Miracle-dz/UniFAR-FTUs huggingface dataset:
Reproductive Energetics and Female Attendance Patterns of Cape Fur Seals (Arctocephalus pusillus pusillus) During Early Lactation

Abstract The Cape fur seal (Arctocephalus pusillus pusillus) is a common pinniped species along the southern African coastline. While much information is available about this species, little is known concerning its reproductive energetics. With radio telemetry, we monitored attendance patterns for 1 to 4 mo from 1996–1999. Attendance of 29 nursing females averaged 1.86 ± 0.77 d on shore and 5.23 ± 2.87 d at sea. Mean cycle time was 7.09 ± 4.33 d, of which 29.6% was spent on shore and 70.4% at sea. A significant increase in length of time at sea was observed during the first 4 mo of lactation. Reproductive condition was quantified using measures of body condition and milk composition. Milk ingestion and field metabolic rates established a baseline for the energetics of 1 mo old pups. Body composition averaged 11.05 ± 2.31% lipid and 19.15 ± 2.60% protein. Milk from 17 individuals was composed of 58.07 ± 6.79% water, 2.02 ± 0.58% ash, 10.76 ± 1.23% protein and 23.16 ± 8.24% lipid. Milk energy content averaged 11.02 ± 3.10 kJ ml−1. Thirteen pups consumed milk at a rate of 136.58 ± 29.11 ml kg−1 d−1. Mean energy intake was 1505.10 ± 320.80 kJ kg−1 d−1, with 37.16 ± 8.04% going to body maintenance and 62.84 ± 8.04% to growth. Fat and protein tissue was deposited at a rate of 16.10 ± 34.93 g d−1 and 21.40 ± 59.20 g d−1, respectively. Pup mass gain averaged 99.30 ± 3154.10 g d−1. Male and female pups differed significantly in body mass and field metabolic rate.
A new coordinate-transformed discretization method for minimum entropy moment approximations of linear kinetic equations

In this contribution we derive and analyze a new numerical method for kinetic equations based on a coordinate transformation of the moment approximation. Classical minimum-entropy moment ($M_N$) closures are a class of reduced models for kinetic equations that conserve many of the fundamental physical properties of the solutions. However, their practical use is limited by their high-computational cost, as an optimization problem has to be solved for every cell in the space-time grid. In addition, implementation of numerical solvers for these models is hampered by the fact that the optimization problems are only well-defined if the moment vectors stay within the realizable set. For the same reason, further reducing these models by, e.g., reduced-basis methods is not a simple task. Our new method overcomes these disadvantages of classical approaches. The coordinate transformation is performed on the semi-discretized level which makes them applicable to a wide range of kinetic schemes and replaces the nonlinear optimization problems by inversion of the positive-definite Hessian matrix. As a result, the new scheme gets rid of the realizability-related problems. Moreover, our numerical experiments demonstrate that our new method is often several times faster than the standard optimization-based scheme.
WOOD THRUSH NESTS IN DENSE UNDERSTORY MAY BE VULNERABLE TO PREDATORS

Abstract We used Mayfield logistic regression and an information-theoretic approach to examine habitat characteristics associated with nesting success of Wood Thrushes (Hylocichla mustelina) across an urban to forested gradient in southwestern Pennsylvania in 2003 and 2004. Both nest placement and number of understory stems provided equally plausible models. Mayfield success was 20% higher for nests >3 m above ground level while dense understory was associated with low nest height. Wood Thrush nests in the forest interior averaged 2 m higher with a third less understory than edge nests. Urbanization and distance to the forest edge were not useful predictors of Wood Thrush nest success. The analysis was confounded by low breeding density at the most urbanized sites, but we found moderate success (42%, n = 63) across a fragmented landscape with minimal core forest area. Interior nests in a large contiguous forest were twice as successful (60%, n = 31) compared to edge nests (25%, n = 33) adjacent to a small housing development. We do not know the mechanism underlying increased predation of low understory nest sites that we observed. The ability of Wood Thrushes to see and/or effectively attack a predator in the area may be important for nest defense; changes in the predator community associated with forest edges may also explain differences in nest success. The relationship between nest placement, nest defense, and the predator community needs further study.
Integrated Sampling System (ISS) for Ocean Worlds

