Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft-tissue Preservation in Miocene Frogs from Libros, Spain: Insights into the Genesis of Decay Microenvironments

Abstract The Late Miocene Libros biota is a lacustrine-hosted, Konservat-Lagerstätte from Libros, near Teruel in northeast Spain. Adult frogs are characterized by the preservation of their soft tissues, some in histological detail. The soft tissues of the body outline are preserved as a layered structure, which comprises a central carbonaceous bacterial biofilm enveloped by the phosphatized remains of the mid-dermal Eberth-Katschenko layer, external to which is a second, thinner, carbonaceous bacterial biofilm. Bacterial autolithification is restricted to limited phosphatization of the cell margins of bacteria adjacent to phosphatized dermis. Phosphatization occurred during the late stages of decay; phosphate was sourced primarily from the dermis itself. Other tissues and organs are also defined in authigenic minerals: nervous tissue (aragonite), the stomach (calcium phosphate), and collagen fibers of the dermal stratum compactum (calcium sulphate); bone marrow is organically preserved. The disparate modes of soft-tissue preservation within individual specimens reflects development of several highly localized, chemically distinct microenvironments within the frog carcasses during decay. These microenvironments correspond to individual organs and tissues, were established at different times during decay, and varied in their duration. The preservation of soft tissues via multiple taphonomic pathways was controlled ultimately by anatomical and physiological factors.
The nature of CR7 revealed with MUSE: a young starburst powering extended Ly α emission at z = 6.6

CR7 is among the most luminous Ly α emitters (LAEs) known at z = 6.6 and consists of at least three UV components that are surrounded by Ly α emission. Previous studies have suggested that it may host an extreme ionizing source. Here, we present deep integral field spectroscopy of CR7 with VLT/Multi Unit Spectroscopic Explorer (MUSE). We measure extended emission with a similar halo scale length as typical LAEs at z ≈ 5. CR7’s Ly α halo is clearly elongated along the direction connecting the multiple components, likely tracing the underlying gas distribution. The Ly α emission originates almost exclusively from the brightest UV component, but we also identify a faint kinematically distinct Ly α emitting region nearby a fainter component. Combined with new near-infrared data, the MUSE data show that the rest-frame Ly α equivalent width (EW) is ≈100 Å. This is a factor 4 higher than the EW measured in low-redshift analogues with carefully matched Ly α profiles (and thus arguably H i column density), but this EW can plausibly be explained by star formation. Alternative scenarios requiring active galactic nucleus (AGN) powering are also disfavoured by the narrower and steeper Ly α spectrum and much smaller IR to UV ratio compared to obscured AGN in other Ly α blobs. CR7’s Ly α emission, while extremely luminous, resembles the emission in more common LAEs at lower redshifts very well and is likely powered by a young metal-poor starburst.
Fast Nonconvex $T_2^*$ Mapping Using ADMM

Magnetic resonance (MR)-$T_2^*$ mapping is widely used to study hemorrhage, calcification and iron deposition in various clinical applications, it provides a direct and precise mapping of desired contrast in the tissue. However, the long acquisition time required by conventional 3D high-resolution $T_2^*$ mapping method causes discomfort to patients and introduces motion artifacts to reconstructed images, which limits its wider applicability. In this paper we address this issue by performing $T_2^*$ mapping from undersampled data using compressive sensing (CS). We formulate the reconstruction as a nonconvex problem that can be decomposed into two subproblems. They can be solved either separately via the standard approach or jointly via the alternating direction method of multipliers (ADMM). Compared to previous CS-based approaches that only apply sparse regularization on the spin density $\boldsymbol X_0$ and the relaxation rate $\boldsymbol R_2^*$, our formulation enforces additional sparse priors on the $T_2^*$-weighted images at multiple echoes to improve the reconstruction performance. We performed convergence analysis of the proposed algorithm, evaluated its performance on in vivo data, and studied the effects of different sampling schemes. Experimental results showed that the proposed joint-recovery approach generally outperforms the state-of-the-art method, especially in the low-sampling rate regime, making it a preferred choice to perform fast 3D $T_2^*$ mapping in practice. The framework adopted in this work can be easily extended to other problems arising from MR or other imaging modalities with non-linearly coupled variables.
Switching Controls for Analytic Semigroups and Applications to Parabolic Systems

In this work, we push further the analysis of the problem of switching controls proposed in [31]. The problem consists in the following one: assuming that one can control a system using two or more actuators, does there exist a control strategy such that at all times, only one actuator is active? We answer positively to this question when the controlled system corresponds to an analytic semigroup spanned by a positive self-adjoint operator which is null-controllable in arbitrary small times. Similarly as in [31], our proof relies on analyticity arguments and will also work in finite dimensional setting and under some further spectral assumptions when the operator spans an analytic semigroup but is not necessarily self-adjoint.
Modelling epidemic dynamics under collective decision making

