Miracle-dz/UniFAR-FTUs huggingface dataset:
Recovery of a Luther-Emery phase in the three-band Hubbard model with longer-range hopping

A lightly doped single-band Hubbard model on a two leg ladder exhibits a Luther-Emery phase, while the three-band Hubbard ladder behaves as a Luttinger liquid upon hole doping. In order to understand this discrepancy, we present a systematic density-matrix renormalization group study of the three-band Hubbard model on two-leg cylinders with further-neighbor particle hoppings. The inclusion of the longer-range hopping is motivated by the studies of the single-band Hubbard model in which the further-neighbor hopping terms are suggested to be crucial for the unconventional superconductivity. When the longer-range hopping parameters are small, the ground state is a Luttinger liquid having mutually commensurate superconducting, charge and spin density wave correlations. Increasing the longer-range hopping drives a transition into a Luther-Emery phase with quasi-long ranged superconducting and charge orders but short-ranged spin-spin correlations. By down-folding the three-band Hubbard model into an effective $t$-$t'$-$J$-$J'$ model, we find that in the Luther-Emery phase, both the nearest and second neighbor kinetic energies are enhanced due to an effective increase of copper-oxygen hybridization. Amplifying inter-cell oxygen orbital hopping mirrors the benefits of reducing the charge transfer energy, causing doped holes to favor oxygen orbitals and strengthening superconducting pairing.
An effective description of the impact of inhomogeneities on the movement of the kink front in 2+1 dimensions

In the present work we explore the interaction of a one-dimensional kink-like front of the sine-Gordon equation moving in 2-dimensional spatial domains. We develop an effective equation describing the kink motion, characterizing its center position dynamics as a function of the transverse variable. The relevant description is valid both in the Hamiltonian realm and in the non-conservative one bearing gain and loss. We subsequently examine a variety of different scenarios, without and with a spatially-dependent heterogeneity. The latter is considered both to be one-dimensional ($y$-independent) and genuinely two-dimensional. The spectral features and the dynamical interaction of the kink with the heterogeneity are considered and comparison with the effective quasi-one-dimensional description (characterizing the kink center as a function of the transverse variable) is also provided. Generally, good agreement is found between the analytical predictions and the computational findings in the different cases considered.
First-order electronic phase transition in $\alpha$-(BEDT-TTF)$_2$I$_3$ revealed by temperature-dependent generalized ellipsometry

The nature of correlation-driven metal-insulator transitions remains a longstanding puzzle in solid-state physics. While some theories suggest a second-order character, various experimental observations in these materials indicate first-order phase transitions. Despite considerable progress over the last decades in understanding the underlying driving mechanisms of metal-insulator transitions, in particular the phase coexistence remains poorly understood on a microscopic scale. Here, we employ Mueller matrix spectroscopic and temperature-dependent ellipsometry to determine the anisotropic dielectric functions of the two-dimensional charge-transfer salt $\alpha$-(BEDT-TTF)$_2$I$_3$ across its charge-order metal-insulator transition. Our results offer valuable insights into temperature-dependent changes of the dielectric functions along the different crystallographic axes. Furthermore, we apply an effective-medium approximation to quantify the correlation between the metal-to-insulator transition and the volume fraction of the metallic phase embedded within the insulating phase. Through this comprehensive approach, generalized ellipsometry unravels the nature of the correlation-driven metal-insulator transition.
Towards Erd\H{o}s-Hajnal property for dp-minimal graphs

We introduce the notion of strongly $\binom{k}{2}$-free graphs, which contain dp-minimal graphs. We show that under some sparsity assumption, given a rainbow $\binom{k}{2}$-free blockade we can find a rainbow $\binom{k-1}{2}$-free blockade. This might serve as an intermediate step towards Erd\H os-Hajnal property for dp-minimal graphs.
What You See Is What You Detect: Towards better Object Densification in 3D detection

Recent works have demonstrated the importance of object completion in 3D Perception from Lidar signal. Several methods have been proposed in which modules were used to densify the point clouds produced by laser scanners, leading to better recall and more accurate results. Pursuing in that direction, we present, in this work, a counter-intuitive perspective: the widely-used full-shape completion approach actually leads to a higher error-upper bound especially for far away objects and small objects like pedestrians. Based on this observation, we introduce a visible part completion method that requires only 11.3\% of the prediction points that previous methods generate. To recover the dense representation, we propose a mesh-deformation-based method to augment the point set associated with visible foreground objects. Considering that our approach focuses only on the visible part of the foreground objects to achieve accurate 3D detection, we named our method What You See Is What You Detect (WYSIWYD). Our proposed method is thus a detector-independent model that consists of 2 parts: an Intra-Frustum Segmentation Transformer (IFST) and a Mesh Depth Completion Network(MDCNet) that predicts the foreground depth from mesh deformation. This way, our model does not require the time-consuming full-depth completion task used by most pseudo-lidar-based methods. Our experimental evaluation shows that our approach can provide up to 12.2\% performance improvements over most of the public baseline models on the KITTI and NuScenes dataset bringing the state-of-the-art to a new level. The codes will be available at \textcolor[RGB]{0,0,255}{\url{{https://github.com/Orbis36/WYSIWYD}}
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning

Offline-to-online reinforcement learning (RL) is a training paradigm that combines pre-training on a pre-collected dataset with fine-tuning in an online environment. However, the incorporation of online fine-tuning can intensify the well-known distributional shift problem. Existing solutions tackle this problem by imposing a policy constraint on the policy improvement objective in both offline and online learning. They typically advocate a single balance between policy improvement and constraints across diverse data collections. This one-size-fits-all manner may not optimally leverage each collected sample due to the significant variation in data quality across different states. To this end, we introduce Family Offline-to-Online RL (FamO2O), a simple yet effective framework that empowers existing algorithms to determine state-adaptive improvement-constraint balances. FamO2O utilizes a universal model to train a family of policies with different improvement/constraint intensities, and a balance model to select a suitable policy for each state. Theoretically, we prove that state-adaptive balances are necessary for achieving a higher policy performance upper bound. Empirically, extensive experiments show that FamO2O offers a statistically significant improvement over various existing methods, achieving state-of-the-art performance on the D4RL benchmark. Codes are available at https://github.com/LeapLabTHU/FamO2O.
Large Language Models as Generalizable Policies for Embodied Tasks

We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take as input text instructions and visual egocentric observations and output actions directly in the environment. Using reinforcement learning, we train LLaRP to see and act solely through environmental interactions. We show that LLaRP is robust to complex paraphrasings of task instructions and can generalize to new tasks that require novel optimal behavior. In particular, on 1,000 unseen tasks it achieves 42% success rate, 1.7x the success rate of other common learned baselines or zero-shot applications of LLMs. Finally, to aid the community in studying language conditioned, massively multi-task, embodied AI problems we release a novel benchmark, Language Rearrangement, consisting of 150,000 training and 1,000 testing tasks for language-conditioned rearrangement. Video examples of LLaRP in unseen Language Rearrangement instructions are at https://llm-rl.github. io.
Twist- and gate-tunable proximity spin-orbit coupling, spin relaxation anisotropy, and charge-to-spin conversion in heterostructures of graphene and transition metal dichalcogenides

We present a DFT-based investigation of the twist-angle dependent proximity spin-orbit coupling (SOC) in graphene/TMDC structures. We find that for Mo-based TMDCs the proximity valley-Zeeman SOC exhibits a maximum at around 15--20{\deg}, and vanishes at 30{\deg}, while for W-based TMDCs we find an almost linear decrease of proximity valley-Zeeman SOC when twisting from 0{\deg} to 30{\deg}. The induced Rashba SOC is rather insensitive to twisting, while acquiring a nonzero Rashba phase angle, $\varphi \in [-20;40]${\deg}, for twist angles different from 0{\deg} and 30{\deg}. This finding contradicts earlier tight-binding predictions that the Rashba angle can be 90{\deg} in the studied systems. In addition, we study the influence of several tunability knobs on the proximity SOC for selected twist angles. By applying a transverse electric field in the limits of $\pm 2$ V/nm, mainly the Rashba SOC can be tuned by about 50\%. The interlayer distance provides a giant tunability, since the proximity SOC can be increased by a factor of 2--3, when reducing the distance by about 10\%. Encapsulating graphene between two TMDCs, both twist angles are important to control the interference of the individual proximity SOCs, allowing to precisely tailor the valley-Zeeman SOC in graphene, while the Rashba SOC becomes suppressed. Finally, based on our effective Hamiltonians with fitted parameters, we calculate experimentally measurable quantities such as spin lifetime anisotropy and charge-to-spin conversion efficiencies. The spin lifetime anisotropy can become giant, up to $10^4$, in encapsulated structures. The charge-to-spin conversion, which is due to spin-Hall and Rashba-Edelstein effects, can lead to twist-tunable non-equilibrium spin-density polarizations that are perpendicular and parallel to the applied charge current.
Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation

Occlusion is a long-standing problem in computer vision, particularly in instance segmentation. ACM MMSports 2023 DeepSportRadar has introduced a dataset that focuses on segmenting human subjects within a basketball context and a specialized evaluation metric for occlusion scenarios. Given the modest size of the dataset and the highly deformable nature of the objects to be segmented, this challenge demands the application of robust data augmentation techniques and wisely-chosen deep learning architectures. Our work (ranked 1st in the competition) first proposes a novel data augmentation technique, capable of generating more training samples with wider distribution. Then, we adopt a new architecture - Hybrid Task Cascade (HTC) framework with CBNetV2 as backbone and MaskIoU head to improve segmentation performance. Furthermore, we employ a Stochastic Weight Averaging (SWA) training strategy to improve the model's generalization. As a result, we achieve a remarkable occlusion score (OM) of 0.533 on the challenge dataset, securing the top-1 position on the leaderboard. Source code is available at this https://github.com/nguyendinhson-kaist/MMSports23-Seg-AutoID.
The role of turbulence in high-mass star formation: Subsonic and transonic turbulence are ubiquitously found at early stages

