Miracle-dz/UniFAR-FTUs huggingface dataset:
Stacking order and Coulomb correlation effect in the layered charge density wave phase of 
1T−NbS2

Two-dimensional (2D) layered materials have attracted tremendous interest from the perspective of basic physics and technological applications in the last decade. Especially, the artificially assembled van der Waals (vdW) heterostructures and twisted 2D materials bring out fascinating properties, and render promising applications possible by engineering the stacking order. Here, based on first-principles calculations, we explored the interplay between stacking effect and electron-electron correlation in the layered vdW material of bulk 1T-NbS2 with a 2D charge density wave (CDW) order. Without considering the Coulomb correlation, two energetically favorable out-of-plane stacking configurations are identified: one is a metallic phase with a single-layer stacking pattern, another is a band insulator with a paired-bilayer stacking configuration. Even though the Coulomb correlation is taken into account, the two energetic favorable stacking orders are still far more stable than other stacking orders. Furthermore, increasing the Coulomb interaction, the paired-bilayer stacking configuration transforms from nonmagnetic band insulator to antiferromagnetic insulator, while the single-layer stacking undergoes a Slater-Mott metal-insulator transition, which indicates the non-negligible role of electron-electron correlation interactions. In addition, the electronic structure and magnetic ground state change drastically among different stacking configurations, providing a platform to tune the electronic structures and interlayer magnetic interactions by altering the stacking order. In contrast to the widely accepted scenario of Mott localization as the driving force behind the gap formation in the CDW phase of layered transition metal dichalcogenides, our results not only highlight the crucial role of stacking order in the electronic structures of 1T-NbS2, but also shed fresh light on the distinct effects of Coulomb interaction in different stacking arrangements. * Email: mingxing@glut.edu. cn (Xing Ming)
Optimal Partitioning of Non-Convex Environments for Minimum Turn Coverage Planning

In this letter, we tackle the problem of planning an optimal coverage path for a robot operating indoors. Many existing approaches attempt to discourage turns in the path by covering the environment along the least number of coverage lines, i.e., straight-line paths. This is because turning not only slows down the robot but also negatively affects the quality of coverage, e.g., tools like cameras and cleaning attachments commonly have poor performance around turns. The problem of minimizing coverage lines however is typically solved using heuristics that do not guarantee optimality. In this work, we propose a turn-minimizing coverage planning method that computes the optimal number of axis-parallel (horizontal/vertical) coverage lines for the environment in polynomial time. We do this by formulating a linear program (LP) that optimally partitions the environment into axis-parallel ranks (non-intersecting rectangles of width equal to the tool width). We then generate coverage paths for a set of real-world indoor environments and compare the results with state-of-the-art coverage approaches.
Direct solution of multiple excitations in a matrix product state with block Lanczos

Matrix product state methods are known to be efficient for computing ground states of local, gapped Hamiltonians, particularly in one dimension. We introduce the multi-targeted density matrix renormalization group method that acts on a bundled matrix product state, holding many excitations. The use of a block or banded Lanczos algorithm allows for the simultaneous, variational optimization of the bundle of excitations. The method is demonstrated on a Heisenberg model and other cases of interest. A large of number of excitations can be obtained at a small bond dimension with highly reliable local observables throughout the chain.
THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting

Group interactions arise in our daily lives (email communications, on-demand ride sharing, comment interactions on online communities, to name a few), and they together form hypergraphs that evolve over time. Given such temporal hypergraphs, how can we describe their underlying design principles? If their sizes and time spans are considerably different, how can we compare their structural and temporal characteristics? In this work, we define 96 temporal hypergraph motifs (TH-motifs), and propose the relative occurrences of their instances as an answer to the above questions. TH-motifs categorize the relational and temporal dynamics among three connected hyperedges that appear within a short time. For scalable analysis, we develop THYME+, a fast and exact algorithm for counting the instances of TH-motifs in massive hypergraphs, and show that THYME+ is at most ${2,163}\times$ faster while requiring less space than baseline. Using it, we investigate 11 real-world temporal hypergraphs from various domains. We demonstrate that TH-motifs provide important information useful for downstream tasks and reveal interesting patterns, including the striking similarity between temporal hypergraphs from the same domain.
LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition

