Reading comprehension is a complex cognitive process involving many human brain activities. Plenty of works have studied the reading patterns and attention allocation mechanisms in the reading process. However, little is known about what happens in h
uman brain during reading comprehension and how we can utilize this information as implicit feedback to facilitate information acquisition performance. With the advances in brain imaging techniques such as EEG, it is possible to collect high-precision brain signals in almost real time. With neuroimaging techniques, we carefully design a lab-based user study to investigate brain activities during reading comprehension. Our findings show that neural responses vary with different types of contents, i.e., contents that can satisfy users information needs and contents that cannot. We suggest that various cognitive activities, e.g., cognitive loading, semantic-thematic understanding, and inferential processing, at the micro-time scale during reading comprehension underpin these neural responses. Inspired by these detectable differences in cognitive activities, we construct supervised learning models based on EEG features for two reading comprehension tasks: answer sentence classification and answer extraction. Results show that it is feasible to improve their performance with brain signals. These findings imply that brain signals are valuable feedback for enhancing human-computer interactions during reading comprehension.
Recently, Information Retrieval community has witnessed fast-paced advances in Dense Retrieval (DR), which performs first-stage retrieval with embedding-based search. Despite the impressive ranking performance, previous studies usually adopt brute-fo
rce search to acquire candidates, which is prohibitive in practical Web search scenarios due to its tremendous memory usage and time cost. To overcome these problems, vector compression methods have been adopted in many practical embedding-based retrieval applications. One of the most popular methods is Product Quantization (PQ). However, although existing vector compression methods including PQ can help improve the efficiency of DR, they incur severely decayed retrieval performance due to the separation between encoding and compression. To tackle this problem, we present JPQ, which stands for Joint optimization of query encoding and Product Quantization. It trains the query encoder and PQ index jointly in an end-to-end manner based on three optimization strategies, namely ranking-oriented loss, PQ centroid optimization, and end-to-end negative sampling. We evaluate JPQ on two publicly available retrieval benchmarks. Experimental results show that JPQ significantly outperforms popular vector compression methods. Compared with previous DR models that use brute-force search, JPQ almost matches the best retrieval performance with 30x compression on index size. The compressed index further brings 10x speedup on CPU and 2x speedup on GPU in query latency.
The cuprate superconductors are characterized by numerous ordering tendencies, with the nematic order being the most distinct form of order. Here the intertwinement of the electronic nematicity with superconductivity in cuprate superconductors is stu
died based on the kinetic-energy-driven superconductivity. It is shown that the optimized Tc takes a dome-like shape with the weak and strong strength regions on each side of the optimal strength of the electronic nematicity, where the optimized Tc reaches its maximum. This dome-like shape nematic-order strength dependence of Tc indicates that the electronic nematicity enhances superconductivity. Moreover, this nematic order induces the anisotropy of the electron Fermi surface (EFS), where although the original EFS with the four-fold rotation symmetry is broken up into that with a residual two-fold rotation symmetry, this EFS with the two-fold rotation symmetry still is truncated to form the Fermi arcs with the most spectral weight that locates at the tips of the Fermi arcs. Concomitantly, these tips of the Fermi arcs connected by the wave vectors ${bf q}_{i}$ construct an octet scattering model, however, the partial wave vectors and their respective symmetry-corresponding partners occur with unequal amplitudes, leading to these ordered states being broken both rotation and translation symmetries. As a natural consequence, the electronic structure is inequivalent between the $k_{x}$ and $k_{y}$ directions. These anisotropic features of the electronic structure are also confirmed via the result of the autocorrelation of the single-particle excitation spectra, where the breaking of the rotation symmetry is verified by the inequivalence on the average of the electronic structure at the two Bragg scattering sites. Furthermore, the strong energy dependence of the order parameter of the electronic nematicity is also discussed.
