arXiv subjects

Yi Li

Publications and source records attributed to Yi Li.

At least 505 records · Page 28Linked to original sources

arXiv2019

Minorization-Maximization-based Steepest Ascent for Large-scale Survival Analysis with Time-Varying Effects: Application to the National Kidney Transplant Dataset

The time-varying effects model is a flexible and powerful tool for modeling the dynamic changes of covariate effects. However, in survival analysis, its computational burden increases quickly as the number of sample sizes or predictors grows. Traditional methods that perform well for moderate sample sizes and low-dimensional data do not scale to massive data. Analysis of national kidney transplant data with a massive sample size and large number of predictors defy any existing statistical methods and software. In view of these difficulties, we propose a Minorization-Maximization-based steepest ascent procedure for estimating the time-varying effects. Leveraging the block structure formed by the basis expansions, the proposed procedure iteratively updates the optimal block-wise direction along which the approximate increase in the log-partial likelihood is maximized. The resulting estimates ensure the ascent property and serve as refinements of the previous step. The performance of the proposed method is examined by simulations and applications to the analysis of national kidney transplant data.

stat.CO↗

arXiv2019

Robust Estimation and Variable Selection for the Accelerated Failure Time Model

This paper considers robust modeling of the survival time for cancer patients. Accurate prediction can be helpful for developing therapeutic and care strategies. We propose a unified Expectation-Maximization approach combined with the L1-norm penalty to perform variable selection and obtain parameter estimation simultaneously for the accelerated failure time model with right-censored survival data. Our approach can be used with general loss functions, and reduces to the well-known Buckley-James method when the squared-error loss is used without regularization. To mitigate the effects of outliers and heavy-tailed noise in the real application, we advocate the use of robust loss functions under our proposed framework. Simulation studies are conducted to evaluate the performance of the proposed approach with different loss functions, and an application to an ovarian carcinoma study is provided. Meanwhile, we extend our approach by incorporating the group structure of covariates.

stat.ME↗

arXiv2019

Evolving ab initio trading strategies in heterogeneous environments

Securities markets are quintessential complex adaptive systems in which heterogeneous agents compete in an attempt to maximize returns. Species of trading agents are also subject to evolutionary pressure as entire classes of strategies become obsolete and new classes emerge. Using an agent-based model of interacting heterogeneous agents as a flexible environment that can endogenously model many diverse market conditions, we subject deep neural networks to evolutionary pressure to create dominant trading agents. After analyzing the performance of these agents and noting the emergence of anomalous superdiffusion through the evolutionary process, we construct a method to turn high-fitness agents into trading algorithms. We backtest these trading algorithms on real high-frequency foreign exchange data, demonstrating that elite trading algorithms are consistently profitable in a variety of market conditions---even though these algorithms had never before been exposed to real financial data. These results provide evidence to suggest that developing \textit{ab initio} trading strategies by repeated simulation and evolution in a mechanistic market model may be a practical alternative to explicitly training models with past observed market data.

cs.NE↗

arXiv2019

On Low-Risk Heavy Hitters and Sparse Recovery Schemes

We study the heavy hitters and related sparse recovery problems in the low-failure probability regime. This regime is not well-understood, and has only been studied for non-adaptive schemes. The main previous work is one on sparse recovery by Gilbert et al.(ICALP'13). We recognize an error in their analysis, improve their results, and contribute new non-adaptive and adaptive sparse recovery algorithms, as well as provide upper and lower bounds for the heavy hitters problem with low failure probability.

cs.DS↗

arXiv2019

A Survey on Theorem Provers in Formal Methods

Mechanical reasoning is a key area of research that lies at the crossroads of mathematical logic and artificial intelligence. The main aim to develop mechanical reasoning systems (also known as theorem provers) was to enable mathematicians to prove theorems by computer programs. However, these tools evolved with time and now play vital role in the modeling and reasoning about complex and large-scale systems, especially safety-critical systems. Technically, mathematical formalisms and automated reasoning based-approaches are employed to perform inferences and to generate proofs in theorem provers. In literature, there is a shortage of comprehensive documents that can provide proper guidance about the preferences of theorem provers with respect to their designs, performances, logical frameworks, strengths, differences and their application areas. In this work, more than 40 theorem provers are studied in detail and compared to present a comprehensive analysis and evaluation of these tools. Theorem provers are investigated based on various parameters, which includes: implementation architecture, logic and calculus used, library support, level of automation, programming paradigm, programming language, differences and application areas.

