Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal Sequence Prediction With Point Processes and Self-Organizing Decision Trees

We study the spatiotemporal prediction problem and introduce a novel point-process-based prediction algorithm. Spatiotemporal prediction is extensively studied in machine learning literature due to its critical real-life applications, such as crime, earthquake, and social event prediction. Despite these thorough studies, specific problems inherent to the application domain are not yet fully explored. Here, we address the nonstationary spatiotemporal prediction problem on both densely and sparsely distributed sequences. We introduce a probabilistic approach that partitions the spatial domain into subregions and models the event arrivals in each region with interacting point processes. Our algorithm can jointly learn the spatial partitioning and the interaction between these regions through a gradient-based optimization procedure. Finally, we demonstrate the performance of our algorithm on both simulated data and two real-life datasets. We compare our approach with baseline and state-of-the-art deep learning-based approaches, where we achieve significant performance improvements. Moreover, we also show the effect of using different parameters on the overall performance through empirical results and explain the procedure for choosing the parameters.
Patterns of reproductive traits of fucoid species in core and marginal populations

Fucoid macroalgae are important primary producers and habitat modifiers on North Atlantic intertidal rocky shores. With decreasing latitude, western European fucoid populations display reduced levels of abundance, biomass and recruitment, while experiencing higher levels of physical environmental stress during summer months. We hypothesized that such reduction in the south is accompanied by a detectable decline in fucoid reproductive capacity. To test this hypothesis, morphological and reproductive traits of core (Welsh) and marginal (Portuguese) populations of two common fucoid species, Fucus vesiculosus and F. spiralis (Ochrophyta, Fucales), were examined. Morphological measurements showed that for a given thallus length, both fucoid species had smaller thallus volume and lower biomass in the southerly marginal part of the range. Significantly lower biomass of reproductive tissue of F. vesiculosus and a smaller number of receptacles per individual on specimens of both species indicate that levels of reproductive output are probably lower in southern populations. Despite the differences in reproductive traits observed between regions, reproductive effort (measured as the percentage of total dry biomass represented by reproductive tissue) of both species remained similar, as algae from both regions made similar investments in reproduction. The results indicate that stressful conditions reduced growth and number of receptacles of both species and amount of reproductive biomass of F. vesiculosus in the south but do not seem to change the way these algal species invest their energy. The decline in mass and reproductive biomass of specimens from southern shores found in this study, when combined with the lower abundance of adults and lower recruitment levels previously observed, is a strong indication of fucoid populations with lower levels of propagule output. This is an important factor when considering responses of these populations to a changing environment.
Mobile Robot Path Planning in Dynamic Environments: A Survey

The environment that the robot operating in is becoming more and more complex, which poses great challenges on robot navigation. This paper gives an overview of the navigation framework for robot running in dense environment. The path planning in the navigation framework of mobile robots is divided into global planning and local planning according to the planning scope and the executability. Robot navigation is a multi-objective problem, which not only needs to complete the given tasks but also needs to simultaneously maintain the social comfort level. Consequently, we focus on the reinforcement learning-based path planning algorithms and analyze the development status, advantages, and disadvantages of the existing algorithms. Besides, path planning in a dynamic environment for robots will be further studied in the future in the areas of the advanced algorithm, hybrid algorithm, mu10.15878/j.cnki. instrumentation.2019. 02.010lti-robot collaboration, social model, and artificial intelligence algorithm combination.
Prediction with Gaussian Process Dynamical Models

The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques for modeling of modern systems, e.g., soft robotics or human-robot interaction, is often challenging or even infeasible due to the complexity of the system dynamics. In contrast, data-driven approaches need only a minimum of prior knowledge and scale with the complexity of the system. In particular, Gaussian process dynamical models (GPDMs) provide very promising results for the modeling of complex dynamics. However, the control properties of these GP models are just sparsely researched, which leads to a "blackbox" treatment in modeling and control scenarios. In addition, the sampling of GPDMs for prediction purpose respecting their non-parametric nature results in non-Markovian dynamics making the theoretical analysis challenging. In this article, we present the relation of the sampling procedure and non-Markovian dynamics for two types of GPDMs and analyze the control theoretical properties focusing on the transfer between common sampling approximations. The outcomes are illustrated with numerical examples and discussed with respect to the application in control settings.
Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation

Existing face datasets often lack sufficient representation of occluding objects, which can hinder recognition, but also supply meaningful information to understand the visual context. In this work, we introduce Extended Labeled Faces in-the-Wild (ELFW), a dataset supplementing with additional face-related categories -- and also additional faces -- the originally released semantic labels in the vastly used Labeled Faces in-the-Wild (LFW) dataset. Additionally, two object-based data augmentation techniques are deployed to synthetically enrich under-represented categories which, in benchmarking experiments, reveal that not only segmenting the augmented categories improves, but also the remaining ones benefit.
NGC 4104: A shell galaxy in a forming fossil group

