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We propose to realize the quantum nonlinear Hall effect and the inverse Faraday effect through the acoustic wave in a time-reversal invariant but inversion broken Dirac insulator. We focus on the acoustic frequency much lower than the Dirac gap such that the interband transition is suppressed and these effects arise solely from the intrinsic valley-contrasting band topology. The corresponding acoustoelectric conductivity and magnetoacoustic susceptibility are both proportional to the quantized valley Chern number and independent of the quasiparticle lifetime. The linear and nonlinear components of the longitudinal and transverse topological currents can be tuned by adjusting the polarization and propagation directions of the surface acoustic wave. The static magnetization generated by a circularly polarized acoustic wave scales linearly with the acoustic frequency as well as the strain-induced charge density. Our results unveil a quantized nonlinear topological acoustoelectric response of gapped Dirac materials, like hBN and transition-metal dichalcogenide, paving the way toward room-temperature acoustoelectric devices due to their large band gaps., Comment: 6 pages, 2 figures in main text, and 7 pages, 5 figures, 2 tables in supplement

This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many routine tasks. On the other hand, researchers, whose work is not only time-consuming but also highly expertise-demanding, face increasing challenges as they have to spend more time reading, writing, and reviewing papers. This raises the question: how can LLMs potentially assist researchers in alleviating their heavy workload? This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability. To address this, we constructed the ReviewCritique dataset, which includes two types of information: (i) NLP papers (initial submissions rather than camera-ready) with both human-written and LLM-generated reviews, and (ii) each review comes with "deficiency" labels and corresponding explanations for individual segments, annotated by experts. Using ReviewCritique, this study explores two threads of research questions: (i) "LLMs as Reviewers", how do reviews generated by LLMs compare with those written by humans in terms of quality and distinguishability? (ii) "LLMs as Metareviewers", how effectively can LLMs identify potential issues, such as Deficient or unprofessional review segments, within individual paper reviews? To our knowledge, this is the first work to provide such a comprehensive analysis.

We present a quantum theory of phonon magnetic moment in doped Dirac semimetals. Our theory is based on an emergent gauge field approach to the electron-phonon coupling, applicable to both gapless and gapped systems. We find that the magnetic moment is directly proportional to the electrical Hall conductivity through the phonon Hall viscosity. Our theory is combined with the first-principles calculations, allowing us to quantitatively implement it to realistic materials. Magnetic moments are found to be on the order of Bohr magneton for certain phonon modes in graphene and $\text{Cd}_3 \text{As}_2$. Our results provide practical guidance for the dynamical generation of large magnetization in the topological quantum materials., Comment: 7 pages, 3 figures, supplemental materials included

Limited-angle and sparse-view computed tomography (LACT and SVCT) are crucial for expanding the scope of X-ray CT applications. However, they face challenges due to incomplete data acquisition, resulting in diverse artifacts in the reconstructed CT images. Emerging implicit neural representation (INR) techniques, such as NeRF, NeAT, and NeRP, have shown promise in under-determined CT imaging reconstruction tasks. However, the unsupervised nature of INR architecture imposes limited constraints on the solution space, particularly for the highly ill-posed reconstruction task posed by LACT and ultra-SVCT. In this study, we introduce the Diffusion Prior Driven Neural Representation (DPER), an advanced unsupervised framework designed to address the exceptionally ill-posed CT reconstruction inverse problems. DPER adopts the Half Quadratic Splitting (HQS) algorithm to decompose the inverse problem into data fidelity and distribution prior sub-problems. The two sub-problems are respectively addressed by INR reconstruction scheme and pre-trained score-based diffusion model. This combination first injects the implicit image local consistency prior from INR. Additionally, it effectively augments the feasibility of the solution space for the inverse problem through the generative diffusion model, resulting in increased stability and precision in the solutions. We conduct comprehensive experiments to evaluate the performance of DPER on LACT and ultra-SVCT reconstruction with two public datasets (AAPM and LIDC), an in-house clinical COVID-19 dataset and a public raw projection dataset created by Mayo Clinic. The results show that our method outperforms the state-of-the-art reconstruction methods on in-domain datasets, while achieving significant performance improvements on out-of-domain (OOD) datasets., Comment: 16 pages, 11 figures

Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms. Interestingly, our results show that a single-agent LLM with strong prompts can achieve almost the same performance as the best existing discussion approach on a wide range of reasoning tasks and backbone LLMs. We observe that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt. Further study reveals the common interaction mechanisms of LLMs during the discussion., Comment: 22 pages, 5 figures, 10 tables

