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The properties of fractional Chern insulator (FCI) phase and the phase transitions between FCI and Mott insulators (MI) in bosonic systems are well studied. The continuous transitions between FCI and superfluid (SF), however, despite the inspiring field theoretical predictions, have not been directly verified. The existing numerical results of FCI-SF transition are either indirect or clearly first-order. Here, by simply tuning the bandwidth of Haldane honeycomb lattice model, we find direct transitions from a bosonic FCI at $\nu$ = 1/2 filling of a flat Chern band to two SF states with bosons condensed at momenta $M$ and $\Gamma$, respectively. While the FCI-SF($M$) transition is first-order, the FCI-SF($\Gamma$) transition is found continuous. Through finite size criticality analysis, the obtained critical exponents $\beta\approx$0.35(5) and $\nu\approx$0.62(12) are both compatible with those of the 3D XY universality class and more exotic beyond-Landau ones. Our work thence presents a direct numerical evidence of a continuous FCI-SF transition between topological ordered phase and spontaneous continuous symmetry-breaking phase, and further indicates the zero-field bosonic FCI might be realized from a SF state by gradually flattening the dispersion of Chern band, through the (quasi)adiabatic preparation in ultracold atom systems.

The recent developments in twisted MoTe$_2$ and rhombohedral multilayer graphene have generated widespread attention to the general features of fractional quantum anomalous Hall (FQAH) states, including their possible coexistence with and transition to various symmetry breaking charge ordered states. These attentions are pushing forward our knowledge of the relation between the topological order in FQAH states and the Landau-type of symmetry breaking order such as the 1D smectic electronic liquid crystal and 2D charge-density-wave (CDW) solid. Although the transitions from topological states to symmetry breaking states with trivial topology have been discussed, the road from one topological ordered state to another with the same Hall conductance and broken translational symmetry has not been found. Here we show the intriguing evidence that the FQAH to FQAH Smectic (FQAHS) transition is robustly realizable in the archetypal correlated flat Chern-band model at filling $\nu$ = 2/3. This transition is novel in that: i) the FQAHS acquires the same fractional Hall conductance as FQAH, which cannot be explained by mean-field band folding. The formation of smectic order can be viewed as perturbation around the transition point, and thus, do not destroy or change the original topology; ii) the charge excitation remains gapped across the transition although the neutral gap is closed at transition point; and iii) the transition is triggered by the softening of roton mode with the same wave vector as the smectic order. Our discovery opens countless new possibilities, both theoretical and experimental, in the fast-growing field of robust fractional Chern insulators.

The interplay of topological order and charge order exhibits rich physics. Recent experiments that succesfully realized the frational quantum anomalous Hall (FQAH) effect in twisted MoTe$_2$ bilayers and rhombohedral multilayer graphene without external magnetic field further call for deeper understanding of the relation between topological order and charge order in quantum moir\'e materials. In the archetypal correlated flat-band model on checkerboard lattice, a FQAH smectic state with coexistent topological order and smectic charge order has been numerically discovered at filling $\nu$ = 2/3. In this work, we explore the global ground-state phase diagram of the model with competing interactions and find a C = 2/3 FQAH phase surrounded by four different charge density wave (CDW) phases. In particular, we identify a FQAH-CDW transition triggered by roton condensation, in that, the minimal roton gap continues to decrease at the same finite momentum, along with the diverging density flucuations at the transition point, after which the system enters into a CDW metal phase with the same ordered wavevector. Our discovery points out that the charge-neutral roton modes can play a significant role in a transition from FQAH topological order to CDW symmetry-breaking order, discussed in FQH literature while severely neglected in FQAH systems.

This paper introduces a conversion matrix method for linear periodically time-variant (LPTV) digital phase-locked loop (DPLL) phase noise modeling that offers precise and computationally efficient results to enable rapid design iteration and optimization. Unlike many previous studies, which either assume linear time-invariance (LTI) and therefore overlook phase noise aliasing effects, or solve LPTV systems with noise folding and multiple sampling rate conversions that heightens modeling and computational complexity, the proposed conversion matrix method allows the designer to represent LPTV systems using intuitive LTI-like transfer functions with excellent accuracy. Additionally, the uncorrelated upsampling method addresses the cross-correlated spectrum of cyclostationary noise sources by a simple matrix multiplication. This eliminates the need to consider the beat frequency of the upsampled noise source and the system with different sampling rates, thus improving computational efficiency. The proposed algorithm is applied to modeling a integer-N DPLL with time-varying proportional loop gain, and the modeling accuracy is validated with Simulink transient simulations., Comment: 13 pages, 24 figures