Context. Traditionally, supersonic turbulence is considered to be one of the most likely mechanisms to slow down the gravitational collapse in dense clumps, thereby enabling the formation of massive stars. However, several recent studies have raised differing points of view based on observations carried out with sufficiently high spatial and spectral resolution. These studies call for a re-evaluation of the role turbulence plays in massive star-forming regions. Aims. Our aim is to study the gas properties, especially the turbulence, in a sample of massive star-forming regions with sufficient spatial and spectral resolution, which can both resolve the core fragmentation and the thermal line width. Methods. We observed NH3 metastable lines with the Very Large Array (VLA) to assess the intrinsic turbulence. Results. Analysis of the turbulence distribution histogram for 32 identified NH3 cores reveals the presence of three distinct components. Furthermore, our results suggest that (1) sub- and transonic turbulence is a prevalent (21 of 32) feature of massive star-forming regions and those cold regions are at early evolutionary stage. This investigation indicates that turbulence alone is insufficient to provide the necessary internal pressure required for massive star formation, necessitating further exploration of alternative candidates; and (2) studies of seven multi-core systems indicate that the cores within each system mainly share similar gas properties and masses. However, two of the systems are characterized by the presence of exceptionally cold and dense cores that are situated at the spatial center of each system. Our findings support the hub-filament model as an explanation for this observed distribution
Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice

Hyperparameters play a critical role in machine learning. Hyperparameter tuning can make the difference between state-of-the-art and poor prediction performance for any algorithm, but it is particularly challenging for structure learning due to its unsupervised nature. As a result, hyperparameter tuning is often neglected in favour of using the default values provided by a particular implementation of an algorithm. While there have been numerous studies on performance evaluation of causal discovery algorithms, how hyperparameters affect individual algorithms, as well as the choice of the best algorithm for a specific problem, has not been studied in depth before. This work addresses this gap by investigating the influence of hyperparameters on causal structure learning tasks. Specifically, we perform an empirical evaluation of hyperparameter selection for some seminal learning algorithms on datasets of varying levels of complexity. We find that, while the choice of algorithm remains crucial to obtaining state-of-the-art performance, hyperparameter selection in ensemble settings strongly influences the choice of algorithm, in that a poor choice of hyperparameters can lead to analysts using algorithms which do not give state-of-the-art performance for their data.
Enhancing Enterprise Network Security: Comparing Machine-Level and Process-Level Analysis for Dynamic Malware Detection

Analysing malware is important to understand how malicious software works and to develop appropriate detection and prevention methods. Dynamic analysis can overcome evasion techniques commonly used to bypass static analysis and provide insights into malware runtime activities. Much research on dynamic analysis focused on investigating machine-level information (e.g., CPU, memory, network usage) to identify whether a machine is running malicious activities. A malicious machine does not necessarily mean all running processes on the machine are also malicious. If we can isolate the malicious process instead of isolating the whole machine, we could kill the malicious process, and the machine can keep doing its job. Another challenge dynamic malware detection research faces is that the samples are executed in one machine without any background applications running. It is unrealistic as a computer typically runs many benign (background) applications when a malware incident happens. Our experiment with machine-level data shows that the existence of background applications decreases previous state-of-the-art accuracy by about 20.12% on average. We also proposed a process-level Recurrent Neural Network (RNN)-based detection model. Our proposed model performs better than the machine-level detection model; 0.049 increase in detection rate and a false-positive rate below 0.1.
How Re-sampling Helps for Long-Tail Learning?

Long-tail learning has received significant attention in recent years due to the challenge it poses with extremely imbalanced datasets. In these datasets, only a few classes (known as the head classes) have an adequate number of training samples, while the rest of the classes (known as the tail classes) are infrequent in the training data. Re-sampling is a classical and widely used approach for addressing class imbalance issues. Unfortunately, recent studies claim that re-sampling brings negligible performance improvements in modern long-tail learning tasks. This paper aims to investigate this phenomenon systematically. Our research shows that re-sampling can considerably improve generalization when the training images do not contain semantically irrelevant contexts. In other scenarios, however, it can learn unexpected spurious correlations between irrelevant contexts and target labels. We design experiments on two homogeneous datasets, one containing irrelevant context and the other not, to confirm our findings. To prevent the learning of spurious correlations, we propose a new context shift augmentation module that generates diverse training images for the tail class by maintaining a context bank extracted from the head-class images. Experiments demonstrate that our proposed module can boost the generalization and outperform other approaches, including class-balanced re-sampling, decoupled classifier re-training, and data augmentation methods. The source code is available at https://www.lamda. nju.edu. cn/code_CSA. ashx.
Well-posedness analysis of the Cahn-Hilliard-Biot model

