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A complete classification is established for continuous and SL(n) covariant matrix-valued valuations on Lp(Rn,|x|2dx). The assumption of matrix symmetry is eliminated. For n>2, such valuation is uniquely characterized by the moment matrix of measurable function. In the 2-dimensional case, while the rotation matrix shows up.

Long-term time series forecasting (LTSF) has been widely applied in finance, traffic prediction, and other domains. Recently, patch-based transformers have emerged as a promising approach, segmenting data into sub-level patches that serve as input tokens. However, existing methods mostly rely on predetermined patch lengths, necessitating expert knowledge and posing challenges in capturing diverse characteristics across various scales. Moreover, time series data exhibit diverse variations and fluctuations across different temporal scales, which traditional approaches struggle to model effectively. In this paper, we propose a dynamic tokenizer with a dynamic sparse learning algorithm to capture diverse receptive fields and sparse patterns of time series data. In order to build hierarchical receptive fields, we develop a multi-scale Transformer model, coupled with multi-scale sequence extraction, capable of capturing multi-resolution features. Additionally, we introduce a group-aware rotary position encoding technique to enhance intra- and inter-group position awareness among representations across different temporal scales. Our proposed model, named DRFormer, is evaluated on various real-world datasets, and experimental results demonstrate its superiority compared to existing methods. Our code is available at: https://github.com/ruixindingECNU/DRFormer.

Recent advances in prompt optimization have notably enhanced the performance of pre-trained language models (PLMs) on downstream tasks. However, the potential of optimized prompts on domain generalization has been under-explored. To explore the nature of prompt generalization on unknown domains, we conduct pilot experiments and find that (i) Prompts gaining more attention weight from PLMs' deep layers are more generalizable and (ii) Prompts with more stable attention distributions in PLMs' deep layers are more generalizable. Thus, we offer a fresh objective towards domain-generalizable prompts optimization named "Concentration", which represents the "lookback" attention from the current decoding token to the prompt tokens, to increase the attention strength on prompts and reduce the fluctuation of attention distribution. We adapt this new objective to popular soft prompt and hard prompt optimization methods, respectively. Extensive experiments demonstrate that our idea improves comparison prompt optimization methods by 1.42% for soft prompt generalization and 2.16% for hard prompt generalization in accuracy on the multi-source domain generalization setting, while maintaining satisfying in-domain performance. The promising results validate the effectiveness of our proposed prompt optimization objective and provide key insights into domain-generalizable prompts., Comment: Submitted to NeurIPS 2024, Preprint, Under review

Large language models have shown their ability to become effective few-shot learners with prompting, revoluting the paradigm of learning with data scarcity. However, this approach largely depends on the quality of prompt initialization, and always exhibits large variability among different runs. Such property makes prompt tuning highly unreliable and vulnerable to poorly constructed prompts, which limits its extension to more real-world applications. To tackle this issue, we propose to treat the hard prompt and soft prompt as separate inputs to mitigate noise brought by the prompt initialization. Furthermore, we optimize soft prompts with contrastive learning for utilizing class-aware information in the training process to maintain model performance. Experimental results demonstrate that \sysname outperforms state-of-the-art methods by 7.20% in accuracy and reduces the standard deviation by 2.02 on average. Furthermore, extensive experiments underscore its robustness and stability across 7 datasets covering various tasks., Comment: Submitted to ACL 2024

The burgeoning generative capabilities of large language models (LLMs) have raised growing concerns about abuse, demanding automatic machine-generated text detectors. DetectGPT, a zero-shot metric-based detector, first introduces perturbation and shows great performance improvement. However, in DetectGPT, the random perturbation strategy could introduce noise, and logit regression depends on the threshold, harming the generalizability and applicability of individual or small-batch inputs. Hence, we propose a novel fine-tuned detector, Pecola, bridging metric-based and fine-tuned methods by contrastive learning on selective perturbation. Selective strategy retains important tokens during perturbation and weights for multi-pair contrastive learning. The experiments show that Pecola outperforms the state-of-the-art (SOTA) by 1.20% in accuracy on average on four public datasets. And we further analyze the effectiveness, robustness, and generalization of the method.