Introduction: One of the most pressing questions in exploration is whether life exists or existed anywhere else in the universe. Water is a critical prerequisite for life as we-know it, thus targets for extraterrestrial life are bodies that have or had liquid water, i.e. Ocean Worlds such as Titan or Europa. Due to the large radiation fluxes at Europa, or the possibility for burial by aeolian sediment at Titan, the search for life demands the capability to sample subsurface locations.
Arceuthobium tsugense subsp. Amabilae, a New Subspecies of Hemlock Dwarf Mistletoe (Viscaceae) from Oregon

ABSTRACT The dwarf mistletoe severely parasitizing Pacific silver fir in Oregon is described as a new subspecies of hemlock dwarf mistletoe. This classification is based on morphological and host range differences between hemlock dwarf mistletoe, Arceuthobium tsugense (Rosendahl) G. N. Jones subsp. tsugense, and the new subspecies, Pacific silver fir dwarf mistletoe, Arceuthobium tsugense subsp. amabilae Mathiasen & C. Daugherty.
Flat band superconductivity in type-II Weyl-semimetal WTe2 induced by a normal metal contact

WTe$_2$ is a material with rich topological properties: it is a 2D topological insulator as a monolayer and a Weyl-semimetal and higher-order topological insulator (HOTI) in the bulk form. Inducing superconductivity in topological materials is a way to obtain topological superconductivity, which lays at the foundation for many proposals of fault tolerant quantum computing. Here, we demonstrate the emergence of superconductivity at the interface between WTe$_2$ and the normal metal palladium. The superconductivity has a critical temperature of about 1.2 K. By studying the superconductivity in perpendicular magnetic field, we obtain the coherence length and the London penetration depth. These parameters hint to a possible origin of superconductivity due to the formation of flat bands. Furthermore, the critical in-plane magnetic field exceeds the Pauli limit, suggesting a non-trivial nature of the superconducting state.
Attention-based Residual Speech Portrait Model for Speech to Face Generation

Given a speaker's speech, it is interesting to see if it is possible to generate this speaker's face. One main challenge in this task is to alleviate the natural mismatch between face and speech. To this end, in this paper, we propose a novel Attention-based Residual Speech Portrait Model (AR-SPM) by introducing the ideal of the residual into a hybrid encoder-decoder architecture, where face prior features are merged with the output of speech encoder to form the final face feature. In particular, we innovatively establish a tri-item loss function, which is a weighted linear combination of the L2-norm, L1-norm and negative cosine loss, to train our model by comparing the final face feature and true face feature. Evaluation on AVSpeech dataset shows that our proposed model accelerates the convergence of training, outperforms the state-of-the-art in terms of quality of the generated face, and achieves superior recognition accuracy of gender and age compared with the ground truth.
Evo-RL: evolutionary-driven reinforcement learning

In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (Evo-RL), embeds the reinforcement learning algorithm in an evolutionary cycle, where we distinctly differentiate between purely evolvable (instinctive) behaviour versus purely learnable behaviour. Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing Evo-RL to be adaptive to different environments. In addition, Evo-RL facilitates learning on environments with reward-less states, which makes it more suited for real-world problems with incomplete information. To show that Evo-RL leads to state-of-the-art performance, we present the performance of different state-of-the-art reinforcement learning algorithms when operating within Evo-RL and compare it with the case when these same algorithms are executed independently. Results show that reinforcement learning algorithms embedded within our Evo-RL approach significantly outperform the stand-alone versions of the same RL algorithms on OpenAI Gym control problems with rewardless states constrained by the same computational budget.
Stochastic Model of Randomly End-Linked Polymer Network Microregions

