairness, FairDG ensures a stronger short-term fairness guarantee, which holds in every round. Finally, we perform extensive simulations to verify the effectiveness of the proposed algorithms in terms of the time-average utility and fairness satisfaction.

cs.GT↗

arXiv2021

A fast-converging scheme for the Phonon Boltzmann equation with dual relaxation times

Callaway's dual relaxation times model, which takes into account the normal and resistive scatterings of phonon, is used to describe the heat conduction in materials like graphene. For steady-state problems, the Callaway model is usually solved by the conventional iterative scheme (CIS), which is efficient in the ballistic regime, but inefficient in the diffusive/hydrodynamic regime. In this paper, a general synthetic iterative scheme (GSIS) is proposed to expedite the convergence to steady-state solutions. First, macroscopic synthetic equations are designed to guide the evolution of equilibrium distribution functions for normal and resistive scatterings, so that fast convergence can be achieved even in the diffusive/hydrodynamic regime. Second, the Fourier stability analysis is conducted to find the convergence rate for both CIS and GSIS, which rigorously proves the efficiency of GSIS over CIS. Finally, several numerical simulations are carried out to demonstrate the accuracy and efficiency of GSIS, where up to three orders of magnitude of convergence acceleration is achieved.

physics.comp-ph↗

arXiv2021

Silicon Photonics in Optical Access Networks for 5G Communications

Only radio access networks can provide connectivity across multiple antenna sites to achieve the great leap forward in capacity targeted by 5G. Optical fronthaul remains a sticking point in that connectivity, and we make the case for analog radio over fiber signals and an optical access network smartedge to achieve the potential of radio access networks. The edge of the network would house the intelligence that coordinates wireless transmissions to minimize interference and maximize throughput. As silicon photonics provides a hardware platform well adapted to support optical fronthaul, it is poised to drive smart edge adoption. We draw out the issues in adopting oursolution, propose a strategy for network densification, and cite recent demonstrations to support our approach.

cs.NI↗