Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Open-vocabulary Segmentation with Foundation Models

Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception and thus a crucial objective in computer vision research. However, this task is heavily impeded by the lack of large-scale and diverse 3D open-vocabulary segmentation datasets for training robust and generalizable models. Distilling knowledge from pre-trained 2D open-vocabulary segmentation models helps but it compromises the open-vocabulary feature significantly as the 2D models are mostly finetuned with close-vocabulary datasets. We tackle the challenges in 3D open-vocabulary segmentation by exploiting the open-vocabulary multimodal knowledge and object reasoning capability of pre-trained foundation models CLIP and DINO, without necessitating any fine-tuning. Specifically, we distill open-vocabulary visual and textual knowledge from CLIP into a neural radiance field (NeRF) which effectively lifts 2D features into view-consistent 3D segmentation. Furthermore, we introduce the Relevancy-Distribution Alignment loss and Feature-Distribution Alignment loss to respectively mitigate the ambiguities of CLIP features and distill precise object boundaries from DINO features, eliminating the need for segmentation annotations during training. Extensive experiments show that our method even outperforms fully supervised models trained with segmentation annotations, suggesting that 3D open-vocabulary segmentation can be effectively learned from 2D images and text-image pairs.
Description and origin of some talus‐foot debris accumulations, Aghla Mountains, Co. Donegal, Ireland

The morphology and site characteristics of talus-foot debris accumulations in the Aghla Mountains, Ireland, suggest that these landforms are fossil protalus ramparts and talus-derived rock glaciers. The ramparts are linear or arcuate ridges or benches located within 45 m of the talus foot. The rock glaciers comprise several transverse ridges and depressions, interpreted as flow structures, and extend further from the talus foot than the ramparts. At least one rock glacier developed from a rampart. The ramparts indicate the former existence of perennia snowbeds and the rock glaciers are considered to result from the deformation and creep of interstitial ice (permafrost). There is no clear evidence of large scale-slope failures or glacier ice having influenced debris accumulation. The landforms are probably of Nahanagan Stadial age (c. 11-10 ka BP) and testify to the susceptibility of well-jointed quartzite cliffs and talus to significant modification under the severe climatic regime of the stadial.
Proposal Doctor of Philosophy Quantitative modeling of the neural representation of nouns and phrases

Recent advances in functional Magnetic Resonance Imaging (fMRI) offer a significant new approach to studying semantic representations in humans by making it possible to directly observe brain activity while people comprehend words and sentences. In the proposed work, we used fMRI to study the cortical systems that underpin semantic representation while people comprehended linguistic concepts like concrete objects, adjective-noun phrases, or noun-noun concept combinations. The thesis of this research is that the distributed pattern of neural activity encodes the meanings of linguistic concepts and intermediate semantic representation can be used to model how brain composes the meaning of words or phrases in terms of more primitive semantic features. In an object-contemplation task, participants were presented with line drawings and/or text labels of objects and were instructed to think of the same properties of the stimulus object consistently during multiple presentations of each item. By learning 1) the correspondence between features in semantic models and neural activity, and 2) the semantic composition model that governs how words are combined to form phrases, this work enables a predictive theory that is capable of extrapolating the model of the neural activity to previously unseen words and phrases.
Graph Kernels Based on Multi-scale Graph Embeddings

—Graph kernels are conventional methods for com- puting graph similarities. However, most of the R-convolution graph kernels face two challenges: 1) They cannot compare graphs at multiple different scales, and 2) they do not consider the distributions of substructures when computing the kernel matrix. These two challenges limit their performances. To mitigate the two challenges, we propose a novel graph kernel called the Multi- scale Path-pattern Graph kernel (MPG), at the heart of which is the multi-scale path-pattern node feature map. Each element of the path-pattern node feature map is the number of occurrences of a path-pattern around a node. A path-pattern is constructed by the concatenation of all the node labels in a path of a truncated BFS tree rooted at each node. Since the path-pattern node feature map can only compare graphs at local scales, we incorporate into it the multiple different scales of the graph structure, which are captured by the truncated BFS trees of different depth. We use the Wasserstein distance to compute the similarity between the multi-scale path-pattern node feature maps of two graphs, considering the distributions of substructures. We empirically validate MPG on various benchmark graph datasets and demonstrate that it achieves state-of-the-art performance.
Breakthrough of attenuating contaminant plumes in pumping wells: Analytical model and implications for integral pumping tests