Stroke is a common disabling neurological condition that affects about one-quarter of the adult population over age 25; more than half of patients still have poor outcomes, such as permanent functional dependence or even death, after the onset of acute stroke. The aim of this study is to investigate the efficacy of diffusion-weighted MRI modalities combining with structured health profile on predicting the functional outcome to facilitate early intervention. A deep fusion learning network is proposed with two-stage training: the first stage focuses on cross-modality representation learning and the second stage on classification. Supervised contrastive learning is exploited to learn discriminative features that separate the two classes of patients from embeddings of individual modalities and from the fused multimodal embedding. The network takes as the input DWI and ADC images, and structured health profile data. The outcome is the prediction of the patient needing long-term care at 3 months after the onset of stroke. Trained and evaluated with a dataset of 3297 patients, our proposed fusion model achieves 0.87, 0.80 and 80.45% for AUC, F1-score and accuracy, respectively, outperforming existing models that consolidate both imaging and structured data in the medical domain. If trained with comprehensive clinical variables, including NIHSS and comorbidities, the gain from images on making accurate prediction is not considered substantial, but significant. However, diffusion-weighted MRI can replace NIHSS to achieve comparable level of accuracy combining with other readily available clinical variables for better generalization., Comment: 12 pages, 5 figures, 5 tables

Commonsense knowledge graph completion is a new challenge for commonsense knowledge graph construction and application. In contrast to factual knowledge graphs such as Freebase and YAGO, commonsense knowledge graphs (CSKGs; e.g., ConceptNet) utilize free-form text to represent named entities, short phrases, and events as their nodes. Such a loose structure results in large and sparse CSKGs, which makes the semantic understanding of these nodes more critical for learning rich commonsense knowledge graph embedding. While current methods leverage semantic similarities to increase the graph density, the semantic plausibility of the nodes and their relations are under-explored. Previous works adopt conceptual abstraction to improve the consistency of modeling (event) plausibility, but they are not scalable enough and still suffer from data sparsity. In this paper, we propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class, which indicates a similar level of plausibility. Each node in CSKG finds its top entailed nodes using a finetuned transformer over natural language inference (NLI) tasks, which sufficiently capture textual entailment signals. The entailment relation between these nodes are further utilized to: 1) build new connections between source triplets and entailed nodes to densify the sparse CSKGs; 2) enrich the generalization ability of node representations by comparing the node embeddings with a contrastive loss. Experiments on two standard CSKGs demonstrate that our proposed framework EntailE can improve the performance of CSKG completion tasks under both transductive and inductive settings., Comment: 10 pages, 5 figures, 9 tables

The recent experimental discovery of the zero-field fractional Chern insulator (FCI) in twisted $\mathrm{MoTe_2}$ moir\'e superlattices has sparked immense interest in this exotic topological quantum state. The FCI has also been observed in previous experiments in magic angle twisted bilayer graphene (TBG) under a finite magnetic field of about 5 Tesla. Generally, the stabilization of FCI requires fine-tuning the topological band to satisfy certain conditions. It would still be helpful to have an intuitive picture to understand the different behaviors in twisted $\mathrm{MoTe_2}$ and TBG. Here, we compare them through the lens of emergent gauge fields. In TBG, the system can be mapped to two Dirac fermions coupled to emergent gauge fields with opposite signs. In contrast, the twisted $\mathrm{MoTe_2}$ reduces to a hole with parabolic dispersion coupled to an emergent gauge field. This contrasting gauge structure provides a new perspective on the observed difference: the zero-field FCI is stable in $\mathrm{MoTe_2}$ but absent in TBG. Based on this understanding, we will explore potential strategies for stabilizing FCI in both moir\'e superlattices.

The shift current in bulk photovoltaics is commonly attributed to a shift in non-interacting charge centres. We show that the origin of shift currents in the many-body picture is fundamentally different. Here the shift current arises largely from a many-body shift vector that also involves shifts in charge centres, but these are associated with pairs of nearly-degenerate excitons overlapping in k-space. The shift current can thus be enhanced by nearly-degenerate, optically-active and tightly-bound excitons, spread out in k-space. Ab initio results are presented for BaTiO$_3$, NbOCl$_2$ and MoS$_2$.

