arXiv subjects

Di Zhang

Publications and source records attributed to Di Zhang.

At least 325 records · Page 18Linked to original sources

arXiv2020

Towards establishing the second $b$-flavored CKM unitarity triangle

Some fine differences between the twin $b$-flavored unitarity triangles are calculated by means of a generalized Wolfenstein parametrization of the CKM matrix, and a possibility of experimentally establishing the second triangle is briefly discussed. We find that the apexes of these two triangles, characterized respectively by $(\overlineρ, \overlineη)$ and $(\widetildeρ, \widetildeη)$, are located on the same circular arc in the complex plane. This observation provides us with a new way to test consistency of the CKM picture of CP violation in the quark sector and probe possible new physics. The differences between the apexes (i.e., $\widetildeρ - \overlineρ$ and $\widetildeη - \overlineη$) are found to be of ${\cal O}(λ^2)$ with $λ\simeq 0.22$ being the Wolfenstein expansion parameter, and the shapes of these two triangles are found to be insensitive to the two-loop renormalization-group-equation running effects up to the accuracy of ${\cal O}\left(λ^4\right)$.

hep-ph↗

arXiv2020

RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning

Today high-performance computing (HPC) platforms are still dominated by batch jobs. Accordingly, effective batch job scheduling is crucial to obtain high system efficiency. Existing HPC batch job schedulers typically leverage heuristic priority functions to prioritize and schedule jobs. But, once configured and deployed by the experts, such priority functions can hardly adapt to the changes of job loads, optimization goals, or system settings, potentially leading to degraded system efficiency when changes occur. To address this fundamental issue, we present RLScheduler, an automated HPC batch job scheduler built on reinforcement learning. RLScheduler relies on minimal manual interventions or expert knowledge, but can learn high-quality scheduling policies via its own continuous 'trial and error'. We introduce a new kernel-based neural network structure and trajectory filtering mechanism in RLScheduler to improve and stabilize the learning process. Through extensive evaluations, we confirm that RLScheduler can learn high-quality scheduling policies towards various workloads and various optimization goals with relatively low computation cost. Moreover, we show that the learned models perform stably even when applied to unseen workloads, making them practical for production use.

cs.DC↗

arXiv2020

On the mean curvature flow of submanifolds in the standard Gaussian space $^†$

In this paper, we study the regular geometric behavior of the mean curvature flow (MCF) of submanifolds in the standard Gaussian metric space $({\mathbb R}^{m+p},e^{-|x|^2/m}\ol g)$ where $({\mathbb R}^{m+p},\ol g)$ is the standard Euclidean space and $x\in{\mathbb R}^{m+p}$ denotes the position vector. Note that, as a special Riemannian manifold, $({\mathbb R}^{m+p},e^{-|x|^2/m}\ol g)$ has an unbounded curvature. Up to a family of diffeomorphisms on $M^m$, the mean curvature flow we considered here turns out to be equivalent to a special variation of the ``{\em conformal mean curvature flow}\,'' which we have introduced previously. The main theorem of this paper indicates, geometrically, that any immersed compact submanifold in the standard Gaussian space, with the square norm of the position vector being not equal to $m$, will blow up at a finite time under the mean curvature flow, in the sense that either the position or the curvature blows up to infinity; Moreover, by this main theorem, the interval $[0,T)$ of time in which the flowing submanifolds keep regular has some certain optimal upper bound, and it can reach the bound if and only if the initial submanifold either shrinks to the origin or expands uniformly to infinity under the flow. Besides the main theorem, we also obtain some other interesting conclusions which not only play their key roles in proving the main theorem but also characterize in part the geometric behavior of the flow, being of independent significance.

math.DG↗

arXiv2020

On the two-loop radiative origin of the smallest neutrino mass and the associated Majorana CP phase