Symmetry-breaking orders can not only compete with each other, but also be interwined, and the interwined topological and symmetry-breaking orders make the situation more intriguing. This work examines the archetypal correlated flat band model on a checkerboard lattice at filling $\nu=2/3$ and we find the unique interplay between smectic charge order and topological order gives rise to two novel quantum states. As the interaction strength increases, the system first transitions from a Fermi liquid into FQAH smectic (FQAHS) state, where FQAH topological order coexists cooperatively with smectic charge order with enlarged ground-state degeneracy and interestingly, the Hall conductivity is $\sigma_{xy}=\nu=2/3$, different from the band-folding or doping scenarios. Further increasing the interaction strength, the system undergoes another quantum phase transition and evolves into a polar smectic metal (PSM) state. This emergent PSM is an anisotropic non-Fermi liquid, whose interstripe tunneling is irrelevant while it is metallic inside each stripe. Different from the FQAHS and conventional smectic orders, this PSM spontaneously breaks the two-fold rotational symmetry, resulting in a nonzero electric dipole moment and ferroelectric order. In addition to the exotic ground states, large-scale numerical simulations are also used to study low-energy excitations and thermodynamic characteristics. We find the onset temperature of the incompressible FQAHS state, which also coincides with the onset of non-polar smectic order, is dictated by the magneto-roton modes. Above this onset temperature, the PSM state exists at intermediate-temperature regime. Although the T = 0 quantum phase transition between PSM and FQAHS is first order, the thermal FQAHS-PSM transition could be continuous. We expect the features of the exotic states and thermal phase transitions could be accessed in future experiments.

Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms. While continuous improvements have been made in domain-specific LSM, limited work has been done in cross-domain LSM, which considers modeling of lifelong sequences of both target domain and source domain. In this paper, we propose Lifelong Cross Network (LCN) to incorporate cross-domain LSM to improve the click-through rate (CTR) prediction in the target domain. The proposed LCN contains a LifeLong Attention Pyramid (LAP) module that comprises of three levels of cascaded attentions to effectively extract interest representations with respect to the candidate item from lifelong sequences. We also propose Cross Representation Production (CRP) module to enforce additional supervision on the learning and alignment of cross-domain representations so that they can be better reused on learning of the CTR prediction in the target domain. We conducted extensive experiments on WeChat Channels industrial dataset as well as on benchmark dataset. Results have revealed that the proposed LCN outperforms existing work in terms of both prediction accuracy and online performance., Comment: Accepted by KDD 2024

Integer and fractional Chern insulators have been extensively explored in correlated flat band models. Recently, the prediction and experimental observation of fractional quantum anomalous Hall (FQAH) states with spontaneous time-reversal-symmetry breaking have garnered attention. While the thermodynamics of integer quantum anomalous Hall (IQAH) states have been systematically studied, our theoretical knowledge on thermodynamic properties of FQAH states has been severely limited. Here, we delve into the general thermodynamic response and collective excitations of both IQAH and FQAH states within the paradigmatic flat Chern-band model with remote band considered. Our key findings include: i) In both $\nu$ = 1 IQAH and $\nu$ = 1/3 FQAH states, even without spin fluctuations, the charge-neutral collective excitations would lower the onset temperature of these topological states, to a value significantly smaller than the charge gap, due to band-mixing and multi-particle scattering; ii) By employing large-scale thermodynamic simulations in FQAH states in the presence of strong inter-band mixing between C = $\pm1$ bands, we find that the lowest collective excitations manifest as the zero-momentum excitons in the IQAH state, whereas in the FQAH state, they take the form of magneto-rotons with finite momentum; iii) The unique charge oscillations in FQAH states are exhibited with distinct experimental signatures, which we propose to detect in future experiments.

Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power-law relationship with model size or data size, which provides important guidance for the development of large-scale models. However, there is still limited understanding on the scaling effect of user behavior models in recommender systems, where the unique data characteristics (e.g. data scarcity and sparsity) pose new challenges to explore the scaling effect in recommendation tasks. In this work, we focus on investigating the scaling laws in large sequential recommendation models. Specially, we consider a pure ID-based task formulation, where the interaction history of a user is formatted as a chronological sequence of item IDs. We don't incorporate any side information (e.g. item text), because we would like to explore how scaling law holds from the perspective of user behavior. With specially improved strategies, we scale up the model size to 0.8B parameters, making it feasible to explore the scaling effect in a diverse range of model sizes. As the major findings, we empirically show that scaling law still holds for these trained models, even in data-constrained scenarios. We then fit the curve for scaling law, and successfully predict the test loss of the two largest tested model scales. Furthermore, we examine the performance advantage of scaling effect on five challenging recommendation tasks, considering the unique issues (e.g. cold start, robustness, long-term preference) in recommender systems. We find that scaling up the model size can greatly boost the performance on these challenging tasks, which again verifies the benefits of large recommendation models.

