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In rare earth nickelates (RENiO$_3$), electron-lattice coupling drives a concurrent metal-to-insulator and bond disproportionation phase transition whose microscopic origin has long been the subject of active debate. Of several proposed mechanisms, here we test the hypothesis that pairs of self-doped ligand holes spatially condense to provide local spin moments that are antiferromagnetically coupled to Ni spins. These singlet-like states provide a basis for long-range bond and spiral spin order. Using magnetic resonant X-ray scattering on NdNiO$_3$ thin films, we observe the chiral nature of the spin-disproportionated state, with spin spirals propagating along the crystallographic (101)$_\mathrm{ortho}$ direction. These spin spirals are found to preferentially couple to X-ray helicity, establishing the presence of a hitherto-unobserved macroscopic chirality. The presence of this chiral magnetic configuration suggests a potential multiferroic coupling between the noncollinear magnetic arrangement and improper ferroelectric behavior as observed in prior studies on NdNiO$_3$ (101)$_\mathrm{ortho}$ films and RENiO$_3$ single crystals. Experimentally constrained theoretical double-cluster calculations confirm the presence of an energetically stable spin-disproportionated state with Zhang-Rice singlet-like combinations of Ni and ligand moments., Comment: 6 pages, 4 figures

The proliferation of location-based services and applications has brought significant attention to data and location privacy. While general secure computation and privacy-enhancing techniques can partially address this problem, one outstanding challenge is to provide near latency-free search and compatibility with mainstream geographic search techniques, especially the Discrete Global Grid Systems (DGGS). This paper proposes a new construction, namely GridSE, for efficient and DGGS-compatible Secure Geographic Search (SGS) with both backward and forward privacy. We first formulate the notion of a semantic-secure primitive called \textit{symmetric prefix predicate encryption} (SP$^2$E), for predicting whether or not a keyword contains a given prefix, and provide a construction. Then we extend SP$^2$E for dynamic \textit{prefix symmetric searchable encryption} (pSSE), namely GridSE, which supports both backward and forward privacy. GridSE only uses lightweight primitives including cryptographic hash and XOR operations and is extremely efficient. Furthermore, we provide a generic pSSE framework that enables prefix search for traditional dynamic SSE that supports only full keyword search. Experimental results over real-world geographic databases of sizes (by the number of entries) from $10^3$ to $10^7$ and mainstream DGGS techniques show that GridSE achieves a speedup of $150\times$ - $5000\times$ on search latency and a saving of $99\%$ on communication overhead as compared to the state-of-the-art. Interestingly, even compared to plaintext search, GridSE introduces only $1.4\times$ extra computational cost and $0.9\times$ additional communication cost. Source code of our scheme is available at https://github.com/rykieguo1771/GridSE-RAM.

The relative role of electron-electron and electron-lattice interactions in driving the metal-insulator transition in perovskite nickelates opens a rare window into the non-trivial interplay of the two important degrees of freedom in solids. The most promising solution is to extract the electronic and lattice contributions during the phase transition by performing high-resolution spectroscopy measurements. Here, we present a three-dimensional electronic structure study of Nd1-xSrxNiO3 (x = 0 and 0.175) thin films with unprecedented accuracy, in which the low energy fermiology has a quantitative agreement with model simulations and first-principles calculations. Two characteristic phonons, the octahedral rotational and breathing modes, are illustrated to be coupled with the electron dynamics in the metallic phase, showing a kink structure along the band dispersion, as well as a hump feature in the energy spectrum. Entering the insulating state, the electron-phonon interaction is amplified by strong electron correlations, transforming the mobile large polarons at high temperatures to localized small polarons in the ground state. Moreover, the analysis of quasiparticle residue enables us to establish a transport-spectroscopy correspondence in Nd1-xSrxNiO3 thin films. Our findings demonstrate the essential role of electron-lattice interaction enhanced by the electronic correlation to stabilize the insulating phase in the perovskite nickelates., Comment: 26 pages, 4 + 7 figures

