Miracle-dz/UniFAR-FTUs huggingface dataset:
Future changes in autumn atmospheric river events in British Columbia, Canada, as projected by CMIP5 global climate models

Atmospheric rivers (ARs) often trigger extreme precipitation events in British Columbia (BC), Canada. Here we analyze how well the autumn AR events with the highest probability for extreme precipitation over BC, henceforth called AR‐extreme events, are simulated in five Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) and how these AR‐extreme events are projected to change by the end of the century. We examine the daily synoptic patterns of integrated water vapor transport (IVT) over the Pacific Ocean that favor the formation of AR‐extreme events. Our analysis and comparison with AR‐extreme events in four reanalysis products for the period 1979–2010 reveal that the GCMs more successfully resolve their seasonality and interannual variability than their frequencies and amount of precipitation brought to BC. For the CMIP5 scenario's Representative Concentration Pathway (RCP) 4.5 and RCP8.5, the frequency of AR‐extreme events will increase for the period 2070–2100 with the largest increase in December. All models project an increase in total precipitation over BC, due to the increase in frequency and intensity of the AR‐extreme events; however, the dominant factor is the increase in frequency, especially of those events with precipitation exceeding 20 mmd−1. The path of the ARs during the AR‐extreme events is projected to move northward, bringing stronger IVT and more precipitation to the north coast of BC, while the south coast may become drier than at the present day. The shift in the ARs is driven by the northward shift in the Aleutian Low pressure system, especially in RCP8.5.
Potential feedback of recent vegetation changes on summer rainfall in the Sahel

The Sahel region of Northern Africa is home to more than 50 million people for whom summer rainfall is a crucial water resource in terms of food security and societal stability. Using satellite-based Normalized Difference Vegetation Index (NDVI) and gridded observational precipitation records during 1982–2012, we detected a significant increase (p-value < 0.01) in both vegetation greenness and monsoon rainfall over the Sahel since the early 1980s. A significant positive association between NDVI and precipitation was observed for most of the Sahel during the boreal summer. In further efforts to examine the potential causal association behind the positive correlation, we found that summer vegetation greenness Granger-causes summer rainfall in the Sahel. Regarding the physical process behind this identified Granger causality, we inferred that significantly increasing latent heat flux and specific humidity resulted in increasing summer rainfall during the years of high NDVI in the Sahel. A significant increase in the percentage of land used for crops and pastures was a potential cause of the recent vegetation change. Our findings indicated that the positive effect of vegetation cover through agricultural activities on regional precipitation could lead to a positive feedback between the vegetation and climate in the water-limited Sahel region.
Husbandry and breeding practices of dromedary camels among pastoral communities of Afar and Somali regional states, Ethiopia

The objectives of this paper were to identify and describe husbandry practices, herd structure, owners' trait preferences, breeding practices, and production constraints of camel in the two major camel rearing regions of Ethiopia viz. Afar and Somali, to generate baseline information that would help to plan possible breed improvement strategies and options for the different camel populations. The study sites were selected purposively while households from each of the sites randomly. Data were collected using formal questionnaires and focus group discussion. Results showed that average camel population per household was higher in Mille (28.06±2.27), Gode (27.51±2.02), and Moyale (24.07±2.13) districts. Female camel populations with age of >1 year contributes 78-83 % of the total camel herd population in all the study districts. Higher number of female animals in the herd in the arid environment means providing continuous supply of milk and allows a rapid recovery of herd numbers after a disease outbreak or drought occurrence. This shows that pastoralists breeding objectives are in relation to the arid environment and female population in the herd. Most of the pastoral communities utilize a single breeding male camel per 40-50 female camels and this will affect productivity and heterogeneity of camel population. With regard to trait preference, all pastoral communities ranked milk yield as the first trait of choice, except Moyale district in which adaptation trait was the primary preference. Growth trait ranked second in Mille, Gode, Liben, and Jijiga pastoral communities where as adaptation trait ranked second in Amibara and Shinille pastoral communities. The major camel production constraints were feed, diseases, and lack of water in that order and the major cause of the constraints
MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation

We introduce MVControl, a novel neural network architecture that enhances existing pre-trained multi-view 2D diffusion models by incorporating additional input conditions, e.g. edge maps. Our approach enables the generation of controllable multi-view images and view-consistent 3D content. To achieve controllable multi-view image generation, we leverage MVDream as our base model, and train a new neural network module as additional plugin for end-to-end task-specific condition learning. To precisely control the shapes and views of generated images, we innovatively propose a new conditioning mechanism that predicts an embedding encapsulating the input spatial and view conditions, which is then injected to the network globally. Once MVControl is trained, score-distillation (SDS) loss based optimization can be performed to generate 3D content, in which process we propose to use a hybrid diffusion prior. The hybrid prior relies on a pre-trained Stable-Diffusion network and our trained MVControl for additional guidance. Extensive experiments demonstrate that our method achieves robust generalization and enables the controllable generation of high-quality 3D content. Code available at https://github.com/WU-CVGL/MVControl/.
Towards Transferable Multi-modal Perception Representation Learning for Autonomy: NeRF-Supervised Masked AutoEncoder