In the realm of Computational Fluid Dynamics (CFD), the demand for memory and computation resources is extreme, necessitating the use of leadership-scale computing platforms for practical domain sizes. This intensive requirement renders traditional checkpointing methods ineffective due to the significant slowdown in simulations while saving state data to disk. As we progress towards exascale and GPU-driven High-Performance Computing (HPC) and confront larger problem sizes, the choice becomes increasingly stark: to compromise data fidelity or to reduce resolution. To navigate this challenge, this study advocates for the use of in situ analysis and visualization techniques. These allow more frequent data "snapshots" to be taken directly from memory, thus avoiding the need for disruptive checkpointing. We detail our approach of instrumenting NekRS, a GPU-focused thermal-fluid simulation code employing the spectral element method (SEM), and describe varied in situ and in transit strategies for data rendering. Additionally, we provide concrete scientific use-cases and report on runs performed on Polaris, Argonne Leadership Computing Facility's (ALCF) 44 Petaflop supercomputer and J\"ulich Wizard for European Leadership Science (JUWELS) Booster, J\"ulich Supercomputing Centre's (JSC) 71 Petaflop High Performance Computing (HPC) system, offering practical insight into the implications of our methodology.

Unconventional superconductivity on a honeycomb lattice has received increasing interest since the discovery of graphene primarily due to the similarities between materials with a honeycomb lattice and cuprate superconductors. Many theoretical studies have been conducted on superconductivity on a honeycomb lattice, however, a consistent picture is still lacking. In this article we have extended the theory of kinetic-energy-driven superconductivity, which has been developed to investigate unconventional superconductivity in cuprate superconductors, to explore superconductivity on a honeycomb lattice within the $t$-$J$ model. Our results demonstrate that the charge-carrier pair gap parameter with $d_{x^{2}-y^{2}}+{\rm i}d_{xy}$-wave symmetry exhibits a dome-like shape as a function of doping, with superconductivity emerging at a certain doping concentration and disappearing at high doping levels, similar to what has been observed in cuprate and cobaltate superconductors. Furthermore, the charge-carrier pair gap parameter decreases with increasing the value of $J/t$ (the antiferromagnetic exchange coupling constant relative to the nearest-neighbor hopping integral), and approaches zero when $J/t$ reaches a sufficiently large value. This indicates that the antiferromagnetic order will suppress the superconducting state and a sufficiently strong exchange coupling will completely destroy the superconductivity. Taking into account our present results together with the corresponding results of cuprate and cobaltate superconductors, it appears that the dome-like shape of the doping dependence of the charge-carrier pair gap parameter may be a common feature in doped Mott insulators., Comment: 8 pages, 2 figures

We demonstrate NekRS performance results on various advanced GPU architectures. NekRS is a GPU-accelerated version of Nek5000 that targets high performance on exascale platforms. It is being developed in DOE's Center of Efficient Exascale Discretizations, which is one of the co-design centers under the Exascale Computing Project. In this paper, we consider Frontier, Crusher, Spock, Polaris, Perlmutter, ThetaGPU, and Summit. Simulations are performed with 17x17 rod-bundle geometries from small modular reactor applications. We discuss strong-scaling performance and analysis., Comment: 24 pages, 13 figures, 2 tables