cs.SE↗

arXiv2019

Combining Program Analysis and Statistical Language Model for Code Statement Completion

Automatic code completion helps improve developers' productivity in their programming tasks. A program contains instructions expressed via code statements, which are considered as the basic units of program execution. In this paper, we introduce AutoSC, which combines program analysis and the principle of software naturalness to fill in a partially completed statement. AutoSC benefits from the strengths of both directions, in which the completed code statement is both frequent and valid. AutoSC is first trained on a large code corpus to derive the templates of candidate statements. Then, it uses program analysis to validate and concretize the templates into syntactically and type-valid candidate statements. Finally, these candidates are ranked by using a language model trained on the lexical form of the source code in the code corpus. Our empirical evaluation on the large datasets of real-world projects shows that AutoSC achieves 38.9-41.3% top-1 accuracy and 48.2-50.1% top-5 accuracy in statement completion. It also outperforms a state-of-the-art approach from 9X-69X in top-1 accuracy.

cs.SE↗

arXiv2019

Small scale reconstruction in three-dimensional Kolmogorov flows using four-dimensional variational data assimilation

We apply the four dimensional variational method to reconstruct the small scales of three-dimensional turbulent velocity fields with a moderate Reynolds number, given a time sequence of measurement data on a coarse set of grid points. The results show that, reconstruction is successful when the resolution of the measurement data, given in terms of the wavenumber, is at the order of the threshold value $k_c = 0.2η_K^{-1}$ where $η_K$ is the Kolmogorov length scale of the flow. When the data are available over a period of one large eddy turn-over time scale, the filtered enstrophy and other small scale quantities are reconstructed with a $30\%$ or smaller normalized point-wise error, and a $90\%$ point-wise correlation. The spectral correlation between the reconstructed and target fields is higher than $80\%$ for all wavenumbers. Minimum volume enclosing ellipsoids (MVEEs) and MVEE trees are introduced to quantitatively compare the geometry of non-local structures. Results show that, for the majority samples, errors in the locations and the sizes of the reconstructed structures are within $15\%$, and those in the orientations is within $15^\circ$. This investigation demonstrates that satisfactory reconstruction of the scales two or more octaves smaller is possible if data at large scales are available for at least one large eddy turn-over time. In comparison, a direct substitution scheme results in three times bigger point-wise discrepancy in enstrophy. The spectral difference between the reconstructed and target velocity fields is more than ten times higher than what is obtained with the four dimensional variational method.

physics.flu-dyn↗

arXiv2019

Magnetic damping modulation in $IrMn_{3}/Ni_{80}Fe_{20}$ via the magnetic spin Hall effect

Non-collinear antiferromagnets can have additional spin Hall effects due to the net chirality of their magnetic spin structure, which provides for more complex spin-transport phenomena compared to ordinary non-magnetic materials. Here we investigated how ferromagnetic resonance of permalloy ($Ni_{80}Fe_{20}$) is modulated by spin Hall effects in adjacent epitaxial $IrMn_{3}$ films. We observe a large dc modulation of the ferromagnetic resonance linewidth for currents applied along the [001] $IrMn_{3}$ direction. This very strong angular dependence of spin-orbit torques from dc currents through the bilayers can be explained by the magnetic spin Hall effect where $IrMn_{3}$ provides novel pathways for modulating magnetization dynamics electrically.

cond-mat.mtrl-sci↗

arXiv2019

Path Planning Games

Path planning is a fundamental and extensively explored problem in robotic control. We present a novel economic perspective on path planning. Specifically, we investigate strategic interactions among path planning agents using a game theoretic path planning framework. Our focus is on economic tension between two important objectives: efficiency in the agents' achieving their goals, and safety in navigating towards these. We begin by developing a novel mathematical formulation for path planning that trades off these objectives, when behavior of other agents is fixed. We then use this formulation for approximating Nash equilibria in path planning games, as well as to develop a multi-agent cooperative path planning formulation. Through several case studies, we show that in a path planning game, safety is often significantly compromised compared to a cooperative solution.