Given a massless neutrino at a superhigh energy scale $Λ$ (e.g., in the minimal seesaw model with only two heavy Majorana neutrinos), we calculate quantum corrections to its initially vanishing mass $m^{}_1$ (or $m^{}_3$) and the associated Majorana CP phase $ρ$ (or $\varrho$) at the Fermi scale $Λ^{}_{\rm F}$ by means of the two-loop renormalization-group equations (RGEs) in the standard model and with the help of the latest neutrino oscillation data. The numerical results obtained from our analytical approximations are in good agreement with those achieved by numerically solving the two-loop RGEs. In particular, we confirm that a nonzero value of $m^{}_1$ (or $m^{}_3$) of ${\cal O}(10^{-13})$ eV at $Λ^{}_{\rm F}$ can be radiatively generated from $m^{}_1 =0$ (or $m^{}_3 =0$) at $Λ\simeq 10^{14}$ GeV in the SM, and find that $ρ$ (or $\varrho$) may accordingly acquire an appreciable physical value. As a nontrivial by-product, the evolution of all the other (initially nonzero) flavor parameters of massive neutrinos is studied both analytically and numerically, by just keeping their leading (i.e., one-loop) RGE-induced effects.

hep-ph↗

arXiv2020

A direct link between unflavored leptogenesis and low-energy CP violation via the one-loop quantum corrections

In the type-I seesaw mechanism the Casas-Ibarra (CI) parametrization provides a convenient description of the Dirac neutrino mass matrix in terms of the light and heavy Majorana neutrino masses, the lepton flavor mixing matrix $U$ and an unknown complex orthogonal matrix $O$. If $O$ is assumed to be real, it will be impossible to generate {\it unflavored} thermal leptogenesis via the lepton-number-violating and CP-violating decays of the lightest heavy Majorana neutrino. We find that this observation can be invalidated after small but unavoidable quantum corrections to the CI parametrization are taken into account with the help of the one-loop renormalization-group equations (RGEs) between the seesaw and electroweak scales. We illustrate a novel and viable unflavored leptogenesis scenario of this kind based on the RGEs in the seesaw-extended standard model, and show its direct link to the CP-violating phases of $U$ at low energies.

hep-ph↗

arXiv2020

Bridging resonant leptogenesis and low-energy CP violation with an RGE-modified seesaw relation

We propose a special type-I seesaw scenario in which the Yukawa coupling matrix $Y^{}_ν$ can be fully reconstructed by using the light Majorana neutrino masses $m^{}_i$, the heavy Majorana neutrino masses $M^{}_i$ and the PMNS lepton flavor mixing matrix $U$. It is the RGE-induced correction to the seesaw relation that helps interpret the observed baryon-antibaryon asymmetry of the Universe via flavored resonant thermal leptogenesis with $M^{}_1 \simeq M^{}_2 \ll M^{}_3$. We show that our idea works well in either the $τ$-flavored regime with equilibrium temperature $T \simeq M^{}_1 \in (10^9, 10^{12}]$ GeV or the $(μ+τ)$-flavored regime with $T \simeq M^{}_1 \in (10^5, 10^9]$ GeV, provided the light neutrinos have a normal mass ordering. We find that the same idea is also viable for a {\it minimal} type-I seesaw model with two nearly degenerate heavy Majorana neutrinos.

hep-ph↗

arXiv2020

Distinguishing between the twin $b$-flavored unitarity triangles on a circular arc

With the help of the generalized Wolfenstein parametrization of quark flavor mixing and CP violation, we calculate fine differences between the twin $b$-flavored unitarity triangles defined by $V^{*}_{ub} V^{}_{ud} + V^{*}_{cb} V^{}_{cd} + V^{*}_{tb} V^{}_{td} = 0$ and $V^{*}_{ud} V^{}_{td} + V^{*}_{us} V^{}_{ts} + V^{*}_{ub} V^{}_{tb} = 0$ in the complex plane. We find that vertices of the rescaled versions of these two triangles, described respectively by $\barρ + {\rm i} \barη = -\left(V^{*}_{ub} V^{}_{ud}\right)/\left(V^{*}_{cb} V^{}_{cd}\right)$ and $\widetildeρ + {\rm i} \widetildeη = -\left(V^{*}_{ub} V^{}_{tb}\right)/\left(V^{*}_{us} V^{}_{ts}\right)$, are located on a circular arc whose center and radius are given by $O = \left(0.5, 0.5 \cotα\right)$ and $R = 0.5 \cscα$ with $α$ being their common inner angle. The small difference between $(\barρ, \barη)$ and $(\widetildeρ, \widetildeη)$ is characterized by $\widetildeρ - \barρ \sim \widetildeη - \barη \sim {\cal O}(λ^2)$ with $λ\simeq 0.22$ being the Wolfenstein expansion parameter, and these two vertices are insensitive to the two-loop renormalization-group running effects up to the accuracy of ${\cal O}(λ^4)$. Some comments are also made on similar features of three pairs of the rescaled unitarity triangles of lepton flavor mixing and CP violation.