Prompt-based pre-trained language models (PLMs) paradigm have succeeded substantially in few-shot natural language processing (NLP) tasks. However, prior discrete prompt optimization methods require expert knowledge to design the base prompt set and identify high-quality prompts, which is costly, inefficient, and subjective. Meanwhile, existing continuous prompt optimization methods improve the performance by learning the ideal prompts through the gradient information of PLMs, whose high computational cost, and low readability and generalizability are often concerning. To address the research gap, we propose a Dialogue-comprised Policy-gradient-based Discrete Prompt Optimization ($DP_2O$) method. We first design a multi-round dialogue alignment strategy for readability prompt set generation based on GPT-4. Furthermore, we propose an efficient prompt screening metric to identify high-quality prompts with linear complexity. Finally, we construct a reinforcement learning (RL) framework based on policy gradients to match the prompts to inputs optimally. By training a policy network with only 0.67% of the PLM parameter size on the tasks in the few-shot setting, $DP_2O$ outperforms the state-of-the-art (SOTA) method by 1.52% in accuracy on average on four open-source datasets. Moreover, subsequent experiments also demonstrate that $DP_2O$ has good universality, robustness, and generalization ability., Comment: AAAI 2024 Main Track

Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery. Thanks to the recent advances in both neuroscience and artificial intelligence, we have been able to record the visually evoked brain activities and mimic the visual perception ability through computational approaches. In this paper, we pay attention to visual stimuli reconstruction by reconstructing the observed images based on portably accessible brain signals, i.e., electroencephalography (EEG) data. Since EEG signals are dynamic in the time-series format and are notorious to be noisy, processing and extracting useful information requires more dedicated efforts; In this paper, we propose a comprehensive pipeline, named NeuroImagen, for reconstructing visual stimuli images from EEG signals. Specifically, we incorporate a novel multi-level perceptual information decoding to draw multi-grained outputs from the given EEG data. A latent diffusion model will then leverage the extracted information to reconstruct the high-resolution visual stimuli images. The experimental results have illustrated the effectiveness of image reconstruction and superior quantitative performance of our proposed method., Comment: A preprint version of an ongoing work

The multistage robust unit commitment (UC) is of paramount importance for achieving reliable operations considering the uncertainty of renewable realizations. The typical affine decision rule method and the robust feasible region method may achieve uneconomic dispatches as the dispatch decisions just rely on the current-stage information. Through approximating the future cost-to-go functions, the dual dynamic programming based methods have been shown adaptive to the multistage robust optimization problems, while suffering from high computational complexity. Thus, we propose the robust approximate dynamic programming (RADP) method to promote the computational speed and the economic performance for large-scale robust UC problems. RADP initializes the candidate points for guaranteeing the feasibility of upper bounding the value functions, solves the linear McCormick relaxation based bilinear programming to obtain the worst cases, and combines the primal and dual updates for this hybrid binary and continuous decision-making problem to achieve fast convergence. We can verify that the RADP method enjoys a finite termination guarantee for the multistage robust optimization problems with achieving suboptimal solutions. Numerical tests on 118-bus and 2383-bus transmission systems have demonstrated that RADP can approach the suboptimal economic performance at significantly improved computational efficiency., Comment: 9 pages, 3 figures, 4 tables

Machine-Generated Text (MGT) detection, a task that discriminates MGT from Human-Written Text (HWT), plays a crucial role in preventing misuse of text generative models, which excel in mimicking human writing style recently. Latest proposed detectors usually take coarse text sequences as input and fine-tune pretrained models with standard cross-entropy loss. However, these methods fail to consider the linguistic structure of texts. Moreover, they lack the ability to handle the low-resource problem which could often happen in practice considering the enormous amount of textual data online. In this paper, we present a coherence-based contrastive learning model named CoCo to detect the possible MGT under low-resource scenario. To exploit the linguistic feature, we encode coherence information in form of graph into text representation. To tackle the challenges of low data resource, we employ a contrastive learning framework and propose an improved contrastive loss for preventing performance degradation brought by simple samples. The experiment results on two public datasets and two self-constructed datasets prove our approach outperforms the state-of-art methods significantly. Also, we surprisingly find that MGTs originated from up-to-date language models could be easier to detect than these from previous models, in our experiments. And we propose some preliminary explanations for this counter-intuitive phenomena. All the codes and datasets are open-sourced., Comment: Accepted by EMNLP 2023 main cofference

Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations for the collection of test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future.

