Miracle-dz/UniFAR-FTUs huggingface dataset:
The Density and Survival of Fucus vesiculosus L. (Fucales, Phaeophyta) on Different Bedrock Types on a Baltic Sea Moraine Coast

Abstract The glacial moraine along the Baltic Sea of the south-eastern coast of Sweden is composed mainly of porous and soft Lower Cambrian sandstone and hard granite together with small amounts of alkaline amphibolite and diabase. Our objective was to determine whether the chemical and structural properties of these bedrock types, in combination with wave exposure, affect germling survival and adult plant density of Fucus vesiculosus L. commonly found growing on stones and boulders in the area. The number of surviving F. vesiculosus germlings was found to be similar on the alkaline amphibolite and diabase (1.0x104±0.8x103 ind.m−2). It was significantly higher than on the acidic porphyry, sandstone, granite, and gneiss (0.6x104±0.5x103 ind.m−2) after two months of growth. These results indicate that small-scale differences in surface structures such as porosity, holes and fissures may affect the survival of early stages of F. vesiculosus germlings in the Baltic Sea. Also the natural density of F. vesiculosus individuals at the wave-exposed site was shown to differ depending on bedrock type, with the highest number of plants on sandstone (1893±125 ind. m−2) followed by granite (405±28 ind.m−2) and amphibolite (374±33 ind.m−2). Contrary to the wave-exposed site, the density of plants was similar on all three types at the sheltered site. Further, the attachment strength of adult F. vesiculosus specimens was similar, i.e. 57.6±4.1 N and 61.8±6.8 N on granite and sandstone, respectively, and significantly higher than on amphibolite (36.3±4.2 N). Thus we suggest that the distribution and proportion of different bedrock types on a moraine coast may affect both the survival of Fucus germlings on a very small scale and may affect the density of plants due to differences in attachment strength and impact of waves.
F-rank-width of (edge-colored) graphs

Rank-width is a complexity measure equivalent to the clique-width of undirected graphs and has good algorithmic and structural properties. It is in particular related to the vertex-minor relation. We discuss an extension of the notion of rank-width to all types of graphs - directed or not, with edge colors or not -, named F-rank-width. We extend most of the results known for the rank-width of undirected graphs to the F-rank-width of graphs: cubic-time recognition algorithm, characterisation by excluded configurations under vertex-minor and pivot-minor, and algebraic characterisation by graph operations. We also show that the rank-width of undirected graphs is a special case of F-rank-width.
Evolution of Oxygenic Photosynthesis

The origin of oxygenic photosynthesis was the most important metabolic innovation in Earth history. It allowed life to generate energy and reducing power directly from sunlight and water, freeing it from the limited resources of geochemically derived reductants. This greatly increased global primary productivity and restructured ecosystems. The release of O2 as an end product of water oxidation led to the rise of oxygen, which dramatically altered the redox state of Earth's atmosphere and oceans and permanently changed all major biogeochemical cycles. Furthermore, the biological availability of O2 allowed for the evolution of aerobic respiration and novel biosynthetic pathways, facilitating much of the richness we associate with modern biology, including complex multicellularity. Here we critically review and synthesize information from the geological and biological records for the origin and evolution of oxygenic photosynthesis. Data from both of these archives illustrate that this metabolism first appea.
Robust parameter estimation in dynamical systems via Statistical Learning with an application to epidemiological models

We propose a robust parameter estimation method for dynamical systems based on Statistical Learning techniques which aims to estimate a set of parameters that well fit the dynamics in order to obtain robust evidences about the qualitative behaviour of its trajectory. The method is quite general and flexible, since it dos not rely on any specific property of the dynamical system, and represents a mathematical formalisation of the procedure consisting of sampling and testing parameters, in which evolutions generated by candidate parameters are tested against observed data to assess goodness-of-fit. The Statistical Learning framework introduces a mathematically rigorous scheme to this general approach for parameter estimation, adding to the great field of parameter estimation in dynamical systems. The method is specially useful for estimating parameters in epidemiological compartmental models. We illustrate it in simulated and real data about COVID-19 spread in the US in order to assess qualitatively the peak of deaths by the disease.
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis

this paper proposed a parallel-channel model to process the textual and visual information in memes and then analyze the sentiment polarity of memes. In the shared task of identifying and categorizing memes, we preprocess the dataset according to the language behaviors on social media. Then, we adapt and fine-tune the Bidirectional Encoder Representations from Transformers (BERT), and two types of convolutional neural network models (CNNs) were used to extract the features from the pictures. We applied an ensemble model that combined the BiLSTM, BIGRU, and Attention models to perform cross domain suggestion mining. The officially released results show that our system performs better than the baseline algorithm
Distributionally Robust Losses for Latent Covariate Mixtures