During the course of an epidemic, individuals constantly make decisions on how to fight against epidemic spreading. Collectively, these individual decisions are critical to the global outcome of the epidemic, especially when no pharmaceutical interventions are available. However, existing epidemic models lack the ability to capture this complex decision-making process, which is shaped by an interplay of factors including government-mandated policy interventions, expected socio-economic costs, perceived infection risks and social influences. Here, we introduce a novel parsimonious model, grounded in evolutionary game theory, able to capture decision-making dynamics over heterogeneous time scales. Using real data, we analyse three case studies in the spreading of gonorrhoea, the 1918--19 Spanish flu and COVID-19. Behavioural factors shaping the course of the epidemic are intelligibly mapped onto a minimal set of model parameters, and their interplay gives rise to characteristic phenomena, such as sustained periodic outbreaks, multiple epidemic waves, or a successful eradication of the disease. Our model enables a direct assessment of the epidemiological and socio-economic impact of different policy interventions implemented to combat epidemic outbreaks. Besides the common-sense finding that stringent non-pharmaceutical interventions are essential to taming the initial phases of the outbreak, the duration of such interventions and the way they are phased out are key for an eradication in the medium-to-long term. Surprisingly, our findings reveal that social influence is a double-edged sword in the control of epidemics, helping strengthen collective adoption of self-protective behaviours during the early stages of the epidemic, but then accelerating their rejection upon lifting of non-pharmaceutical containment interventions.
Coupling between Particle Shape and Long-Range Interaction in the High-Density Regime

By using long-range interacting polygons, we experimentally probe the coupling between particle shape and long-range interaction. For two typical space-filling polygons, square and triangle, we find two types of coupling modes that predominantly control the structure formation. Specifically, the rotational ordering of squares brings a lattice deformation that produces a hexagonal-to-rhombic transition in the high-density regime, whereas the alignment of triangles introduces a large geometric frustration that causes an order-to-disorder transition. Moreover, the two coupling modes lead to small and large "internal roughness" of the two systems, and thus predominantly control their structure relaxations. Our study thus provides a physical picture to the coupling between long-range interaction effect and short-range shape effect in the high-density regime unexplored before.
Risk-sensitive Dynkin games with heterogeneous Poisson random intervention times.

The paper solves constrained Dynkin games with risk-sensitive criteria, where two players are allowed to stop at two independent Poisson random intervention times, via the theory of backward stochastic differential equations. This generalizes the previous work of [Liang and Sun, Dynkin games with Poisson random intervention times, SIAM Journal on Control and Optimization, 2019] from the risk-neutral criteria and common signal times for both players to the risk-sensitive criteria and two heterogenous signal times. Furthermore, the paper establishes a connection of such constrained risk-sensitive Dynkin games with a class of stochastic differential games via Krylov's randomized stopping technique.
New nodes attack strategies for real complex weighted networks

In this work we introduce a new nodes attack strategy removing nodes with highest conditional weighted betweenness centrality (CondWBet). We compare its efficacy with well-known attack strategies from literature over five real-world complex weighted networks. We use the network weighted efficiency (WEFF) like a measure encompassing the weighted structure of the network in addition to the commonly used binary-topological measure, the largest connected cluster (LCC). We find that the recently proposed conditional betweenness strategy (CondBet) (Nguyen et al. 2019) is the best to fragment the LCC in all cases. Further, we find that the introduced CondWBet strategy is the best to decrease the network efficiency (WEFF) in 3 out of 5 cases. Last, CondWBet is be the most effective strategy to reduce WEFF at the beginning of the removal process whereas the Strength that removes nodes with highest link weights first, shows the highest efficacy in the final phase of the removal process when the network is broken in many small clusters. These last outcomes would suggest that a better attacking strategy could be a combination of the CondWBet and Strength strategies
Sketching Datasets for Large-Scale Learning (long version)

This article considers "sketched learning," or "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification, or regression) is performed. In particular, a "sketch" is first constructed by computing carefully chosen nonlinear random features (e.g., random Fourier features) and averaging them over the whole dataset. Parameters are then learned from the sketch, without access to the original dataset. This article surveys the current state-of-the-art in sketched learning, including the main concepts and algorithms, their connections with established signal-processing methods, existing theoretical guarantees-on both information preservation and privacy preservation, and important open problems.
Compact Graph Architecture for Speech Emotion Recognition