Nek5000/RS is a highly-performant open-source spectral element code for simulation of incompressible and low-Mach fluid flow, heat transfer, and combustion with a particular focus on turbulent flows in complex domains. It is based on high-order discretizations that realize the same (or lower) cost per gridpoint as traditional low-order methods. State-of-the-art multilevel preconditioners, efficient high-order time-splitting methods, and runtime-adaptive communication strategies are built on a fast OCCA-based kernel library, libParanumal, to provide scalability and portability across the spectrum of current and future high-performance computing platforms. On Summit, Nek5000/RS has recently achieved an milestone in the simulation of nuclear reactors: the first full-core computational fluid dynamics simulations of reactor cores, including pebble beds with > 350,000 pebbles and 98M elements advanced in less than 0.25 seconds per Navier-Stokes timestep. With carefully tuned algorithms, it is possible to simulate a single flow-through time for a full reactor core in less than six hours on all of Summit., Comment: 9 pages, 3 figures, 6 tables

In this paper we describe the research and development activities in the Center for Efficient Exascale Discretization within the US Exascale Computing Project, targeting state-of-the-art high-order finite-element algorithms for high-order applications on GPU-accelerated platforms. We discuss the GPU developments in several components of the CEED software stack, including the libCEED, MAGMA, MFEM, libParanumal, and Nek projects. We report performance and capability improvements in several CEED-enabled applications on both NVIDIA and AMD GPU systems.

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially-assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications., Comment: 22 pages, 18 figures

We develop an all-hex meshing strategy for the interstitial space in beds of densely packed spheres that is tailored to turbulent flow simulations based on the spectral element method (SEM). The SEM achieves resolution through elevated polynomial order N and requires two to three orders of magnitude fewer elements than standard finite element approaches do. These reduced element counts place stringent requirements on mesh quality and conformity. Our meshing algorithm is based on a Voronoi decomposition of the sphere centers. Facets of the Voronoi cells are tessellated into quads that are swept to the sphere surface to generate a high-quality base mesh. Refinements to the algorithm include edge collapse to remove slivers, node insertion to balance resolution, localized refinement in the radial direction about each sphere, and mesh optimization. We demonstrate geometries with 10^2-10^5 spheres using approximately 300 elements per sphere (for three radial layers), along with mesh quality metrics, timings, flow simulations, and solver performance., Comment: 13 pages, 10 figures

The development of NekRS, a GPU-oriented thermal-fluids simulation code based on the spectral element method (SEM) is described. For performance portability, the code is based on the open concurrent compute abstraction and leverages scalable developments in the SEM code Nek5000 and in libParanumal, which is a library of high-performance kernels for high-order discretizations and PDE-based miniapps. Critical performance sections of the Navier-Stokes time advancement are addressed. Performance results on several platforms are presented, including scaling to 27,648 V100s on OLCF Summit, for calculations of up to 60B gridpoints., Comment: 14 pages, 8 figures

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 are 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., Comment: 9 pages, 6 figures, added references and discussions; accepted for publication in Physical Review B. arXiv admin note: text overlap with arXiv:2001.01054

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 bonding 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., Comment: 26 pages, 16 figures, added discussions and updated the references

Fast power system state estimation (SE) solution is of paramount importance for achieving real-time decision making in power grid operations. Semidefinite programming (SDP) reformulation has been shown effective to obtain the global optimum for the nonlinear SE problem, while suffering from high computational complexity. Thus, we leverage the recent advances in nonconvex SDP approach that allows for the simple first-order gradient-descent (GD) updates. Using the power system model, we can verify that the SE objective function enjoys nice properties (strongly convex, smoothness) which in turn guarantee a linear convergence rate of the proposed GD-based SE method. To further accelerate the convergence speed, we consider the accelerated gradient descent (AGD) extension, as well as their robust versions under outlier data and a hybrid GD-based SE approach with additional synchrophasor measurements. Numerical tests on the IEEE 118-bus, 300-bus and the synthetic ACTIVSg2000-bus systems have demonstrated that FGD-SE and AGD-SE, can approach the near-optimal performance of the SDP-SE solution at significantly improved computational efficiency, especially so for AGD-SE.

