arXiv subjects

Ning Chen

Publications and source records attributed to Ning Chen.

At least 127 records · Page 7Linked to original sources

arXiv2016

Higgs pair productions in the CP-violating two-Higgs-doublet model

In this work, we study the SM-like Higgs pair productions in the framework of the general CP-violating two-Higgs-doublet model. Several constraints are imposed to the model sequentially, including the SM-like Higgs boson signal fits, the precise measurements of the electric dipole moments, the perturbative unitarity and stability bounds to the Higgs potential, and the most recent LHC searches for the heavy Higgs bosons. We show how are the CP-violating mixing angles related to the Higgs cubic self couplings in this setup. Afterwards, we estimate the cross sections of the future LHC/SppC searches for the Higgs pair productions, as well as other possible decay modes for the heavy Higgs bosons.

hep-ph↗

arXiv2016

Degenerate Higgs bosons: hiding a second Higgs at 125 GeV

More than one Higgs boson may be present near the currently discovered Higgs mass, which can not be properly resolved due to the limitations in the intrinsic energy resolution at the Large Hadron Collider. We investigated the scenarios where two $CP$-even Higgs bosons are degenerate in mass. To correctly predict the Higgs signatures, quantum interference effects between the two Higgs bosons must be taken into account, which, however, has been often neglected in the literature. We carried out a global analysis including the interference effects for a variety of Higgs searching channels at the Large Hadron Collider, which suggests that the existence of two degenerate Higgs bosons near 125 GeV is highly likely. Prospects of distinguishing the degenerate Higgs case from the single Higgs case are discussed.

hep-ph↗

arXiv2016

LHC searches for heavy neutral Higgs bosons with a top jet substructure analysis

We study the LHC searches for the heavy $CP$-odd Higgs boson $A$ and $CP$-even Higgs boson $H$ in the context of general two-Higgs-doublet model. Specifically, we consider the decay mode of $A/H\to t \bar t $ through the $b \bar b$ or $t \bar t $ associated production channels. In the so-called "alignment limit" of the two-Higgs-doublet model, this decay mode can be the most dominant one. By employing the HEPTopTagger and the multi-variable-analysis method, we present the search sensitivities for both $CP$-odd Higgs boson $A$ and $CP$-even Higgs boson $H$ via these channels at the high-luminosity LHC runs.

hep-ph↗

arXiv2016

A hidden confining world on the 750 GeV diphoton excess

We explain the recent diphoton excesses around $750$ GeV by both ATLAS and CMS as a singlet scalar $Φ$ which couples to SM gluon and neutral gauge bosons only through higher dimensional operators. A natural explanation is that $Φ$ is a pseudo-Nambu-Goldstone boson (pNGB) which receives parity violation through anomaly if there exists a hidden strong dynamics. The singlet and other light pNGBs will decay into two SM gauge bosons and even serves as the meta-stable coloured states which can be probed in the future. By accurately measuring their relative decay and the total production rate in the future, we will learn the underlying strong dynamics parameter. The lightest baryon in this confining theory could serve as a viable dark matter candidate.

hep-ph↗

arXiv2015

Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic RegBayes (regularized Bayesian inference) framework, we handily incorporate the prediction loss with probabilistic inference of a Bayesian model; set distinct regularization parameters for different types of links to handle the imbalance issue in real networks; and unify the analysis of both the smooth logistic log-loss and the piecewise linear hinge loss. For the nonconjugate posterior inference, we present a simple Gibbs sampler via data augmentation, without making restricting assumptions as done in variational methods. We further develop an approximate sampler using stochastic gradient Langevin dynamics to handle large networks with hundreds of thousands of entities and millions of links, orders of magnitude larger than what existing LFRM models can process. Extensive studies on various real networks show promising performance.

cs.LG↗

arXiv2015

Dropout Training for SVMs with Data Augmentation

Dropout and other feature noising schemes have shown promising results in controlling over-fitting by artificially corrupting the training data. Though extensive theoretical and empirical studies have been performed for generalized linear models, little work has been done for support vector machines (SVMs), one of the most successful approaches for supervised learning. This paper presents dropout training for both linear SVMs and the nonlinear extension with latent representation learning. For linear SVMs, to deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square (IRLS) algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a re-weighted least square problem, where the re-weights are analytically updated. For nonlinear latent SVMs, we consider learning one layer of latent representations in SVMs and extend the data augmentation technique in conjunction with first-order Taylor-expansion to deal with the intractable expected non-smooth hinge loss and the nonlinearity of latent representations. Finally, we apply the similar data augmentation ideas to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions, and we further develop a non-linear extension of logistic regression by incorporating one layer of latent representations. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs. In addition, the nonlinear SVMs further improve the prediction performance on several image datasets.