cs.MA↗

arXiv2019

A Survey of Deep Facial Attribute Analysis

Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation. First, we summarize a general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction. Additionally, we introduce the underlying theories of this two-stage pipeline for both FAE and FAM. Second, the datasets and performance metrics commonly used in facial attribute analysis are presented. Third, we create a taxonomy of state-of-the-art methods and review deep FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.

cs.CV↗

arXiv2019

Counterroating incommensurate magnetic order and strong quantum fluctuations in the honeycomb layers of $\rm NaNi_2BiO_6$

We report the magnetic structure and electronic properties of the honeycomb antiferromagnet $\rm NaNi_2BiO_{5.66}$. We find magnetic order with moments along the $c$ axis for temperatures below $T_{c1}=6.3(1)\>{\rm K}$ and then in the honeycomb plane for $T < T_{c2}=4.8(1)\>{\rm K}$ with a counterrotating pattern and an ordering wave vector ${\bf q}=(\frac{1}{3},\> \frac{1}{3},\> 0.15(1))$. Density functional theory and electron spin resonance indicate this is high-spin Ni$^{3+}$ magnetism near a high to low spin transition. The ordering wave vector, in-plane magnetic correlations, missing entropy, spin state, and superexchange pathways are all consistent with bond-dependent Kitaev-$Γ$-Heisenberg exchange interactions in $\rm NaNi_2BiO_{6-δ}$.

cond-mat.str-el↗

arXiv2019

Distinguishing antiferromagnetic spin sublattices via the spin Seebeck effect

Antiferromagnets are beneficial for future spintronic applications due to their zero magnetic moment and ultrafast dynamics. But gaining direct access to their antiferromagnetic order and identifying the properties of individual magnetic sublattices, especially in thin films and small-scale devices, remains a formidable challenge. So far, the existing read-out techniques such as anisotropic magnetoresistance, tunneling anisotropic magnetoresistance, and spin-Hall magnetoresistance, are even functions of sublattice magnetization and thus allow us to detect different orientations of the Néel order for antiferromagnets with multiple easy axes. In contrast direct electrical detection of oppositely oriented spin states along the same easy axes (e.g., in uniaxial antiferromagnets) requires sensitivity to the direction of individual sublattices and thus is more difficult. In this study, using spin Seebeck effect, we report the electrical detection of the two sublattices in a uniaxial antiferromagnet Cr2O3. We find the rotational symmetry and hysteresis behavior of the spin Seebeck signals measured at the top and bottom surface reflect the dierction of the surface sublattice moments, but not the Néel order or the net moment in the bulk. Our results demonstrate the important role of interface spin sublattices in generating the spin Seebeck voltages, which provide a way to access each sublattice independently, enables us to track the full rotation of the magnetic sublattice, and distinguish different and antiparallel antiferromagnetic states in uniaxial antiferromagnets.

cond-mat.mtrl-sci↗

arXiv2019

Directed-Weighting Group Lasso for Eltwise Blocked CNN Pruning

Eltwise layer is a commonly used structure in the multi-branch deep learning network. In a filter-wise pruning procedure, due to the specific operation of the eltwise layer, all its previous convolutional layers should vote for which filters by index should be pruned. Since only an intersection of the voted filters is pruned, the compression rate is limited. This work proposes a method called Directed-Weighting Group Lasso (DWGL), which enforces an index-wise incremental (directed) coefficient on the filterlevel group lasso items, so that the low index filters getting high activation tend to be kept while the high index ones tend to be pruned. When using DWGL, much fewer filters are retained during the voting process and the compression rate can be boosted. The paper test the proposed method on the ResNet series networks. On CIFAR-10, it achieved a 75.34% compression rate on ResNet-56 with a 0.94% error increment, and a 52.06% compression rate on ResNet-20 with a 0.72% error increment. On ImageNet, it achieved a 53% compression rate with ResNet-50 with a 0.6% error increment, speeding up the network by 2.23 times. Furthermore, it achieved a 75% compression rate on ResNet-50 with a 1.2% error increment, speeding up the network by 4 times.