Context. Groups are the most common association of galaxies in the Universe and they are found in different configuration states, such as loose, compact, and fossil groups. Aims. We studied the galaxy group MKW 4s, dominated by the giant early-type galaxy NGC 4104 at z = 0.0282, with the aim of understanding the evolutionary stage of this group and to place it within the framework of the standard ΛCDM cosmological scenario. Methods. We obtained deep optical data with CFHT/Megacam (g and r bands) and we applied both the GALFIT 2D image fitting program and the IRAF/ELLIPSE 1D radial method to model the brightest group galaxy (BGG) and its extended stellar envelope. We also analysed the publicly available XMM-Newton and Chandra X-ray data. From N-body simulations of dry-mergers with different mass ratios of the infalling galaxy, we were able to constrain the dynamical stage of this system. Results. Our results show a stellar shell system feature in NGC 4104 and an extended envelope that was reproduced by our numerical simulations of a collision with a satellite galaxy taking place about 4−6 Gyr ago. The initial pair of galaxies had a mass ratio of at least 1:3. Taking into account the stellar envelope contribution to the total r band magnitude and the X-ray luminosity, MKW 4s falls into the category of a fossil group. Conclusions. Our results show that we are witnessing a rare case of a shell elliptical galaxy in a forming fossil group.
Wire resonator as a broadband Huygens superscatterer

Interference phenomena allow tailoring propagation of electromagnetic waves by controlling phases of several scattering channels. Huygens element, being a representative example of this approach, enables enhancement of the scattering from an object in a forward direction, while the reflection is suppressed. However, a typical resonant realization of Huygens element employs constructive interference between electric and magnetic dipolar resonances that makes it relatively narrowband. Here we develop the concept of a broadband resonant Huygens element, based on a circular array of vertically aligned near-field coupled metal wires. Accurate management of multipole interference in an electrically small structure results in directional scattering over a large bandwidth, acceding 10% of the carrier frequency. Being constructed from nonmagnetic materials, this structure demonstrates a strong magnetic response appearing in dominating magnetic multipoles over electric counterparts. Moreover, we predict and observe higher-order magnetic multipoles, including hexadecapole (M16-pole) and magnetic triakontadipole (M32-pole) with quality factors, approaching 6000. The experimental demonstration is performed at the low GHz spectral range. Broadband Huygens elements can be employed in a set of practical applications, where compact electromagnetic devices for tailoring wave propagation are needed, i.e., antenna devices, directional reflectors, and even solar cells, given that the concept is scaled to the optical frequency range.
Data-dependent Pruning to find the Winning Lottery Ticket

The Lottery Ticket Hypothesis postulates that a freshly initialized neural network contains a small subnetwork that can be trained in isolation to achieve similar performance as the full network. Our paper examines several alternatives to search for such subnetworks. We conclude that incorporating a data dependent component into the pruning criterion in the form of the gradient of the training loss -- as done in the SNIP method -- consistently improves the performance of existing pruning algorithms.
Normalized solutions to a Schr\"odinger-Bopp-Podolsky system under Neumann boundary conditions

In this paper we study a Schrodinger-Bopp-Podolsky system of partial differential equations in a bounded and smooth domain of $\mathbb R^3$ with a non constant coupling factor. Under a compatibility condition on the boundary data we deduce existence and multiplicity of solutions by means of the Ljusternik-Schnirelmann theory.
Rank-metric codes over arbitrary Galois extensions and rank analogues of Reed-Muller codes

This paper extends the study of rank-metric codes in extension fields $\mathbb{L}$ equipped with an arbitrary Galois group $G = \mathrm{Gal}(\mathbb{L}/\mathbb{K})$. We propose a framework for studying these codes as subspaces of the group algebra $\mathbb{L}[G]$, and we relate this point of view with usual notions of rank-metric codes in $\mathbb{L}^N$ or in $\mathbb{K}^{N\times N}$, where $N = [\mathbb{L} : \mathbb{K}]$. We then adapt the notion of error-correcting pairs to this context, in order to provide a non-trivial decoding algorithm for these codes. We then focus on the case where $G$ is abelian, which leads us to see codewords as elements of a multivariate skew polynomial ring. We prove that we can bound the dimension of the vector space of zeroes of these polynomials, depending of their degree. This result can be seen as an analogue of Alon-F\"uredi theorem -- and by means, of Schwartz-Zippel lemma -- in the rank metric. Finally, we construct the counterparts of Reed-Muller codes in the rank metric, and we give their parameters. We also show the connection between these codes and classical Reed-Muller codes in the case where $\mathbb{L}$ is a Kummer extension.
Circumvention by design - dark patterns in cookie consent for online news outlets