Reliable Machine Learning via Structured Distributionally Robust Optimization Data sets used to train machine learning (ML) models often suffer from sampling biases and underrepresent marginalized groups. Standard machine learning models are trained to optimize average performance and perform poorly on tail subpopulations. In “Distributionally Robust Losses for Latent Covariate Mixtures,” John Duchi, Tatsunori Hashimoto, and Hongseok Namkoong formulate a DRO approach for training ML models to perform uniformly well over subpopulations. They design a worst case optimization procedure over structured distribution shifts salient in predictive applications: shifts in (a subset of) covariates. The authors propose a convex procedure that controls worst case subpopulation performance and provide finite-sample (nonparametric) convergence guarantees. Empirically, they demonstrate their worst case procedure on lexical similarity, wine quality, and recidivism prediction tasks and observe significantly improved performance across unseen subpopulations.
A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users’ requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This article provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.
Multi-camera Torso Pose Estimation using Graph Neural Networks

Estimating the location and orientation of humans is an essential skill for service and assistive robots. To achieve a reliable estimation in a wide area such as an apartment, multiple RGBD cameras are frequently used. Firstly, these setups are relatively expensive. Secondly, they seldom perform an effective data fusion using the multiple camera sources at an early stage of the processing pipeline. Occlusions and partial views make this second point very relevant in these scenarios. The proposal presented in this paper makes use of graph neural networks to merge the information acquired from multiple camera sources, achieving a mean absolute error below 125mm for the location and 10° for the orientation using low-resolution RGB images. The experiments, conducted in an apartment with three cameras, benchmarked two different graph neural network implementations and a third architecture based on fully connected layers. The software used has been released as open-source in a public repository1.
Dynamic Ensemble of Contextual Bandits to Satisfy Users' Changing Interests

Recommender systems have to handle a highly non-stationary environment, due to users' fast changing interests over time. Traditional solutions have to periodically rebuild their models, despite high computational cost. But this still cannot empower them to automatically adjust to abrupt changes in trends caused by timely information. It is important to note that the changes of reward distributions caused by a non-stationary environment can also be context dependent. When the change is orthogonal to the given context, previously maintained models should be reused for better recommendation prediction. In this work, we focus on contextual bandit algorithms for making adaptive recommendations. We capitalize on the unique context-dependent property of reward changes to conquer the challenging non-stationary environment for model update. In particular, we maintain a dynamic ensemble of contextual bandit models, where each bandit model's reward estimation quality is monitored regarding given context and possible environment changes. Only the admissible models to the current environment will be used for recommendation. We provide a rigorous upper regret bound analysis of our proposed algorithm. Extensive empirical evaluations on both synthetic and three real-world datasets confirmed the algorithm's advantage against existing non-stationary solutions that simply create new models whenever an environment change is detected.
Accounting for missing actors in interaction network inference from abundance data

Network inference aims at unravelling the dependency structure relating jointly observed variables. Graphical models provide a general framework to distinguish between marginal and conditional dependency. Unobserved variables (missing actors) may induce apparent conditional dependencies. In the context of count data, we introduce a mixture of Poisson log‐normal distributions with tree‐shaped graphical models, to recover the dependency structure, including missing actors. We design a variational EM algorithm and assess its performance on synthetic data. We demonstrate the ability of our approach to recover environmental drivers on two ecological data sets. The corresponding R package is available from github.com/Rmomal/nestor.
Potential for carbon sequestration in European soils: preliminary estimates for five scenarios using results from long‐term experiments

