Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods for calculating coexistence mechanisms: beyond scaling factors

How do species coexist? A framework known as Modern Coexistence Theory measures mechanisms of coexistence by comparing a species perturbed to low density (the invader) to other species that remain at their typical densities (the residents); this invader-resident comparison measures a rare-species advantage that results from specialization. However, there are several reasonable ways (i.e., methods) to compare invaders and residents, each differing in practicality and biological interpretation. Here, using theoretical arguments and case studies, we compare four such methods for calculating coexistence mechanisms: 1) Scaling factors, the traditional approach where resident growth rates are scaled by a measure of relative sensitivity to competition, obtained by solving a system of linear equations; 2) The simple comparison, which gives equal weight to all resident species; 3) Speed conversion factors, a novel method in which resident growth rates are scaled by a ratio of generation times, and; 4) The invader-invader comparison, another novel method in which a focal species is compared to itself at high vs. low density. We conclude that the conventional scaling factors can be useful in some theoretical research, but are not recommended for empirical applications, i.e., determining the mechanisms of coexistence in real communities. Instead, we recommend the simple comparison and speed conversion factor methods. The speed conversion factors are most useful when comparing species with dissimilar generation times. However, ecologists often study coexistence in guilds of species with similar life-histories, and therefore, similar generation times. In such scenarios, the easier-to-use simple comparison method is reasonable.
Multiscale mobility patterns and the restriction of human mobility under lockdown

Strict lockdown measures have been put in place in many countries around the world to constrain human mobility in response to the unparalleled challenges posed by the COVID-19 pandemic. Here we apply network-theoretic tools to analyse a geolocalised dataset of human mobility of 16 million UK Facebook users from March to July 2020. A special emphasis lies on dynamical perspectives of network analysis and multi-scale community detection with Markov Stability analysis is performed to identify signatures for the mobility contraction in the UK. Thereby, a new quantitative criterion for the scale selection in Markov Stability analysis is proposed, which reveals different scales of mobility in a semi-automated manner. The analysis of the UK mobility network reveals a pronounced decline of human mobility under COVID-19 and suggests that local community structure has been strengthened under lockdown. In particular, human mobility does not follow along purely geographic and administrative lines but the flow-based approach allows for the identification of intrinsic mobility patterns that may inform future interventions to prevent COVID-19 transmission.
Spatial Structures in the Globular Cluster Distribution of Fornax Cluster Galaxies

We report the discovery of statistically significant spatial structures in the projected two-dimensional distributions of Globular Cluster (GC) systems of 10 galaxies that are among the brightest in the Fornax Cluster. We use a catalog of GCs extracted from the Hubble Space Telescope Advanced Camera for Surveys Fornax Cluster Survey imaging data. We characterize the size, shape, and location relative to the host galaxies of the GC structures and suggest a classification based on their morphology and location that is suggestive of different formation mechanisms. We also investigate the GC structures in the context of the positions of their host galaxies relative to the general spatial distributions of galaxies and intracluster GCs in the Fornax Cluster. We finally estimate the dynamical masses of the progenitors of some GC structures under the assumption that they are the relics of past accretion events of satellite galaxies by their hosts.
SC-LiDAR-SLAM: A Front-end Agnostic Versatile LiDAR SLAM System

Accurate 3D point cloud map generation is a core task for various robot missions or even for data-driven urban analysis. To do so, light detection and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) technology have been elaborated. To compose a full SLAM system, many odometry and place recognition methods have independently been proposed in academia. However, they have hardly been integrated or too tightly combined so that exchanging (upgrading) either single odometry or place recognition module is very effort demanding. Recently, the performance of each module has been improved a lot, so it is necessary to build a SLAM system that can effectively integrate them and easily replace them with the latest one. In this paper, we release such a front-end agnostic LiDAR SLAM system, named SC-LiDAR-SLAM. We built a complete SLAM system by designing it modular, and successfully integrating it with Scan Context++ and diverse existing open-source LiDAR odometry methods to generate an accurate point cloud map.
Robust Scatterer Number Density Segmentation of Ultrasound Images