hep-ph↗

arXiv2020

A modular $A_4$ symmetry realization of two-zero textures of the Majorana neutrino mass matrix

We show how to realize two-zero textures of the Majorana neutrino mass matrix $M_ν$ based on modular $A_4$ invariant models without flavons. In these models, all matter fields are assigned to three inequivalent singlets, ${\bf 1}$, ${\bf 1^\prime}$ and ${\bf 1^{\prime\prime}}$, of the finite modular group $Γ_3 \simeq A_4$. Considering tensor products of the $A_4$ group, it is easy to make the charged lepton mass matrix $M_\ell$ diagonal. Since not all modular forms of a specific weight and level 3 can be arranged into three inequivalent singlets of $A_4$ simultaneously, we can always make some entries in $M_ν$ vanish by properly assigning the representations and modular weights for the matter fields. We consider two cases where neutrino masses originate from the Weinberg operator and the type-I seesaw mechanism, respectively. For the former case, all seven viable two-zero textures of $M_ν$ (${\bf A_{1,2}}$, ${\bf B_{1,2,3,4}}$ and ${\bf C}$) can be realized successfully. For the latter case, only five of them (namely ${\bf A_{1,2}}$, ${\bf B_{3,4}}$ and ${\bf C}$) can be achieved due to the intrinsic structure of the right-handed neutrino mass matrix $M_{\rm R}$ in our assumption for simplicity.

hep-ph↗

arXiv2019

New Realization of the Conversion Calculation for Reactor Antineutrino Fluxes

Validation of the effective conversion method in the reactor antineutrino flux calculation is examined using the \textit{ab initio} calculation of the electron and antineutrino spectra from the state-of-the-art nuclear database. It turns out that neglecting the shape variation of beta decay branches between the allowed and forbidden transitions would induce significant bias in the total inverse-beta-decay yields and energy spectral distributions. We propose a new realization of the conversion method with both the allowed and forbidden virtual branches, and apply it to both the \textit{simulated} data from the nuclear database and \textit{real} data from the fission measurements at ILL by virtue of statistical properties of the allowed and forbidden decays in the database. Two kinds of dominant uncertainty sources are identified and it turns out that the new realization of the conversion calculation can largely reduce the rate and spectral bias and thus present a reliable prediction of the antineutrino fluxes if accurate beta decay information is available in the high endpoint energy range.

hep-ph↗

arXiv2019

A Bayesian analysis of sneutrino DM in the NMSSM with Type-I seesaw mechanism

In the Next-to-Minimal Supersymmetric Standard Model (NMSSM) with extra heavy neutrino superfields, neutrino may acquire its mass via a seesaw mechanism and sneutrino may act as a viable dark matter (DM) candidate. Given the strong tension between the naturalness for $Z$ boson mass and the DM direct detection experiments for customary neutralino DM candidate, we augment the NMSSM with Type-I seesaw mechanism, which is the simplest extension of the theory to predict neutrino mass, and study the scenarios of sneutrino DM. We construct likelihood function with LHC Higgs data, B-physics measurements, DM relic density and its direct and indirect search limits, and perform a comprehensive scan over the parameter space of the theory by Nested Sampling method. We adopt both Bayesian and frequentist statistical quantities to illustrate the favored parameter space of the scenarios, the DM annihilation mechanism as well as the features of DM-nucleon scattering. We find that the scenarios are viable over broad parameter regions, especially the Higgsino mass $μ$ can be below about $250 {\rm GeV}$ for a significant part of the region, which predicts $Z$ boson mass in a natural way. We also find that the DM usually co-annihilated with the Higgsinos to get the measured relic density, and consequently the DM-nucleon scattering rate is naturally suppressed to coincide with the recent XENON-1T results even for light Higgsinos. Other issues, such as the LHC search for the Higgsinos, are also addressed.