Coexisting orders are key features of strongly correlated materials and underlie many intriguing phenomena from unconventional superconductivity to topological orders. Here, we report the coexistence of two interacting charge-density-wave (CDW) orders in EuTe4, a layered crystal that has drawn considerable attention owing to its anomalous thermal hysteresis and a semiconducting CDW state despite the absence of perfect FS nesting. By accessing unoccupied conduction bands with time- and angle-resolved photoemission measurements, we find that mono- and bi-layers of Te in the unit cell host different CDWs that are associated with distinct energy gaps. The two gaps display dichotomous evolutions following photoexcitation, where the larger bilayer CDW gap exhibits less renormalization and faster recovery. Surprisingly, the CDW in the Te monolayer displays an additional momentum-dependent gap renormalization that cannot be captured by density-functional theory calculations. This phenomenon is attributed to interlayer interactions between the two CDW orders, which account for the semiconducting nature of the equilibrium state. Our findings not only offer microscopic insights into the correlated ground state of EuTe4 but also provide a general non-equilibrium approach to understand coexisting, layer-dependent orders in a complex system., Comment: To appear in PRL

We proposed an end-to-end system design towards utilizing Retrieval Augmented Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) for domain-specific and time-sensitive queries related to private knowledge-bases. Our system integrates RAG pipeline with upstream datasets processing and downstream performance evaluation. Addressing the challenge of LLM hallucinations, we finetune models with a curated dataset which originates from CMU's extensive resources and annotated with the teacher model. Our experiments demonstrate the system's effectiveness in generating more accurate answers to domain-specific and time-sensitive inquiries. The results also revealed the limitations of fine-tuning LLMs with small-scale and skewed datasets. This research highlights the potential of RAG systems in augmenting LLMs with external datasets for improved performance in knowledge-intensive tasks. Our code and models are available on Github., Comment: These authors contributed equally to this work

Recent theoretical studies have suggested that transition metal perovskite oxide membranes can enable surface phonon polaritons in the infrared range with low loss and much stronger subwavelength confinement than bulk crystals. Such modes, however, have not been experimentally observed so far. Here, using a combination of far-field Fourier-transform infrared (FTIR) spectroscopy and near-field synchrotron infrared nanospectroscopy (SINS) imaging, we study the phonon-polaritons in a 100 nm thick freestanding crystalline membrane of SrTiO3 transferred on metallic and dielectric substrates. We observe a symmetric-antisymmetric mode splitting giving rise to epsilon-near-zero and Berreman modes as well as highly confined (by a factor of 10) propagating phonon polaritons, both of which result from the deep-subwavelength thickness of the membranes. Theoretical modeling based on the analytical finite-dipole model and numerical finite-difference methods fully corroborate the experimental results. Our work reveals the potential of oxide membranes as a promising platform for infrared photonics and polaritonics.

Brillouin microscopy, which maps elastic modulus from the frequency shift of scattered light, has evolved to a faster speed for the investigation of rapid biomechanical changes. Impulsive stimulated Brillouin scattering (ISBS) spectroscopy has the potential to speed up measurement through the resonant amplification interaction from pulsed excitation and time-domain continuous detection. However, significant progress has not been achieved due to the limitation in signal-to-noise ratio (SNR) and the corresponding need for excessive averaging to maintain high spectral precision. Moreover, the limited spatial resolution also hinders its application in mechanical imaging. Here, by scrutinizing the SNR model, we design a high-speed ISBS microscope through multi-parameter optimization including phase, reference power, and acquisition time. Leveraging this, with the further assistance of the Matrix Pencil method for data processing, three-dimensional mechanical images are mapped under multiple contrast mechanisms for a millimeter-scale polydimethylsiloxane pattern immersed in methanol, enabling the identification of these two transparent materials without any contact or labeling. Our experimental results demonstrate the capability to maintain high spectral precision and resolution at a sub-millisecond integration time for one pixel. With a two-order improvement in the speed and a tenfold improvement in the spatial resolution over the state-of-the-art systems, this method makes it possible for ISBS microscopes to sensitively investigate rapid mechanical changes in time and space.

Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from a process specifically designed to optimize cohensive and distinctive sentence representations helps rank significant sentences. To do so, we propose a novel graph pre-training auto-encoder to obtain sentence embeddings by explicitly modelling intra-sentential distinctive features and inter-sentential cohesive features through sentence-word bipartite graphs. These pre-trained sentence representations are then utilized in a graph-based ranking algorithm for unsupervised summarization. Our method produces predominant performance for unsupervised summarization frameworks by providing summary-worthy sentence representations. It surpasses heavy BERT- or RoBERTa-based sentence representations in downstream tasks., Comment: Accepted by the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)