This work proposes a unified self-supervised pre-training framework for transferable multi-modal perception representation learning via masked multi-modal reconstruction in Neural Radiance Field (NeRF), namely NeRF-Supervised Masked AutoEncoder (NS-MAE). Specifically, conditioned on certain view directions and locations, multi-modal embeddings extracted from corrupted multi-modal input signals, i.e., Lidar point clouds and images, are rendered into projected multi-modal feature maps via neural rendering. Then, original multi-modal signals serve as reconstruction targets for the rendered multi-modal feature maps to enable self-supervised representation learning. Extensive experiments show that the representation learned via NS-MAE shows promising transferability for diverse multi-modal and single-modal (camera-only and Lidar-only) perception models on diverse 3D perception downstream tasks (3D object detection and BEV map segmentation) with diverse amounts of fine-tuning labeled data. Moreover, we empirically find that NS-MAE enjoys the synergy of both the mechanism of masked autoencoder and neural radiance field. We hope this study can inspire exploration of more general multi-modal representation learning for autonomous agents.
Physicochemical Assessment of Soil in Doiwala Dehradun District of Uttrakhand (India)

In the present research work, studies on soils with physical and chemical properties of soils have been done. Soil samples were collected from Four differ-ent locations covering Doiwala Dehradun, District (Uttrakhand) India. The soil parameters like soil moisture, pH, EC, Carbon, Calcium carbonate, TDS, Magnesium, Calcium,Nitrogen, Copper, Potassium and Phosphorous content, were analyzed in the month of October 2010 to May 2011. The values of pH indicated that all samples of the soils are alkaline, all samples were containing moderate amounts of available micronutrients.
Visual Dexterity: In-hand Dexterous Manipulation from Depth

In-hand object reorientation is necessary for performing many dexterous manipulation tasks, such as tool use in less structured environments that remain beyond the reach of current robots. Prior works built reorientation systems assuming one or many of the following: reorienting only specific objects with simple shapes, limited range of reorientation, slow or quasistatic manipulation, simulation-only results, the need for specialized and costly sensor suites, and other constraints which make the system infeasible for real-world deployment. We present a general object reorientation controller that does not make these assumptions. It uses readings from a single commodity depth camera to dynamically reorient complex and new object shapes by any rotation in realtime, with the median reorientation time being close to seven seconds. The controller is trained using reinforcement learning in simulation and evaluated in the real world on new object shapes not used for training, including the most
Possibilities for methanogenic and acetogenic life in molecular cloud

There are life forms in space and the ancestor of Earth life came from interstellar space according to models like panspermia. Naturally, life may also exist in molecular clouds. Here the author discusses the possibility of methanogenic life in Molecular Cloud with methane as the final metabolism product. According to the calculations, it is easy to see that the chemical reaction through methanogenesis can release sufficient free energy. If methanogenic life exist in the pre-solar nebula, then they may be the ancestor of Earth's life and there are already some tentative evidences by several molecular biology studies.
Expanders Satisfy the Weak Meyniel Conjecture

We show that if $\{G_n\}_{n\geq 1}$ is a sequence of graphs of order $n$ with bounded maximum degree and isoperimetric function $\Phi(G_n,n^{1-\alpha})$ bounded away from $0$ as $n\rightarrow \infty$, then the cop number of $G_n$ is at most $O(n^{\frac{1+\alpha}{2}+o(1)})$. It is unclear if this bound is tight given our assumption that the maximum degree of our sequence is bounded. All the same, recent work by Hosseini, Mohar, and Gonzalez Hermosillo de la Maza strongly motivates considering the bounded degree case, as they show that proving that the cop number of graphs of bounded max degree is $O(n^{1-\epsilon})$ would also prove that for all graphs the cop number is $O(n^{1-\alpha})$ for some $\alpha>0$. This would resolve a weak version of a notable conjecture by Meyniel. Next, we prove that certain unintuitive graphs necessarily exist and have large cop number if the weak version Meyniel's conjecture is false. We concluding by conjecturing that any such unintuitive graphs must be expanders and would therefore not have large cop number by the upper bound we show. Thus, our conjecture would imply a weak version of Meyniel's conjecture.
Electrically tunable correlated domain wall network in twisted bilayer graphene