Retrieval-based place recognition is an efficient and effective solution for re-localization within a pre-built map, or global data association for Simultaneous Localization and Mapping (SLAM). The accuracy of such an approach is heavily dependant on the quality of the extracted scene-level representation. While end-to-end solutions - which learn a global descriptor from input point clouds - have demonstrated promising results, such approaches are limited in their ability to enforce desirable properties at the local feature level. In this paper, we introduce a local consistency loss to guide the network towards learning local features which are consistent across revisits, hence leading to more repeatable global descriptors resulting in an overall improvement in 3D place recognition performance. We formulate our approach in an end-to-end trainable architecture called LoGG3D-Net. Experiments on two large-scale public benchmarks (KITTI and MulRan) show that our method achieves mean F1max scores of 0.939 and 0.968 on KITTI and MulRan respectively, achieving state-of-the-art performance while operating in near real-time. The open-source implementation is available at: https://github.com/csiro-robotics/LoGG3D-Net.
Distilling Linguistic Context for Language Model Compression

A computationally expensive and memory intensive neural network lies behind the recent success of language representation learning. Knowledge distillation, a major technique for deploying such a vast language model in resource-scarce environments, transfers the knowledge on individual word representations learned without restrictions. In this paper, inspired by the recent observations that language representations are relatively positioned and have more semantic knowledge as a whole, we present a new knowledge distillation objective for language representation learning that transfers the contextual knowledge via two types of relationships across representations: Word Relation and Layer Transforming Relation. Unlike other recent distillation techniques for the language models, our contextual distillation does not have any restrictions on architectural changes between teacher and student. We validate the effectiveness of our method on challenging benchmarks of language understanding tasks, not only in architectures of various sizes but also in combination with DynaBERT, the recently proposed adaptive size pruning method.
Powerful supercontinuum vortices generated by femtosecond vortex beams with thin plates

We demonstrate numerically and experimentally the generation of powerful supercontinuum vortices from femtosecond vortex beams by using multiple thin fused silica plates. The supercontinuum vortices are shown to preserve the vortex phase profile of the initial beam for spectral components ranging from 500 nm to 1200 nm. The transfer of the vortex phase profile results from the inhibition of multiple filamentation and the preservation of vortex ring with relatively uniform intensity distribution by means of the thin-plate scheme, where the supercontinuum is mainly generated from the self-phase modulation and self-steepening effects. Our scheme works for vortex beams with different topological charges, which provides a simple and effective method to generate supercontinuum vortices with high power. © 2021 Chinese Laser Press
From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba

Time series forecasting (TSF) is fundamentally required in many real-world applications, such as electricity consumption planning and sales forecasting. In e-commerce, accurate time-series sales forecasting (TSSF) can significantly increase economic benefits. TSSF in e-commerce aims to predict future sales of millions of products. The trend and seasonality of products vary a lot, and the promotion activity heavily influences sales. Besides the above difficulties, we can know some future knowledge in advance except for the historical statistics. Such future knowledge may reflect the influence of the future promotion activity on current sales and help achieve better accuracy. However, most existing TSF methods only predict the future based on historical information. In this work, we make up for the omissions of future knowledge. Except for introducing future knowledge for prediction, we propose Aliformer based on the bidirectional Transformer, which can utilize the historical information, current factor, and future knowledge to predict future sales. Specifically, we design a knowledge-guided self-attention layer that uses known knowledge's consistency to guide the transmission of timing information. And the future-emphasized training strategy is proposed to make the model focus more on the utilization of future knowledge. Extensive experiments on four public benchmark datasets and one proposed large-scale industrial dataset from Tmall demonstrate that Aliformer can perform much better than state-of-the-art TSF methods. Aliformer has been deployed for goods selection on Tmall Industry Tablework, and the dataset will be released upon approval.
NetTraj: A Network-Based Vehicle Trajectory Prediction Model With Directional Representation and Spatiotemporal Attention Mechanisms

Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and location-based recommendations. Existing methods typically represent a trajectory as a sequence of grid cells, road segments or intention sets. None of them is ideal, as the cell-based representation ignores the road network structures and the other two are less efficient in analyzing city-scale road networks. Moreover, previous models barely leverage spatial dependencies or only consider them at the grid cell level, ignoring the non-Euclidean spatial structure shaped by irregular road networks. To address these problems, we propose a network-based vehicle trajectory prediction model named NetTraj, which represents each trajectory as a sequence of intersections and associated movement directions, and then feeds them into a LSTM encoder-decoder network for future trajectory generation. Furthermore, we introduce a local graph attention mechanism to capture network-level spatial dependencies of trajectories, and a temporal attention mechanism with a sliding context window to capture both short- and long-term temporal dependencies in trajectory data. Extensive experiments based on two real-world large-scale taxi trajectory datasets show that NetTraj outperforms the existing state-of-the-art methods for vehicle trajectory prediction, validating the effectiveness of the proposed trajectory representation method and spatiotemporal attention mechanisms.
Grasping in the Dark: Zero-Shot Object Grasping Using Tactile Feedback

