Miracle-dz/UniFAR-FTUs huggingface dataset:
MULTI-AGENT REINFORCEMENT LEARNING

Multi-robot path finding in dynamic environments is a highly challenging classic problem. In the movement process, robots need to avoid collisions with other moving robots while minimizing their travel distance. Previous methods for this problem either continuously replan paths using heuristic search methods to avoid conflicts or choose appropriate collision avoidance strategies based on learning approaches. The former may result in long travel distances due to frequent replanning, while the latter may have low learning efficiency due to low sample exploration and utilization, and causing high training costs for the model. To address these issues, we propose a path planning method, MAPPOHR, which combines heuristic search, empirical rules, and multiagent reinforcement learning. The method consists of two layers: a real-time planner based on the multi-agent reinforcement learning algorithm, MAPPO, which embeds empirical rules in the action output layer and reward functions, and a heuristic search planner used to create a global guiding path. During movement, the heuristic search planner replans new paths based on the instructions of the real-time planner. We tested our method in 10 different conflict scenarios. The experiments show that the planning performance of MAPPOHR is better than that of existing learning and heuristic methods. Due to the utilization of empirical knowledge and heuristic search, the learning efficiency of MAPPOHR is higher than that of existing learning methods.
Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation

This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS), which learns to segment objects of arbitrary classes using mere image-text pairs. Existing works turn to enhance the vanilla vision transformer by introducing explicit grouping recognition, i.e., employing several group tokens/centroids to cluster the image tokens and perform the group-text alignment. Nevertheless, these methods suffer from a granularity inconsistency regarding the usage of group tokens, which are aligned in the all-to-one v.s. one-to-one manners during the training and inference phases, respectively. We argue that this discrepancy arises from the lack of elaborate supervision for each group token. To bridge this granularity gap, this paper explores explicit supervision for the group tokens from the prototypical knowledge. To this end, this paper proposes the non-learnable prototypical regularization (NPR) where non-learnable prototypes are estimated from source features to serve as supervision and enable contrastive matching of the group tokens. This regularization encourages the group tokens to segment objects with less redundancy and capture more comprehensive semantic regions, leading to increased compactness and richness. Based on NPR, we propose the prototypical guidance segmentation network (PGSeg) that incorporates multi-modal regularization by leveraging prototypical sources from both images and texts at different levels, progressively enhancing the segmentation capability with diverse prototypical patterns. Experimental results show that our proposed method achieves state-of-the-art performance on several benchmark datasets. The source code is available at https://github.com/Ferenas/PGSeg.
Изменения растительности и климата на северо-западе Cреднерусской возвышенности в голоцене

The article presents the results of spore-pollen and rhizopod analyses, the study of botanical composition of peat and the radiocarbon dating of peat deposits of the Cranberry (Klukva) bog (Belev district, Tula oblast) and the reconstruction of changes in vegetation and climate in the Holocene. The obtained data showed that the spread of broad-leaved forests of oak, linden and elm on the North-West of the Central Russian Upland had begun around 7800 years BP (before present), and up to the last few centuries broad-leaved forests were the dominant component of the vegetation cover. Drastic vegetation changes occurred only in historic times as a result of human activities. In period 9500–7500 years BP climatic conditions were cooler than at present, and in the interval 7500–5200 years BP they were warmer and drier. A general cooling of the climate in the second half of the Holocene had included the climatic oscillations of the second order: a series of cold spells (5000–4000, 3600–3200, 2500–2000, 1400–800 years BP) and warming (4000–3600, 2000–1400 years BP, and about 1000–700 years BP).
〈Research Reports〉 Performance of a MIROC3.2-hires climate scenario in eastern Mongolian Steppe and its improvement by statistical downscaling

The spatial resolution of Global Circulation Models is too coarse to represent regional climate variations at the scales required for environmental impact assessments. Studies on downscaling over Mongolia were comparatively few, except dynamical downscaling studies, which have done by Kimura and Sato (2004), and Sato et al. (2007), especially on statistical downscaling. In this study, two climate variables (monthly mean temperature and monthly precipitation amount) of the output from MIROC3-2- hires climate model were downscaled and compared its performance with a bias correction method. Results showed that values from MIROC3-2-hires climate model overestimated the monthly accumulated precipitation and represented well the monthly mean temperature in eastern Mongolia during the control period from 1971 to 2000. The cumulative density function made it possible to compensate the difference by 36% for precipitation and by 16% for temperature.
Impact of 2000–2050 Climate Change on Fine Particulate Matter (PM2.5) Air Quality Inferred from a Multi-Model Analysis of Meteorological Modes