One of the main options for carbon mitigation identified by the IPCC is the sequestration of carbon in soils. In this paper we use statistical relationships derived from European long‐term experiments to explore the potential for carbon sequestration in soils in the European Union. We examine five scenarios, namely (a) the amendment of arable soils with animal manure, (b) the amendment of arable soils with sewage sludge, (c) the incorporation of cereal straw into the soils in which it was grown, (d) the afforestation of surplus arable land through natural woodland regeneration, and (e) extensification of agriculture through ley‐arable farming. Our calculations suggest only limited potential to increase soil carbon stocks over the next century by addition of animal manure, sewage sludge or straw ( 15 Tg C y–1), but greater potential through extensification of agriculture (≈ 40 Tg C y–1) or through the afforestation of surplus arable land (≈ 50 Tg C y–1). We estimate that extensification could increase the total soil carbon stock of the European Union by 17%. Afforestation of 30% of present arable land would increase soil carbon stocks by about 8% over a century and would substitute up to 30 Tg C y–1 of fossil fuel carbon if the wood were used as biofuel. However, even the afforestation scenario, with the greatest potential for carbon mitigation, can sequester only 0.8% of annual global anthropogenic CO2‐carbon. Our figures suggest that, although efforts in temperate agriculture can contribute to global carbon mitigation, the potential is small compared to that available through reducing anthropogenic CO2 emissions by halting tropical and sub‐tropical deforestation or by reducing fossil fuel burning.
EXPO-HD: Exact Object Perception using High Distraction Synthetic Data

We present a new labeled visual dataset intended for use in object detection and segmentation tasks. This dataset consists of 5,000 synthetic photorealistic images with their corresponding pixel-perfect segmentation ground truth. The goal is to create a photorealistic 3D representation of a specific object and utilize it within a simulated training data setting to achieve high accuracy on manually gathered and annotated real-world data. Expo Markers were chosen for this task, fitting our requirements of an exact object due to the exact texture, size and 3D shape. An additional advantage is the availability of this object in offices around the world for easy testing and validation of our results. We generate the data using a domain randomization technique that also simulates other photorealistic objects in the scene, known as distraction objects. These objects provide visual complexity, occlusions, and lighting challenges to help our model gain robustness in training. We are also releasing our manually-labeled real-image test dataset. This white-paper provides strong evidence that photorealistic simulated data can be used in practical real world applications as a more scalable and flexible solution than manually-captured data. this https URL
A Hybrid Approach to Audio-to-Score Alignment

Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece. Standard alignment methods are based on Dynamic Time Warping (DTW) and employ handcrafted features. We explore the usage of neural networks as a preprocessing step for DTW-based automatic alignment methods. Experiments on music data from different acoustic conditions demonstrate that this method generates robust alignments whilst being adaptable at the same time.
Legitimacy and Adoption of a Scientific Biological Control Program: an Institutional Analysis of Hoddle

The enemy of my enemy is my friend. This adage might well be the title of this issue’s paper advocating the scientific introduction of natural enemies to achieve biological pest control (Hoddle 2004 [this issue]). In “Restoring Balance: Using Exotics to Control Invasive Exotics,” Hoddle advocates science-based biological control systems to reduce the damage from invasive pests. The article is moderate in tone and grounded in substantial scientific evidence, both general and detailed. And it is balanced: Hoddle provides a valuable resource documenting a long list of failed exotic-on-exotic pest-control efforts, reassures the reader that science has advanced beyond these, and demonstrates how such failures could be avoided in the future. From another view, Hoddle makes huge assumptions about the malleability of human institutions, not just the control of exotic pests: “The rate at which invasive species degrade valued habitats continues to accelerate, and human management is essential if areas and species of conservation importance are to be preserved. ” The legitimacy of Hoddle’s argument depends not only on a consensus of biological scientists, but also on a variety of other critical stakeholders who will engage in the “human management. ” The paper’s timing, the nature of its scientific claims, and the power struggles based on the interests and beliefs it implies all suggest that institutional change will be as important as scientific proof if the enemy-ofmy-enemy logic is to be implemented widely.
Experimental investigation on torque scaling in turbulent Taylor-Couette flow

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Experimental investigation on torque scaling in turbulent Taylor-Couette flow S. Merbold, C. Egbers
Linear Delay-cell Design for Low-energy Delay Multiplication and Accumulation