hep-ph↗

arXiv2019

Seesaw mirroring between light and heavy Majorana neutrinos with the help of the $S^{}_3$ reflection symmetry

In the canonical seesaw mechanism we require the relevant neutrino mass terms to be invariant under the $S^{}_3$ charge-conjugation transformations of left- and right-handed neutrino fields. Then both the Dirac mass matrix $M^{}_{\rm D}$ and the right-handed neutrino mass matrix $M^{}_{\rm R}$ are well constrained, so is the effective light Majorana neutrino mass matrix $M^{}_ν$ via the seesaw formula. We find that these mass matrices can be classified into 22 categories, among which some textures respect the well-known $μ$-$τ$ permutation or reflection symmetry and flavor democracy. It is also found that there exist remarkable structural equalities or similarities between $M^{}_ν$ and $M^{}_{\rm R}$, reflecting a seesaw mirroring relationship between light and heavy Majorana neutrinos. We calculate the corresponding light neutrino masses and flavor mixing parameters as well as the CP-violating asymmetries in decays of the lightest heavy Majorana neutrino, and show that only the flavored leptogenesis mechanism is possible to work for three categories of $M^{}_{\rm D}$ and $M^{}_{\rm R}$ in the $S^{}_3$ reflection symmetry limit.

hep-ph↗

arXiv2019

Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections

For very large datasets, random projections (RP) have become the tool of choice for dimensionality reduction. This is due to the computational complexity of principal component analysis. However, the recent development of randomized principal component analysis (RPCA) has opened up the possibility of obtaining approximate principal components on very large datasets. In this paper, we compare the performance of RPCA and RP in dimensionality reduction for supervised learning. In Experiment 1, study a malware classification task on a dataset with over 10 million samples, almost 100,000 features, and over 25 billion non-zero values, with the goal of reducing the dimensionality to a compressed representation of 5,000 features. In order to apply RPCA to this dataset, we develop a new algorithm called large sample RPCA (LS-RPCA), which extends the RPCA algorithm to work on datasets with arbitrarily many samples. We find that classification performance is much higher when using LS-RPCA for dimensionality reduction than when using random projections. In particular, across a range of target dimensionalities, we find that using LS-RPCA reduces classification error by between 37% and 54%. Experiment 2 generalizes the phenomenon to multiple datasets, feature representations, and classifiers. These findings have implications for a large number of research projects in which random projections were used as a preprocessing step for dimensionality reduction. As long as accuracy is at a premium and the target dimensionality is sufficiently less than the numeric rank of the dataset, randomized PCA may be a superior choice. Moreover, if the dataset has a large number of samples, then LS-RPCA will provide a method for obtaining the approximate principal components.

stat.ML↗

arXiv2018

Distributed Cache Enabled V2X Networks: Proposals, Research Trends and Challenging Issues

Nowadays, the internet of vehicles (IoV) has been evolved into the stage of vehicle to everything (V2X). However, the majority of existing work focuses on the motor-vehicles. In contrast, the sharing bicycle system is vastly and rapidly deployed as a feasible internet of things (IoT) application scene for the last mile problem (e.g., from station to home/office). Moreover, the internet access of current V2X is relied on the back-haul to roadside unit (RSU) connections. In this paper, other than prior work, we propose a versatile V2X system with a distributed framework and heterogeneous caching method. All the vehicles and devices on-the-road (motor-vehicle, non-motor-vehicle, pedestrian, etc.) are comprehensively included in the proposed networks. We further introduce a heterogeneous cache method for effective wireless transmission while utilizing the massive connected devices. The potential research trends on achieving high speed transmission, deep programming dedicated network slicing are highlighted as well as the big data, machine learning (ML), fog computing based image recognition and reconstruction, to provide some insights for future studies. Finally, the challenging issues, i.e., urban street canyon path loss and channel models, ultra reliable communication and low latency requirements, are discussed.