In today's data-driven landscape, the delicate equilibrium between safeguarding user privacy and unleashing data potential stands as a paramount concern. Federated learning, which enables collaborative model training without necessitating data sharing, has emerged as a privacy-centric solution. This decentralized approach brings forth security challenges, notably poisoning and backdoor attacks where malicious entities inject corrupted data. Our research, initially spurred by test-time evasion attacks, investigates the intersection of adversarial training and backdoor attacks within federated learning, introducing Adversarial Robustness Unhardening (ARU). ARU is employed by a subset of adversaries to intentionally undermine model robustness during decentralized training, rendering models susceptible to a broader range of evasion attacks. We present extensive empirical experiments evaluating ARU's impact on adversarial training and existing robust aggregation defenses against poisoning and backdoor attacks. Our findings inform strategies for enhancing ARU to counter current defensive measures and highlight the limitations of existing defenses, offering insights into bolstering defenses against ARU., Comment: 8 pages, 6 main pages of text, 4 figures, 2 tables. Made for a Neurips workshop on backdoor attacks

This paper presents a novel modular robot system that can self-reconfigure to achieve omnidirectional movements for collaborative object transportation. Each robotic module is equipped with a steerable omni-wheel for navigation and is shaped as a regular icositetragon with a permanent magnet installed on each corner for stable docking. After aggregating multiple modules and forming a structure that can cage a target object, we have developed an optimization-based method to compute the distribution of all wheels' heading directions, which enables efficient omnidirectional movements of the structure. By implementing a hierarchical controller on our prototyped system in both simulation and experiment, we validated the trajectory tracking performance of an individual module and a team of six modules in multiple navigation and collaborative object transportation settings. The results demonstrate that the proposed system can maintain a stable caging formation and achieve smooth transportation, indicating the effectiveness of our hardware and locomotion designs.

Atomically thin transition metal dichalcogenides can exhibit markedly different electronic properties compared to their bulk counterparts. In the case of NbSe$_2$, the question of whether its charge density wave (CDW) phase is enhanced in the monolayer limit has been the subject of intense debate, primarily due to the difficulty of decoupling this order from its environment. Here, we address this challenge by using a misfit crystal that comprises NbSe$_2$ monolayers separated by SnSe rock-salt spacers, a structure that allows us to investigate a monolayer crystal embedded in a bulk matrix. We establish an effective monolayer electronic behavior of the misfit crystal by studying its transport properties and visualizing its electronic structure by angle-resolved photoemission measurements. We then investigate the emergence of the CDW by tracking the temperature dependence of its collective modes. Our findings reveal a nearly sixfold enhancement in the CDW transition temperature, providing compelling evidence for the profound impact of dimensionality on charge order formation in NbSe$_2$.

We investigate the sequential manipulation planning problem for unmanned aerial manipulators (UAMs). Unlike prior work that primarily focuses on one-step manipulation tasks, sequential manipulations require coordinated motions of a UAM's floating base, the manipulator, and the object being manipulated, entailing a unified kinematics and dynamics model for motion planning under designated constraints. By leveraging a virtual kinematic chain (VKC)-based motion planning framework that consolidates components' kinematics into one chain, the sequential manipulation task of a UAM can be planned as a whole, yielding more coordinated motions. Integrating the kinematics and dynamics models with a hierarchical control framework, we demonstrate, for the first time, an over-actuated UAM achieves a series of new sequential manipulation capabilities in both simulation and experiment.

Discrete frequency-bin entanglement is an essential resource for applications in quantum information processing. In this Letter, we propose and demonstrate a scheme to generate discrete frequency-bin entanglement with a single piece of periodically poled lithium niobate waveguide in a modified Sagnac interferometer. Correlated two-photon states in both directions of the Sagnac interferometer are generated through cascaded second-order optical nonlinear processes. A relative phase difference between the two states is introduced by changing the polarization state of pump light, thus manipulating the two-photon state at the output of the Sagnac interferometer. The generated two-photon state is sent into a fiber polarization splitter, then a pure discrete frequency-bin entangled two-photon state is obtained by setting the pump light. The frequency entanglement property is measured by a spatial quantum beating with a visibility of $96.0 \pm 6.1\%$. The density matrix is further obtained with a fidelity of $98.0 \pm 3.0\%$ to the ideal state. Our demonstration provides a promising method for the generation of pure discrete frequency-bin entanglement at telecom band, which is desired in quantum photonics., Comment: 4 pages. 3 figures