Studies of the effect of climate change on fine particulate matter (PM2.5) air quality using general circulation models (GCMs) show inconsistent results including in the sign of the effect. This reflects uncertainty in the GCM simulations of the regional meteorological variables affecting PM2.5. Here we use the CMIP3 archive of data from fifteen different IPCC AR4 GCMs to obtain improved statistics of 21st-century trends in the meteorological modes driving PM2.5 variability over the contiguous US. We analyze 1999– 2010 observations to identify the dominant meteorological modes driving interannual PM 2.5 variability and their synoptic periods T. We find robust correlations ( r > 0.5) of annual mean PM2.5 with T, especially in the eastern US where the dominant modes represent frontal passages. The GCMs all have significant skill in reproducing present-day statistics for T and we show that this reflects their ability to simulate atmospheric baroclinicity. We then use the local PM 2.5-toperiod sensitivity (dPM2.5/dT) from the 1999–2010 observations to project PM2.5 changes from the 2000–2050 changes in T simulated by the 15 GCMs following the SRES A1B greenhouse warming scenario. By weighted-average statistics of GCM results we project a likely 2000–2050 increase of ∼ 0.1 μg m−3 in annual mean PM2.5 in the eastern US arising from less frequent frontal ventilation, and a likely decrease albeit with greater inter-GCM variability in the Pacific Northwest due to more frequent maritime inflows. Potentially larger regional effects of 2000–2050 climate change on PM2.5 may arise from changes in temperature, biogenic emissions, wildfires, and vegetation, but are still unlikely to affect annual PM2.5 by more than 0.5 μg m−3.
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields

Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture are ubiquitous in nature and engineering, which often lead to catastrophic failure of structures. However, understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Molecular simulations offer an important tool to resolve the progressive microstructural changes at crack fronts and are widely used to explore processes therein, such as mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g., kinking, branching). Empirical force fields developed based on local descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, even for the nonlinear, anisotropic stress-strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of rare events during fracture, which, unfortunately, have not been taken into account in both the state-of-the-art empirical and machine-learning force fields. Based on data generated by first-principles calculations, we develop a neural network-based force field for fracture, NN-F$^3$, by combining pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F$^3$ is demonstrated by studying the rupture of h-BN and twisted bilayer graphene as model problems. The simulation results confirm recent experimental findings and highlight the necessity to include the knowledge of electronic structures from first-principles calculations in predicting extreme mechanical processes.
Geological setting of vertebrate microfossil localities across the Cretaceous–Paleogene boundary in southwestern Saskatchewan, Canada

The Frenchman and Ravenscrag formations of southwestern Saskatchewan, Canada, record an apparently continuous sequence of nonmarine clastic sediments across the Cretaceous–Paleogene (K–Pg) boundary. Extensive exposures of these fossil-rich sediments occur in the Frenchman River Valley, near the towns of Ravenscrag, Eastend, and Shaunavon, and have been a focus of study since the 1970s. Despite this long history of investigation, a comprehensive account of the geographic and stratigraphic positions of many of the significant fossil localities has yet to be published. Given this state of affairs, the goals of this paper are to (i) document the geographic locations, stratigraphic positions, and lithologies of 11 key vertebrate microfossil localities, including several new localities that have been recently discovered, and (ii) provide an update on the status of these fossil localities, the majority of which have not been sampled in the last 20 years. Four fossil localities are known from the lower Paleocene .
Late Devonian glacigenic and associated facies from the central Appalachian Basin, eastern United States

Late Devonian strata in the eastern United States are generally considered as having been deposited under warm tropical conditions. However, a stratigraphically restricted Late Devonian succession of diamictite-mudstone-sandstone within the Spechty Kopf and Rockwell Formations that extends for more than 400 km along depositional strike within the central Appalachian Basin may indicate otherwise. This lithologic association unconformably overlies the Catskill Formation, where a 3- to 5-m-thick interval of deformed strata occurs immediately below the diamictite strata. The diamictite facies consists of several subfacies that are interpreted to be subglacial, englacial, supraglacial meltout, and resedimented deposits. The mudstone facies that overlies the diamictite consists of subfacies of chaotically bedded, clast-poor mudstone, and laminated mudstone subfacies that represent subaqueous proximal debris flows and distal glaciolacustrine rhythmites or varvites, respectively. The pebbly sandstone facies is interpreted as proglacial braided outwash deposits that both preceded glacial advance and followed glacial retreat. Both the tectonic and depositional frameworks suggest that the facies were deposited in a terrestrial setting within the Appalachian foreland basin during a single glacial advance and retreat. Regionally, areas that were not covered by ice were subject to increased rainfall as indicated by wet-climate paleosols. River systems eroded deeper channels in response to sea-level drop during glacial advance. Marine facies to the west contain iceborne dropstone boulders preserved within contemporaneous units of the Cleveland Shale Member of the Ohio Shale. The stratigraphic interval correlative with sea-level drop, climate change, and glacigenic succession represents one of the Appalachian Basin's most prolific oil- and gas-producing intervals and is contemporaneous with a global episode of sea-level drop responsible for the deposition of the Hangenberg Shale/Sandstone of Europe. This interval records the Hangenberg biotic crisis near the Devonian-Carboniferous boundary.
Unexpected Improvements to Expected Improvement for Bayesian Optimization

Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often exceeded by that of more recent methods. Notably, EI and its variants, including for the parallel and multi-objective settings, are challenging to optimize because their acquisition values vanish numerically in many regions. This difficulty generally increases as the number of observations, dimensionality of the search space, or the number of constraints grow, resulting in performance that is inconsistent across the literature and most often sub-optimal. Herein, we propose LogEI, a new family of acquisition functions whose members either have identical or approximately equal optima as their canonical counterparts, but are substantially easier to optimize numerically. We demonstrate that numerical pathologies manifest themselves in"classic"analytic EI, Expected Hypervolume Improvement (EHVI), as well as their constrained, noisy, and parallel variants, and propose corresponding reformulations that remedy these pathologies. Our empirical results show that members of the LogEI family of acquisition functions substantially improve on the optimization performance of their canonical counterparts and surprisingly, are on par with or exceed the performance of recent state-of-the-art acquisition functions, highlighting the understated role of numerical optimization in the literature.
Chain-of-Thought Embeddings for Stance Detection on Social Media

Stance detection on social media is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit stance labels. Chain-of-Thought (COT) prompting has recently been shown to improve performance on stance detection tasks -- alleviating some of these issues. However, COT prompting still struggles with implicit stance identification. This challenge arises because many samples are initially challenging to comprehend before a model becomes familiar with the slang and evolving knowledge related to different topics, all of which need to be acquired through the training data. In this study, we address this problem by introducing COT Embeddings which improve COT performance on stance detection tasks by embedding COT reasonings and integrating them into a traditional RoBERTa-based stance detection pipeline. Our analysis demonstrates that 1) text encoders can leverage COT reasonings with minor errors or hallucinations that would otherwise distort the COT output label. 2) Text encoders can overlook misleading COT reasoning when a sample's prediction heavily depends on domain-specific patterns. Our model achieves SOTA performance on multiple stance detection datasets collected from social media.
SWIPT in Mixed Near- and Far-Field Channels: Joint Beam Scheduling and Power Allocation

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks by exploiting massive number of antennas for generating pencil-like beamforming. This also leads to a fundamental paradigm shift from conventional far-field communications towards the new near-field communications. In contrast to the existing works that mostly considered simultaneous wireless information and power transfer (SWIPT) in the far field, we consider in this paper a new and practical scenario, called mixed near- and far-field SWIPT, where energy harvesting (EH) and information decoding (ID) receivers are located in the near- and far-field regions of the XL-array base station (BS), respectively. Specifically, we formulate an optimization problem to maximize the weighted sum-power harvested at all EH receivers by jointly designing the BS beam scheduling and power allocation, under the constraints on the maximum sum-rate and BS transmit power. First, for the general case with multiple EH and ID receivers, we propose an efficient algorithm to obtain a suboptimal solution by utilizing the binary variable elimination and successive convex approximation methods. To obtain useful insights, we then study the joint design for special cases. In particular, we show that when there are multiple EH receivers and one ID receiver, in most cases, the optimal design is allocating a portion of power to the ID receiver for satisfying the rate constraint, while the remaining power is allocated to one EH receiver with the highest EH capability. This is in sharp contrast to the conventional far-field SWIPT case, for which all powers should be allocated to ID receivers. Numerical results show that our proposed joint design significantly outperforms other benchmark schemes without the optimization of beam scheduling and/or power allocation.
Tests for Non-Gaussian Statistics in the DMR Four-Year Sky Maps