Quantitative ultrasound (QUS) aims to reveal information about the tissue microstructure using backscattered echo signals from clinical scanners. Among different QUS parameters, scatterer number density is an important property that can affect the estimation of other QUS parameters. Scatterer number density can be classified into high or low scatterer densities. If there are more than ten scatterers inside the resolution cell, the envelope data are considered as fully developed speckle (FDS) and, otherwise, as underdeveloped speckle (UDS). In conventional methods, the envelope data are divided into small overlapping windows (a strategy here we refer to as patching), and statistical parameters, such as SNR and skewness, are employed to classify each patch of envelope data. However, these parameters are system-dependent, meaning that their distribution can change by the imaging settings and patch size. Therefore, reference phantoms that have known scatterer number density are imaged with the same imaging settings to mitigate system dependency. In this article, we aim to segment regions of ultrasound data without any patching. A large dataset is generated, which has different shapes of scatterer number density and mean scatterer amplitude using a fast simulation method. We employ a convolutional neural network (CNN) for the segmentation task and investigate the effect of domain shift when the network is tested on different datasets with different imaging settings. Nakagami parametric image is employed for multitask learning to improve performance. Furthermore, inspired by the reference phantom methods in QUS, a domain adaptation stage is proposed, which requires only two frames of data from FDS and UDS classes. We evaluate our method for different experimental phantoms and in vivo data.
Improving on "Data mining reconsidered" by K.D. Hoover and S.J. Perez

Kevin Hoover and Stephen Perez take important steps towards resolving some key issues in econometric methodology. They simulate general-to-specific selection for linear, dynamic regression models, and find that their algorithm performs well in re-mining the ? Lovell database? We discuss developments that improve on their results, automated in PcGets. Monte Carlo experiments and re-analyses of empirical studies show that pre-selection F-tests, encompassing tests, and sub-sample reliability checks all help eliminate ? spuriously-significant? regressors, without impugning recovery of the correct specification.
Organic Molecules in Interstellar Space: Latest Advances

Although first considered as too diluted for the formation of molecules in-situ and too harsh an environment for their survival, the interstellar medium has turned out to host a rich palette of molecular species: to date, 256 species, not counting isotopologues, have been identified. The last decade, and more particularly the last 2 years, have seen an explosion of new detections, including those of a number of complex organic species, which may be dubbed as prebiotic. Organic molecules have been discovered not just in interstellar clouds from the Solar neighbourhood, but also throughout the Milky-Way, as well as in nearby galaxies, or some of the most distant quasars. These discoveries were made possible by the completion of large sub-millimetre and radio facilities. Equipped with new generation receivers, those instruments have provided the orders of magnitude leap in sensitivity required to detect the vanishingly weak rotational lines that allowed the molecule identifications. Last 2 years, 30 prebiotic molecules have been detected in TMC-1, a dust-enshrouded gaseous cloud located at 400 light-years from the Sun in the Taurus constellation. Ten new molecular species, have been identified in the arm of a spiral galaxy seven billion light-yr distant, and 12 molecular species observed in a quasar at 11 billion light-yr. We present the latest spectral observations of this outlying quasar and discuss the implications of those detections in these 3 archetypal sources. The basic ingredients involved in the Miller-Urey experiment and related experiments (H2, H2O, CH4, NH3, CO, H2S, … ) appeared early after the formation of the first galaxies and are widespread throughout the Universe. The chemical composition of the gas in distant galaxies seems not much different from that in the nearby interstellar clouds. It presumably comprises, like for TMC-1, aromatic rings and complex organic molecules putative precursors of the RNA nucleobases, except the lines of such complex species are too weak to be detected that far.
Automatic Correction of Syntactic Dependency Annotation Differences

Annotation inconsistencies between data sets can cause problems for low-resource NLP, where noisy or inconsistent data cannot be easily replaced. We propose a method for automatically detecting annotation mismatches between dependency parsing corpora, along with three related methods for automatically converting the mismatches. All three methods rely on comparing unseen examples in a new corpus with similar examples in an existing corpus. These three methods include a simple lexical replacement using the most frequent tag of the example in the existing corpus, a GloVe embedding-based replacement that considers related examples, and a BERT-based replacement that uses contextualized embeddings to provide examples fine-tuned to our data. We evaluate these conversions by retraining two dependency parsers—Stanza and Parsing as Tagging (PaT)—on the converted and unconverted data. We find that applying our conversions yields significantly better performance in many cases. Some differences observed between the two parsers are observed. Stanza has a more complex architecture with a quadratic algorithm, taking longer to train, but it can generalize from less data. The PaT parser has a simpler architecture with a linear algorithm, speeding up training but requiring more training data to reach comparable or better performance.
Transverse positron polarization in the polarized 
μ+
 decay related with the muonium-to-antimuonium transition