Polymerization and formation of crosslinked polymer networks are important processes in manufacturing, materials fabrication, and in the case of hydrated polymer networks, synthesis of biomedical materials, drug delivery, and tissue engineering. While considerable research has been devoted to the modeling of polymer networks to determine averaged, mean-field, global properties, there are fewer studies that specifically examine the variance of the composition across "micro-regions" (composed of a large, but finite, number of polymer network strands) within the larger polymer network.Here, we mathematically model the stochastic formation of polymer networks comprised of linear homobifunctional network strands that undergo an end-linking gelation process. We introduce a master equation that describes the evolution of the probabilities of possible network micro-region configurations as a function of time and extent of reaction. We specifically focus on the dynamics of network formation and the statistical variability of the gel micro-regions, particularly at intermediate extents of reaction. We also consider possible annealing effects and study how cooperative binding between the two end-groups on a single network-strand affects network formation. Our results allow for a more detailed and thorough understanding of polymer network dynamics and variability of network properties.
Closing the Random Graph Gap in Tuza's Conjecture through the Online Triangle Packing Process

A long-standing conjecture of Zsolt Tuza asserts that the triangle covering number $\tau(G)$ is at most twice the triangle packing number $\nu(G)$, where the triangle packing number $\nu(G)$ is the maximum size of a set of edge-disjoint triangles in $G$ and the triangle covering number $\tau(G)$ is the minimal size of a set of edges intersecting all triangles. In this paper, we prove that Tuza's conjecture holds in the Erdős-Renyi random graph $G(n,m)$ for all range of $m$, closing the gap in what was previously known. (Recently, this result was also independently proved by Jeff Kahn and Jinyoung Park.) We employ a random greedy process called the online triangle packing process to produce a triangle packing in $G(n,m)$ and analyze this process by using the differential equations method.
When Humans and Machines Make Joint Decisions: A Non-Symmetric Bandit Model

How can humans and machines learn to make joint decisions? This has become an important question in domains such as medicine, law and finance. We approach the question from a theoretical perspective and formalize our intuitions about human-machine decision making in a non-symmetric bandit model. In doing so, we follow the example of a doctor who is assisted by a computer program. We show that in our model, exploration is generally hard. In particular, unless one is willing to make assumptions about how human and machine interact, the machine cannot explore efficiently. We highlight one such assumption, policy space independence, which resolves the coordination problem and allows both players to explore independently. Our results shed light on the fundamental difficulties faced by the interaction of humans and machines. We also discuss practical implications for the design of algorithmic decision systems.
The UU-Net: Reversible Face De-Identification for Visual Surveillance Video Footage

We propose a reversible face de-identification method for video surveillance data, where landmark-based techniques cannot be reliably used. Our solution generates a photorealistic de-identified stream that meets the data protection regulations and can be publicly released under minimal privacy concerns. Notably, such stream still encapsulates the information required to later reconstruct the original scene, which is useful for scenarios, such as crime investigation, where subjects identification is of most importance. Our learning process jointly optimizes two main components: 1) a public module, that receives the raw data and generates the de-identified stream; and 2) a private module, designed for security authorities, that receives the public stream and reconstructs the original data, disclosing the actual IDs of the subjects in a scene. The proposed solution is landmarks-free and uses a conditional generative adversarial network to obtain synthetic faces that preserve pose, lighting, background information and even facial expressions. Also, we keep full control over the set of soft facial attributes to be preserved/changed between the raw/de-identified data, which extends the range of applications for the proposed solution. Our experiments were conducted in three visual surveillance datasets (BIODI, MARS and P-DESTRE) plus one video face data set (YouTube Faces), showing highly encouraging results. The source code is available at https://github.com/hugomcp/uu-net.
Fast neural Poincaré maps for toroidal magnetic fields

