Yi Li - The Johns Hopkins University, USA

Yi Li
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Name
Yi Li
Affiliation
The Johns Hopkins University, USA
City
Baltimore
Country
United States

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Computer Science - Computer Vision and Pattern Recognition (10)
 
Quantum Physics (4)
 
Physics - Strongly Correlated Electrons (4)
 
Computer Science - Information Theory (3)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (3)
 
Computer Science - Data Structures and Algorithms (3)
 
Mathematics - Information Theory (3)
 
Mathematics - Analysis of PDEs (3)
 
High Energy Physics - Theory (3)
 
Physics - Materials Science (2)
 
Statistics - Machine Learning (2)
 
Physics - Superconductivity (2)
 
Computer Science - Learning (2)
 
Cosmology and Nongalactic Astrophysics (2)
 
Computer Science - Information Retrieval (1)
 
Instrumentation and Methods for Astrophysics (1)
 
High Energy Astrophysical Phenomena (1)
 
Mathematics - Differential Geometry (1)
 
Computer Science - Robotics (1)
 
Physics - Instrumentation and Detectors (1)
 
High Energy Physics - Phenomenology (1)
 
Computer Science - Graphics (1)
 
High Energy Physics - Experiment (1)
 
Computer Science - Computation and Language (1)
 
Computer Science - Human-Computer Interaction (1)
 
Statistics - Methodology (1)
 
Computer Science - Artificial Intelligence (1)
 
Statistics - Applications (1)
 
Computer Science - Other (1)
 
Mathematics - Mathematical Physics (1)
 
Physics - Atomic Physics (1)
 
Mathematics - Classical Analysis and ODEs (1)
 
Mathematical Physics (1)
 
Physics - Accelerator Physics (1)
 
Computer Science - Logic in Computer Science (1)
 
Computer Science - Software Engineering (1)
 
Physics - Optics (1)

Publications Authored By Yi Li

Rankings are widely used in many information systems. In information retrieval, a ranking is a list of ordered documents, in which a document with lower position has higher ranking score than the documents behind it. This paper studies the consensus measure for a given set of rankings, in order to understand the degree to which the rankings agree and the extent to which the rankings are related. Read More

Recently, wireless caching techniques have been studied to satisfy lower delay requirements and offload traffic from peak periods. By storing parts of the popular files at the mobile users, users can locate some of their requested files in their own caches or the caches at their neighbors. In the latter case, when a user receives files from its neighbors, device-to-device (D2D) communication is enabled. Read More

Digital sculpting is a popular means to create 3D models but remains a challenging task for many users. This can be alleviated by recent advances in data-driven and procedural modeling, albeit bounded by the underlying data and procedures. We propose a 3D sculpting system that assists users in freely creating models without predefined scope. Read More

We study light propagation through a slab of cold gas using both the standard electrodynamics of polarizable media, and massive atom-by-atom simulations of the electrodynamics. The main finding is that the predictions from the two methods may differ qualitatively when the density of the atomic sample $\rho$ and the wavenumber of resonant light $k$ satisfy $\rho k^{-3}\gtrsim 1$. The reason is that the standard electrodynamics is a mean-field theory, whereas for sufficiently strong light-mediated dipole-dipole interactions the atomic sample becomes correlated. Read More

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. Read More

Five year post-transplant survival rate is an important indicator on quality of care delivered by kidney transplant centers in the United States. To provide a fair assessment of each transplant center, an effect that represents the center-specific care quality, along with patient level risk factors, is often included in the risk adjustment model. In the past, the center effects have been modeled as either fixed effects or Gaussian random effects, with various pros and cons. Read More

Recent years have witnessed an increasing popularity of algorithm design for distributed data, largely due to the fact that massive datasets are often collected and stored in different locations. In the distributed setting communication typically dominates the query processing time. Thus it becomes crucial to design communication efficient algorithms for queries on distributed data. Read More