Monitoring of contaminant plumes and predicting their future fate are essential for effective management of groundwater contaminants. Solute breakthrough curves from operating pumping wells can provide information on the water quality in relatively large aquifer regions, which may be unavailable to instrumentation and direct measurement for practical and/or economical reasons. Relations between spatially varying aquifer concentrations C0(x, y) initially surrounding a well and temporally varying concentrations of subsequently extracted well water, Cp(t), then need to be quantified. However, limited applicability of analytical expressions and numerical inaccuracies related to solving transport equations for converging flow fields hamper such quantifications even in homogeneous aquifers. We use a stream‐tube approach and provide a general problem formulation that accounts for first‐order degradation and linear, instantaneous, sorption/retardation in heterogeneous aquifers. An analytical expression is obtained for homogeneous aquifer conditions (in the well vicinity), relating any given initial C0(x, y) function and the subsequent contaminant breakthrough Cp(t) in the well. Results for wide plumes subject to first‐order degradation show that concentrations at the extraction well will increase as a function of pumping time. This increase is despite the fact that late‐time data reflect longer transport paths (to the well), along which mass is removed through degradation. We also derive unique solutions for the inverse problem, in particular considering how the average contaminant concentration Cav (averaged along a control plane through the well within its capture zone, perpendicular to the mean groundwater flow direction) depends on the measurable Cp(t). The solutions demonstrate that the longer the pumping time, the more sensitive the solutions for Cav become to degradation rate constants, which if needed can be determined in situ using multiple control planes.
Channel management in IEEE 802.22 WRAN systems

Channel management policy is one of the key functions of any cognitive radio system. The channel management decision in a cognitive radio system depends on various factors such as government regulations, limitations of transmission power between communicating devices, availability of geo-location information, and the quality of various available channels. The main purpose of channel management in a cognitive radio system is to protect the incumbent users (licensed users). The first international standard based on cognitive radio technology is IEEE 802.22, Wireless Regional Area Networks, which aims at providing broadband access in rural areas by effectively utilizing the unused TV channels. This standard aims at describing both PHY and MAC layer functionalities in an infrastructure- based broadband wireless access network for communication between consumer premises equipment using a base station. This article presents the cognitive radio functionality of this standard in detail. The cognitive functionality is provided by the dynamic channel management using channel sensing, channel classification, and maintenance of channel information. Using these techniques, this standard effectively protects the incumbent users and dynamically manages the channel allocation in IEEE 802.22 WRAN systems.
Learning to close the loop from 3D point clouds

This paper presents a new solution to the loop closing problem for 3D point clouds. Loop closing is the problem of detecting the return to a previously visited location, and constitutes an important part of the solution to the Simultaneous Localisation and Mapping (SLAM) problem. It is important to achieve a low level of false alarms, since closing a false loop can have disastrous effects in a SLAM algorithm. In this work, the point clouds are described using features, which efficiently reduces the dimension of the data by a factor of 300 or more. The machine learning algorithm AdaBoost is used to learn a classifier from the features. All features are invariant to rotation, resulting in a classifier that is invariant to rotation. The presented method does neither rely on the discretisation of 3D space, nor on the extraction of lines, corners or planes. The classifier is extensively evaluated on publicly available outdoor and indoor data, and is shown to be able to robustly and accurately determine whether a pair of point clouds is from the same location or not. Experiments show detection rates of 63% for outdoor and 53% for indoor data at a false alarm rate of 0%. Furthermore, the classifier is shown to generalise well when trained on outdoor data and tested on indoor data in a SLAM experiment.
Towards Designer Modeling through Design Style Clustering

We propose modeling designer style in mixed-initiative game content creation tools as archetypical design traces. These design traces are formulated as transitions between design styles; these design styles are in turn found through clustering all intermediate designs along the way to making a complete design. This method is implemented in the Evolutionary Dungeon Designer, a prototype mixed-initiative system for roguelike games. We present results both in the form of design styles for rooms, which can be analyzed to better understand the kind of rooms designed by users, and in the form of archetypical sequences between these rooms. We further discuss how the results here can be used to create style-sensitive suggestions. Such suggestions would allow the system to be one step ahead of the designer, offering suggestions for the next phase, assuming that the designer will follow one of the archetypical design traces.
Water Resources and Natural Gas Production from the Marcellus Shale

