Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Light Curves of Bipolar Core Collapse Supernovae from the Equatorial Plane

We use the two-components bipolar toy model of core collapse supernova (CCSN) ejecta to fit the rapid decline from maximum luminosity in the light curve of the type IIb CCSN SN 2018gk (ASASSN-18am). In this toy model we use a template light curve from a different CCSN that is similar to SN 2018gk, but that has no rapid drop in its light curve. The bipolar morphology that we model with a polar ejecta and an equatorial ejecta increases the maximum luminosity and causes a steeper decline for an equatorial observer, relative to a similar spherical explosion. The total energy and mass of our toy model for SN 2018gk are E SN = 5 × 10 51 erg and M SN = 2.7 M ⊙ . This explosion energy is more than what a neutrino driven explosion mechanism can supply, implying that jets exploded SN 2018gk. These energetic jets likely shaped the ejecta to a bipolar morphology, as our toy model requires. We crudely estimate that f ≈ 2%–5% of all CCSNe show this behavior, most being hydrogen deficient (stripped-envelope) CCSNe, as we observe them from the equatorial plane. We estimate the overall fraction of CCSNe that have a pronounced bipolar morphology to be fbip ≈ 5%–15% of all CCSNe.
Is there slow particle transport in the MBL phase?

We analyze the saturation value of the bipartite entanglement and number entropy starting from a random product state deep in the MBL phase. By studying the probability distributions of these entropies we find that the growth of the saturation value of the entanglement entropy stems from a significant reshuffling of the weight in the probability distributions from the bulk to the exponential tails. In contrast, the probability distributions of the saturation value of the number entropy are converged with system size, and exhibit a sharp cut-off for values of the number entropy which correspond to one particle fluctuating across the boundary between the two halves of the system. Our results therefore rule out slow particle transport deep in the MBL phase and confirm that the slow entanglement entropy production stems uniquely from configurational entanglement.
On the optimality of upper estimates near blow-up in quasilinear Keller–Segel systems

ABSTRACT Solutions to the chemotaxis system in a ball , , wherein and are given parameters with m−q>−1, cannot blow up in finite time provided u is uniformly-in-time bounded in for some . For radially symmetric solutions, we show that, if u is only bounded in and the technical condition is fulfilled, then, for any , there is C>0 with denoting the maximal existence time. This is essentially optimal in the sense that, if this estimate held for any , then u would already be bounded in for some .
Learning Stable Manoeuvres in Quadruped Robots from Expert Demonstrations

With the research into development of quadruped robots picking up pace, learning based techniques are being explored for developing locomotion controllers for such robots. A key problem is to generate leg trajectories for continuously varying target linear and angular velocities, in a stable manner. In this paper, we propose a two pronged approach to address this problem. First, multiple simpler policies are trained to generate trajectories for a discrete set of target velocities and turning radius. These policies are then augmented using a higher level neural network for handling the transition between the learned trajectories. Specifically, we develop a neural network based filter that takes in target velocity, radius and transforms them into new commands that enable smooth transitions to the new trajectory. This transformation is achieved by learning from expert demonstrations. An application of this is the transformation of a novice user’s input into an expert user’s input, thereby ensuring stable manoeuvres regardless of the user’s experience. Training our proposed architecture requires much less expert demonstrations compared to standard neural network architectures. Finally, we demonstrate experimentally these results in the in-house quadruped Stoch 2.
TESS Reveals a Short-period Sub-Neptune Sibling (HD 86226c) to a Known Long-period Giant Planet

The Transiting Exoplanet Survey Satellite mission was designed to find transiting planets around bright, nearby stars. Here, we present the detection and mass measurement of a small, short-period (≈4 days) transiting planet around the bright (V = 7.9), solar-type star HD 86226 (TOI-652, TIC 22221375), previously known to host a long-period (∼1600 days) giant planet. HD 86226c (TOI-652.01) has a radius of 2.16 ± 0.08 R⊕ and a mass of M⊕, based on archival and new radial velocity data. We also update the parameters of the longer-period, not-known-to-transit planet, and find it to be less eccentric and less massive than previously reported. The density of the transiting planet is 3.97 g cm−3, which is low enough to suggest that the planet has at least a small volatile envelope, but the mass fractions of rock, iron, and water are not well-constrained. Given the host star brightness, planet period, and location of the planet near both the “radius gap” and the “hot Neptune desert,” HD 86226c is an interesting candidate for transmission spectroscopy to further refine its composition.
Some Die Filthy Rich: The Diverse Molecular Gas Contents of Post-starburst Galaxies Probed by Dust Absorption