We investigate the domain wall network in twisted bilayer graphene (TBG) under the influence of interlayer bias and screening effect from the layered structure. Starting from the continuum model, we analyze the low-energy domain wall modes within the moir\'e bilayer structure and obtain an analytic form representing charge density distributions of the two-dimensional structure. By computing the screened electron-electron interaction strengths both within and between the domain walls, we develop a bosonized model that describes the correlated domain wall network. We demonstrate that these interaction strengths can be modified through an applied interlayer bias, screening length and dielectric materials, and show how the model can be employed to investigate various properties of the domain wall network and its stability. We compute correlation functions both without and with phonons. Including electron-phonon coupling in the network, we establish phase diagrams from these correlation functions. These diagrams illustrate electrical tunability of the network between various phases, such as density wave states and superconductivity. Our findings reveal the domain wall network as a promising platform for the experimental manipulation of electron-electron interactions in low dimensions and the study of strongly correlated matter. We point out that our investigation not only enhances the understanding of domain wall modes in TBG but also has broader implications for the development of moir\'e devices.
Maximizing Discrimination Capability of Knowledge Distillation with Energy-based Score

To apply the latest computer vision techniques that require a large computational cost in real industrial applications, knowledge distillation methods (KDs) are essential. Existing logit-based KDs apply the constant temperature scaling to all samples in dataset, limiting the utilization of knowledge inherent in each sample individually. In our approach, we classify the dataset into two categories (i.e., low energy and high energy samples) based on their energy score. Through experiments, we have confirmed that low energy samples exhibit high confidence scores, indicating certain predictions, while high energy samples yield low confidence scores, meaning uncertain predictions. To distill optimal knowledge by adjusting non-target class predictions, we apply a higher temperature to low energy samples to create smoother distributions and a lower temperature to high energy samples to achieve sharper distributions. When compared to previous logit-based and feature-based methods, our energy-based KD (Energy KD) achieves better performance on various datasets. Especially, Energy KD shows significant improvements on CIFAR-100-LT and ImageNet datasets, which contain many challenging samples. Furthermore, we propose high energy-based data augmentation (HE-DA) for further improving the performance. We demonstrate that meaningful perfor-∗
Learning Saliency From Fixations

We present a novel approach for saliency prediction in images, leveraging parallel decoding in transformers to learn saliency solely from fixation maps. Models typically rely on continuous saliency maps, to overcome the difficulty of optimizing for the discrete fixation map. We attempt to replicate the experimental setup that generates saliency datasets. Our approach treats saliency prediction as a direct set prediction problem, via a global loss that enforces unique fixations prediction through bipartite matching and a transformer encoder-decoder architecture. By utilizing a fixed set of learned fixation queries, the cross-attention reasons over the image features to directly output the fixation points, distinguishing it from other modern saliency predictors. Our approach, named Saliency TRansformer (SalTR), achieves metric scores on par with state-of-the-art approaches on the Salicon and MIT300 benchmarks.
Correcting soil nutrient deficiencies with organic materials in the atoll soils of the Marshall Islands

The coralline soils of atolls suffer from multiple nutrient deficiencies that severely limit crop growth. This study was conducted to assess the nutrient status of the soils of the inhabited atolls within the Marshall Islands (MI), and to determine what local materials could be used to correct deficiencies limiting crop growth. Surface and subsoils from 25 atolls were collected and analyzed for their chemical properties, and soil test results were evaluated with a missing element pot study. Soil tests revealed that the MI soils were severely deficient in K (0.12 cmolc kg"l) and marginally deficient in Cu (0.13 ug g'I). The missing element study showed that the soil was deficient in K, S, N, P, and Cu. An incubation experiment and a series of greenhouse experiments were conducted to evaluate the ability oflocally available organic materials to mineralize N and supply adequate nutrients to crops. Vigna marina and fish meal showed the highest N mineralization capacity, and the Gompertz equation provided the best fit. Chinese cabbage plants grew as well in soils amended with chicken manure, Vigna marina, and copra cake as they did in soil treated with chemical fertilizers. Plants grown in soil amended with fish meal did not grow as well due to inadequate K supply. Nitrogen recovery was highest in V. marina treatment at 92% followed by the chemical control (83%), chicken manure (34%), fish-meal (18%), and copra cake (9%). Added coconut leaves immobilized N and resulted in very poor cabbage growth. Comparisons between relative growth rate (RGR) and nutrient relative accumulation rate (RAR) showed that nutrients supplied from the V. marina amendment to the cabbage plant matched plant demand. In a rate
The CO-to-H2 conversion factor of Galactic giant molecular clouds using CO isotopologues: high-resolution XCO maps