Gait recognition plays a vital role in human identification since gait is a unique biometric feature that can be perceived at a distance. Although existing gait recognition methods can learn gait features from gait sequences in different ways, the pe
rformance of gait recognition suffers from insufficient labeled data, especially in some practical scenarios associated with short gait sequences or various clothing styles. It is unpractical to label the numerous gait data. In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones. Specifically, we employ the horizontal pyramid mapping (HPM) and micro-motion template builder (MTB) as our spatiotemporal backbones to capture the multi-scale spatiotemporal representations. Experiments on CASIA-B and OU-MVLP benchmark gait datasets demonstrate the effectiveness of the proposed SelfGait compared with four state-of-the-art gait recognition methods. The source code has been released at https://github.com/EchoItLiu/SelfGait.
The recent experiments revealed a remarkable possibility for the absence of the disparity between the phase diagrams of the electron- and hole-doped cuprate superconductors, while such an aspect should be also reflected in the dressing of the electro
ns. Here the phase diagram of the electron-doped cuprate superconductors and the related exotic features of the anisotropic dressing of the electrons are studied based on the kinetic-energy driven superconductivity. It is shown that although the optimized Tc in the electron-doped side is much smaller than that in the hole-doped case, the electron- and hole-doped cuprate superconductors rather resemble each other in the doping range of the superconducting dome, indicating an absence of the disparity between the phase diagrams of the electron- and hole-doped cuprate superconductors. In particular, the anisotropic dressing of the electrons due to the strong electrons coupling to a strongly dispersive spin excitation leads to that the electron Fermi surface is truncated to form the disconnected Fermi arcs centered around the nodal region. Concomitantly, the dip in the peak-dip-hump structure of the quasiparticle excitation spectrum is directly associated with the corresponding peak in the quasiparticle scattering rate, while the dispersion kink is always accompanied by the corresponding inflection point in the total self-energy, as the dip in the peak-dip-hump structure and dispersion kink in the hole-doped counterparts. The theory also predicts that both the normal and anomalous self-energies exhibit the well-pronounced low-energy peak-structures.
The recently deduced normal and anomalous self-energies from photoemission spectra of cuprate superconductors via the machine learning technique are calling for an explanation. Here the normal and anomalous self-energies in cuprate superconductors ar
e analyzed within the framework of the kinetic-energy-driven superconductivity. It is shown that the exchanged spin excitations give rise to the well-pronounced low-energy peak-structures in both the normal and anomalous self-energies, however, they do not cancel in the total self-energy. In particular, the peak-structure in the normal self-energy is mainly responsible for the peak-dip-hump structure in the single-particle excitation spectrum, and can persist into the normal-state, while the sharp peak in the anomalous self-energy gives rise to a crucial contribution to the superconducting gap, and vanishes in the normal-state. Moreover, the evolution of the peak-structure with doping and momentum are also analyzed.
The angle-resolved photoemission spectroscopy (ARPES) autocorrelation in the electron-doped cuprate superconductors is studied based on the kinetic-energy driven superconducting (SC) mechanism. It is shown that the strong electron correlation induces
the electron Fermi surface (EFS) reconstruction, where the most of the quasiparticles locate at around the hot spots on EFS, and then these hot spots connected by the scattering wave vectors ${bf q}_{i}$ construct an {it octet} scattering model. In a striking analogy to the hole-doped case, the sharp ARPES autocorrelation peaks are directly correlated with the scattering wave vectors ${bf q}_{i}$, and are weakly dispersive in momentum space. However, in a clear contrast to the hole-doped counterparts, the position of the ARPES autocorrelation peaks move toward to the opposite direction with the increase of doping. The theory also indicates that there is an intrinsic connection between the ARPES autocorrelation and quasiparticle scattering interference (QSI) in the electron-doped cuprate superconductors.