Recently, large language models (LLMs) have shown great potential in recommender systems, either improving existing recommendation models or serving as the backbone. However, there exists a large semantic gap between LLMs and recommender systems, since items to be recommended are often indexed by discrete identifiers (item ID) out of the LLM's vocabulary. In essence, LLMs capture language semantics while recommender systems imply collaborative semantics, making it difficult to sufficiently leverage the model capacity of LLMs for recommendation. To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems. Our approach can directly generate items from the entire item set for recommendation, without relying on candidate items. Specifically, we make two major contributions in our approach. For item indexing, we design a learning-based vector quantization method with uniform semantic mapping, which can assign meaningful and non-conflicting IDs (called item indices) for items. For alignment tuning, we propose a series of specially designed tuning tasks to enhance the integration of collaborative semantics in LLMs. Our fine-tuning tasks enforce LLMs to deeply integrate language and collaborative semantics (characterized by the learned item indices), so as to achieve an effective adaptation to recommender systems. Extensive experiments demonstrate the effectiveness of our method, showing that our approach can outperform a number of competitive baselines including traditional recommenders and existing LLM-based recommenders. Our code is available at https://github.com/RUCAIBox/LC-Rec/., Comment: Accepted by ICDE 2024

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to the training and utilizing of recommendation models. However, most existing studies approach the CTR prediction as a classification task on static datasets, assuming that the train and test sets are independent and identically distributed (a.k.a, i.i.d. assumption). To bridge this gap, we formulate the CTR prediction problem in streaming scenarios as a Streaming CTR Prediction task. Accordingly, we propose dedicated benchmark settings and metrics to evaluate and analyze the performance of the models in streaming data. To better understand the differences compared to traditional CTR prediction tasks, we delve into the factors that may affect the model performance, such as parameter scale, normalization, regularization, etc. The results reveal the existence of the ''streaming learning dilemma'', whereby the same factor may have different effects on model performance in the static and streaming scenarios. Based on the findings, we propose two simple but inspiring methods (i.e., tuning key parameters and exemplar replay) that significantly improve the effectiveness of the CTR models in the new streaming scenario. We hope our work will inspire further research on streaming CTR prediction and help improve the robustness and adaptability of recommender systems.

Quadratic band touching (QBT) points are widely observed in 2D and 3D materials, including bilayer graphene and Luttinger semimetals, and attract significant attention from theory to experiment. However, even in its simplest form, the 2D checkerboard lattice QBT model, the phase diagram characterized by temperature and interaction strength still remains unknown beyond the weak-coupling regime. Intense debates persist regarding the existence of various interaction-driven insulating states in this system. To address these uncertainties, we employ thermal tensor network simulations, specifically exponential tensor renormalization group and tangent space tensor renormalization group, along with density matrix renormalization group calculations to provide a comprehensive finite-temperature phase diagram for this model and shed light on previous ambiguities. Notably, our findings reveal the emergence of a robust bond-nematic Dirac semimetal phase with distinct thermodynamic properties that set it part from the nematic insulating state and other symmetry broken states. This previously overlooked feature is found to be ubiquitous in interacting QBT systems. We also discuss the implications of these results for experimental systems such as bilayer graphene and iridate compounds.

Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks. Along this line of research, this work aims to investigate the capacity of LLMs that act as the ranking model for recommender systems. We first formalize the recommendation problem as a conditional ranking task, considering sequential interaction histories as conditions and the items retrieved by other candidate generation models as candidates. To solve the ranking task by LLMs, we carefully design the prompting template and conduct extensive experiments on two widely-used datasets. We show that LLMs have promising zero-shot ranking abilities but (1) struggle to perceive the order of historical interactions, and (2) can be biased by popularity or item positions in the prompts. We demonstrate that these issues can be alleviated using specially designed prompting and bootstrapping strategies. Equipped with these insights, zero-shot LLMs can even challenge conventional recommendation models when ranking candidates are retrieved by multiple candidate generators. The code and processed datasets are available at https://github.com/RUCAIBox/LLMRank., Comment: Accepted by ECIR 2024

While the ground state phase diagram of the correlated flat-band systems have been intensively investigated, the dynamic and thermodynamic properties of such lattice models are less explored, but it is the latter which is most relevant to the experimental probes (transport, quantum capacitance and spectroscopy) of the quantum moir\'e materials such as twisted bilayer graphene and transition metal dichalcogenides. Here we show, by means of momentum-space quantum Monte Carlo and exact diagonalization, there exists a unique thermodynamic characteristic for the correlated flat-band models with interaction-driven quantum anomalous Hall (QAH) ground state, namely, the transition from the QAH insulator to the metallic state takes place at a much lower temperature compared with the zero-temperature single-particle gap generated by the long-range Coulomb interaction. Such low transition temperature comes from the proliferation of excitonic particle-hole excitations, which "quantum teleport" the electrons across the gap between different topological bands to restore the broken time-reversal symmetry and give rise to a pronounced enhancement in the charge compressibility. Future experiments, to verify such generic thermodynamic characteristics, are proposed., Comment: 5 pages, 3 figures with supplemental material

The interaction-driven quantum anomalous Hall (QAH) insulator has been sought for a long time in a Dirac semimetal with linear band touching points at the Fermi level. By combining exact diagonalization, density matrix renormalization group, and analytical methods, we study a spinless fermion system on the checkerboard lattice with two fold rotational symmetry, which realizes two Dirac band touching points in the absence of interaction. At weak coupling, the Dirac semimetal is stable. At a finite density-density repulsive interaction, we analyze possible symmetry broken states, and find that an QAH state is stabilized when the interaction strength exceeds the energy scale controlling the separation between the Dirac points. Through numerical simulations, we verify the existence of the QAH phase with spontaneous time-reversal symmetry breaking and quantized Chern number $C = 1$., Comment: 14 pages, 12 figures