Given an $n \times d$ matrix $A$, its Schatten-$p$ norm, $p \geq 1$, is defined as $\|A\|_p = \left (\sum_{i=1}^{\textrm{rank}(A)}\sigma_i(A)^p \right )^{1/p}$, where $\sigma_i(A)$ is the $i$-th largest singular value of $A$. These norms have been studied in functional analysis in the context of non-commutative $\ell_p$-spaces, and recently in data stream and linear sketching models of computation. Basic questions on the relations between these norms, such as their embeddability, are still open. Read More

In case of a severe accident, the key to saving lives is the time between the incident and when the victim receives treatment from the first-responders. In areas with well designed emergency medical systems, the time for an ambulance to arrive at the accident location is often not too long. However, in many low and middle income countries, it usually takes much longer for an ambulance to arrive at the accident location due to lack of proper services. Read More

Visual tracking is a fundamental problem in computer vision. Recently, some deep-learning-based tracking algorithms have been achieving record-breaking performances. However, due to the high complexity of deep learning, most deep trackers suffer from low tracking speed, and thus are impractical in many real-world applications. Read More

The primordial non-Gaussianity induces scale-dependent bias of the HI with respect to the underlying dark matter, which exhibits features on the very large scale of 21-cm power spectrum potentially observable with HI intensity mapping observations. We forecast the prospective constraints on the four fundamental shapes of primordial non-Gaussianity (local, equilateral, orthogonal and enfolded), with the current and future HI intensity mapping experiments, BINGO, FAST and SKA-I. With the current configuration of the experiments and assumed one-year observation time, we find that the SKA-I will provide tighter constraints on local shape of primoridal non-Gaussianity than Planck. Read More

For the perturbed Gelfand's equation on the unit ball in two dimensions, Y. Du and Y. Lou [4] proved that the curve of positive solutions is exactly $S$-shaped, for sufficiently small values of the secondary parameter. Read More

We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation and instance mask proposal. It performs instance mask prediction and classification jointly. Read More

Phase coupling between auto-oscillators is central for achieving coherent responses such as synchronization. Here we present an experimental approach to probe it in the case of two dipolarly coupled spin-torque vortex nano-oscillators using an external microwave field. By phase-locking one oscillator to the microwave field, we observe frequency pulling on the second oscillator. Read More

Shortcut to adiabaticity in various quantum systems has attracted much attention with the wide applications in quantum information processing and quantum control. In this paper, we concentrate on stimulated Raman shortcut-to-adiabatic passage in quantum three-level systems. To implement counter-diabatic driving but without additional coupling, we first reduce the quantum three-level systems to effective two-level problems at large intermediate-level detuning, or on resonance, apply counter-diabatic driving along with the unitary transformation, and eventually modify the pump and Stokes pulses for achieving fast and high-fidelity population transfer. Read More

Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work. This paper introduces a novel and computationally feasible multivariate screening and classification method for ultrahigh-dimensional data. Leveraging inter-feature correlations, the proposed method enables detection of marginally weak and sparse signals and recovery of the true informative feature set, and achieves asymptotic optimal misclassification rates. Read More

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 particle 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. Read More

Hybrid systems exhibit both continuous and discrete behavior. Analyzing hybrid systems is known to be hard. Inspired by the idea of concolic testing (of programs), we investigate whether we can combine random sampling and symbolic execution in order to effectively verify hybrid systems. Read More

The proof test and debugging of the multi-pulsed electron accelerator, Dragon-2,requires a thorough comprehension of the quality of the beams. This puts forward a rigid requirement on the measurement system that it should have the ability that not only differentiates the three pulses on the whole but also tells the details of each pulse.In the measurements, beam energy is converted by a target to the Optical Transition Radiation (OTR) light, the information carried by which provides a good approach to measure beam profile and divergence simultaneously. Read More

We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection. Read More

We explore the modification of the entropic formulation of uncertainty principle in quantum mechanics which measures the incompatibility of measurements in terms of Shannon entropy. The deformation in question is the type so called generalized uncertainty principle that is motivated by thought experiments in quantum gravity and string theory and is characterized by a parameter of Planck scale. The corrections are evaluated for small deformation parameters by use of the Gaussian wave function and numerical calculation. Read More