cs.LG↗

arXiv2015

The leptophilic dark matter with $Z'$ interaction: from indirect searches to future $e^+ e^-$ collider searches

We investigate the scenario where the dark matter only interacts with the charged leptons in the standard model via a neutral vector mediator $Z'$. Such a scenario with a 430 GeV dark matter can fit the recent positron fluxes observed by the AMS-02 Collaborations, with the reasonable boost factors. We study the possibility of searching such leptophilic $Z'$ via its lepton final states and invisible decay modes at the future electron-positron colliders, such as the International Linear Collider (ILC) and the Compact Linear Collider (CLIC). We find that for the benchmark models with $Z'$ mass from 1.0 TeV to 1.5 TeV, the searches for the invisible decays of $Z'\to \bar χχ$ is easily achieved at the CLIC 1.5 TeV runs via the mono-photon process. However, lighter $Z'$ with mass from 0.5 TeV to 0.8 TeV are challenging to see. The di-lepton plus single photon channel can reveal the $Z'$ mass at the ILC and CLIC with moderate luminosities.

hep-ph↗

arXiv2014

On Revenue Maximization with Sharp Multi-Unit Demands

We consider markets consisting of a set of indivisible items, and buyers that have {\em sharp} multi-unit demand. This means that each buyer $i$ wants a specific number $d_i$ of items; a bundle of size less than $d_i$ has no value, while a bundle of size greater than $d_i$ is worth no more than the most valued $d_i$ items (valuations being additive). We consider the objective of setting prices and allocations in order to maximize the total revenue of the market maker. The pricing problem with sharp multi-unit demand buyers has a number of properties that the unit-demand model does not possess, and is an important question in algorithmic pricing. We consider the problem of computing a revenue maximizing solution for two solution concepts: competitive equilibrium and envy-free pricing. For unrestricted valuations, these problems are NP-complete; we focus on a realistic special case of "correlated values" where each buyer $i$ has a valuation $v_i\qual_j$ for item $j$, where $v_i$ and $\qual_j$ are positive quantities associated with buyer $i$ and item $j$ respectively. We present a polynomial time algorithm to solve the revenue-maximizing competitive equilibrium problem. For envy-free pricing, if the demand of each buyer is bounded by a constant, a revenue maximizing solution can be found efficiently; the general demand case is shown to be NP-hard.

cs.GT↗

arXiv2014

Competitive analysis via benchmark decomposition

We propose a uniform approach for the design and analysis of prior-free competitive auctions and online auctions. Our philosophy is to view the benchmark function as a variable parameter of the model and study a broad class of functions instead of a individual target benchmark. We consider a multitude of well-studied auction settings, and improve upon a few previous results. (1) Multi-unit auctions. Given a $β$-competitive unlimited supply auction, the best previously known multi-unit auction is $2β$-competitive. We design a $(1+β)$-competitive auction reducing the ratio from $4.84$ to $3.24$. These results carry over to matroid and position auctions. (2) General downward-closed environments. We design a $6.5$-competitive auction improving upon the ratio of $7.5$. Our auction is noticeably simpler than the previous best one. (3) Unlimited supply online auctions. Our analysis yields an auction with a competitive ratio of $4.12$, which significantly narrows the margin of $[4,4.84]$ previously known for this problem. A particularly important tool in our analysis is a simple decomposition lemma, which allows us to bound the competitive ratio against a sum of benchmark functions. We use this lemma in a "divide and conquer" fashion by dividing the target benchmark into the sum of simpler functions.

cs.GT↗

arXiv2014

LHC searches for the CP-odd Higgs by the jet substructure analysis

The LHC searches for the CP-odd Higgs boson is studied (with masses from 300 GeV to 1 TeV) in the context of the general two-Higgs-doublet model. With the discovery of the 125 GeV Higgs boson at the LHC, we highlight one promising discovery channel of the hZ. This channel can become significant after the global signal fitting to the 125 GeV Higgs boson in the general two-Higgs-doublet model. It is particularly important in the scenario where two CP-even Higgs bosons in the two-Higgs-doublet model have the common mass of 125 GeV. Since the final states involve a Standard-Model-like Higgs boson, we apply the jet substructure analysis of the fat Higgs jet in order to eliminate the Standard Model background sufficiently. After performing the kinematic cuts, we present the LHC search sensitivities for the CP-odd Higgs boson with mass up to 1 TeV via this channel.