We investigated the correlation between intensities of the 12CO and 13CO (J = 1–0) lines towards the Galactic giant molecular clouds (GMCs) W51A, W33, N35–N36 complex, W49A, M17SW, G12.02–00.03, W43, and M16 using the FUGIN (FOREST Unbiased Galactic plane Imaging survey with the Nobeyama 45-m telescope) CO line data. All the GMCs show intensity saturation in the 12CO line when the brightness temperature of 13CO is higher than a threshold temperature of about ∼5 K. We obtained high-resolution (∼20 arcsec) distribution maps of the XCO factor (XCO,iso) in individual GMCs using correlation diagrams of the CO isotopologues. It is shown that XCO,iso is variable in each GMC within the range of XCO,iso ∼ (0.9–5) × 1020 cm−2 (K km s−1)−1. Despite the variability in the GMCs, the average value among the GMCs is found to be nearly constant at XCO,iso = (2.17 ± 0.27) × 1020 cm−2 (K km s−1)−1, which is consistent with that from previous studies in the Milky Way.
Human Machine Co-Creation. A Complementary Cognitive Approach to Creative Character Design Process Using GANs

Recent advances in Generative Adversarial Networks GANs applications continue to attract the attention of researchers in different fields. In such a framework, two neural networks compete adversely to generate new visual contents indistinguishable from the original dataset. The objective of this research is to create a complementary codesign process between humans and machines to augment character designers abilities in visualizing and creating new characters for multimedia projects such as games and animation. Driven by design cognitive scaffolding, the proposed approach aims to inform the process of perceiving, knowing, and making. The machine generated concepts are used as a launching platform for character designers to conceptualize new characters. A labelled dataset of 22,000 characters was developed for this work and deployed using different GANs to evaluate the most suited for the context, followed by mixed methods evaluation for the machine output and human derivations. The discussed results substantiate the value of the proposed cocreation framework and elucidate how the generated concepts are used as cognitive substances that interact with designers competencies in a versatile manner to influence the creative processes of conceptualizing novel characters.
Perceptual Image Compression with Cooperative Cross-Modal Side Information

The explosion of data has resulted in more and more associated text being transmitted along with images. Inspired by from distributed source coding, many works utilize image side information to enhance image compression. However, existing methods generally do not consider using text as side information to enhance perceptual compression of images, even though the benefits of multimodal synergy have been widely demonstrated in research. This begs the following question: How can we effectively transfer text-level semantic dependencies to help image compression, which is only available to the decoder? In this work, we propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff. Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features. This is done by predicting a semantic mask to guide the learned text-adaptive affine transformation at the pixel level. Furthermore, we design a text-conditional generative adversarial networks to improve the perceptual quality of reconstructed images. Extensive experiments involving four datasets and ten image quality assessment metrics demonstrate that the proposed approach achieves superior results in terms of rate-perception trade-off and semantic distortion.
Observation of strong nuclear suppression in exclusive $J/\psi$ photoproduction in Au$+$Au ultra-peripheral collisions at RHIC

We report a measurement of exclusive $J/\psi$ photoproduction in Au$+$Au ultra-peripheral collisions at $\sqrt{s_{_\mathrm{NN}}}=200$ GeV using the STAR detector. For the first time, i) the rapidity correlation between $J/\psi$ photoproduction and neutron emission from nuclear breakups has been experimentally measured; ii) nuclear suppression factors are measured for both the coherent and incoherent $J/\psi$ production. At photon-nucleon center-of-mass energy of 25.0 GeV, the coherent and incoherent $J/\psi$ cross sections of Au nuclei are found to be $71\pm10\%$ and $36\pm7\%$, respectively, of that of free protons. The stronger suppression observed in the incoherent production provides a new experimental handle to study the initial-state parton density in heavy nuclei. Data are compared with theoretical models quantitatively.
Robust Decision Aggregation with Second-order Information

We consider a decision aggregation problem with two experts who each make a binary recommendation after observing a private signal about an unknown binary world state. An agent, who does not know the joint information structure between signals and states, sees the experts' recommendations and aims to match the action with the true state. Under the scenario, we study whether supplemented additionally with second-order information (each expert's forecast on the other's recommendation) could enable a better aggregation. We adopt a minimax regret framework to evaluate the aggregator's performance, by comparing it to an omniscient benchmark that knows the joint information structure. With general information structures, we show that second-order information provides no benefit. No aggregator can improve over a trivial aggregator, which always follows the first expert's recommendation. However, positive results emerge when we assume experts' signals are conditionally independent given the world state. When the aggregator is deterministic, we present a robust aggregator that leverages second-order information, which can significantly outperform counterparts without it. Second, when two experts are homogeneous, by adding a non-degenerate assumption on the signals, we demonstrate that random aggregators using second-order information can surpass optimal ones without it. In the remaining settings, the second-order information is not beneficial. We also extend the above results to the setting when the aggregator's utility function is more general.