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It has long been puzzling that fractional quantum Hall states in the first excited Landau level (1LL) often differ significantly from their counterparts in the lowest Landau level. We show that the dispersion of composite fermions (CFs) is a deterministic factor driving the distinction. We find that CFs with two quantized vortices in the 1LL have a non-quasiconvex dispersion. Consequently, in the filling fraction $7/3$, CFs occupy the second $\Lambda$-level instead of the first. The corresponding ground state wave function, based on the CF wave function ansatz, is identified to be the fermionic Haffnian wave function rather than the Laughlin wave function. The conclusion is supported by numerical evidence from exact diagonalizations in both disk and spherical geometries. Furthermore, we show that the dispersion becomes quasiconvex in wide quantum wells or for CFs with four quantized vortices in the filling fraction $11/5$, coinciding with observations that the distinction between the Landau levels disappears under these circumstances., Comment: 5 pages, 4 figures

The advancing maturity of photonic integrated circuit (PIC) fabrication technology enables the high integration of an increasing number of optical components onto a single chip. With the incremental circuit complexity, the calibration of active phase shifters in a large-scale PIC becomes a crucially important issue. The traditional one-by-one calibration techniques encounter significant hurdles with the propagation of calibration errors, and achieving the decoupling of all phase shifters for independent calibration is not straightforward. To address this issue, we propose a machine-learning approach for globally calibrating the large-scale PIC. Our method utilizes a custom network to simultaneously learn the nonlinear phase-current relations for all thermo-optic phase shifters on the PIC by minimizing the negative likelihood of the measurement datasets. Moreover, the reflectivities of all static beamsplitter components can also be synchronizedly extracted using this calibration method. As an example, a quantum walk PIC with a circuit depth of 12 is calibrated, and a programmable discrete-time quantum walk is experimentally demonstrated. These results will greatly benefit the applications of large-scale PICs in photonic quantum information processing., Comment: 9 pages, 5 figures, and comments are welcome

The intrinsic spin Hall effect (ISHE) proposed in [\href{https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.92.126603} {Sinova \textit{et al.} Phys. Rev. Lett. \textbf{92}, 126603 (2004)}] is driven by the spin Berry curvature. Herein, we establish the concept of \textit{spin quantum metric}, which is the counterpart of the spin Berry curvature in the \textit{spin quantum geometric tensor} defined in a similar way to the quantum geometric tensor. Dual to the $\mathcal{T}$-even ($\mathcal{T}$, time reversal) spin Berry curvature, the spin quantum metric features a $\mathcal{T}$-odd characteristic. Notably, we show that the $\mathcal{T}$-odd spin quantum metric can also drive an ISHE ($\mathcal{T}$-odd ISHE) under a high-frequency electric field. Guided by symmetry, we evaluate this $\mathcal{T}$-odd ISHE in the magnetically tilted surface Dirac cone and in ferromagnetic monolayer MnBi$_2$Te$_4$. We find that this $\mathcal{T}$-odd ISHE dominates when the Fermi level is close to the band (anti)crossing point, where its magnitude can be as large as the $\mathcal{T}$-even ISHE when an infrared driving field is applied. Our work not only uncovers an indispensable ingredient in the emergent community of quantum geometric physics but also offers a novel mechanism for ultrafast spintronics., Comment: Three figures

We study problems of federated control in Markov Decision Processes. To solve an MDP with large state space, multiple learning agents are introduced to collaboratively learn its optimal policy without communication of locally collected experience. In our settings, these agents have limited capabilities, which means they are restricted within different regions of the overall state space during the training process. In face of the difference among restricted regions, we firstly introduce concepts of leakage probabilities to understand how such heterogeneity affects the learning process, and then propose a novel communication protocol that we call Federated-Q protocol (FedQ), which periodically aggregates agents' knowledge of their restricted regions and accordingly modifies their learning problems for further training. In terms of theoretical analysis, we justify the correctness of FedQ as a communication protocol, then give a general result on sample complexity of derived algorithms FedQ-X with the RL oracle , and finally conduct a thorough study on the sample complexity of FedQ-SynQ. Specifically, FedQ-X has been shown to enjoy linear speedup in terms of sample complexity when workload is uniformly distributed among agents. Moreover, we carry out experiments in various environments to justify the efficiency of our methods.