For any real number $p > 0$, we nearly completely characterize the space complexity of estimating $\|A\|_p^p = \sum_{i=1}^n \sigma_i^p$ for $n \times n$ matrices $A$ in which each row and each column has $O(1)$ non-zero entries and whose entries are presented one at a time in a data stream model. Here the $\sigma_i$ are the singular values of $A$, and when $p \geq 1$, $\|A\|_p^p$ is the $p$-th power of the Schatten $p$-norm. We show that when $p$ is not an even integer, to obtain a $(1+\epsilon)$-approximation to $\|A\|_p^p$ with constant probability, any $1$-pass algorithm requires $n^{1-g(\epsilon)}$ bits of space, where $g(\epsilon) \rightarrow 0$ as $\epsilon \rightarrow 0$ and $\epsilon > 0$ is a constant independent of $n$. Read More

Fully convolutional networks (FCNs) have been proven very successful for semantic segmentation, but the FCN outputs are unaware of object instances. In this paper, we develop FCNs that are capable of proposing instance-level segment candidates. In contrast to the previous FCN that generates one score map, our FCN is designed to compute a small set of instance-sensitive score maps, each of which is the outcome of a pixel-wise classifier of a relative position to instances. Read More

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. Read More

Accurate control of a quantum system is a fundamental requirement in many areas of modern science ranging from quantum information processing to high-precision measurements. A significantly important goal in quantum control is to prepare a desired state as fast as possible with sufficiently high fidelity allowed by available resources and experimental constraints. Stimulated Raman adiabatic passage (STIRAP) is a robust way to realize high-fidelity state transfer but it requires a sufficiently long operation time to satisfy the adiabatic criteria. Read More

The sign problem is a major obstacle in quantum Monte Carlo simulations for many-body fermion systems. We examine this problem with a new perspective based on the Majorana reflection positivity and Majorana Kramers positivity. Two sufficient conditions are proven for the absence of the fermion sign problem. Read More

How can web services that depend on user generated content discern fake social engagement activities by spammers from legitimate ones? In this paper, we focus on the social site of YouTube and the problem of identifying bad actors posting inorganic contents and inflating the count of social engagement metrics. We propose an effective method, Leas (Local Expansion at Scale), and show how the fake engagement activities on YouTube can be tracked over time by analyzing the temporal graph based on the engagement behavior pattern between users and YouTube videos. With the domain knowledge of spammer seeds, we formulate and tackle the problem in a semi-supervised manner --- with the objective of searching for individuals that have similar pattern of behavior as the known seeds --- based on a graph diffusion process via local spectral subspace. Read More

In this paper we consider the problem of continuously discovering image contents by actively asking image based questions and subsequently answering the questions being asked. The key components include a Visual Question Generation (VQG) module and a Visual Question Answering module, in which Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN) are used. Given a dataset that contains images, questions and their answers, both modules are trained at the same time, with the difference being VQG uses the images as input and the corresponding questions as output, while VQA uses images and questions as input and the corresponding answers as output. Read More

Fast Radio Bursts are bright, unresolved, non-repeating, broadband, millisecond flashes, found primarily at high Galactic latitudes, with dispersion measures much larger than expected for a Galactic source. The inferred all-sky burst rate is comparable to the core-collapse supernova rate out to redshift 0.5. Read More

Heterogeneous gap among different modalities emerges as one of the critical issues in modern AI problems. Unlike traditional uni-modal cases, where raw features are extracted and directly measured, the heterogeneous nature of cross modal tasks requires the intrinsic semantic representation to be compared in a unified framework. This paper studies the learning of different representations that can be retrieved across different modality contents. Read More

Visual Question Answering (VQA) emerges as one of the most fascinating topics in computer vision recently. Many state of the art methods naively use holistic visual features with language features into a Long Short-Term Memory (LSTM) module, neglecting the sophisticated interaction between them. This coarse modeling also blocks the possibilities of exploring finer-grained local features that contribute to the question answering dynamically over time. Read More