hep-ph↗

arXiv2014

3.5 keV Galactic Emission Line as a Signal from the Hidden Sector

An emission line with energy of $E\sim 3.5$ keV has been observed in galaxy clusters by two experiments. The emission line is consistent with the decay of a dark matter particle with a mass of $\sim 7$ keV. In this work we discuss the possibility that the dark particle responsible for the emission is a real scalar ($ρ$) which arises naturally in a $U(1)_X$ Stueckelberg of MSSM. In the MSSM Stueckelberg extension $ρ$ couples only to other scalars carrying a $U(1)_X$ quantum number. Under the assumption that there exists a vectorlike leptonic generation carrying both $SU(2)_L\times U(1)_Y$ and $U(1)_X$ quantum numbers, we compute the decay of the $ρ$ into two photons via a triangle loop involving scalars. The relic density of the $ρ$ arises via the decay $H^0\to h^0+ ρ$ at the loop level involving scalars, and via the annihilation processes of the vectorlike scalars into $ρ+ h^0$. It is shown that the galactic data can be explained within a multicomponent dark matter model where the 7 keV dark matter is a subdominant component constituting only $(1-10)$\% of the matter relic density with the rest being supersymmetric dark matter such as the neutralino. Thus the direct detection experiments remain viable searches for WIMPs. The fact that the dark scalar $ρ$ with no interactions with the standard model particles arises from a Stueckelberg extension of a hidden $U(1)_X$ implies that the 3.5 KeV galactic line emission is a signal from the hidden sector.

hep-ph↗

arXiv2014

LHC Searches for The Heavy Higgs Boson via Two B Jets plus Diphoton

Extra scalar fields are common in beyond Standard Model (SM) new physics, and they may mix with the 125 GeV SM-like Higgs boson discovered at the LHC. This fact suggests possible discovery channels for these new scalar fields with their decay modes involving the 125 GeV Higgs boson. In this work, we explore the LHC search potential of the heavy CP-even Higgs boson H in the two-Higgs-doublet model. We focus on the channel of H decaying to a pair of light CP-even Higgs bosons h, with two h's decaying to two b jets and diphoton sequentially. This channel is particularly involved when the relevant cubic coupling is enhanced. We find such enhancement to be possible when taking a large CP-odd Higgs mass input for the two-Higgs-doublet model spectrum. Analogous to the SM Higgs self-coupling measurement, the two b jets plus diphoton final states are of particular interest due to the manageable SM background. After performing a cut-based analysis of both signal and background processes, we demonstrate the LHC search sensitivities for the heavy CP-even Higgs boson in a broad mass range via the two b jets plus diphoton final states.

hep-ph↗

arXiv2014

Dropout Training for Support Vector Machines

cs.LG↗

arXiv2014

Study of the material photon and electron background and the liquid argon detector veto efficiency of the CDEX-10 experiment

The China Dark Matter Experiment (CDEX) is located at the China Jinping underground laboratory (CJPL) and aims to directly detect the WIMP flux with high sensitivity in the low mass region. Here we present a study of the predicted photon and electron backgrounds including the background contribution of the structure materials of the germanium detector, the passive shielding materials, and the intrinsic radioactivity of the liquid argon that serves as an anti-Compton active shielding detector. A detailed geometry is modeled and the background contribution has been simulated based on the measured radioactivities of all possible components within the GEANT4 program. Then the photon and electron background level in the energy region of interest (<10^-2 events kg-1 day-1 keV-1 (cpkkd)) is predicted based on Monte Carlo simulations. The simulated result is consistent with the design goal of CDEX-10 experiment, 0.1 cpkkd, which shows that the active and passive shield design of CDEX-10 is effective and feasible.

physics.ins-det↗

arXiv2014

Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs

Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations. While priors can affect posterior distributions through Bayes' rule, imposing posterior regularization is arguably more direct and in some cases more natural and general. In this paper, we present regularized Bayesian inference (RegBayes), a novel computational framework that performs posterior inference with a regularization term on the desired post-data posterior distribution under an information theoretical formulation. RegBayes is more flexible than the procedure that elicits expert knowledge via priors, and it covers both directed Bayesian networks and undirected Markov networks whose Bayesian formulation results in hybrid chain graph models. When the regularization is induced from a linear operator on the posterior distributions, such as the expectation operator, we present a general convex-analysis theorem to characterize the solution of RegBayes. Furthermore, we present two concrete examples of RegBayes, infinite latent support vector machines (iLSVM) and multi-task infinite latent support vector machines (MT-iLSVM), which explore the large-margin idea in combination with a nonparametric Bayesian model for discovering predictive latent features for classification and multi-task learning, respectively. We present efficient inference methods and report empirical studies on several benchmark datasets, which appear to demonstrate the merits inherited from both large-margin learning and Bayesian nonparametrics. Such results were not available until now, and contribute to push forward the interface between these two important subfields, which have been largely treated as isolated in the community.