Within the framework of kinetic-energy-driven superconductivity, the asymmetric doping dependence of superconductivity between the hole- and electron-doped triangular-lattice superconductors has been studied. It is shown that although the superconducting transition temperature has a dome-shaped doping dependence for both the hole- and electron-doped triangular-lattice superconductors, superconductivity appears over a wide doping of range in the hole-doped case, while it only exists in a narrow range of the doping in the electron-doped side. Moreover, the maximum superconducting transition temperature around the optimal doping in the electron-doped triangular-lattice superconductors is lower than that of the hole-doped counterparts. The theory also shows that the asymmetric doping dependence of superconductivity between the hole- and electron-doped cases may be a common feature for a doped Mott insulator., Comment: 6 pages, 2 figures; accepted for publication in Mod. Phys. Lett. B

Superconductivity in copper oxides emerges on doping holes or electrons into their Mott insulating parent compounds. The spin excitations are thought to be the mediating glue for the pairing in superconductivity. Here the momentum and doping dependence of the dynamical spin response in the electron-doped cuprate superconductors is studied based on the kinetic-energy-driven superconducting mechanism. It is shown that the dispersion of the low-energy spin excitations changes strongly upon electron doping, however, the hour-glass-shaped dispersion of the low-energy spin excitations appeared in the hole-doped side is absent in the electron-doped case due to the electron-hole asymmetry. In particular, the commensurate resonance appears in the superconducting-state with the resonance energy that correlates with the dome-shaped doping dependence of the superconducting gap. Moreover, the spectral weight and dispersion of the high-energy spin excitations in the superconducting-state are comparable with those in the corresponding normal-state, indicating that the high-energy spin excitations do not play an important part in the pair formation., Comment: 9 pages, 6 figures, added discussions, accepted for publication in Journal of Low Temperature Physics. arXiv admin note: text overlap with arXiv:1405.7448

The notion of the electron Fermi surface is one of the characteristic concepts in the field of condensed matter physics, and it plays a crucial role in the understanding of the physical properties of doped Mott insulators. Based on the t-J model, we study the nature of the electron Fermi surface in the cobaltates, and qualitatively reproduce the essential feature of the evolution of the electron Fermi surface with doping. It is shown that the underlying hexagonal electron Fermi surface obeys Luttinger's theorem. The theory also predicts a Fermi-arc phenomenon at the low-doped regime, where the region of the hexagonal electron Fermi surface along the \Gamma-K direction is suppressed by the electron self-energy, and then six disconnected Fermi arcs located at the region of the hexagonal electron Fermi surface along the \Gamma-M direction emerge. However, this Fermi-arc phenomenon at the low-doped regime weakens with the increase of doping., Comment: 8 pages, 4 figures, added references and discussions, accepted for publication in J. Phys. Condens. Matter. arXiv admin note: text overlap with arXiv:1510.05384

Within the framework of the fermion-spin theory, the charge transport in the doped Mott insulators on a honeycomb lattice is studied by taking into account the pseudogap effect. It is shown that the conductivity spectrum in the low-doped regime is separated by the pseudogap into a low-energy non-Drude peak followed by a broad midinfrared band. However, the decrease of the pseudogap with the increase of doping leads to a shift of the position of the midinfrared band towards to the low-energy non-Drude peak, and then the low-energy Drude behavior recovers in the high-doped regime. The combined results of both the doped honeycomb-lattice and square-lattice Mott insulators indicate that the two-component conductivity induced by the pseudogap is a universal feature in the doped Mott insulators., Comment: 8 pages, 3 figures