Grasping and manipulating a wide variety of objects is a fundamental skill that would determine the success and wide spread adaptation of robots in homes. Several end-effector designs for robust manipulation have been proposed but they mostly work when provided with prior information about the objects or equipped with external sensors for estimating object shape or size. Such approaches are limited to many-shot or unknown objects and are prone to estimation errors from external estimation systems. We propose an approach to grasp and manipulate previously unseen or zero-shot objects: the objects without any prior of their shape, size, material and weight properties, using only feedback from tactile sensors which is contrary to the state-of-the-art. Such an approach provides robust manipulation of objects either when the object model is not known or when it is estimated incorrectly from an external system. Our approach is inspired by the ideology of how animals or humans manipulate objects, i.e., by using feedback from their skin. Our grasping and manipulation revolves around the simple notion that objects slip if not grasped stably. This slippage can be detected and counteracted for a robust grasp that is agnostic to the type, shape, size, material and weight of the object. At the crux of our approach is a novel tactile feedback based controller that detects and compensates for slip during grasp. We successfully evaluate and demonstrate our proposed approach on many real world experiments using the Shadow Dexterous Hand equipped with BioTac SP tactile sensors for different object shapes, sizes, weights and materials. We obtain an overall success rate of 73.5%
Black hole feeding and star formation in NGC 1808

We report on Atacama Large Millimeter Array (ALMA) observations of CO(3−2) emission in the Seyfert2/starburst galaxy NGC 1808, at a spatial resolution of 4 pc. Our aim is to investigate the morphology and dynamics of the gas inside the central 0.5 kpc and to probe the nuclear feeding and feedback phenomena. We discovered a nuclear spiral of radius 1′′ = 45 pc. Within it, we found a decoupled circumnuclear disk or molecular torus of a radius of 0.13′′ = 6 pc. The HCN(4−3) and HCO(4−3) and CS(7−6) dense gas line tracers were simultaneously mapped and detected in the nuclear spiral and they present the same misalignment in the molecular torus. At the nucleus, the HCN/HCO and HCN/CS ratios indicate the presence of an active galactic nucleus (AGN). The molecular gas shows regular rotation, within a radius of 400 pc, except for the misaligned disk inside the nuclear spiral arms. The computations of the torques exerted on the gas by the barred stellar potential reveal that the gas within a radius of 100 pc is feeding the nucleus on a timescale of five rotations or on an average timescale of ∼60 Myr. Some non-circular motions are observed towards the center, corresponding to the nuclear spiral arms. We cannot rule out that small extra kinematic perturbations could be interpreted as a weak outflow attributed to AGN feedback. The molecular outflow detected at ≥250 pc in the NE direction is likely due to supernovae feedback and it is connected to the kpc-scale superwind.
Low‐dose aspirin use and mortality risk in patients with head and neck cancer: A nationwide cohort study of 10 770 patients

Several recent observational studies have linked low‐dose aspirin use to improved survival in patients with head and neck cancer. However, studies of patterns of aspirin use and risk of cancer‐specific mortality are lacking. This nationwide cohort study included all patients in the Danish Cancer Registry with a primary diagnosis of head and neck squamous cell cancer (HNSCC) during 2000 to 2016, aged 30 to 84 years, without prior cancer (except nonmelanoma skin cancer) and alive 1 year after diagnosis. Nationwide registries provided information on filled prescriptions, mortality and potential confounding factors. For a subpopulation, a clinical database provided additional information, including human papillomavirus (HPV) tumor status. We used Cox proportional hazards regression models to estimate adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) for the association between postdiagnostic low‐dose aspirin use (≥1 prescription within first year after diagnosis) and risk of cancer‐specific mortality. We identified 10 770 patients with HNSCC during a median follow‐up of 3.9 years. Of these, 1799 (16.7%) were low‐dose aspirin users. Postdiagnostic use of low‐dose aspirin was associated with a HR of 0.97 (95% CI 0.82‐1.15) for cancer‐specific mortality. Similar neutral associations were found according to patterns of aspirin use. No apparent trends emerged according to age, sex, topography or stage. A tendency towards a decreased cancer‐specific mortality risk with low‐dose aspirin use was observed among HPV‐positive patients; however, the statistical precision was low. In conclusion, we did not observe an association between postdiagnostic low‐dose aspirin use and cancer‐specific mortality in a nationwide cohort of patients with HNSCC.
Knockdown of NON-YELLOW COLORING 1 (NYC1)-like gene or chlorophyllin application enhanced chlorophyll accumulation with antioxidant roles in suppressing heat-induced leaf senescence in perennial ryegrass.