We search the high-latitude portion of the COBE Differential Microwave Radiometer (DMR) 4 yr sky maps for evidence of a non-Gaussian temperature distribution in the cosmic microwave background. The genus, three-point correlation function, and two-point correlation function of temperature maxima and minima are all in excellent agreement with the hypothesis that the CMB anisotropy on angular scales of 7° or larger represents a random-phase Gaussian field. A likelihood comparison of the DMR sky maps to a set of random-phase non-Gaussian toy models selects the exact Gaussian model as most likely. Monte Carlo simulations show that the two-point correlation of the peaks and valleys in the maps provides the greatest discrimination among the class of models tested.
Application of a decision rule and a D-dimer assay in the diagnosis of pulmonary embolism

Summary Current strategies for diagnosing pulmonary embolism (PE) include a clinical decision rule (CDR), followed by a D-dimer assay in patients with an unlikely clinical probability. We assessed the implementation of the current guidelines for the diagnosis of PE. A first questionnaire was sent to internists and pulmonologists to assess the proportion of physicians that adequately applied the guidelines. Two versions of a second questionnaire were sent presenting five hypothetical cases of which in two cases with an intermediate clinical probability an abnormal D-dimer test result was added to one version. We assessed the variation of the CDR and compared the proportions of a likely clinical probability between the two versions. A total of 65 physicians responded to the first questionnaire (response rate 75%). Half of the physicians (N=29; 46%) indicated that they use a CDR in all patients and 22 physicians (45%) indicated that they review the D-dimer result after they examined patients. Sixty-two physicians responded on the second questionnaire (response rate 36%). A shift was observed from an unlikely to a likely probability when an abnormal D-dimer test result was added to the clinical information (22% to 41%; p=0.22 and 26% to 50%; p<0.05). Our findings indicate that physicians do not use the guidelines for diagnosis of PE consistently. Furthermore, the knowledge of an abnormal D-dimer test result before seeing the patient leads to a higher CDR score. Physicians should therefore first examine patients before taking note of the D-dimer test result.
Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks

This study presents a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli of rocks from their digital CT-scan images. We use the Mapper algorithm to transform 3D digital rock images into graph datasets, encapsulating essential geometrical information. These graphs, after training, prove effective in predicting elastic moduli. Our GNN model shows robust predictive capabilities across various graph sizes derived from various subcube dimensions. Not only does it perform well on the test dataset, but it also maintains high prediction accuracy for unseen rocks and unexplored subcube sizes. Comparative analysis with Convolutional Neural Networks (CNNs) reveals the superior performance of GNNs in predicting unseen rock properties. Moreover, the graph representation of microstructures significantly reduces GPU memory requirements (compared to the grid representation for CNNs), enabling greater flexibility in the batch size selection. This work demonstrates the potential of GNN models in enhancing the prediction accuracy of rock properties and boosting the efficiency of digital rock analysis.
Discussion on the Stratigraphic Boundary between Middle Triassic and Upper Triassic

Considering the dispute over the Yangchang Formation classification,a comparative analysis and investigation was conducted.Sporo-pollen assemblage,petrologic characteristics and basin evolution all suggest the distinct differences between the Ch8-Ch10 oil-bearing formations and Ch1-Ch7 oil bearing formations.Sedimentary period of Ch8-Ch10 oil-bearing formations is characterized by even landform,well developed rivers,deltas and shallow lakes,and low sandstone maturity.Analysis on the samples from the Ch8-Ch10 strata shows that the sporo-pollen is dominated by pteridophyta spore,with high content of Punctatisporites one of the significant members of sporo-pollen living in middle Triassic up to 50.5%. However,the deep lake widened expeditiously at the beginning of the Ch7 depositional period.The quartz content in the Ch7 sandstone increases significantly,resulting in the corresponding change of rock types in the west and the southwest.Therefore,the sedimentary system in the west and southwest margins of the basin are evolved into alluvial fan and fan delta from braided channel and braided channel/delta system.The biotic community is characterized by the appearance of Duplexisporites-an important member of sporo-pollen assemblage in late Triassic.In addition,a suit of tuffs distributed at the bottom of Ch7 in the western,southwestern and central parts of the basin records geological events such as syn-depositional volcanism and magmatic activities,which are the important features of early Indo-Sinian movement.Consequently,the stratigraphic boundary between Ch7 and Ch8 oil bearing formations can be regarded as the boundary between mid-Triassic and upper Triassic as well as the representative of the first episode of the Indo-Sinian Movement.
Author response to reviewers’ comments

We thank both reviewers for their careful revision of our manuscript and constructive comments. Below we provide our responses in a point-by-point manner. The reviewers’ comments are reproduced in italics , our responses in blue and quotes from the revised manuscript in red bold font. In addition, we have made a few minor edits to the manuscript in places not directly indicated by the reviewers, to further clarify some of the points.