The study of superconductivity arising from doping a Mott insulator has become a central issue in the area of superconductivity. Within the framework of the kinetic-energy-driven superconducting mechanism, we discuss the thermodynamic properties in triangular-lattice superconductors. It is shown that a sharp peak in the specific-heat appears at the superconducting transition temperature Tc, and then the specific-heat varies exponentially as a function of temperature for the temperatures T

Superconductivity in cuprate superconductors occurs upon charge-carrier doping Mott insulators, where a central question is what mechanism causes the loss of electrical resistance below the superconducting (SC) transition temperature? In this review, we attempt to summarize the basic idea of the kinetic-energy driven SC mechanism in the description of superconductivity in cuprate superconductors. The mechanism of the kinetic-energy driven superconductivity is purely electronic without phonons, where the charge-carrier pairing interaction arises directly from the kinetic energy by the exchange of spin excitations in the higher powers of the doping concentration. This kinetic-energy driven d-wave SC-state is controlled by both the SC gap and quasiparticle coherence, which leads to that the maximal SC transition temperature occurs around the optimal doping, and then decreases in both the underdoped and overdoped regimes. In particular, the same charge-carrier interaction mediated by spin excitations that induces the SC-state in the particle-particle channel also generates the normal-state pseudogap state in the particle-hole channel. The normal-state pseudogap crossover temperature is much larger than the SC transition temperature in the underdoped and optimally doped regimes, and then monotonically decreases upon the increase of doping, eventually disappearing together with superconductivity at the end of the SC dome. This kinetic-energy driven SC mechanism also indicates that the strong electron correlation favors superconductivity, since the main ingredient is identified into a charge-carrier pairing mechanism not from the external degree of freedom such as the phonon but rather solely from the internal spin degree of freedom of the electron. The typical properties of cuprate superconductors discussed within the framework of the kinetic-energy driven SC mechanism are also reviewed., Comment: 81 pages, 30 figures, Review article, Updated references

Within the framework of the kinetic energy driven superconducting mechanism, the dynamical spin response of cuprate superconductors is studied from low-energy to high-energy. The spin self-energy is evaluated explicitly in terms of the collective charge carrier modes in the particle-hole and particle-particle channels, and employed to calculate the dynamical spin structure factor. Our results show the existence of damped but well-defined dispersive spin excitations in the whole doping phase diagram. In particular, the low-energy spin excitations in the superconducting-state have an hour-glass-shaped dispersion, with commensurate resonance that appears in the superconducting-state only, while the low-energy incommensurate spin fluctuations can persist into the normal-state. The high-energy spin excitations in the superconducting-state on the other hand retain roughly constant energy as a function of doping, with spectral weights and dispersion relations comparable to those in the corresponding normal-state. The theory also shows that the unusual magnetic correlations in cuprate superconductors can be ascribed purely to the spin self-energy effects which arise directly from the charge carrier-spin interaction in the kinetic energy of the system., Comment: 14 pages, 9 figures, typos corrected and added discussions, accepted for publication

The interplay between the superconducting gap and normal-state pseudogap in the bilayer cuprate superconductors is studied based on the kinetic energy driven superconducting mechanism. It is shown that the charge carrier interaction directly from the interlayer coherent hopping in the kinetic energy by exchanging spin excitations does not provide the contribution to the normal-state pseudogap in the particle-hole channel and superconducting gap in the particle-particle channel, while only the charge carrier interaction directly from the intralayer hopping in the kinetic energy by exchanging spin excitations induces the normal-state pseudogap in the particle-hole channel and superconducting gap in the particle-particle channel, and then the two-gap behavior is a universal feature for the single layer and bilayer cuprate superconductors., Comment: 7 pages, 2 figures