To ensure that users of online services understand what data are collected and how they are used in algorithmic decision-making, the European Union’s General Data Protection Regulation (GDPR) specifies informed consent as a minimal requirement. For online news outlets consent is commonly elicited through interface design elements in the form of a pop-up. We have manually analyzed 300 data collection consent notices from news outlets that are built to ensure compliance with GDPR. The analysis uncovered a variety of strategies or dark patterns that circumvent the intent of GDPR by design. We further study the presence and variety of these dark patterns in these “cookie consents” and use our observations to specify the concept of dark pattern in the context of consent elicitation.
The impact of electrification on rural attractiveness in Senegal

A reliable and affordable access to electricity has become one of the basic needs for humans and is, as such, at the top of the development agenda. It contributes to socio-economic development by transforming the whole spectrum of people's lives - food, education, health care; it spurs new economic opportunities and thus improves livelihoods. Using a comprehensive dataset of pseudonymised mobile phone records, provided by the market share leader, we analyse the impact of electrification on the attractiveness of rural areas in Senegal. We extract communication and mobility flows from the call detail records (CDRs) and show that electrification has a small, yet positive and specific, impact on centrality measures within the communication network and on the volume of incoming visitors. This increased influence is however circumscribed to a limited spatial extent, creating a complex competition with nearby areas. Nevertheless, we found that the volume of visitors between any two sites could be well predicted from the level of electrification at the destination combined with the living standard at the origin. In view of these results, we discuss how to obtain the best outcomes from a rural electrification planning strategy. We determine that electrifying clusters of rural sites is a better solution than attempting to centralise electricity supplies to maximise the development of specifically targeted sites.
Q-NET: A Formula for Numerical Integration of a Shallow Feed-forward Neural Network

Numerical integration is a computational procedure that is widely encountered across disciplines when reasoning about data. We derive a formula in closed form to calculate the multidimensional integral of functions fw that are representable using a shallow feed-forward neural network with weights w and a sigmoid activation function. We demonstrate its applicability in estimating numerical integration of arbitrary functions f over hyper-rectangular domains in the absence of a prior. To achieve this, we first train the network to learn $fw \approx f$ using point-samples of the integrand. We then use our formula to calculate the exact integral of the learned function fw. Our formula operates on the weights w of the trained approximator network. We show that this formula can itself be expressed as a shallow feed-forward network, which we call a Q-NET, with w as its inputs. Although the Q-NET does not have any learnable parameters, we use this abstraction to derive a family of elegant parametric formulae that represent the marginal distributions of the input function over arbitrary subsets of input dimensions in functional form. We perform empirical evaluations of Q-NETs for integrating smooth functions as well as functions with discontinuities.
Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation

We introduce several novel change of measure inequalities for two families of divergences: $f$-divergences and $\alpha$-divergences. We show how the variational representation for $f$-divergences leads to novel change of measure inequalities. We also present a multiplicative change of measure inequality for $\alpha$-divergences and a generalized version of Hammersley-Chapman-Robbins inequality. Finally, we present several applications of our change of measure inequalities, including PAC-Bayesian bounds for various classes of losses and non-asymptotic intervals for Monte Carlo estimates.
The flag manifold as a tool for analyzing and comparing data sets

The shape and orientation of data clouds reflect variability in observations that can confound pattern recognition systems. Subspace methods, utilizing Grassmann manifolds, have been a great aid in dealing with such variability. However, this usefulness begins to falter when the data cloud contains sufficiently many outliers corresponding to stray elements from another class or when the number of data points is larger than the number of features. We illustrate how nested subspace methods, utilizing flag manifolds, can help to deal with such additional confounding factors. Flag manifolds, which are parameter spaces for nested subspaces, are a natural geometric generalization of Grassmann manifolds. To make practical comparisons on a flag manifold, algorithms are proposed for determining the distances between points $[A], [B]$ on a flag manifold, where $A$ and $B$ are arbitrary orthogonal matrix representatives for $[A]$ and $[B]$, and for determining the initial direction of these minimal length geodesics. The approach is illustrated in the context of (hyper) spectral imagery showing the impact of ambient dimension, sample dimension, and flag structure.
A Chemokine, Interferon (IFN)-γ-Inducible Protein 10 kDa, Is Stimulated by IFN-τ and Recruits Immune Cells in the Ovine Endometrium1