T Marcellus Shale is a sedimentary rock formation deposited over 350 million years ago in a shallow inland sea located in the eastern United States where the present-day Appalachian Mountains now stand (de Witt and others, 1993). This shale contains significant quantities of natural gas. New developments in drilling technology, along with higher wellhead prices, have made the Marcellus Shale an important natural gas resource. The Marcellus Shale extends from southern New York across Pennsylvania, and into western Maryland, West Virginia, and eastern Ohio (fig. 1). The production of commercial quantities of gas from this shale requires large volumes of water to drill and hydraulically fracture the rock. This water must be recovered from the well and disposed of before the gas can flow. Concerns about the availability of water supplies needed for gas production, and questions about wastewater disposal have been raised by water-resource agencies and citizens throughout the Marcellus Shale gas development region. This Fact Sheet explains the basics of Marcellus Shale gas production, with the intent of helping the reader better understand the framework of the water-resource questions and concerns. What is the Marcellus Shale?
Similarity-driven Sequence Classification Based on Support Vector Machines and Its Application in Adaptive On-line Handwriting Recognition Paper ID : 755

In this paper, a novel sequence classification method is proposed in the context of Support Vector Machines (SVM). This method is driven by an intuitive similarity measure, namelyER, which directly tells the similarity of two sequences (1or multi-dimensional). If sequence X is very similar to Y (for instance, the similarity byER is above 90%), it is safe to assign X to the same class as Y . ER is plugged into standard SVM to speed up the decision-making of multi-class classification. The immediate application of the method is in the adaptive on-line handwriting recognition, where handwritten characters are represented by 2D sequences of X-,Y-coordinates. Experiments on the benchmark database UNIPEN show that the classification driven byER can be about three times faster than standard SVM while the classification accuracy is enhanced or comparable.
The Oldest Fossil Pirate Spider (Araneae: Mimetidae), in Uppermost Eocene Indian Amber, Imaged Using X-ray Computed Tomography

Summary A fossil pirate spider (Araneae, Mimetidae) is documented in amber from the Eocene Cambay Formation of India. It represents the first described spider from this deposit and the oldest fossil record of the family, extending its known geological range by approximately 3–8 million years from the previously oldest fossils in Eocene Baltic amber. It also represents the first Gondwanan geological locality for fossil mimetids. Despite the application of X-ray computed tomography to the study of this inclusion, the state of preservation of the juvenile spider did not permit identification beyond family level.
A taxonomy and survey of grid resource management systems for distributed computing

The resource management system is the central component of distributed network computing systems. There have been many projects focused on network computing that have designed and implemented resource management systems with a variety of architectures and services. In this paper, an abstract model and a comprehensive taxonomy for describing resource management architectures is developed. The taxonomy is used to identify approaches followed in the implementation of existing resource management systems for very large-scale network computing systems known as Grids. The taxonomy and the survey results are used to identify architectural approaches and issues that have not been fully explored in the research. Copyright  2001 John Wiley & Sons, Ltd.
Learning Matchable Colorspace Transformations for Long-term Metric Visual Localization

Long-term metric localization is an essential capability of autonomous mobile robots, but remains challenging for vision-based systems in the presence of appearance change caused by lighting, weather or seasonal variations. While experience-based mapping has proven to be an effective technique for enabling visual localization across appearance change, the number of experiences required for reliable long-term localization can be large, and methods for reducing the necessary number of experiences are desired. Taking inspiration from physics-based models of color constancy, we propose a method for learning a nonlinear mapping from RGB to grayscale colorspaces that maximizes the number of feature matches for images captured under varying lighting and weather conditions. Our key insight is that useful image transformations can be learned by approximating conventional non-differentiable localization pipelines with a differentiable learned model that can predict a convenient measure of localization quality, such as the number of feature matches, for a given pair of images. Moreover, we ﬁnd that the generality of appearance-robust RGB-to-grayscale mappings can be improved by incorporating a learned low-dimensional context feature computed for a speciﬁc image pair. Using synthetic and real-world datasets, we show that our method substantially improves feature matching across day-night cycles and presents a viable strategy for signiﬁcantly improving the efﬁciency of experience-based visual localization.
Geometric Reinforcement Learning: The Case of Cartesian Space Orientation