The constructions of the new high-intensity muon beamlines are progressing in facilities around the world, and new physics searches related to the muons are expected. The facilities can observe the transverse positron polarization of the polarized µ + decay to test the standard model. The transition of muonium into antimuonium (Mu-to-Mu transition), which is one of the interesting possibilities in the models beyond the standard model, can be also tested. The near-future observation of the transition gives us a great impact since it indicates that there is an approximate discrete symmetry in the lepton sector. If the Mu-to-Mu transition operator is generated, a new muon decay operator can exist and it may interfere with the standard model muon decay operator to induce the corrections to the transverse positron polarization in the µ + decay. In this paper, we examine the possibility that the Mu-to-Mu transition and the correction to the transverse positron polarization are related, and we show that those two are related in the model of a neutral ﬂavor gauge boson. We also investigate the models to generate the Mu-to-Mu transition, such as an inert SU (2) L doublet, a SU (2) L triplet for the type-II seesaw model, a dilepton gauge boson, and a left-right model. The non-zero value of the transverse polarization for one of
The Magnetic Field versus Density Relation in Star-forming Molecular Clouds

We study the magnetic field to density (B–ρ) relation in turbulent molecular clouds with dynamically important magnetic fields using nonideal three-dimensional magnetohydrodynamic simulations. Our simulations show that there is a distinguishable break density ρ T between the relatively flat low-density regime and a power-law regime at higher densities. We present an analytic theory for ρ T based on the interplay of the magnetic field, turbulence, and gravity. The break density ρ T scales with the strength of the initial Alfvén Mach number A0 for sub-Alfvénic ( A0<1 ) and trans-Alfvénic ( A0∼1 ) clouds. We fit the variation of ρ T for model clouds as a function of A0 , set by different values of initial sonic Mach number 0 and the initial ratio of gas pressure to magnetic pressure β 0. This implies that ρ T, which denotes the transition in mass-to-flux ratio from the subcritical to the supercritical regime, is set by the initial turbulent compression of the molecular cloud.
STABILITY OF CRANK-NICOLSON COMPACT SCHEME FOR CONVECTION-DIFFUSION EQUATIONS USING MATRIX METHOD

The convection–diffusion equation is ubiquitous in several phenomenons, for example, option pricing problems in stock market [1, 2], computational fluid dynamics [3, 4], and in various other physical systems [5, 6, 7, 8]. The analytical solution of the convection-diffusion equations is only obtained in a few cases and it is not available in general. Therefore, rich theory of numerical methods is essential to solve such problems. In literature, various numerical methods have been developed for solving convection-diffusion equation [9, 10, 11]. Second order accurate finite difference method (FDM) is one of the most established numerical approach because of its effortless implementation and desirable stability properties [12, 13, 14]. Here, the stability of an FDM implies that the errors made at one time step do not magnify as the computations are carried forward in time. In fact, high-order accurate FDM can be developed by increasing the number of grid points in computational stencil. However the implementation of boundary conditions becomes tedious in those cases. Moreover, the corresponding coefficient matrices in fully discrete problem have more non-zero entries. Further, stability condition may become more restrictive. Therefore, high-order accurate FDMs have been developed using smaller (compact) stencils where the non-zero entries of coefficient matrix are cumbersome but tractable. Such high-order accurate FDMs, as studied in [15, 16, 17], are commonly known as compact schemes. Due to the above mentioned parsimony and high-order accuracy of compact scheme as compared to FDM, the former is even more crucial for solving multi-dimensional PDEs [18, 19]. Since compact approximations to compute the derivatives of functions have been derived in many ways, let us briefly discuss the available literature in this direction. A MATLAB suit for compact approximations of various order derivatives is presented in [20]. A fundamental work about compact approximations and its its characterstics is presented in [21]. In [22] and [23], the method of undetermined coefficients and the polynomial interpolation were adopted to derive the compact approximations on non-uniform grids respectively.
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection

In collaborative outlier detection, multiple participants exchange their local detectors trained on decentralized devices without exchanging their own data. A key problem of collaborative outlier detection is efficiently aggregating multiple local detectors to form a global detector without breaching the privacy of participants’ data and degrading the detection accuracy. We study locality-sensitive hashing-based ensemble methods to detect collaborative outliers since they are mergeable and compatible with differentially private mechanisms. Our proposed LSH iTables is simple and outperforms recent ensemble competitors on centralized and decentralized scenarios over many real-world data sets.
A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening

—Diabetic retinopathy refers to the pathology of the retina induced by diabetes and is one of the leading causes of preventable blindness in the world. Early detection of diabetic retinopathy is critical to avoid vision problem through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time-consuming and requires intense effort due to the small size of lesion and low contrast of the images. Thus, computer-assisted diagnosis of diabetic retinopathy based on the detection of red lesions is actively being explored recently. In this paper, we present a convolutional neural network with residual skip connection for the segmentation of exudates in retinal images. To improve the performance of network architecture, a suitable image augmen- tation technique is used. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. Comparative performance analysis of three benchmark databases: HEI-MED, E-ophtha, and DiaretDB1 is presented. It is shown that the proposed method achieves accuracy (0.98, 0.99, 0.98) and sensitivity (0.97, 0.92, and 0.95) on E-ophtha, HEI-MED, and DiaReTDB1, respectively.
Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks

The multi-agent pickup and delivery (MAPD) problem, in which multiple agents iteratively carry materials without collisions, has received significant attention. However, many conventional MAPD algorithms assume a specifically designed grid-like environment, such as an automated warehouse. Therefore, they have many pickup and delivery locations where agents can stay for a lengthy period, as well as plentiful detours to avoid collisions owing to the freedom of movement in a grid. By contrast, because a maze-like environment such as a search-and-rescue or construction site has fewer pickup/delivery locations and their numbers may be unbalanced, many agents concentrate on such locations resulting in inefficient operations, often becoming stuck or deadlocked. Thus, to improve the transportation efficiency even in a maze-like restricted environment, we propose a deadlock avoidance method, called standby-based deadlock avoidance (SBDA). SBDA uses standby nodes determined in real-time using the articulation-point-finding algorithm, and the agent is guaranteed to stay there for a finite amount of time. We demonstrated that our proposed method outperforms a conventional approach. We also analyzed how the parameters used for selecting standby nodes affect the performance.
Towards Reducing Manual Workload in Technology-Assisted Reviews: Estimating Ranking Performance

Conducting a systematic review (SR) is comprised of multiple tasks: (i) collect documents (studies) that are likely to be relevant from digital libraries (eg., PubMed), (ii) manually read and label the documents as relevant or irrelevant, (iii) extract information from the relevant studies, and (iv) analyze and synthesize the information and derive a conclusion of SR. When researchers label studies, they can screen ranked documents where relevant documents are higher than irrelevant ones. This practice, known as screening prioritization (ie., document ranking approach), speeds up the process of conducting a SR as the documents labelled as relevant can move to the next tasks earlier. However, the approach is limited in reducing the manual workload because the total number of documents to screen remains the same. Towards reducing the manual workload in the screening process, we investigate the quality of document ranking of SR. This can signal researchers whereabouts in the ranking relevant studies are located and let them decide where to stop the screening. After extensive analysis on SR document rankings from different ranking models, we hypothesize 'topic broadness' as a factor that affects the ranking quality of SR. Finally, we propose a measure that estimates the topic broadness and demonstrate that the proposed measure is a simple yet effective method to predict the qualities of document rankings for SRs.
Risk bounds for PU learning under Selected At Random assumption

Positive-unlabeled learning (PU learning) is known as a special case of semi-supervised binary classification where only a fraction of positive examples are labeled. The challenge is then to find the correct classifier despite this lack of information. Recently, new methodologies have been introduced to address the case where the probability of being labeled may depend on the covariates. In this paper, we are interested in establishing risk bounds for PU learning under this general assumption. In addition, we quantify the impact of label noise on PU learning compared to standard classification setting. Finally, we provide a lower bound on minimax risk proving that the upper bound is almost optimal.
Double Retrieval and Ranking for Accurate Question Answering

Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering. This step is performed by aggregating the embeddings of top $k$ answer candidates to support the verification of a target answer. Although the approach is intuitive and sound, it still shows two limitations: (i) the supporting candidates are ranked only according to the relevancy with the question and not with the answer, and (ii) the support provided by the other answer candidates is suboptimal as these are retrieved independently of the target answer. In this paper, we address both drawbacks by proposing (i) a double reranking model, which, for each target answer, selects the best support; and (ii) a second neural retrieval stage designed to encode question and answer pair as the query, which finds more specific verification information. The results on well-known datasets for Answer Sentence Selection show significant improvement over the state of the art.