Abstract Proper distribution of immune cells in the uterus is a prerequisite for successful implantation and subsequent placentation, but biochemical signals that govern such events have not been well characterized. In the present study, the cDNA of a chemokine, interferon (IFN)-γ-inducible protein 10 kDa (IP-10), was identified from a cDNA subtraction study between uterine endometrial tissues from Day 17 pregnant and Day 15 cyclic ewes. The effect of IFN-τ on IP-10 expression and the involvement of IP-10 in the recruitment of immune cells were then investigated. Northern blot analysis revealed that large amounts of IP-10 mRNA were present during conceptus attachment to maternal endometrium and early placentation. IP-10 mRNA was localized to monocytes distributed in the subepithelial stroma of pregnant but not cyclic uteri. This finding was supported by the discovery of IP-10 mRNA expression in monocytes but not in lymphocytes, uterine epithelial cells, or stromal cells. Moreover, the expression of IP-10 mRNA by the monocytes was stimulated by IFN-α, IFN-γ, and IFN-τ in a dose-dependent manner, but the expression of IP-10 mRNA by the endometrial explants was most stimulated by IFN-τ. In a chemotaxis assay, migration of peripheral blood mononuclear cells was stimulated by the addition of IFN-τ stimulated-endometrial culture medium, and the effect was significantly reduced by neutralization with an anti-IP-10 antibody. These results suggest that endometrial IP-10 regulated by conceptus IFN-τ regulates recruitment and/or distribution of immune cells seen in the early pregnant uterus.
Isolation, diversity, and antimicrobial activity of fungal endophytes from Rohdea chinensis (Baker) N.Tanaka (synonym Tupistra chinensis Baker) of Qinling Mountains, China

Endophytic fungi have been emerged as fruitful resources for producing structurally fascinating and biologically active secondary metabolites. However, endophytic fungi from medicinal plants of Qinling Mountains–the most important natural climatic boundary between the subtropical and warm temperate zones of China with an astonishingly high level of biodiversity–have rarely been explored as potential sources of novel fungal species and active secondary metabolites. In this study, a total of 371 fungal colonies were successfully isolated from 510 tissue segments of the medicinal Tupistra chinensis Baker collected from Qinling Mountains, China. Roots of T. chinensis Baker are used as a folk medicine to ameliorate pharyngitis and treat rheumatic diseases. A total of 100 representative morphotype strains were identified according to ITS rDNA sequence analyses and were grouped into three phyla (Ascomycota, Basidiomycota, Mucoromycota), seven classes (Dothideomycetes, Sordariomycetes, Eurotiomycetes, Microbotryomycetes, Agaricomycetes, Leotiomycetes, Mortierellomycetes), and at least 35 genera. The genera of Collectotrichum (IF, 29.92%), Fusarium (IF, 8.36%), Aspergillus (IF, 8.09%), and Dactylonectria (IF, 5.39%) were most frequently isolated from the tissues of T. chinensis Baker. The Species Richness Index (S, 65) and the Shannon-Wiener Index (H′, 3.7914) indicated that T. chinensis Baker harbored abundant fungal resources. Moreover, five isolates were potential new taxa because of low similarity of ITS sequences ranged from 95.09%∼96.61%. Fifteen out of 100 endophytic fungal ethyl acetate extracts exhibited inhibitory activities against at least one pathogenic bacterium or fungus. Two important lead compounds produced by two stains (F8047 and F8075) with high antimicrobial activities were identified using high performance liquid chromatography (HPLC) and ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC–QTOF MS) analyses. In addition, it was noteworthy that the strain F8001, which may be a potential new species, showed antimicrobial activity and should be investigated further. Overall, these results indicated that the endophytic fungi from T. chinensis Baker could be exploited as a novel source of bioactive compounds.
Temporal and Spatial Activity Patterns of Three Species in the Leptodactylus fuscus Group (Amphibia, Leptodactylidae)

Abstract. Space and time are two of the main dimensions of the ecological niche. Because of their high dependence upon aquatic environments during breeding activity, anuran assemblages are interesting models for studying inter-populational relationships. The overlap in habitat use could be particularly high, especially for seasonal species and among taxa that share a common phylogenetic history. Three species of the Leptodactylus fuscus group occur in a semi-permanent pond at Cerro Verde (Rocha, Uruguay): L. latinasus, L. mystacinus and L. gracilis. The aim of this work was to describe the spatial and temporal calling patterns of the three species and the effects of environmental conditions upon them on a daily scale. Their relative abundance was quantified over four weeks (December 2004–January 2005) during the first half of the night along a transect using acoustic surveys. Nestedness, segregation, Jaccard's index and association analysis were calculated, revealing a nested pattern with low overlap between species in the occupation of microhabitats. On the temporal axis, the calling behavior of the species seems to be better described by the time of night than by temperature, humidity, or atmospheric pressure. These analyses also showed a non-linear association of activity with time of night, indicating that higher activity occurs at different hours for each species.