Poincaré maps for toroidal magnetic fields are routinely employed to study gross confinement properties in devices built to contain hot plasmas. In most practical applications, evaluating a Poincaré map requires numerical integration of a magnetic field line, a process that can be slow and that cannot be easily accelerated using parallel computations. We propose a novel neural network architecture, the HénonNet, and show that it is capable of accurately learning realistic Poincaré maps from observations of a conventional field-line-following algorithm. After training, such learned Poincaré maps evaluate much faster than the field-line integration method. Moreover, the HénonNet architecture exactly reproduces the primary physics constraint imposed on field-line Poincaré maps: flux preservation. This structure-preserving property is the consequence of each layer in a HénonNet being a symplectic map. We demonstrate empirically that a HénonNet can learn to mock the confinement properties of a large magnetic island by using coiled hyperbolic invariant manifolds to produce a sticky chaotic region at the desired island location. This suggests a novel approach to designing magnetic fields with good confinement properties that may be more flexible than ensuring confinement using KAM tori.
Inductive Relational Matrix Completion

Data sparsity and cold-start issues emerge as two major bottlenecks for matrix completion in the context of user-item interaction matrix. We propose a novel method that can fundamentally address these issues. The main idea is to partition users into support users, which have many observed interactions (i.e., non-zero entries in the matrix), and query users, which have few observed entries. For support users, we learn their transductive preference embeddings using matrix factorization over their interactions (a relatively dense sub-matrix). For query users, we devise an inductive relational model that learns to estimate the underlying relations between the two groups of users. This allows us to attentively aggregate the preference embeddings of support users in order to compute inductive embeddings for query users. This new method can address the data sparsity issue by generalizing the behavior patterns of warm-start users to others and thus enables the model to also work effectively for cold-start users with no historical interaction. As theoretical insights, we show that a general version of our model does not sacrifice any expressive power on query users compared with transductive matrix factorization under mild conditions. Also, the generalization error on query users is bounded by the numbers of support users and query users' observed interactions. Moreover, extensive experiments on real-world datasets demonstrate that our model outperforms several state-of-the-art methods by achieving significant improvements on MAE and AUC for warm-start, few-shot (sparsity) and zero-shot (cold-start) recommendation.
Heuristic-Based Weak Learning for Automated Decision-Making

Machine learning systems impact many stakeholders and groups of users, often disparately. Prior studies have reconciled conflicting user preferences by aggregating a high volume of manually labeled pairwise comparisons, but this technique may be costly or impractical. How can we lower the barrier to participation in algorithm design? Instead of creating a simplified labeling task for a crowd, we suggest collecting ranked decision-making heuristics from a focused sample of affected users. With empirical data from two use cases, we show that our weak learning approach, which requires little to no manual labeling, agrees with participants' pairwise choices nearly as often as fully supervised approaches.
Modelling of calcium handling in genetically modified mice

This thesis develops biophysically-based data-driven mat hematical models of intracellular calcium dynamics in ventricular myocytes for both normal an d genetically modified mouse hearts, based on speciesand temperature-consistent expe rimental data. The models were subsequently applied to quantitatively examine the change s in calcium dynamics in mice with cardiomyocyte-specific knockout (KO) of the cardiac sa rco/endoplasmic reticulum ATPase (SERCA2) gene, to determine the contributing mechan isms which underlie the ultimate development of heart failure in these animals. In C hapter 1, with emphasis on calcium dynamics and calcium regulation in heart failure, a n overview of cardiac electrophysiology, excitation-contraction coupling and math ematical models of cardiac electrophysiology is provided. In Chapter 2, models of calcium d ynamics in the ventricular myocytes from the C57BL/6 mouse heart at a physiological tem perature is developed and validated based on speciesand temperature-consistent me asurements. In Chapter 3, the C57BL/6 model framework is re-parameterised to experiment al data from the control and SERCA2 KO mice at 4 weeks after gene deletion. The models are t hen used to quantitatively characterise changes in calcium dynamics in the KO animals and the role of the compensatory mechanisms. In Chapter 4, the model framework is extended to include differential distributions of ion channels in the sarcolem ma and the calcium dynamics in the sub-sarcolemmal space, with parameters in these sub-co mponents fitted to experimentally measured calcium dynamics from the control and KO card iomyocytes at 7-week after gene deletion. Finally in Chapter 5, conclusions are drawn, the limitations of this study are discussed, and the future extensions to this study are de scrib d.
A New Spin on an Old Black Hole: NuSTAR Spectroscopy of EXO 1846–031