It is well acknowledged that incorporating explicit knowledge graphs (KGs) can benefit question answering. Existing approaches typically follow a grounding-reasoning pipeline in which entity nodes are first grounded for the query (question and candidate answers), and then a reasoning module reasons over the matched multi-hop subgraph for answer prediction. Although the pipeline largely alleviates the issue of extracting essential information from giant KGs, efficiency is still an open challenge when scaling up hops in grounding the subgraphs. In this paper, we target at finding semantically related entity nodes in the subgraph to improve the efficiency of graph reasoning with KG. We propose a grounding-pruning-reasoning pipeline to prune noisy nodes, remarkably reducing the computation cost and memory usage while also obtaining decent subgraph representation. In detail, the pruning module first scores concept nodes based on the dependency distance between matched spans and then prunes the nodes according to score ranks. To facilitate the evaluation of pruned subgraphs, we also propose a graph attention network (GAT) based module to reason with the subgraph data. Experimental results on CommonsenseQA and OpenBookQA demonstrate the effectiveness of our method., Comment: 8 pages, 4 figures, accepted to *SEM 2024

Terahertz technologies are important for a number of emerging applications, such as for next generation communications. We predict that transition metal substitutional defects in two-dimensional transition metal dichalcogenides (TMDs) can serve as quantum defects for terahertz technologies. Central to this prediction is the finding that the zero field splittings between spin sublevels in such defects are typically in the sub-terahertz to terahertz range due to the large spin-orbit coupling in these systems. As a proof of concept, we consider different transition metal impurities from across the periodic table, in prototypical TMDs, MoS2 and WSe2. Using first principles calculations, we demonstrate that selected spin triplet defects can potentially serve as qubits operating in the terahertz regime. We also propose defects that can potentially be quantum sources of terahertz radiation. Our research broadens the scope of advancements in quantum information science and lays a foundation for their integration with THz technologies.

Logical reasoning remains a pivotal component within the realm of artificial intelligence. The recent evolution of large language models (LLMs) has marked significant progress in this domain. The adoption of strategies like chain-of-thought (CoT) has enhanced the performance of LLMs across diverse reasoning tasks. Nonetheless, logical reasoning that involves proof planning, specifically those that necessitate the validation of explanation accuracy, continues to present stumbling blocks. In this study, we first evaluate the efficacy of LLMs with advanced CoT strategies concerning such tasks. Our analysis reveals that LLMs still struggle to navigate complex reasoning chains, which demand the meticulous linkage of premises to derive a cogent conclusion. To address this issue, we finetune a smaller-scale language model, equipping it to decompose proof objectives into more manageable subgoals. We also introduce contrastive decoding to stepwise proof generation, making use of negative reasoning paths to strengthen the model's capacity for logical deduction. Experiments on EntailmentBank underscore the success of our method in augmenting the proof planning abilities of language models.

Two ways has been discussed to unlock the reasoning capability of a large language model. The first one is prompt engineering and the second one is to combine the multiple inferences of large language models, or the multi-agent discussion. Theoretically, this paper justifies the multi-agent discussion mechanisms from the symmetry of agents. Empirically, this paper reports the empirical results of the interplay of prompts and discussion mechanisms, revealing the empirical state-of-the-art performance of complex multi-agent mechanisms can be approached by carefully developed prompt engineering. This paper also proposes a scalable discussion mechanism based on conquer and merge, providing a simple multi-agent discussion solution with simple prompts but state-of-the-art performance., Comment: Working in progress, and code will be released soon

Moir\'e superlattices of semiconducting transition metal dichalcogenides (TMDCs) enable unprecedented spatial control of electron wavefunctions in an artificial lattice with periodicities more than ten times larger than that of atomic crystals, leading to emerging quantum states with fascinating electronic and optical properties. The breaking of translational symmetry further introduces a new degree of freedom inside each moir\'e unit cell: high symmetry points of energy minima called moir\'e sites, behaving as spatially separated quantum dots. The superposition of a quasiparticle wavefunction between different moir\'e sites will enable a new platform for quantum information processing but is hindered by the suppressed electron tunneling between moir\'e sites. Here we demonstrate the superposition between two moir\'e sites by constructing an angle-aligned trilayer WSe2/monolayer WS2 moir\'e heterojunction. The two moir\'e sites with energy minimum allow the formation of two different interlayer excitons, with the hole residing in either moir\'e site of the first WSe2 layer interfacing the WS2 layer and the electron in the third WSe2 layer. An external electric field can drive the hybridization of either of the interlayer excitons with the intralayer excitons in the third WSe2 layer, realizing the continuous tuning of interlayer exciton hopping between two moir\'e sites. Therefore, a superposition of the two interlayer excitons localized at different moir\'e sites can be realized, which can be resolved in the electric-field-dependent optical reflectance spectra, distinctly different from that of the natural trilayer WSe2 in which the moir\'e modulation is absent. Our study illustrates a strategy of harnessing the new moir\'e site degree of freedom for quantum information science, a new direction of twistronics.