cs.LG↗

arXiv2014

Optimal Competitive Auctions

We study the design of truthful auctions for selling identical items in unlimited supply (e.g., digital goods) to n unit demand buyers. This classic problem stands out from profit-maximizing auction design literature as it requires no probabilistic assumptions on buyers' valuations and employs the framework of competitive analysis. Our objective is to optimize the worst-case performance of an auction, measured by the ratio between a given benchmark and revenue generated by the auction. We establish a sufficient and necessary condition that characterizes competitive ratios for all monotone benchmarks. The characterization identifies the worst-case distribution of instances and reveals intrinsic relations between competitive ratios and benchmarks in the competitive analysis. With the characterization at hand, we show optimal competitive auctions for two natural benchmarks. The most well-studied benchmark $\mathcal{F}^{(2)}(\cdot)$ measures the envy-free optimal revenue where at least two buyers win. Goldberg et al. [13] showed a sequence of lower bounds on the competitive ratio for each number of buyers n. They conjectured that all these bounds are tight. We show that optimal competitive auctions match these bounds. Thus, we confirm the conjecture and settle a central open problem in the design of digital goods auctions. As one more application we examine another economically meaningful benchmark, which measures the optimal revenue across all limited-supply Vickrey auctions. We identify the optimal competitive ratios to be $(\frac{n}{n-1})^{n-1}-1$ for each number of buyers n, that is $e-1$ as $n$ approaches infinity.

cs.GT↗

arXiv2013

Solving Linear Programming with Constraints Unknown

What is the value of input information in solving linear programming? The celebrated ellipsoid algorithm tells us that the full information of input constraints is not necessary; the algorithm works as long as there exists an oracle that, on a proposed candidate solution, returns a violation in the format of a separating hyperplane. Can linear programming still be efficiently solved if the returned violation is in other formats? We study this question in a trial-and-error framework: there is an oracle that, upon a proposed solution, returns the index of a violated constraint (with the content of the constraint still hidden). When more than one constraint is violated, two variants in the model are investigated. (1) The oracle returns the index of a "most violated" constraint, measured by the Euclidean distance of the proposed solution and the half-spaces defined by the constraints. In this case, the LP can be efficiently solved. (2) The oracle returns the index of an arbitrary (i.e., worst-case) violated constraint. In this case, we give an algorithm with running time exponential in the number of variables. We then show that the exponential dependence on n is unfortunately necessary even for the query complexity. These results put together shed light on the amount of information that one needs in order to solve a linear program efficiently. The proofs of the results employ a variety of geometric techniques, including McMullen's Upper Bound Theorem, the weighted spherical Voronoi diagram, and the furthest Voronoi diagram. In addition, we give an alternative proof to a conjecture of László Fejes Tóth on bounding the number of disconnected components formed by the union of m convex bodies in R^n. Our proof, inspired by the Gauss-Bonnet Theorem in global differential geometry, is independent of the known and reveals more clear insights into the problem and the bound.

cs.DS↗

arXiv2013

Mining Crash Fix Patterns

During the life cycle of software development, developers have to fix different kinds of bugs reported by testers or end users. The efficiency and effectiveness of fixing bugs have a huge impact on the reliability of the software as well as the productivity of the development team. Software companies usually spend a large amount of money and human resources on the testing and bug fixing departments. As a result, a better and more reliable way to fix bugs is highly desired by them. In order to achieve such goal, in depth studies on the characteristics of bug fixes from well maintained, highly popular software projects are necessary. In this paper, we study the bug fixing histories extracted from the Eclipse project, a well maintained, highly popular open source project. After analyzing more than 36,000 bugs that belongs to three major kinds of exception types, we are able to reveal some common fix types that are frequently used to fix certain kinds of program exceptions. Our analysis shows that almost all of the exceptions that belong to a certain exception can be fixed by less than ten fix types. Our result implies that most of the bugs in software projects can be and should be fixed by only a few common fix patterns.

cs.SE↗