We combine new Cosmic Microwave Background (CMB) data from Planck with Baryon Acoustic Oscillation (BAO) data to constrain the Brans-Dicke (BD) theory, in which the gravitational constant $G$ evolves with time. Observations of type Ia supernovae (SNeIa) provide another important set of cosmological data, as they may be regarded as standard candles after some empirical corrections. However, in theories that include modified gravity like the BD theory, there is some risk and complication when using the SNIa data because their luminosity may depend on $G$. Read More

In this paper we study the long time existence of the Ricci-harmonic flow in terms of scalar curvature and Weyl tensor which extends Cao's result \cite{Cao2011} in the Ricci flow. In dimension four, we also study the integral bound of the "Riemann curvature" for the Ricci-harmonic flow generalizing a recently result of Simon \cite{Simon2015}. Read More

We present a generative model which can automatically summarize the stroke composition of free-hand sketches of a given category. When our model is fit to a collection of sketches with similar poses, it discovers and learns the structure and appearance of a set of coherent parts, with each part represented by a group of strokes. It represents both consistent (topology) as well as diverse aspects (structure and appearance variations) of each sketch category. Read More

In this paper, energy efficiency of hybrid automatic repeat request (HARQ) schemes with statistical queuing constraints is studied for both constant-rate and random Markov arrivals by characterizing the minimum energy per bit and wideband slope. The energy efficiency is investigated when either an outage constraint is imposed and (the transmission rate is selected accordingly) or the transmission rate is optimized to maximize the throughput. In both cases, it is also assumed that there is a limitation on the number of retransmissions due to deadline constraints. Read More

We generalize the concept of Berry connection of the single-electron band structure to the two-particle Cooper pair states between two Fermi surfaces with opposite Chern numbers. Because of underlying Fermi surface topology, the pairing Berry phase acquires non-trivial monopole structure. Consequently, pairing gap functions have the topologically-protected nodal structure as vortices in the momentum space with the total vorticity solely determined by the monopole charge $q_p$. Read More

In this paper, the throughput of relay networks with multiple source-destination pairs under queueing constraints has been investigated for both variable-rate and fixed-rate schemes. When channel side information (CSI) is available at the transmitter side, transmitters can adapt their transmission rates according to the channel conditions, and achieve the instantaneous channel capacities. In this case, the departure rates at each node have been characterized for different system parameters, which control the power allocation, time allocation and decoding order. Read More

Inertial magnetization dynamics have been predicted at ultrahigh speeds, or frequencies approaching the energy relaxation scale of electrons, in ferromagnetic metals. Here we identify inertial terms to magnetization dynamics in thin Ni$_{79}$Fe$_{21}$ and Co films near room temperature. Effective magnetic fields measured in high-frequency ferromagnetic resonance (115-345 GHz) show an additional stiffening term which is quadratic in frequency and $\sim$ 80 mT at the high frequency limit of our experiment. Read More