We propose a deep graph approach to address the task of speech emotion recognition. A compact, efficient and scalable way to represent data is in the form of graphs. Following the theory of graph signal processing, we propose to model speech signal as a cycle graph or a line graph. Such graph structure enables us to construct a Graph Convolution Network (GCN)-based architecture that can perform an accurate graph convolution in contrast to the approximate convolution used in standard GCNs. We evaluated the performance of our model for speech emotion recognition on the popular IEMOCAP and MSP-IMPROV databases. Our model outperforms standard GCN and other relevant deep graph architectures indicating the effectiveness of our approach. When compared with existing speech emotion recognition methods, our model achieves comparable performance to the state-of-the-art with significantly fewer learnable parameters (~30K) indicating its applicability in resource-constrained devices. Our code is available at /github.com/AmirSh15/Compact_SER.
Evolution of an Evolutionary Hypothesis: A History of Changing Ideas about the Adaptive Significance of Viviparity in Reptiles

Abstract Most reptiles are oviparous (egg-laying), but viviparity (live-bearing) has evolved about 100 times in lizards and snakes. Geographic biases in the distribution of viviparous species stimulated the “cold-climate hypothesis,” proposed independently by three field-based researchers in the early 1900s. Mell (in China) and Weekes (in Australia) viewed viviparity as a mechanism for thermal buffering of embryos from nighttime frosts, whereas Sergeev (in Russia) suggested that eggs retained in utero would develop faster because they were kept warmer, enabling early hatching. Although alternative adaptationist hypotheses proliferated over the next few decades, many had logical flaws (failing to consider intermediate stages). Phylogenetically based analyses identified around 100 independent origins of viviparity from oviparity, and revealed a strong trend for viviparity to evolve in cold climates. After experimental studies showed that incubation temperatures affect offspring phenotypes as well as rates of development and survival, I proposed (in 1995) that by retaining eggs in utero, a mother can manipulate the phenotypic traits of her offspring (and thus, enhance their fitness). This mother of all hypotheses subsumes the cold-climate hypothesis, and has been supported by studies on both oviparous and viviparous squamates, and in tropical as well as cold-climate areas. The history of this research topic illustrates the emergence of similar ideas from different researchers (sometimes creating tensions); the major roles of happenstance and field experience in suggesting ideas and allowing them to be tested; and the vital contributions of concepts, methodologies, and technologies from other fields to our understanding of reptilian viviparity.
Shallow Univariate ReLU Networks as Splines: Initialization, Loss Surface, Hessian, and Gradient Flow Dynamics

Understanding the learning dynamics and inductive bias of neural networks (NNs) is hindered by the opacity of the relationship between NN parameters and the function represented. Partially, this is due to symmetries inherent within the NN parameterization, allowing multiple different parameter settings to result in an identical output function, resulting in both an unclear relationship and redundant degrees of freedom. The NN parameterization is invariant under two symmetries: permutation of the neurons and a continuous family of transformations of the scale of weight and bias parameters. We propose taking a quotient with respect to the second symmetry group and reparametrizing ReLU NNs as continuous piecewise linear splines. Using this spline lens, we study learning dynamics in shallow univariate ReLU NNs, finding unexpected insights and explanations for several perplexing phenomena. We develop a surprisingly simple and transparent view of the structure of the loss surface, including its critical and fixed points, Hessian, and Hessian spectrum. We also show that standard weight initializations yield very flat initial functions, and that this flatness, together with overparametrization and the initial weight scale, is responsible for the strength and type of implicit regularization, consistent with previous work. Our implicit regularization results are complementary to recent work, showing that initialization scale critically controls implicit regularization via a kernel-based argument. Overall, removing the weight scale symmetry enables us to prove these results more simply and enables us to prove new results and gain new insights while offering a far more transparent and intuitive picture. Looking forward, our quotiented spline-based approach will extend naturally to the multivariate and deep settings, and alongside the kernel-based view, we believe it will play a foundational role in efforts to understand neural networks. Videos of learning dynamics using a spline-based visualization are available at http://shorturl.at/tFWZ2.
Feeding strategies of sedentary large herbivores in East Africa, with emphasis on the African buffalo, Syncerus coffer