We investigate the well-posedness of the recently proposed Cahn-Hilliard-Biot model. The model is a three-way coupled PDE of elliptic-parabolic nature, with several nonlinearities and the fourth order term known to the Cahn-Hilliard system. We show existence of weak solutions to the variational form of the equations and uniqueness under certain conditions of the material parameters and secondary consolidation, adding regularizing effects. Existence is shown by discretizing in space and applying ODE-theory (the Peano-Cauchy theorem) to prove existence of the discrete system, followed by compactness arguments to retain solutions of the continuous system. In addition, the continuous dependence of solutions on the data is established, in particular implying uniqueness. Both results build strongly on the inherent gradient flow structure of the model.
Canted antiferromagnetism in polar 
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 with high Néel temperature

MnSiN$_2$ is a transition metal nitride with Mn and Si ions displaying an ordered distribution on the cation sites of a distorted wurtzite-derived structure. The Mn$^{2+}$ ions reside on a 3D diamond-like covalent network with strong superexchange pathways. We simulate its electronic structure and find that the N anions in MnSiN$_2$ act as $\sigma$- and $\pi$-donors, which serve to enhance the N-mediated superexchange, leading to the high N\'{e}el ordering temperature of $T_N$ = 443 K. Polycrystalline samples of MnSiN$_2$ were prepared to reexamine the magnetic structure and resolve previously reported discrepancies. An additional magnetic canting transition is observed at $T_\mathrm{cant}$ = 433 K and the precise canted ground state magnetic structure has been resolved using a combination of DFT calculations and powder neutron diffraction. The calculations favor a $G$-type antiferromagnetic spin order with lowering to $Pc^\prime$. Irreducible representation analysis of the magnetic Bragg peaks supports the lowering of the magnetic symmetry. The computed model includes a 10$^\circ$ rotation of the magnetic spins away from the crystallographic $c$-axis consistent with measured powder neutron diffraction data modeling and a small canting of 0.6$^\circ$.
Mitigating the impacts of street lighting on biodiversity and ecosystem functioning

Street lights are not only a major source of direct light pollution emissions, but stock has been transitioning to light-emitting diode (LED) technology in many parts of the world, resulting in increases in the blue part of the visible spectrum that is more harmful to biodiversity and human health. But LEDs can be modified more easily than conventional sodium lamps by adjusting their intensity, spectral output and other features of street light systems. In this Opinion piece, I provide an updated overview of street light mitigation strategies and contend that research in this area has been slow. I show how experimental lighting rigs that mimic real street lights can be used for mitigation testing, since invertebrate behaviour, abundances and interactions can respond quickly and measurably. I demonstrate how advances in network ecology that use species interaction data can provide much-needed assessments of the impacts of street lights on biodiversity and ecosystem functioning, and ultimately provide new tools and metrics for biomonitoring. I acknowledge the limitations of measuring local, short-term responses of biodiversity and identify promising avenues for collaborating with industry and government agencies in new or existing road lighting schemes, to minimize the negative long-term impacts at marginal cost. This article is part of the theme issue ‘Light pollution in complex ecological systems’.
Spectral composition of light-emitting diodes impacts aquatic and terrestrial invertebrate communities with potential implications for cross-ecosystem subsidies

Resource exchanges in the form of invertebrate fluxes are a key component of aquatic-terrestrial habitat coupling, but this interface is susceptible to human activities, including the imposition of artificial light at night. To better understand the effects of spectral composition of light-emitting diodes (LEDs)—a technology that is rapidly supplanting other lighting types—on emergent aquatic insects and terrestrial insects, we experimentally added LED fixtures that emit different light spectra to the littoral zone and adjacent riparian habitat of a pond. We installed four replicate LED treatments of different wavelengths (410, 530 and 630 nm), neutral white (4000 k) and a dark control, and sampled invertebrates in both terrestrial and over-water littoral traps. Invertebrate communities differed among light treatments and between habitats, as did total insect biomass and mean individual insect size. Proportional allochthonous biomass was greater in the riparian habitat and among some light treatments, demonstrating an asymmetrical effect of differently coloured LEDs on aquatic-terrestrial resource exchanges. Overall, our findings demonstrate that variation in wavelength from LEDs may impact the flux of resources between systems, as well as the communities of insects that are attracted to particular spectra of LED lighting, with probable implications for consumers. This article is part of the theme issue ‘Light pollution in complex ecological systems’.