2015Jul
Authors: Fengpeng An, Guangpeng An, Qi An, Vito Antonelli, Eric Baussan, John Beacom, Leonid Bezrukov, Simon Blyth, Riccardo Brugnera, Margherita Buizza Avanzini, Jose Busto, Anatael Cabrera, Hao Cai, Xiao Cai, Antonio Cammi, Guofu Cao, Jun Cao, Yun Chang, Shaomin Chen, Shenjian Chen, Yixue Chen, Davide Chiesa, Massimiliano Clemenza, Barbara Clerbaux, Janet Conrad, Davide D'Angelo, Herve De Kerret, Zhi Deng, Ziyan Deng, Yayun Ding, Zelimir Djurcic, Damien Dornic, Marcos Dracos, Olivier Drapier, Stefano Dusini, Stephen Dye, Timo Enqvist, Donghua Fan, Jian Fang, Laurent Favart, Richard Ford, Marianne Goger-Neff, Haonan Gan, Alberto Garfagnini, Marco Giammarchi, Maxim Gonchar, Guanghua Gong, Hui Gong, Michel Gonin, Marco Grassi, Christian Grewing, Mengyun Guan, Vic Guarino, Gang Guo, Wanlei Guo, Xin-Heng Guo, Caren Hagner, Ran Han, Miao He, Yuekun Heng, Yee Hsiung, Jun Hu, Shouyang Hu, Tao Hu, Hanxiong Huang, Xingtao Huang, Lei Huo, Ara Ioannisian, Manfred Jeitler, Xiangdong Ji, Xiaoshan Jiang, Cecile Jollet, Li Kang, Michael Karagounis, Narine Kazarian, Zinovy Krumshteyn, Andre Kruth, Pasi Kuusiniemi, Tobias Lachenmaier, Rupert Leitner, Chao Li, Jiaxing Li, Weidong Li, Weiguo Li, Xiaomei Li, Xiaonan Li, Yi Li, Yufeng Li, Zhi-Bing Li, Hao Liang, Guey-Lin Lin, Tao Lin, Yen-Hsun Lin, Jiajie Ling, Ivano Lippi, Dawei Liu, Hongbang Liu, Hu Liu, Jianglai Liu, Jianli Liu, Jinchang Liu, Qian Liu, Shubin Liu, Shulin Liu, Paolo Lombardi, Yongbing Long, Haoqi Lu, Jiashu Lu, Jingbin Lu, Junguang Lu, Bayarto Lubsandorzhiev, Livia Ludhova, Shu Luo, Vladimir Lyashuk, Randolph Mollenberg, Xubo Ma, Fabio Mantovani, Yajun Mao, Stefano M. Mari, William F. McDonough, Guang Meng, Anselmo Meregaglia, Emanuela Meroni, Mauro Mezzetto, Lino Miramonti, Thomas Mueller, Dmitry Naumov, Lothar Oberauer, Juan Pedro Ochoa-Ricoux, Alexander Olshevskiy, Fausto Ortica, Alessandro Paoloni, Haiping Peng, Jen-Chieh Peng, Ezio Previtali, Ming Qi, Sen Qian, Xin Qian, Yongzhong Qian, Zhonghua Qin, Georg Raffelt, Gioacchino Ranucci, Barbara Ricci, Markus Robens, Aldo Romani, Xiangdong Ruan, Xichao Ruan, Giuseppe Salamanna, Mike Shaevitz, Valery Sinev, Chiara Sirignano, Monica Sisti, Oleg Smirnov, Michael Soiron, Achim Stahl, Luca Stanco, Jochen Steinmann, Xilei Sun, Yongjie Sun, Dmitriy Taichenachev, Jian Tang, Igor Tkachev, Wladyslaw Trzaska, Stefan van Waasen, Cristina Volpe, Vit Vorobel, Lucia Votano, Chung-Hsiang Wang, Guoli Wang, Hao Wang, Meng Wang, Ruiguang Wang, Siguang Wang, Wei Wang, Yi Wang, Yi Wang, Yifang Wang, Zhe Wang, Zheng Wang, Zhigang Wang, Zhimin Wang, Wei Wei, Liangjian Wen, Christopher Wiebusch, Bjorn Wonsak, Qun Wu, Claudia-Elisabeth Wulz, Michael Wurm, Yufei Xi, Dongmei Xia, Yuguang Xie, Zhi-zhong Xing, Jilei Xu, Baojun Yan, Changgen Yang, Chaowen Yang, Guang Yang, Lei Yang, Yifan Yang, Yu Yao, Ugur Yegin, Frederic Yermia, Zhengyun You, Boxiang Yu, Chunxu Yu, Zeyuan Yu, Sandra Zavatarelli, Liang Zhan, Chao Zhang, Hong-Hao Zhang, Jiawen Zhang, Jingbo Zhang, Qingmin Zhang, Yu-Mei Zhang, Zhenyu Zhang, Zhenghua Zhao, Yangheng Zheng, Weili Zhong, Guorong Zhou, Jing Zhou, Li Zhou, Rong Zhou, Shun Zhou, Wenxiong Zhou, Xiang Zhou, Yeling Zhou, Yufeng Zhou, Jiaheng Zou