A practical deep neural network's (DNN) evaluation involves thousands of multiply-and-accumulate (MAC) operations. To extend DNN's superior inference capabilities to energy constrained devices, architectures and circuits that minimize energy-per-MAC must be developed. In this respect, analog delay-based MAC is advantageous due to reasons both extrinsic and intrinsic to the MAC implementation - (1) lower fixed-point precision requirement for a DNN's evaluation, (2) better dynamic range than charge-based accumulation, for smaller technology nodes, and (3) simpler analog-digital interfacing. Implementing DNNs using delay-based MAC requires mixed-signal delay multipliers that accept digitally stored weights and analog voltages as arguments. To this end, a novel, linearly tune-able delay-cell is proposed, wherein, the delay is realized using an inverted MOS capacitor's (C*) steady discharge from a linearly input-voltage dependent initial charge. The cell is analytically modeled, constraints for its functional validity are determined, and jitter-models are developed. Multiple cells with scaled delays, corresponding to each bit of the digital argument, must be cascaded to form the multiplier. To realize such bit-wise delay-scaling of the cells, a biasing circuit is proposed that generates sub-threshold gate-voltages to scale C*'s discharging rate, and thus area-expensive transistor width-scaling is avoided. For 130nm CMOS technology, the theoretical constraints and limits on jitter are used to find the optimal design-point and quantify the jitter versus bits-per-multiplier trade-off. Schematic-level simulations show a worst-case energy-consumption close to the state-of-art, and thus, feasibility of the cell.
Subsampled Directed-Percolation Models Explain Scaling Relations Experimentally Observed in the Brain

Recent experimental results on spike avalanches measured in the urethane-anesthetized rat cortex have revealed scaling relations that indicate a phase transition at a specific level of cortical firing rate variability. The scaling relations point to critical exponents whose values differ from those of a branching process, which has been the canonical model employed to understand brain criticality. This suggested that a different model, with a different phase transition, might be required to explain the data. Here we show that this is not necessarily the case. By employing two different models belonging to the same universality class as the branching process (mean-field directed percolation) and treating the simulation data exactly like experimental data, we reproduce most of the experimental results. We find that subsampling the model and adjusting the time bin used to define avalanches (as done with experimental data) are sufficient ingredients to change the apparent exponents of the critical point. Moreover, experimental data is only reproduced within a very narrow range in parameter space around the phase transition.
RMPflow: A Geometric Framework for Generation of Multitask Motion Policies

Generating robot motion for multiple tasks in dynamic environments is challenging, requiring an algorithm to respond reactively while accounting for complex nonlinear relationships between tasks. In this article, we develop a novel policy synthesis algorithm, Riemannian motion policy (RMP)flow, based on geometrically consistent transformations of RMPs. RMPs are a class of reactive motion policies that parameterize non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task spaces. Given a set of RMPs designed for individual tasks, RMPflow can combine these policies to generate an expressive global policy, while simultaneously exploiting sparse structure for computational efficiency. We study the geometric properties of RMPflow and provide sufficient conditions for stability. Finally, we experimentally demonstrate that accounting for the natural Riemannian geometry of task policies can simplify classically difficult problems, such as planning through the clutter on high-degree-of-freedom manipulation systems. Note to Practitioners—Requirements on safety and responsiveness for collaborative robots have driven a need for new ideas in control design that bridge between standard objectives in low-level control (such as trajectory tracking) and high-level behavioral objectives (such as collision avoidance) often relegated to planning systems. Modern results from geometric control, which promise stable controllers that can smoothly and safely transition between many behavioral tasks, therefore, become highly relevant. However, for years, this field has remained inaccessible due to its mathematical complexity. This article aims to: 1) make those ideas accessible to robotics and control experts by recasting them in a concrete algorithmic framework amenable to controller design and 2) to additionally generalize them to better satisfy the specific needs of robotic behavior generation. Our experiments demonstrate that the resulting controllers can engender natural behavior that adapts instantaneously to changing surroundings with zero planning while performing manipulation tasks. The framework is gaining traction within the robotics community, finding increasing application in areas, such as autonomous navigation, tactile servoing, and multi-agent systems. Future research will address learning these controllers from data to simplify that process of design and tuning, which at present can require experience.