Transition metal dichalcogenide (TMDC) moir\'e superlattices, owing to the moir\'e flatbands and strong correlation, can host periodic electron crystals and fascinating correlated physics. The TMDC heterojunctions in the type-II alignment also enable long-lived interlayer excitons that are promising for correlated bosonic states, while the interaction is dictated by the asymmetry of the heterojunction. Here we demonstrate a new excitonic state, quadrupolar exciton, in a symmetric WSe2-WS2-WSe2 trilayer moir\'e superlattice. The quadrupolar excitons exhibit a quadratic dependence on the electric field, distinctively different from the linear Stark shift of the dipolar excitons in heterobilayers. This quadrupolar exciton stems from the hybridization of WSe2 valence moir\'e flatbands. The same mechanism also gives rise to an interlayer Mott insulator state, in which the two WSe2 layers share one hole laterally confined in one moir\'e unit cell. In contrast, the hole occupation probability in each layer can be continuously tuned via an out-of-plane electric field, reaching 100% in the top or bottom WSe2 under a large electric field, accompanying the transition from quadrupolar excitons to dipolar excitons. Our work demonstrates a trilayer moir\'e system as a new exciting playground for realizing novel correlated states and engineering quantum phase transitions.

Understanding the interplay of interactions and disorder in quantum transport poses long-standing scientific challenges, with many-body quantum transport phenomena in high-dimensional disordered systems remaining largely unexplored experimentally. We utilize a momentum space lattice platform using quasi-periodically kicked ultracold atomic gases to experimentally investigate many-body effects on the three-dimensional Anderson metal-insulator transition. We observe interaction-driven sub-diffusion and a divergence of delocalization onset time on approaching the many-body phase boundary. Mean-field numerical simulations are in qualitative agreement with experimental observations., Comment: 14 pages, 11 figures, 2 tables, including supplementary materials

We show that excitonic resonances and interexciton transitions can enhance the probability of spontaneous parametric down-conversion, a second-order optical response which generates entangled photon pairs. We benchmark our ab initio many-body calculations using experimental polar plots of second harmonic generation in NbOI$_2$, clearly demonstrating the relevance of excitons in the nonlinear response. A strong double-exciton resonance in 2D NbOCl$_2$ leads to giant enhancement in the second order susceptibility. Our work paves the way for the realization of efficient ultrathin quantum light sources.

Commonsense reasoning tasks such as commonsense knowledge graph completion and commonsense question answering require powerful representation learning. In this paper, we propose to learn commonsense knowledge representation by MICO, a Multi-alternative contrastve learning framework on COmmonsense knowledge graphs (MICO). MICO generates the commonsense knowledge representation by contextual interaction between entity nodes and relations with multi-alternative contrastive learning. In MICO, the head and tail entities in an $(h,r,t)$ knowledge triple are converted to two relation-aware sequence pairs (a premise and an alternative) in the form of natural language. Semantic representations generated by MICO can benefit the following two tasks by simply comparing the distance score between the representations: 1) zero-shot commonsense question answering task; 2) inductive commonsense knowledge graph completion task. Extensive experiments show the effectiveness of our method., Comment: 9 pages, 2 figures

As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the lexical semantics of words under specific contexts. Benefited from the large-scale annotation, current WSD systems have achieved impressive performances in English by combining supervised learning with lexical knowledge. However, such success is hard to be replicated in other languages, where we only have limited annotations.In this paper, based on the multilingual lexicon BabelNet describing the same set of concepts across languages, we propose building knowledge and supervised-based Multilingual Word Sense Disambiguation (MWSD) systems. We build unified sense representations for multiple languages and address the annotation scarcity problem for MWSD by transferring annotations from rich-sourced languages to poorer ones. With the unified sense representations, annotations from multiple languages can be jointly trained to benefit the MWSD tasks. Evaluations of SemEval-13 and SemEval-15 datasets demonstrate the effectiveness of our methodology., Comment: 8 pages, 5 figures

Using high-throughput first-principles calculations to search for layered van der Waals materials with the largest piezoelectric stress coefficients, we discover NbOI2 to be the one among 2940 monolayers screened. The piezoelectric performance of NbOI2 is independent of thickness, and its electromechanical coupling factor of near unity is a hallmark of optimal interconversion between electrical and mechanical energy. Laser scanning vibrometer studies on bulk and few-layer NbOI2 crystals verify their huge piezoelectric responses, which exceed internal references such as In2Se3 and CuInP2S6. Furthermore, we provide insights into the atomic origins of anti-correlated piezoelectric and ferroelectric responses in NbOX2 (X = Cl, Br, I), based on bond covalency and structural distortions in these materials. Our discovery that NbOI2 has the largest piezoelectric stress coefficients among 2D materials calls for the development of NbOI2-based flexible nanoscale piezoelectric devices.