We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraints while $N$ training agents are located in $N$ different environments with limited access to the constraint signals and they are expected to collaboratively learn a policy satisfying all constraint signals. Such learning problems are prevalent in scenarios of Large Language Model (LLM) fine-tuning and healthcare applications. To solve the problem, we propose federated primal-dual policy optimization methods based on traditional policy gradient methods. Specifically, we introduce $N$ local Lagrange functions for agents to perform local policy updates, and these agents are then scheduled to periodically communicate on their local policies. Taking natural policy gradient (NPG) and proximal policy optimization (PPO) as policy optimization methods, we mainly focus on two instances of our algorithms, ie, {FedNPG} and {FedPPO}. We show that FedNPG achieves global convergence with an $\tilde{O}(1/\sqrt{T})$ rate, and FedPPO efficiently solves complicated learning tasks with the use of deep neural networks.

In this study, surface-enhanced Raman scattering (SERS) spectra of different rare earth (RE) ion-citrate complexes were investigated for the first time for the qualitative classification of RE3+ ions. With the addition of RE3+ ions to citrate-capped silver nanoparticles in aqueous solutions, the Raman signals of RE-citrate complexes were enhanced, and characteristic peaks appeared near 1065 cm-1 and 1315 cm-1. The I1065/I1315 ratios of La-citrate and Gd-citrate were approximately 1 and 0.5, respectively. Thus, different RE3+ ions were classified based on the ratio of characteristic SERS peaks near 1065 cm-1 and 1315 cm-1. In addition, the effects of RE3+ ions in the RE-citrate complexes were analyzed based on density functional theory (DFT) calculations. Calculation results show that these characteristic peaks are attributed to the coordination of carboxyl and hydroxyl groups of citrates with the RE3+ ions, suggesting that these are spin-related bands of the RE-citrate complexes.

Creating visually pleasing stylized ink paintings from 3D models is a challenge in robotic manipulation. We propose a semi-automatic framework that can extract expressive strokes from 3D models and draw them in oriental ink painting styles by using a robotic arm. The framework consists of a simulation stage and a robotic drawing stage. In the simulation stage, geometrical contours were automatically extracted from a certain viewpoint and a neural network was employed to create simplified contours. Then, expressive digital strokes were generated after interactive editing according to user's aesthetic understanding. In the robotic drawing stage, an optimization method was presented for drawing smooth and physically consistent strokes to the digital strokes, and two oriental ink painting styles termed as Noutan (shade) and Kasure (scratchiness) were applied to the strokes by robotic control of a brush's translation, dipping and scraping. Unlike existing methods that concentrate on generating paintings from 2D images, our framework has the advantage of rendering stylized ink paintings from 3D models by using a consumer-grade robotic arm. We evaluate the proposed framework by taking 3 standard models and a user-defined model as examples. The results show that our framework is able to draw visually pleasing oriental ink paintings with expressive strokes., Comment: 12 pages, this manuscript is an extended version of our paper accepted by IEEE RA-L

By combining RMF models and equivparticle models with density-dependent quark masses, we construct explicitly ``a quark Fermi Sea'' and ``a baryonic Fermi surface'' to model the quarkyonic phase, where baryons with momentums ranging from zero to Fermi momentums are included. The properties of nuclear matter, quark matter, and quarkyonic matter are then investigated in a unified manner, where quarkyonic matter is more stable and energy minimization is still applicable to obtain the microscopic properties of dense matter. Three different covariant density functionals TW99, PKDD, and DD-ME2 are adopted in our work, where TW99 gives satisfactory predictions for the properties of nuclear matter both in neutron stars and heavy-ion collisions and quarkyonic transition is unfavorable. Nevertheless, if PKDD with larger slope of symmetry energy $L$ or DD-ME2 with larger skewness coefficient $J$ are adopted, the corresponding EOSs are too stiff according to both experimental and astrophysical constraints. The situation is improved if quarkyonic transition takes place, where the EOSs become softer and can accommodate various experimental and astrophysical constraints.