Within the two-leg $t$-J ladder, the spin dynamics of the pressure-induced two-leg ladder cuprate superconductor Sr$_{14-x}$Ca$_{x}$Cu$_{24}$O$_{41}$ is studied based on the kinetic energy driven superconducting mechanism. It is shown that in the pressure-induced superconducting state, the incommensurate spin correlation appears in the underpressure regime, while the commensurate spin fluctuation emerges in the optimal pressure and overpressure regimes. In particular, the spin-lattice relaxation time is dominated by a temperature linear dependence term at low temperature followed by a peak developed below the superconducting transition temperature, in qualitative agreement with the experimental observation on Sr$_{14-x}$Ca$_{x}$Cu$_{24}$O$_{41}$., Comment: 8 pages, 5 figures, accepted for publication in Journal of Physics: Condensed Matter

In this paper, we review the low energy electronic structure of the kinetic energy driven d-wave cuprate superconductors. We give a general description of the charge-spin separation fermion-spin theory, where the constrained electron is decoupled as the gauge invariant dressed holon and spin. In particular, we show that under the decoupling scheme, the charge-spin separation fermion-spin representation is a natural representation of the constrained electron defined in a restricted Hilbert space without double electron occupancy. Based on the charge-spin separation fermion-spin theory, we have developed the kinetic energy driven superconducting mechanism, where the superconducting state is controlled by both superconducting gap parameter and quasiparticle coherence. Within this kinetic energy driven superconductivity, we have discussed the low energy electronic structure of the single layer and bilayer cuprate superconductors in both superconducting and normal states, and qualitatively reproduced all main features of the angle-resolved photoemission spectroscopy measurements on the single layer and bilayer cuprate superconductors. We show that the superconducting state in cuprate superconductors is the conventional Bardeen-Cooper-Schrieffer like with the d-wave symmetry, so that the basic Bardeen-Cooper-Schrieffer formalism with the d-wave gap function is still valid in discussions of the low energy electronic structure of cuprate superconductors, although the pairing mechanism is driven by the kinetic energy by exchanging spin excitations. We also show that the well pronounced peak-dip-hump structure of the bilayer cuprate superconductors in the superconducting state and double-peak structure in the normal state are mainly caused by the bilayer splitting., Comment: 57 pages, 15 figures, typos corrected

Within the framework of the kinetic energy driven superconductivity, the electronic structure of bilayer cuprate superconductors in the superconducting state is studied. It is shown that the electron spectrum of bilayer cuprate superconductors is split into the bonding and antibonding components by the bilayer splitting, then the observed peak-dip-hump structure around the $[\pi,0]$ point is mainly caused by this bilayer splitting, with the superconducting peak being related to the antibonding component, and the hump being formed by the bonding component. The spectral weight increases with increasing the doping concentration. In analogy to the normal state case, both electron antibonding peak and bonding hump have the weak dispersions around the $[\pi,0]$ point., Comment: 11 pages, 4 figures, replotted figures and added references, accepted for publication in Phys. Rev. B

Within the t-t'-J model, the electron spectrum and quasiparticle dispersion in doped bilayer cuprates in the normal state are discussed by considering the bilayer interaction. It is shown that the bilayer interaction splits the electron spectrum of doped bilayer cuprates into the bonding and antibonding components around the $(\pi,0)$ point. The differentiation between the bonding and antibonding components is essential, which leads to two main flat bands around the $(\pi,0)$ point below the Fermi energy. In analogy to the doped single layer cuprates, the lowest energy states in doped bilayer cuprates are located at the $(\pi/2,\pi/2)$ point. Our results also show that the striking behavior of the electronic structure in doped bilayer cuprates is intriguingly related to the bilayer interaction together with strong coupling between the electron quasiparticles and collective magnetic excitations., Comment: 9 pages, 4 figures, updated references, added figures and discussions, accepted for publication in Phys. Rev. B