A broken translation symmetry has recently been revealed in infinite-layer nickelates, which has piqued considerable interest in its origin and relation to superconductivity, as well as in its comparison to charge order in cuprates. Here, by performing resonant x-ray scattering measurements in thin films of infinite-layer PrNiO2+{\delta}, we find that the superlattice reflection at the Ni L3 absorption edge differs considerably from that at the Pr M5 resonance in their dependence on energy, temperature, and local symmetry, indicating they are two distinct charge orders despite the same in-plane wavevectors. These dissimilarities might be related to the excess oxygen dopants, considering that the resonant reflections were observed in an incompletely reduced PrNiO2+{\delta} film. In addition, our azimuthal analysis suggests that the oxygen ligands should play a pivotal role in the charge modulation revealed at the Ni L3 resonance., Comment: 17 pages, 4 figures

This paper describes Tencent's multilingual machine translation systems for the WMT22 shared task on Large-Scale Machine Translation Evaluation for African Languages. We participated in the $\mathbf{constrained}$ translation track in which only the data and pretrained models provided by the organizer are allowed. The task is challenging due to three problems, including the absence of training data for some to-be-evaluated language pairs, the uneven optimization of language pairs caused by data imbalance, and the curse of multilinguality. To address these problems, we adopt data augmentation, distributionally robust optimization, and language family grouping, respectively, to develop our multilingual neural machine translation (MNMT) models. Our submissions won the $\mathbf{1st\ place}$ on the blind test sets in terms of the automatic evaluation metrics. Codes, models, and detailed competition results are available at https://github.com/wxjiao/WMT2022-Large-Scale-African., Comment: WMT 2022, 8 pages

Strongly correlated materials often respond sensitively to the external perturbations. In the recently discovered superconducting infinite-layer nickelates, the superconducting transition temperature can be dramatically enhanced via only ~1% compressive strain-tuning enabled by substrate design. However, the root of such enhancement remains elusive. While the superconducting pairing mechanism is still not settled, magnetic Cooper pairing - similar to the cuprates has been proposed. Using resonant inelastic x-ray scattering, we investigate the magnetic excitations in infinite-layer PrNiO2 thin films for different strain conditions. The magnon bandwidth of PrNiO2 shows only marginal response to strain-tuning, in sharp contrast to the striking enhancement of the superconducting transition temperature Tc in the doped superconducting samples. These results suggest the enhancement of Tc is not mediated by spin excitations and thus provide important empirics for the understanding of superconductivity in infinite-layer nickelates., Comment: 17 pages, 4 figures

Tracking position and orientation independently affords more agile maneuver for over-actuated multirotor Unmanned Aerial Vehicles (UAVs) while introducing undesired downwash effects; downwash flows generated by thrust generators may counteract others due to close proximity, which significantly threatens the stability of the platform. The complexity of modeling aerodynamic airflow challenges control algorithms from properly compensating for such a side effect. Leveraging the input redundancies in over-actuated UAVs, we tackle this issue with a novel control allocation framework that considers downwash effects and explores the entire allocation space for an optimal solution. This optimal solution avoids downwash effects while providing high thrust efficiency within the hardware constraints. To the best of our knowledge, ours is the first formal derivation to investigate the downwash effects on over-actuated UAVs. We verify our framework on different hardware configurations in both simulation and experiment.

Copper oxide high temperature superconductors universally exhibit multiple forms of electronically ordered phases that break the native translational symmetry of the CuO2 planes. The interplay between these orders and the superconducting ground state, as well as how they arise through doping a Mott insulator, is essential to decode the mechanisms of high-temperature superconductivity. Over the years, various forms of electronic liquid crystal phases including charge/spin stripes and incommensurate charge-density-waves (CDWs) were found to emerge out of a correlated metallic ground state in underdoped cuprates. Early theoretical studies also predicted the emergence of a Coulomb-frustrated 'charge crystal' phase in the very lightly-doped, insulating limit of the CuO2 planes. Here, we use resonant X-ray scattering, electron transport, and muon spin rotation measurements to fully resolve the electronic and magnetic ground state and search for signatures of charge order in very lightly hole-doped cuprates from the RBa2Cu3O7-d family (RBCO; R: Y or rare earth). X-ray scattering data from RBCO films reveal a breaking of translational symmetry more pervasive than was previously known, extending down to the Mott limit. The ordering vector of this charge crystal state is linearly connected to the charge-density-waves of underdoped RBCO, suggesting that the former phase is a precursor to the latter as hole doping is increased. Most importantly, the coexistence of charge and spin order in RBCO suggests that this electronic symmetry-breaking state is common to the CuO2 planes in the very lightly-doped regime. These findings bridge the gap between the Mott insulating state and the underdoped metallic state and underscore the prominent role of Coulomb-frustrated electronic phase separation among all cuprates.