Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201

Quantum criticality and nodal superconductivity in the FeAs-based superconductor KFe2As2

The in-plane resistivity $\rho$ and thermal conductivity $\kappa$ of FeAs-based superconductor KFe$_2$As$_2$ single crystal were measured down to 50 mK. We observe non-Fermi-liquid behavior $\rho(T) \sim T^{1.5}$ at $H_{c_2}$ = 5 T, and the development of a Fermi liquid state with $\rho(T) \sim T^2$ when further increasing field. This suggests a field-induced quantum critical point, occurring at the superconducting upper critical field $H_{c_2}$. In zero field there is a large residual linear term $\kappa_0/T$, and the field dependence of $\kappa_0/T$ mimics that in d-wave cuprate superconductors. This indicates that the superconducting gaps in KFe$_2$As$_2$ have nodes, likely d-wave symmetry. Such a nodal superconductivity is attributed to the antiferromagnetic spin fluctuations near the quantum critical point.Comment: 4 pages, 4 figures - replaces arXiv:0909.485

Magnetic Interaction in the Geometrically Frustrated Triangular Lattice Antiferromagnet CuFeO2\rm CuFeO_2

The spin wave excitations of the geometrically frustrated triangular lattice antiferromagnet (TLA) $\rm CuFeO_2$ have been measured using high resolution inelastic neutron scattering. Antiferromagnetic interactions up to third nearest neighbors in the ab plane (J_1, J_2, J_3, with $J_2/J_1 \approx 0.44$ and $J_3/J_1 \approx 0.57$), as well as out-of-plane coupling (J_z, with $J_z/J_1 \approx 0.29$) are required to describe the spin wave dispersion relations, indicating a three dimensional character of the magnetic interactions. Two energy dips in the spin wave dispersion occur at the incommensurate wavevectors associated with multiferroic phase, and can be interpreted as dynamic precursors to the magnetoelectric behavior in this system.Comment: 4 pages, 4 figures, published in Phys. Rev. Let

A hybrid prognostics approach for motorized spindle-tool holder remaining useful life prediction

The quality and efficiency of high-speed machining are restricted by the matching performance of the motorized spindle-tool holder. In high speed cutting process, the mating surface is subjected to alternating torque, repeated clamping wear and centrifugal force, which results in serious degradation of mating performance. Therefore, for the purpose of the optimum maintenance time, periodic evaluation and prediction of remaining useful life (RUL) should be carried out. Firstly, the mapping model between the current of the motorized spindle and matching performance was extracted, and the degradation characteristics of spindle-tool holder were emphatically analyzed. After the original current is de-noised by an adaptive threshold function, the extent of degradation was identified by the amplitudes of wavelet packet entropy. A hybrid prognostics combining Relevance Vector Machine (RVM) i.e. AI-model with power regression i.e. statistical model was proposed to predict the RUL. Finally, the proposed scheme was verified based on a motorized spindle reliability test platform. The experimental results show that the current signal processing method based on wavelet packet and entropy can reflect the change of the degradation characteristics sensitively. Compared with other two similar models, the hybrid model proposed can accurately predict the RUL. This model is suitable for complex and high reliability equipment when Condition Monitoring (CM) data is scarcer

High-temperature electrical and thermal transport properties of fully filled skutterudites RFe_(4)Sb_(12) (R = Ca, Sr, Ba, La, Ce, Pr, Nd, Eu, and Yb)

Fully filled skutterudites RFe_(4)Sb_(12) (R = Ca, Sr, Ba, La, Ce, Pr, Nd, Eu, and Yb) have been prepared and the high-temperature electrical and thermal transport properties are investigated systematically. Lattice constants of RFe_(4)Sb_(12) increase almost linearly with increasing the ionic radii of the fillers, while the lattice expansion in filled structure is weakly influenced by the filler valence charge states. Using simple charge counting, the hole concentration in RFe_(4)Sb_(12) with divalent fillers (R = Ca, Sr, Ba, Eu, and Yb) is much higher than that in RFe4Sb12 with trivalent fillers (R = La, Ce, Pr, and Nd), resulting in relatively high electrical conductivity and low Seebeck coefficient. It is also found that RFe_(4)Sb_(12) filled skutterudites having similar filler valence charge states exhibit comparable electrical conductivity and Seebeck coefficient, and the behavior of the temperature dependence, thereby leading to comparable power factor values in the temperature range from 300 to 800 K. All RFe_(4)Sb_(12) samples possess low lattice thermal conductivity. The correlation between the lattice thermal resistivity WL and ionic radii of the fillers is discussed and a good relationship of W_L ~ (r_(cage)−r_(ion))^3 is observed in lanthanide metal filled skutterudites. CeFe_(4)Sb_(12), PrFe_(4)Sb_(12), and NdFe_(4)Sb_(12) show the highest thermoelectric figure of merit around 0.87 at 750 K among all the filled skutterudites studied in this work

Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform

Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error

Role of the nonperturbative input in QCD resummed Drell-Yan QTQ_T-distributions

We analyze the role of the nonperturbative input in the Collins, Soper, and Sterman (CSS)'s $b$-space QCD resummation formalism for Drell-Yan transverse momentum ($Q_T$) distributions, and investigate the predictive power of the CSS formalism. We find that the predictive power of the CSS formalism has a strong dependence on the collision energy $\sqrt{S}$ in addition to its well-known $Q^2$ dependence, and the $\sqrt{S}$ dependence improves the predictive power at collider energies. We show that a reliable extrapolation from perturbatively resummed $b$-space distributions to the nonperturbative large $b$ region is necessary to ensure the correct $Q_T$ distributions. By adding power corrections to the renormalization group equations in the CSS formalism, we derive a new extrapolation formalism. We demonstrate that at collider energies, the CSS resummation formalism plus our extrapolation has an excellent predictive power for $W$ and $Z$ production at all transverse momenta $Q_T\le Q$. We also show that the $b$-space resummed $Q_T$ distributions provide a good description of Drell-Yan data at fixed target energies.Comment: Latex, 43 pages including 15 figures; typos were correcte

Heavy anion solvation of polarity fluctuations in Pnictides

Once again the condensed matter world has been surprised by the discovery of yet another class of high temperature superconductors. The discovery of iron-pnictide (FeAs) and chalcogenide (FeSe) based superconductors with a $T_c$ of up to 55 K is again evidence of how complex the many body problem really is, or in another view how resourceful nature is. The first reactions would of course be that these new materials must in some way be related to the copper-oxide based superconductors for which a large number of theories exist although a general consensus regarding the correct theory has not yet been reached. Here we point out that the basic physical paradigm of the new iron based superconductors is entirely different from the cuprates. Their fundamental properties, structural and electronic, are dominated by the exceptionally large pnictide polarizabilities