cs.NI↗

arXiv2018

Gear Training: A new way to implement high-performance model-parallel training

The training of Deep Neural Networks usually needs tremendous computing resources. Therefore many deep models are trained in large cluster instead of single machine or GPU. Though major researchs at present try to run whole model on all machines by using asynchronous asynchronous stochastic gradient descent (ASGD), we present a new approach to train deep model parallely -- split the model and then seperately train different parts of it in different speed.

cs.LG↗

arXiv2018

The blow-up of the conformal mean curvature flow

In this paper, we introduce and study the conformal mean curvature flow of submanifolds of higher codimension in the Euclidean space $\bbr^n$. This kind of flow is a special case of a general modified mean curvature flow which is of various origination. As the main result, we prove a blow-up theorem concluding that, under the conformal mean curvature flow in $\bbr^n$, the maximum of the square norm of the second fundamental form of any compact submanifold tends to infinity in finite time. Furthermore, by using the idea of Andrews and Baker for studying the mean curvature flow of submanifolds in the Euclidean space, we also derive some more evolution formulas and inequalities which we believe to be useful in our further study of conformal mean curvature flow. Presently, these computations together with our main theorem are applied to provide a direct proof of a convergence theorem concluding that the external conformal forced mean curvature flow of a compact submanifold in $\bbr^n$ with the same pinched condition as Andrews-Baker's will be convergent to a round point in finite time.

math.DG↗

arXiv2018

Robust Designs of Beamforming and Power Splitting for Distributed Antenna Systems with Wireless Energy Harvesting

In this paper, we investigate a multiuser distributed antenna system with simultaneous wireless information and power transmission under the assumption of imperfect channel state information (CSI). In this system, a distributed antenna port with multiple antennas supports a set of mobile stations who can decode information and harvest energy simultaneously via a power splitter. To design robust transmit beamforming vectors and the power splitting (PS) factors in the presence of CSI errors, we maximize the average worst-case signal-to-interference-plus- noise ratio (SINR) while achieving individual energy harvesting constraint for each mobile station. First, we develop an efficient algorithm to convert the max-min SINR problem to a set of "dual" min-max power balancing problems. Then, motivated by the penalty function method, an iterative algorithm based on semi-definite programming (SDP) is proposed to achieve a local optimal rank-one solution. Also, to reduce the computational complexity, we present another iterative scheme based on the Lagrangian method and the successive convex approximation (SCA) technique to yield a suboptimal solution. Simulation results are shown to validate the robustness and effectiveness of the proposed algorithms.

cs.IT↗

arXiv2017

The $μ-τ$ reflection symmetry of Dirac neutrinos and its breaking effect via quantum corrections

Given the Dirac neutrino mass term, we explore the constraint conditions which allow the corresponding mass matrix to be invariant under the μ-τreflection transformation, leading us to the phenomenologically favored predictions θ_{23} = π/4 and δ= 3π/2 in the standard parametrization of the 3\times 3 lepton flavor mixing matrix. If such a flavor symmetry is realized at a superhigh energy scale Λ_{μτ}, we investigate how it is spontaneously broken via the one-loop renormalization-group equations (RGEs) running from Λ_{μτ} down to the Fermi scale Λ_{\rm F}. Such quantum corrections to the neutrino masses and flavor mixing parameters are derived, and an analytical link is established between the Jarlskog invariants of CP violation at Λ_{μτ} and Λ_{\rm F}. Some numerical examples are also presented in both the minimal supersymmetric standard model and the type-II two-Higgs-doublet model, to illustrate how the octant of θ_{23}, the quadrant of δand the neutrino mass ordering are correlated with one another as a result of the RGE-induced μ-τreflection symmetry breaking effects.

hep-ph↗

arXiv2017

The breaking of flavor democracy in the quark sector

The democracy of quark flavors is a well-motivated flavor symmetry, but it must be properly broken in order to explain the observed quark mass spectrum and flavor mixing pattern. We reconstruct the texture of flavor democracy breaking and evaluate its strength in a novel way, by assuming a parallelism between the Q=+2/3 and Q=-1/3 quark sectors and using a nontrivial parametrization of the flavor mixing matrix. Some phenomenological implications of such democratic quark mass matrices, including their variations in the hierarchy basis and their evolution from the electroweak scale to a superhigh-energy scale, are also discussed.

hep-ph↗