In the absence of an authoritative statement about a rumor, people may expose the truth behind such rumor through their responses on social media. Most rumor detection methods aggregate the information of all the responses and have made great progress. However, due to the different backgrounds of users, the responses have different relevance for discovering th suspicious points hidden in a rumor claim. The methods that focus on all the responding tweets would dilute the effect of the critical ones. Moreover, for a multi-modal rumor claim, the focus of a user may be on several words in the text or an object in the image, so the different modalities should be considered to select the relevant responses and verify the claim. In this paper, we propose a novel multi-modal rumor detection model, termed Focal Reasoning Model (FoRM), to filter out the irrelevant responses and further conduct fine-grained reasoning with the multi-modal claim and corresponding responses. Concretely, there are two main components in our FoRM: the coarse-grained selection and the fine-grained reasoning. The coarse-grained selection component leverages the post-level features of the responses to verify the claim and learns a relevant score of each response. Based on the relevant scores, the most relevant responses are reserved as the critical ones to the further reasoning. In the fine-grained reasoning component, we design a relation attention module to explore the fine-grained relations, i.e., token-to-token and token-to-object relations, between the reserved responses and the multi-modal claim for finding out the valuable clues. Extensive experiments have been conducted on two real-world datasets, and the results demonstrate that our proposed model outperforms all the baselines., Comment: Submitted to TKDE

The DC photocurrent can detect the topology and geometry of quantum materials without inversion symmetry. Herein, we propose that the DC shot noise (DSN), as the fluctuation of photocurrent operator, can also be a diagnostic of quantum materials. Particularly, we develop the quantum theory for DSNs in gapped systems and identify the shift and injection DSNs by dividing the second-order photocurrent operator into off-diagonal and diagonal contributions, respectively. Remarkably, we find that the DSNs can not be forbidden by inversion symmetry, while the constraint from time-reversal symmetry depends on the polarization of light. Furthermore, we show that the DSNs also encode the geometrical information of Bloch electrons, such as the Berry curvature and the quantum metric. Finally, guided by symmetry, we apply our theory to evaluate the DSNs in monolayer GeS and bilayer MoS$_2$ with and without inversion symmetry and find that the DSNs can be larger in centrosymmetric phase., Comment: two figures

We investigate systematically the possible deconfinement phase transition from nuclear matter to quark matter in compact stars. The properties of nuclear matter are fixed by expanding its binding energy to the order of $\rho^3$, while those of quark matter are predicted by an equivparticle model. The Maxwell construction is then applied for the quark-hadron mixed phase. By confronting compact star structures with pulsar observations, we obtain several EOSs that are compatible with the latest observations while supporting quark cores inside the most massive stars. It is found that the quark core is rather small and does not emerge for compact stars with $M\lesssim 2M_\odot$. The in-medium quark condensate of the stellar matter in those stars are then extracted within the framework of an equivparticle model, which decreases nonlinearly with density. At larger densities with pure quark matter, the quark condensate is still large and does not necessary decrease with density, indicating significant nonperturbative contributions within the density regions covered by compact stars.

We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction. We stress the constraint of environment heterogeneity, which means $n$ environments corresponding to these $n$ agents have different state transitions. To obtain a value function or a policy function which optimizes the overall performance in all environments, we propose two federated RL algorithms, \texttt{QAvg} and \texttt{PAvg}. We theoretically prove that these algorithms converge to suboptimal solutions, while such suboptimality depends on how heterogeneous these $n$ environments are. Moreover, we propose a heuristic that achieves personalization by embedding the $n$ environments into $n$ vectors. The personalization heuristic not only improves the training but also allows for better generalization to new environments., Comment: Artificial Intelligence and Statistics 2022