cs.CV↗

arXiv2019

Phonon Transport Controlled by Ferromagnetic Resonance

The resonant coupling of phonons and magnons is important for the interconversion of phononic and spin degrees of freedom. We studied the phonon transmission in LiNbO3 manipulated by the dynamic magnetization in a Ni thin film. It was observed that the phonons could be absorbed strongly through resonant magnon-phonon coupling, which was realized by optimizing the interfacial coupling between Ni and LiNbO3. The line shapes of phonon transmission were further investigated considering the magnon-phonon interconversion in the elastically driven ferromagnetic resonance process. The results promote unique routes for phonon manipulation and detection in the presence of magnetization dynamics.

physics.app-ph↗

arXiv2019

Spin-Wave frequency division multiplexing in an yttrium iron garnet microstripe magnetized by inhomogeneous field

Spin waves are promising candidates for information processing and transmission in a broad frequency range. In the realization of magnonic devices, the frequency depended division of the spin wave frequencies is a critical function for parallel information processing. In this work, we demonstrate a proof-of-concept spin-wave frequency division multiplexing method by magnetizing a homogenous magnetic microstripe with an inhomogeneous field. The symmetry breaking additional field is introduced by a permalloy stripe simply placed in lateral proximity to the waveguide. Spin waves with different frequencies can propagate independently, simultaneously and separately in space along the shared waveguide. This work brings new potentials for parallel information transmission and processing in magnonics.

physics.app-ph↗

arXiv2019

Tight Bounds for the Subspace Sketch Problem with Applications

In the subspace sketch problem one is given an $n\times d$ matrix $A$ with $O(\log(nd))$ bit entries, and would like to compress it in an arbitrary way to build a small space data structure $Q_p$, so that for any given $x \in \mathbb{R}^d$, with probability at least $2/3$, one has $Q_p(x)=(1\pmε)\|Ax\|_p$, where $p\geq 0$, and where the randomness is over the construction of $Q_p$. The central question is: How many bits are necessary to store $Q_p$? This problem has applications to the communication of approximating the number of non-zeros in a matrix product, the size of coresets in projective clustering, the memory of streaming algorithms for regression in the row-update model, and embedding subspaces of $L_p$ in functional analysis. A major open question is the dependence on the approximation factor $ε$. We show if $p\geq 0$ is not a positive even integer and $d=Ω(\log(1/ε))$, then $\tildeΩ(ε^{-2}d)$ bits are necessary. On the other hand, if $p$ is a positive even integer, then there is an upper bound of $O(d^p\log(nd))$ bits independent of $ε$. Our results are optimal up to logarithmic factors, and show in particular that one cannot compress $A$ to $O(d)$ "directions" $v_1,\dots,v_{O(d)}$, such that for any $x$, $\|Ax\|_1$ can be well-approximated from $\langle v_1,x\rangle,\dots,\langle v_{O(d)},x\rangle$. Our lower bound rules out arbitrary functions of these inner products (and in fact arbitrary data structures built from $A$), and thus rules out the possibility of a singular value decomposition for $\ell_1$ in a very strong sense. Indeed, as $ε\to 0$, for $p = 1$ the space complexity becomes arbitrarily large, while for $p = 2$ it is at most $O(d^2 \log(nd))$. As corollaries of our main lower bound, we obtain new lower bounds for a wide range of applications, including the above, which in many cases are optimal.

cs.DS↗

arXiv2019

General expressions for extra-dimensional tree amplitudes and all-plus 1-loop integrands in Q-cut representaion

In this paper, we give the general expressions for a special series of tree amplitudes of the Yang-Mills theory. This series of amplitudes have two adjacent massless spin-1 particles with extra-dimensional momenta and any number of positive helicity gluons. With special helicity choices, we use the spinor helicity formalism to express these n-point amplitudes in compact forms, and find a clever way to use the BCFW recursion relations to prove the results. Then these amplitudes are used to form the complete 1-loop all-plus integrand with any number of gluons, expressed in the Q-cut representation.

hep-th↗

arXiv2019

DeepIM: Deep Iterative Matching for 6D Pose Estimation

Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the observed image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using an untangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over state-of-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.

cs.CV↗