Loss of chlorophyll (Chl) and oxidative damages co-occur during heat-induced leaf senescence. This study aimed to determine the functions of Chl catabolic gene, NON-YELLOW COLORING 1 (NYC1)-like (NOL) in regulating heat-induced leaf senescence and to characterize antioxidant roles of a Chl derivative, sodium copper chlorophyllin (SCC), in suppressing heat-induced leaf senescence. In two separate experiments, one by comparing NOL RNAi transgenic and wild-type plants, and the other by analyzing the effects of sodium copper chlorophyllin (SCC, 1 mM) treatment, perennial ryegrass (Lolium perenne) were exposed to heat stress (38/35 oC, day/night) or optimal temperature (25/20 oC). Results showed that both knockdown of LpNOL and application of SCC suppressed heat-induced leaf senescence, as manifested by increased Chl content, reduced electrolyte leakage, and down-regulation of Chl-catabolic genes and senescence-related genes, as well as enhanced antioxidant capacity in the peroxidase (POD) pathway for H2O2 scavenging. Ex vivo SCC incubation protected membranes from H2O2 damage onto mesophyll protoplasts of perennial ryegrass. The suppression of leaf senescence by knockdown of NOL or chlorophyllin application was associated with enhanced chlorophyll accumulation playing antioxidant roles in protecting leaves from heat-induced oxidative damages.
Effort Estimation in Open Source Software Development: A Case Study

This paper presents first attempts at estimating the effort in open source software projects. First, the possible parties interested in the results of effort estimation, and both hindrances and advantages for effort estimation in this context are explored. Using data concerning an open source project retrieved from public data, several well-known estimation models from literature are applied, and their applicability for this type of development is discussed.
Influence of Hydrogen Sulfide and Redox Reactions on the Surface Properties and Hydrogen Permeability of Pd Membranes

Although hydrogen sulfide (H2S) was always a negative factor leading to the reduction of hydrogen permeability of palladium (Pd) membranes, its proper application could result in a positive effect. In this study, pure Pd membranes were firstly reacted with H2S at 23–450 °C, and then treated by redox reactions. Afterwards, the hydrogen permeability was tested under different reaction conditions using a hydrogen penetrant testing device. Moreover, both products and morphology changes occurred on the Pd membrane surface were analyzed using XRD, XPS and SEM. The results showed that H2S was dissociated to produce sulfides at 23 °C. With a rise of temperature, a regular change took place in the reaction products, morphology of the Pd membrane surface and hydrogen permeability. Adsorbed impurities such as sulfides and free carbon on the Pd membrane surface were removed by the redox treatment. The hydrogen permeability was improved by about 80% for the Pd membrane material subjected to the treatment method stated the above against the untreated one.
Towards a phylogenetic ecology of plant pests and pathogens

Plant–pathogens and insect pests, hereafter pests, play an important role in structuring ecological communities, yet both native and introduced pests impose significant pressure on wild and managed systems, and pose a threat to food security. Global changes in climate and land use, and transportation of plants and pests around the globe are likely to further increase the range, frequency and severity of pest outbreaks in the future. Thus, there is a critical need to expand on current ecological theory to address these challenges. Here, we outline a phylogenetic framework for the study of plant and pest interactions. In plants, a growing body of work has suggested that evolutionary relatedness, phylogeny, strongly structures plant-pest associations—from pest host breadths and impacts, to their establishment and spread in new regions. Understanding the phylogenetic dimensions of plant-pest associations will help to inform models of invasive species spread, disease and pest risk in crops, and emerging pest outbreaks in native plant communities—which will have important implications for protecting food security and biodiversity into the future. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.
Identifying co-phylogenetic hotspots for zoonotic disease

The incidence of zoonotic diseases is increasing worldwide, which makes identifying parasites likely to become zoonotic and hosts likely to harbour zoonotic parasites a critical concern. Prior work indicates that there is a higher risk of zoonotic spillover accruing from closely related hosts and from hosts that are infected with a high phylogenetic diversity of parasites. This suggests that host and parasite evolutionary history may be important drivers of spillover, but identifying whether host–parasite associations are more strongly structured by the host, parasite or both requires co-phylogenetic analyses that combine host–parasite association data with host and parasite phylogenies. Here, we use host–parasite datasets containing associations between helminth taxa and free-range mammals in combination with phylogenetic models to explore whether host, parasite, or both host and parasite evolutionary history influences host–parasite associations. We find that host phylogenetic history is most important for driving patterns of helminth-mammal association, indicating that zoonoses are most likely to come from a host's close relatives. More broadly, our results suggest that co-phylogenetic analyses across broad taxonomic scales can provide a novel perspective for surveying potential emerging infectious diseases. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.