—Reinforcement learning (RL) enables an agent to learn by trial and error while interacting with a dynamic environment. Traditionally, RL is used to learn and predict Euclidean robotic manipulation skills like positions, velocities, and forces. However, in robotics, it is common to have non-Euclidean data like orientation or stiffness, and neglecting their geometric nature can adversely affect learning performance and accuracy. In this paper, we propose a novel framework for RL by using Riemannian geometry, and show how it can be applied to learn manipulation skills with a speciﬁc geometric structure (e.g., robot’s orientation in the task space). The proposed framework is suitable for any policy representation and is independent of the algorithm choice. Speciﬁcally, we propose to apply policy parameterization and learning on the tangent space, then map the learned actions back to the appropriate manifold (e.g., the S 3 manifold for orientation). Therefore, we introduce a geometrically grounded pre-and post-processing step into the typical RL pipeline, which opens the door to all algorithms designed for Euclidean space to learn from non-Euclidean data without changes. Experimental results, obtained both in simulation and on a real robot, support our hypothesis that learning on the tangent space is more accurate and converges to a better solution than approximating non-Euclidean data.
Update on copper and selenium in Canadian cow–calf herds: regional differences and estimation of serum reference values

Abstract Trace mineral supplementation of beef cattle is essential for efficient reproduction and herd health. Understanding regional differences in cow trace mineral status could inform decisions about risks of deficiencies and supplementation management. Cow–calf surveillance projects provided three opportunities to evaluate the trace mineral status of Canadian beef cow herds. Blood samples were collected at pregnancy testing in 2014 from 102 cow–calf herds and in 2016 from 86 cow–calf herds in Western Canada. In 2019, blood samples were collected at pregnancy testing from cows in 163 cow–calf herds from Eastern and Western Canada. Serum samples were analyzed for copper, selenium, and molybdenum concentrations using a plasma mass spectrometer. The prevalence of copper deficient cows sampled from the Western provinces ranged from 24% to 43% across the three periods, and was 20% from Eastern Canada in 2019. The prevalence of selenium deficient cows ranged from 0.2% to 0.4% across the three projects in Western Canada, but was higher in Eastern Canada at 4.6% in 2019. High serum molybdenum was identified in 9.4% to 14% of cows across the three periods in Western Canada and in 15% of cows sampled in Eastern Canada in 2019. Serum copper, selenium, and molybdenum concentrations varied by cow age and month of sample collection. Serum selenium and molybdenum concentrations, but not copper, varied by soil type associated with the location of the farm. A subsample of samples from cows from Western Canadian herds provided body condition score (BCS) data, pregnancy status, and calf survival data and were used to estimate updated serum reference values for adequate concentrations. Age-specific values were required for selenium and molybdenum. Reference intervals (80%) were estimated from 2,406 pregnant beef cows from 99 herds with each cow having a BCS ≥ 2.5/5 and a live calf at 3 wk with no retained placenta: copper for all cows (0.379 to 0.717 ppm), selenium for cows <4 yr (0.052 to 0.152 ppm), and selenium for cows ≥4 yr (0.064 to 0.184 ppm). Upper 90% reference limits were also estimated for serum molybdenum for cows <4 yr (>0.104 ppm) and cows ≥4 yr (>0.110 ppm). The lower limits for the reference intervals for adequate copper and selenium are below those previously reported; nevertheless, they represent a large sample that was specifically applicable to extensively managed beef animals in western Canada.
Projecting land-use and land-cover changes in a tropical mountain forest of Southern Ecuador

Land-use and land-cover changes (LULCC) affect local climate. Human-induced deforestation is a common phenomenon of LULCC. This also holds true for the biodiversity hotspot in the Andes of Ecuador. This study assesses the possibility to project LULCC to future time steps with a focus on deforestation in the San Francisco valley in South Ecuador. A business-as-usual scenario based on two Landsat scenes from 1987 and 2001 was created to project LULCC until 2006. The uncertainty assessment indicated the difficulty of projecting human impact on the ecosystem since circumstances of LULCC in the study area cannot be assumed to stay invariant. The projection performs better than a naive model relying on slope as its suitability factor.
In-Processing Modeling Techniques for Machine Learning Fairness: A Survey

Machine learning models are becoming pervasive in high-stakes applications. Despite their clear benefits in terms of performance, the models could show discrimination against minority groups and result in fairness issues in a decision-making process, leading to severe negative impacts on the individuals and the society. In recent years, various techniques have been developed to mitigate the unfairness for machine learning models. Among them, in-processing methods have drawn increasing attention from the community, where fairness is directly taken into consideration during model design to induce intrinsically fair models and fundamentally mitigate fairness issues in outputs and representations. In this survey, we review the current progress of in-processing fairness mitigation techniques. Based on where the fairness is achieved in the model, we categorize them into explicit and implicit methods, where the former directly incorporates fairness metrics in training objectives, and the latter focuses on refining latent representation learning. Finally, we conclude the survey with a discussion of the research challenges in this community to motivate future exploration.