The black hole candidate EXO 1846-031 underwent an outburst in 2019, after at least 25 yr in quiescence. We observed the system using NuSTAR on 2019 August 3. The 3–79 keV spectrum shows strong relativistic reflection features. Our baseline model gives a nearly maximal black hole spin value of (1σ statistical errors). This high value nominally excludes the possibility of the central engine harboring a neutron star. Using several models, we test the robustness of our measurement to assumptions about the density of the accretion disk, the nature of the corona, the choice of disk continuum model, and the addition of reflection from the outer regions of the accretion disk. All tested models agree on a very high black hole spin value and a high value for the inclination of the inner accretion disk of . We discuss the implications of this spin measurement in the population of stellar mass black holes with known spins, including LIGO and Virgo events.
The Equation of State of Nuclear Matter: From Finite Nuclei to Neutron Stars

Background. We investigate possible correlations between neutron star observables and properties of atomic nuclei. In particular, we explore how the tidal deformability of a 1.4 solar mass neutron star, M1.4, and the neutron-skin thickness of 48Ca and 208Pb are related to the stellar radius and the stiffness of the symmetry energy. Methods. We examine a large set of nuclear equations of state based on phenomenological models (Skyrme, NLWM, DDM) and ab initio theoretical methods (BBG, Dirac–Brueckner, Variational, Quantum Monte Carlo). Results: We find strong correlations between tidal deformability and NS radius, whereas a weaker correlation does exist with the stiffness of the symmetry energy. Regarding the neutron-skin thickness, weak correlations appear both with the stiffness of the symmetry energy, and the radius of a M1.4. Our results show that whereas the considered EoS are compatible with the largest masses observed up to now, only five microscopic models and four Skyrme forces are simultaneously compatible with the present constraints on L and the PREX experimental data on the 208Pb neutron-skin thickness. We find that all the NLWM and DDM models and the majority of the Skyrme forces are excluded by these two experimental constraints, and that the analysis of the data collected by the NICER mission excludes most of the NLWM considered. Conclusion. The tidal deformability of a M1.4 and the neutron-skin thickness of atomic nuclei show some degree of correlation with nuclear and astrophysical observables, which however depends on the ensemble of adopted EoS.
Challenges of AI in Wireless Networks for IoT

The Internet of Things (IoT), hailed as the enabler of the next industrial revolution, will require ubiquitous connectivity, context-aware and dynamic service mobility, and extreme security through the wireless network infrastructure. Artificial Intelligence (AI), thus, will play a major role in the underlying network infrastructure. However, a number of challenges will surface while using the concepts, tools and algorithms of AI in wireless networks used by IoT. In this article, the main challenges in using AI in the wireless network infrastructure that facilitate end-to-end IoT communication are highlighted with potential generalized solution and future research directions.
A Systematic Review on Context-Aware Recommender Systems using Deep Learning and Embeddings

Recommender Systems are tools that improve how users find relevant information in web systems, so they do not face too much information. In order to generate better recommendations, the context of information should be used in the recommendation process. Context-Aware Recommender Systems were created, accomplishing state-of-the-art results and improving traditional recommender systems. There are many approaches to build recommender systems, and two of the most prominent advances in area have been the use of Embeddings to represent the data in the recommender system, and the use of Deep Learning architectures to generate the recommendations to the user. A systematic review adopts a formal and systematic method to perform a bibliographic review, and it is used to identify and evaluate all the research in certain area of study, by analyzing the relevant research published. A systematic review was conducted to understand how the Deep Learning and Embeddings techniques are being applied to improve Context-Aware Recommender Systems. We summarized the architectures that are used to create those and the domains that they are used.