The characteristic features of the renormalization of the electrons in the bilayer cuprate superconductors are investigated within the kinetic-energy driven superconductivity. It is shown that the quasiparticle excitation spectrum is split into its b
onding and antibonding components due to the presence of the bilayer coupling, with each component that is independent. However, in the underdoped and optimally doped regimes, although the bonding and antibonding electron Fermi surface (EFS) contours deriving from the bonding and antibonding layers are truncated to form the bonding and antibonding Fermi arcs, almost all spectral weights in the bonding and antibonding Fermi arcs are reduced to the tips of the bonding and antibonding Fermi arcs, which in this case coincide with the bonding and antibonding hot spots. These hot spots connected by the scattering wave vectors ${bf q}_{i} $ construct an octet scattering model, and then the enhancement of the quasiparticle scattering processes with the scattering wave vectors ${bf q}_{i}$ is confirmed via the result of the autocorrelation of the ARPES spectral intensities. Moreover, the peak-dip-hump (PDH) structure developed in each component of the quasiparticle excitation spectrum along the corresponding EFS is directly related with the peak structure in the quasiparticle scattering rate except for at around the hot spots, where the PDH structure is caused mainly by the bilayer coupling. Although the kink in the quasiparticle dispersion is present all around EFS, when the momentum moves away from the node to the antinode, the kink energy smoothly decreases, while the dispersion kink becomes more pronounced, and in particular, near the cut close to the antinode, develops into a break separating of the fasting dispersing high-energy part of the quasiparticle excitation spectrum from the slower dispersing low-energy part.
The study of the electromagnetic response in cuprate superconductors plays a crucial role in the understanding of the essential physics of these materials. Here the doping dependence of the electromagnetic response in cuprate superconductors is studi
ed within the kinetic-energy driven superconducting mechanism. The kernel of the response function is evaluated based on the linear response approximation for a purely transverse vector potential, and can be broken up into its diamagnetic and paramagnetic parts. In particular, this paramagnetic part exactly cancels the corresponding diamagnetic part in the normal-state, and then the Meissner effect is obtained within the entire superconducting phase. Following this kernel of the response function, the electromagnetic response calculation in terms of the specular reflection model qualitatively reproduces many of the striking features observed in the experiments. In particular, the local magnetic-field profile follows an exponential law, while the superfluid density exhibits the nonlinear temperature behavior at the lowest temperatures, followed by the linear temperature dependence extending over the most of the superconducting temperature range. Moreover, the maximal value of the superfluid density occurs at around the critical doping $delta_{rm critical}sim 0.16$, and then decreases in both lower doped and higher doped regimes. The theory also shows that the nonlinear temperature dependence of the superfluid density at the lowest temperatures can be attributed to the nonlocal effects induced by the d-wave gap nodes on the electron Fermi surface.
Superconductivity is caused by the interaction between electrons by the exchange of collective bosonic excitations, however, this bosonic glue forming electron pairs is manifested itself by the coupling strength of the electrons to collective bosonic
excitations. Here the doping and momentum dependence of the coupling strength of the electrons to spin excitations in cuprate superconductors is studied within the framework of the kinetic-energy-driven superconducting mechanism. The normal self-energy in the particle-hole channel and pairing self-energy in the particle-pariticle channel generated by the interaction between electrons by the exchange of spin excitation are employed to extract the coupling strengths of the electrons to spin excitations in the particle-hole and particle-particle channels, respectively. It is shown that below the superconducting transition temperature, both the coupling strengths in the particle-hole and particle-particle channels around the antinodes consist of two peaks, with a sharp low-energy peak located at around 5 meV in the optimally doped regime, and a broad band with a weak peak centered at around 40 meV. In particular, this two-peak structure in the coupling strength in the particle-hole channel can persist into the normal-state, while as a consequence of the d-wave type symmetry of the superconducting gap, the coupling strength in the particle-particle channel vanishes at the nodes. However, the positions of the peaks in the coupling strengths in the underdoped regime shift towards to higher energies with the increase of doping. More specifically, although the positions of the peaks in the coupling strengths move to lower energies from the antinode to the hot spot on the electron Fermi surface, the weights of the peaks decrease smoothly with the move of the momentum from the antinode to the hot spot, and fade away at the hot spots.