Summary A comparison of the feeding strategy of four large herbivores–buffalo, wildebeest, Burchell's zebra and elephant–was carried out in Lake Manyara National Park, Tanzania. It appears that, especially during the dry season, elephant are time-limited and, presumably to alleviate a shortage in energy intake, switch from grazing to browsing. Zebra and wildebeest show very high grazing times during the day and have very little resting time during the dry season. If these two species are time-limited at that time of the year, it appears to be due to pre-dation risk at night. Buffalo are not time-limited, and show two foraging strategies; namely, an option in which they emphasize bulk feeding, and one in which selection is stressed. They appear to be resource-limited. All four species show a remarkable correspondence in the proportion of time they spend on food collection. On a yearly basis, the amount of time spent on foraging plus moving approximates to 80% of their (day) time. Nevertheless, the feeding strategies of these species are different although they inhabit the same environment and face the same sort of shortages. Resume On a compare les strategies alimentaires de quatre grands herbivores–buffle, gnou, zebre de Burchell et elephant–au Pare National du Lac Manyara, en Tanzanie. II semble que, specialement durant la saison seche, les elephants man-quent de temps et, sans doute pour compenser une insuffisance en apport ener-getique, passent a la consommation de feuilles. Les zebres et les gnous passent un temms considerable a brouter durant le jour et ont tres peu de temps de repos durant la saison seche. Si ces deux especes manquent de temps a cette epoque de l'annee, cela semble etre a cause du rique de predation nocturne. Les buffles ne manquent pas de temps et presentent deux strategies alimentaires: une premiere option favorise l'alimentation de masse, et une autre ou la selection est mani-feste. lis semblent etre limites par les ressources. Ces quatre especes presentent une remarquable correspondance dans la proportion de temps passea la recherche de nourriture. Sur un base annuelle, la proportion de temps passea se nourrir et se deplacer est d'environ 80% du temps diurne. Neanmoins, les strategies alimentaires de ces especes different alors qu'ils vivent dans le meme environnement et subissent les memes contraintes.
THRESHOLD QUANTITIES FOR INFECTIOUS DISEASES IN PERIODIC ENVIRONMENTS

In this short note we give threshold quantities that determine the stability of the infection-free steady state for periodic deterministic systems that describe the spread of infectious diseases in populations whose individuals can be divided into a finite number of distinct groups. We concentrate on "micro-parasitic" infections, but the theory was originally developed for helminth infections. As an example, we treat a simple model of a vector transmitted infection with periodic recruitment in the vector population.
Late time physics of holographic quantum chaos

Quantum chaotic systems are often defined via the assertion that their spectral statistics coincides with, or is well approximated by, random matrix theory. In this paper we explain how the universal content of random matrix theory emerges as the consequence of a simple symmetry-breaking principle and its associated Goldstone modes. This allows us to write down an effective-field theory (EFT) description of quantum chaotic systems, which is able to control the level statistics up to an accuracy {O} \left(e^{-S} \right)O(e−S) with SS the entropy. We explain how the EFT description emerges from explicit ensembles, using the example of a matrix model with arbitrary invariant potential, but also when and how it applies to individual quantum systems, without reference to an ensemble. Within AdS/CFT this gives a general framework to express correlations between ``different universes’’ and we explicitly demonstrate the bulk realization of the EFT in minimal string theory where the Goldstone modes are bound states of strings stretching between bulk spectral branes. We discuss the construction of the EFT of quantum chaos also in higher dimensional field theories, as applicable for example for higher-dimensional AdS/CFT dual pairs.
Value Entropy Model: Metric Method of Service Ecosystem Evolution

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. To meet the customized user demands, service ecosystems begins to emerge with the formation of various IT services collaboration network. However, service ecosystem is a complex social-technology system with the characteristics of natural ecosystems, economic systems and complex networks. Hence, how to realize the multi-dimensional evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this paper proposes a value entropy model to analyze the performance of service ecosystem, which is conducive to integrate evaluation indicators of different dimensions. In addition, a computational experiment system is constructed to verify the effectiveness of value entropy model. The result shows that our model can provide new means and ideas for the analysis of service ecosystem.
Point Proposal Network: Accelerating Point Source Detection Through Deep Learning

Point source detection techniques are used to identify and localise point sources in radio astronomical surveys. With the development of the Square Kilometre Array (SKA) telescope, survey images will see a massive increase in size from Gigapixels to Terapixels. Point source detection has already proven to be a challenge in recent surveys performed by SKA pathfinder telescopes. This paper proposes the Point Proposal Network (PPN): a point source detector that utilises deep convolutional neural networks for fast source detection. Results measured on simulated MeerKAT images show that, although less precise when compared to leading alternative approaches, PPN performs source detection faster and is able to scale to large images, unlike the alternative approaches.
How the learning environment predicts male and female students’ motivational beliefs in algebra-based introductory physics

Societal stereotypes and biases pertaining to who belongs in physics and who can excel in physics can impact motivational beliefs, e.g., of women and racial and ethnic minority students in physics courses. This study investigates how the learning environment predicts male and female students' motivational beliefs including physics self-efficacy, interest, and identity at the end of year long (spanning two-semester) algebra-based introductory physics courses. These were courses at a large university in the US taken primarily by biological science majors many of whom are interested in health professions. Although women are not underrepresented in these physics courses, societal stereotypes and biases internalized by female students over their lifetime can still impact their motivational beliefs about physics. Our findings show gender gap in motivational beliefs favoring men. These findings can be useful to provide support and create an equitable and inclusive learning environment to help all students excel in these courses.