Quenched post-starburst galaxies (QPSBs) are a rare but important class of galaxies that show signs of rapid cessation or recent rejuvenation of star formation. A recent observation shows that about half of QPSBs have large amounts of cold gas. This molecular CO sample is, however, too small and is not without limitations. Our work aims to verify previous results by applying a new method to study a uniformly selected sample, more than 10 times larger. In particular, we present detailed analysis of Hα/Hβ ratios of face-on QPSBs at z = 0.02–0.15 and with . We interpret the Hα/Hβ ratios by applying our recent gas mass calibration, which is based on non-PSB galaxies but predicts gas masses that are consistent with CO observations of ∼100 PSBs. We estimate the molecular gas by either using PSBs with well-measured Hα/Hβ ratios or measuring them from stacked spectra. Our analysis reveals that QPSBs have a wide range of Hα/Hβ ratios and molecular gas fractions that overlap with the typical gas fractions of star-forming or quiescent galaxies: Hα/Hβ ≈ 3–8 and with median , which correspond to . Our results indicate that large reservoirs of cold gas are still present in significant numbers of QPSBs and that they arguably were not removed or destroyed by feedback from active galactic nuclei.
Automatic Detection and Classification of Waste Consumer Medications for Proper Management and Disposal

Every year, millions of pounds of medicines remain unused in the U.S. and are subject to an in-home disposal, i.e., kept in medicine cabinets, flushed in toilet or thrown in regular trash. In-home disposal, however, can negatively impact the environment and public health. The drug take-back programs (drug take-backs) sponsored by the Drug Enforcement Administration (DEA) and its state and industry partners collect unused consumer medications and provide the best alternative to in-home disposal of medicines. However, the drug take-backs are expensive to operate and not widely available. In this paper, we show that artificial intelligence (AI) can be applied to drug take-backs to render them operationally more efficient. Since identification of any waste is crucial to a proper disposal, we showed that it is possible to accurately identify loose consumer medications solely based on the physical features and visual appearance. We have developed an automatic technique that uses deep neural networks and computer vision to identify and segregate solid medicines. We applied the technique to images of about one thousand loose pills and succeeded in correctly identifying the pills with an accuracy of 0.912 and top-5 accuracy of 0.984. We also showed that hazardous pills could be distinguished from non-hazardous pills within the dataset with an accuracy of 0.984. We believe that the power of artificial intelligence could be harnessed in products that would facilitate the operation of the drug take-backs more efficiently and help them become widely available throughout the country.
Higher Derivations of Modules and the Hasse–Schmidt Module

In this paper we revisit Ribenboim's notion of higher derivations of modules and relate it to the recent work of De Fernex and Docampo on the sheaf of differentials of the arc space. In particular, we derive their formula for the Kahler differentials of the Hasse-Schmidt algebra as a consequence of the fact that the Hasse-Schmidt algebra functors commute.
Renormalisation from non-geometric to geometric rough paths

The Hairer-Kelly map has been introduced for establishing a correspondence between geometric and non-geometric rough paths. Recently, a new renormalisation on rough paths has been proposed in (arxiv 1810.12179), built on this map and the Lyons-Victoir extension theorem. In this work, we compare this renormalisation with the existing ones such as BPHZ and the local products renormalisations. We prove that they commute in a certain sense with the Hairer-Kelly map and exhibit an explicit formula in the framework of (arxiv 1810.12179). We also see how the renormalisation behaves in the alternative approach in ( arXiv:1712.01965) for moving from non-geometric to geometric rough paths.
Gravitational-wave Constraints on the Equatorial Ellipticity of Millisecond Pulsars

We present a search for continuous gravitational waves from five radio pulsars, comprising three recycled pulsars (PSR J0437−4715, PSR J0711−6830, and PSR J0737−3039A) and two young pulsars: the Crab pulsar (J0534+2200) and the Vela pulsar (J0835−4510). We use data from the third observing run of Advanced LIGO and Virgo combined with data from their first and second observing runs. For the first time, we are able to match (for PSR J0437−4715) or surpass (for PSR J0711−6830) the indirect limits on gravitational-wave emission from recycled pulsars inferred from their observed spin-downs, and constrain their equatorial ellipticities to be less than 10−8. For each of the five pulsars, we perform targeted searches that assume a tight coupling between the gravitational-wave and electromagnetic signal phase evolution. We also present constraints on PSR J0711−6830, the Crab pulsar, and the Vela pulsar from a search that relaxes this assumption, allowing the gravitational-wave signal to vary from the electromagnetic expectation within a narrow band of frequencies and frequency derivatives.
A Single-Letter Upper Bound on the Mismatch Capacity via Multicast Transmission