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. Read More

We systematically generalize the exotic $^3$He-B phase, which not only exhibits unconventional symmetry but is also isotropic and topologically non-trivial, to arbitrary partial-wave channels with multi-component fermions. The concrete example with four-component fermions is illustrated including the isotropic $f$, $p$ and $d$-wave pairings in the spin septet, triplet, and quintet channels, respectively. The odd partial-wave channel pairings are topologically non-trivial, while pairings in even partial-wave channels are topologically trivial. Read More

We design realizable time-dependent semiclassical pulses to invert the population of a two-level system faster than adiabatically when the rotating-wave approximation cannot be applied. Different approaches, based on the counterdiabatic method or on invariants, may lead to singularities in the pulse functions. Ways to avoid or cancel the singularities are put forward when the pulse spans few oscillations. Read More

A high luminosity Circular Electron Positron Collider (CEPC) as a Higgs Factory will be helpful to the precision measurement of the Higgs mass. The signal-background interference effect is carefully studied for the Higgs diphoton decay mode in the associated Z boson production at the future $e^+e^-$ colliders at energy $246 {\rm GeV}$. The mass shifts go up from about $20 {\rm MeV}$ to $50 {\rm MeV}$ for the experimental mass resolution ranging from $0. Read More

In this paper three dimensional higher spin theories in the Chern-Simons formulation with gauge algebra $sl(N,R)$ are investigated which have Lifshitz symmetry with scaling exponent $z$. We show that an explicit map exists for all $z$ and $N$ relating the Lifshitz Chern-Simons theory to the $(n,m)$ element of the KdV hierarchy. Furthermore we show that the map and hence the conserved charges are independent of $z$. Read More

Retrieving 3D models from 2D human sketches has received considerable attention in the areas of graphics, image retrieval, and computer vision. Almost always in state of the art approaches a large amount of "best views" are computed for 3D models, with the hope that the query sketch matches one of these 2D projections of 3D models using predefined features. We argue that this two stage approach (view selection -- matching) is pragmatic but also problematic because the "best views" are subjective and ambiguous, which makes the matching inputs obscure. Read More

Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking because they require very long training time and a large number of training samples. In this work, we present an efficient and very robust tracking algorithm using a single Convolutional Neural Network (CNN) for learning effective feature representations of the target object, in a purely online manner. Our contributions are multifold: First, we introduce a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation. Read More

Ferromagnets are believed to exhibit strongly anisotropic spin relaxation, with relaxation lengths for spin longitudinal to magnetization significantly longer than those for spin transverse to magnetization. Here we characterize the anisotropy of spin relaxation in Co using the spin pumping contribution to Gilbert damping in noncollinearly magnetized Py$_{1-x}$Cu$_{x}$/Cu/Co trilayer structures. The static magnetization angle between Py$_{1-x}$Cu$_{x}$ and Co, adjusted under field bias perpendicular to film planes, controls the projections of longitudinal and transverse spin current pumped from Py$_{1-x}$Cu$_{x}$ into Co. Read More

To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR). This method first generates a large number of random binary intermediate/derived features based on the original input matrix, and then performs regularized linear/logistic regression on those intermediate/derived features to predict the outcome. Benchmark analyses on a simulated dataset, UCI machine learning repository datasets and a GWAS dataset showed that RBR outperforms other popular methods in accuracy and robustness. Read More

We study itinerant ferromagnetism in a $t_{2g}$ multi-orbital Hubbard system in the cubic lattice, which consists of three planar oriented orbital bands of $d_{xy}$, $d_{yz}$, and $d_{zx}$. Electrons in each orbital band can only move within a two-dimensional plane in the three-dimensional lattice parallel to the corresponding orbital orientation. Electrons of different orbitals interact through the on-site multi-orbital interactions including Hund's coupling. Read More

The rapid experimental progress of ultra-cold dipolar fermions opens up a whole new opportunity to investigate novel many-body physics of fermions. In this article, we review theoretical studies of the Fermi liquid theory and Cooper pairing instabilities of both electric and magnetic dipolar fermionic systems from the perspective of unconventional symmetries. When the electric dipole moments are aligned by the external electric field, their interactions exhibit the explicit $d_{r^2-3z^2}$ anisotropy. Read More