We introduce a new analysis technique to derive a single-letter upper bound on the mismatch capacity of a stationary, single-user, memoryless channel with a decoding metric <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>. Our bound is obtained by considering a multicast transmission over a two-user broadcast channel with decoding metrics <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> at the receivers, referred to as <inline-formula> <tex-math notation="LaTeX">$(q,\rho)$ </tex-math></inline-formula>-surely degraded. This channel has the property that the intersection event of correct <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-decoding of receiver 1 and erroneous <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>-decoding of receiver 2 has zero probability for any fixed-composition codebook of a certain composition <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>. Our bound holds in the strong converse sense of an exponential decay of the probability of correct decoding at rates above the bound. Further, we refine the proof and present a bound that is tighter than that of any choice of <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>. Several examples that demonstrate the strict improvement of our bound compared to previous results are analyzed. Finally, we detect equivalence classes of isomorphic channel-metric pairs <inline-formula> <tex-math notation="LaTeX">$(W,q)$ </tex-math></inline-formula> that share the same mismatch capacity. We prove that if the class contains a matched pair, then our bound is tight and the mismatch capacity of the entire class is fully characterized and is equal to the LM rate, which is achievable by random coding, and may be strictly lower that the matched capacity.
Wide-scale assessment of cultural ecosystem services in coastal areas using graph theory on social media data

Cultural Ecosystem Services (CES) assessment at large scales is crucial in marine ecosystems as they reflect key physical and cognitive interactions between humans and nature. The analysis of social media data with graph theory is a promising approach to provide global information on users' perceptions for different marine ecosystems. Fourteen areas were selected to illustrate the use of graph theory on social media data. The selected areas, known to protect key recreational, educational and heritage attributes of marine ecosystems, were investigated to identify variability in users' preferences. Instagram data (i.e., hashtags associated to photos) was extracted for each area allowing an in-depth assessment of the CES most appreciated by the users. Hashtags were analysed using network centrality measures to identify clusters of words, aspects not normally captured by traditional photo content analysis. The emergent properties of networks of hashtags were explored to characterise visitors' preferences (e.g., cultural heritage or nature appreciation), activities (e.g., diving or hiking), preferred habitats and species (e.g. forest, beach, penguins), and feelings (e.g., happiness or place identity). Network analysis on Instagram hashtags allowed delineating the users' discourse around a natural area, which provides crucial information for effective management of popular natural spaces for people.
RESPONSES IN MICROBES AND PLANTS TO CHANGED TEMPERATURE, NUTRIENT, AND LIGHT REGIMES IN THE ARCTIC

Previous research has shown that experimental perturbations of arctic ecosystems simulating direct and indirect effects of predicted environmental changes have led to strong responses in the plant communities, mostly associated with increased plant nutrient availability. Similarly, changes in decomposition and nutrient mineralization are likely to occur if the soil warms and the soil moisture conditions are altered. Plant and microbial responses have usually been investigated separately, and few, if any, studies have addressed simultaneous responses to environmental changes in plants and soil microorganisms, except in models. We measured simultaneous responses in biomass, nitrogen (N), and phosphorus (P) incorporation in plants and microorganisms after five years of factorial fertilizer addition, air warming, and shading. We expected increased N and P uptake by microorganisms after fertilizer addition and also after warming, due to increases in mineralization rates in warmer soils. Plant productivity and N and P uptake were expected to increase after fertilizer addition but less after warming, because microbes were expected to absorb most of the extra released nutrients. Shading was expected to decrease plant production and also microbial biomass, due to the reduced production of labile carbon (C) in plant root exudates associated with reduced photosynthesis. We found that the plants responded strongly to fertilizer addition by increased biomass accumulation and N and P uptake. They responded less to warming, but more than expected, showing a decline in N and P concentrations in many cases. There were few significant responses to shading. The strongest response was found in combined fertilizer addition and warming treatments. All functional vascular plant groups responded similarly. However, mosses declined under those conditions when vascular plant growth was most pronounced. Contrary to our expectation, microbial C, N, and P did not increase after warming, but microbial N and P increased after shading. As expected, fertilizer addition led to increased microbial P content, whereas microbial N either increased or did not change. In general, microbial C did not change in any treatment. The microbes accumulated extra N and P only when soil inorganic N or P levels increased, suggesting that the soil microorganisms absorbed extra nutrients only in cases of declining N and P sink strength in plants.
Large Scale Subject Category Classification of Scholarly Papers With Deep Attentive Neural Networks

Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neural network (DANN) that classifies scholarly papers using only their abstracts. The network is trained using nine million abstracts from Web of Science (WoS). We also use the WoS schema that covers 104 subject categories. The proposed network consists of two bi-directional recurrent neural networks followed by an attention layer. We compare our model against baselines by varying the architecture and text representation. Our best model achieves micro- F 1 measure of 0.76 with F 1 of individual subject categories ranging from 0.50 to 0.95. The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism. The combination of word vectors with TFIDF outperforms character and sentence level embedding models. We discuss imbalanced samples and overlapping categories and suggest possible strategies for mitigation. We also determine the subject category distribution in CiteSeerX by classifying a random sample of one million academic papers.
Using faecal samples in lizard dietary studies

While the use of faecal pellets is widely accepted as a primary methodological source of data for dietary studies, a recent paper advocated for the use of gut contents. This was due to the fact that faecal samples would give biased results of the diet of arthropod predators, due to a lower representation of soft-bodied prey in faecal pellets. To test this assumption, we compared the spring diet of several populations of two insular lizards from the Balearic Islands (Spain), Podarcis lilfordi and Podarcis pityusensis, using both faecal pellets and gut contents. Our results do not support the supposed bias of dietary analyses based on faecal pellets. Indeed, soft-bodied prey and particularly insect larvae are often equally represented in faecal pellets and gut contents. Alternatively, soft bodied prey are represented in different proportions in gut contents and faecal pellets, but in some cases with higher proportions being observed in the gut contents, and in other cases with higher proportions in faecal samples. We conclude that faecal pellets can be a reliable source of information for dietary studies.
The effect of pH, salts and sugars on the rheological properties of cress seed (Lepidium sativum) gum

Summary Effect of shear rate (15–600 s−1), gum concentration (1–2%), pH (3–9), sucrose (10–40%), lactose (5–15%), NaCl (100–300 mm) and CaCl2 (5–50 mm) was evaluated on apparent viscosity (ηa), flow behaviour index (n), consistency coefficient (K) and yield stress (τ0) indices of cress seed gum (CSG) solutions. Different rheological models were used to fit the experimental data, although the Herschel–Bulkley model was found the best model. An increase in gum concentration led to an increase in τ0, ηa, and k and a decrease in n values. The addition of salts lowered the k value; however, the n value showed slight significant change. The presence of sugar resulted in the enhancement of n, k, τ0 and ηap values. The existence of yield stress and pseudoplastic behaviour of CSG, its stability against salts, wide range of pH and synergic effect of sugar make it a good thickener and stabiliser in food formulations.
Multidimensional analysis of excitonic spectra of monolayers of tungsten disulphide: toward computer-aided identification of structural and environmental perturbations of 2D materials

Despite 2D materials holding great promise for a broad range of applications, the proliferation of devices and their fulfillment of real-life demands are still far from being realized. Experimentally obtainable samples commonly experience a wide range of perturbations (ripples and wrinkles, point and line defects, grain boundaries, strain field, doping, water intercalation, oxidation, edge reconstructions) significantly deviating the properties from idealistic models. These perturbations, in general, can be entangled or occur in groups with each group forming a complex perturbation making the interpretations of observable physical properties and the disentanglement of simultaneously acting effects a highly non-trivial task even for an experienced researcher. Here we generalise statistical correlation analysis of excitonic spectra of monolayer WS2, acquired by hyperspectral absorption and photoluminescence imaging, to a multidimensional case, and examine multidimensional correlations via unsupervised machine learning algorithms. Using principal component analysis we are able to identify four dominant components that are correlated with tensile strain, disorder induced by adsorption or intercalation of environmental molecules, multi-layer regions and charge doping, respectively. This approach has the potential to determine the local environment of WS2 monolayers or other 2D materials from simple optical measurements, and paves the way toward advanced, machine-aided, characterization of monolayer matter.
Stochastic Normalized Gradient Descent with Momentum for Large Batch Training

Stochastic gradient descent (SGD) and its variants have been the dominating optimization methods in machine learning. Compared with small batch training, SGD with large batch training can better utilize the computational power of current multi-core systems like GPUs and can reduce the number of communication rounds in distributed training. Hence, SGD with large batch training has attracted more and more attention. However, existing empirical results show that large batch training typically leads to a drop of generalization accuracy. As a result, large batch training has also become a challenging topic. In this paper, we propose a novel method, called stochastic normalized gradient descent with momentum (SNGM), for large batch training. We theoretically prove that compared to momentum SGD (MSGD) which is one of the most widely used variants of SGD, SNGM can adopt a larger batch size to converge to the $\epsilon$-stationary point with the same computation complexity (total number of gradient computation). Empirical results on deep learning also show that SNGM can achieve the state-of-the-art accuracy with a large batch size.