Miracle-dz/UniFAR-FTUs huggingface dataset:
HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy

The pervasive adoption of Deep Learning (DL) and Graph Processing (GP) makes it a de facto requirement to build large-scale clusters of heterogeneous accelerators including GPUs and FPGAs. The OpenCL programming framework can be used on the individual nodes of such clusters but is not intended for deployment in a distributed manner. Fortunately, the original OpenCL semantics naturally fit into the programming environment of heterogeneous clusters. In this paper, we propose a heterogeneity-aware OpenCL-like (HaoCL) programming framework to facilitate the programming of a wide range of scientific applications including DL and GP workloads on large-scale heterogeneous clusters. With HaoCL, existing applications can be directly deployed on heterogeneous clusters without any modifications to the original OpenCL source code and without awareness of the underlying hardware topologies and configurations. Our experiments show that HaoCL imposes a negligible overhead in a distributed environment, and provides near-liner speedups on standard benchmarks when computation or data size exceeds the capacity of a single node. The system design and the evaluations are presented in this demo paper.
Coupled cluster singles and doubles variational quantum eigensolver ansatz for electronic structure calculations

Variational quantum eigensolver (VQE) for electronic structure calculations is believed to be one major potential application of near term quantum computing. Among all proposed VQE algorithms, the unitary coupled cluster singles and doubles excitations (UCCSD) VQE ansatz has achieved high accuracy and received a lot of research interest. However, first order trotterization UCCSD VQE has gate complexity up-bounded to $O(n^5)$ using Jordan-Wigner transformation, where n is the number of qubits of the Hamiltonian. The high complexity makes UCCSD difficult to be implemented on near term quantum computer. Here we introduce a new VQE ansatz based on the particle preserving exchange gate to achieve excitations. The proposed improved VQE ansatz has gate complexity up-bounded to $O(n^4)$. Numerical results of simple molecular systems such as BeH$_2$, H$_2$O, N$_2$, H$_4$ and H$_6$ using the proposed improved VQE ansatz gives very accurate results within chemical accuracy of about $10^{-3}$ Hartree.
Information-theoretic limits of a multiview low-rank symmetric spiked matrix model

We consider a generalization of an important class of high-dimensional inference problems, namely spiked symmetric matrix models, often used as probabilistic models for principal component analysis. Such paradigmatic models have recently attracted a lot of attention from a number of communities due to their phenomenological richness with statistical-to-computational gaps, while remaining tractable. We rigorously establish the information-theoretic limits through the proof of single-letter formulas for the mutual information and minimum mean-square error. On a technical side we improve the recently introduced adaptive interpolation method, so that it can be used to study low-rank models (i.e., estimation problems of "tall matrices") in full generality, an important step towards the rigorous analysis of more complicated inference and learning models.
Use of global positioning system collars to monitor spatial-temporal movements of co-grazing goats and sheep and their common guardian dog

Goats and sheep often graze together and guardian dogs are commonly used for protection from predators. The objective of this experiment was to characterise how goats, sheep and guardian dogs interact spatially when grazing the same pasture by use of global positioning system (GPS) collars as an unobtrusive means of behaviour monitoring. In 2002 and 2003, three meat goats and two sheep in a group of 12 of each species were randomly chosen and, along with a guard dog, fitted with GPS collars. Minimum distance travelled between consecutive 30-min fixes and distance between any two animals at the same fix time were calculated using spherical geometry. In 2002, the dog travelled the least between fixes during the day but more at night than either goats or sheep. However, in 2003, there was not a significant species difference in distance travelled in 24 h or during the day or night. All species travelled significantly more during day than night but none were stationary at night. Distance amongst goats and between sheep tended to be greater during day than night; distance between goats and sheep was greater than the distance amongst goats or between sheep. Hence, goats and sheep interacted as two separate entities rather than as one large herd/flock. Distance between the dog and goats was closer than between the dog and sheep, indicating a clear preference of the dog for goats that could relate to a difference in previous exposure to the two species. In summary, based on these findings protection by a guardian dog would be greater for a small group of goats than sheep and much greater than for a mixed species group. Or, with a large group of grazing animals, the number of dogs required for a certain level of protection would rank goats < sheep < mixture of goats and sheep.
Coscinocladium, an overlooked endemic and monotypic Mediterranean lichen genus of Physciaceae, reinstated by molecular phylogenetic analysis

The position of the sterile western Mediterranean crustose-placodioid lichen generally known as Lecanora lisbonenesis has been investigated using mitochondrial SSU rDNA sequence data. It proves to belong to an independent genus of Physciaceae, for which the generic name Coscinocladium is available, and the earliest species name is Variolaria gaditana. Previous reports of apothecia in the species prove to be a result of mixtures with other lichen species. A lectotype is selected for L. lisbonensis, a neotype designated for V. gaditana, and the new combination Coscinocladium gaditanum made. The species is described, illustrated, its ecology discussed, and a distribution map provided.
Formation of Giant Planet Satellites

Recent analyses have shown that the concluding stages of giant planet formation are accompanied by the development of a large-scale meridional flow of gas inside the planetary Hill sphere. This circulation feeds a circumplanetary disk that viscously expels gaseous material back into the parent nebula, maintaining the system in a quasi-steady state. Here, we investigate the formation of natural satellites of Jupiter and Saturn within the framework of this newly outlined picture. We begin by considering the long-term evolution of solid material, and demonstrate that the circumplanetary disk can act as a global dust trap, where s• ∼ 0.1–10 mm grains achieve a hydrodynamical equilibrium, facilitated by a balance between radial updraft and aerodynamic drag. This process leads to a gradual increase in the system’s metallicity, and eventually culminates in the gravitational fragmentation of the outer regions of the solid subdisk into km satellitesimals. Subsequently, satellite conglomeration ensues via pair-wise collisions but is terminated when disk-driven orbital migration removes the growing objects from the satellitesimal feeding zone. The resulting satellite formation cycle can repeat multiple times, until it is brought to an end by photoevaporation of the parent nebula. Numerical simulations of the envisioned formation scenario yield satisfactory agreement between our model and the known properties of the Jovian and Saturnian moons.
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (e.g., database tables). In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. In experiments, neural semantic parsers using TaBERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WikiTableQuestions, while performing competitively on the text-to-SQL dataset Spider.
The host galaxies of 106 rapidly evolving transients discovered by the Dark Energy Survey

Rapidly evolving transients (RETs), also termed fast blue optical transients, are a recently discovered group of astrophysical events that display rapid luminosity evolution. RETs typically rise to peak in less than 10 d and fade within 30, a time-scale unlikely to be compatible with the decay of Nickel-56 that drives conventional supernovae (SNe). Their peak luminosity spans a range of −15 < Mg < −22.5, with some events observed at redshifts greater than 1. Their evolution on fast time-scales has hindered high-quality follow-up observations, and thus their origin and explosion/emission mechanism remains unexplained. In this paper, we present the largest sample of RETs to date, comprising 106 objects discovered by the Dark Energy Survey, and perform the most comprehensive analysis of RET host galaxies. Using deep-stacked photometry and emission lines from OzDES spectroscopy, we derive stellar masses and star formation rates (SFRs) for 49 host galaxies, and metallicities ([O/H]) for 37. We find that RETs explode exclusively in star-forming galaxies and are thus likely associated with massive stars. Comparing RET hosts to samples of host galaxies of other explosive transients as well as field galaxies, we find that RETs prefer galaxies with high specific SFRs (〈log (sSFR)〉 ∼ −9.6), indicating a link to young stellar populations, similar to stripped-envelope SNe. RET hosts appear to show a lack of chemical enrichment, their metallicities akin to long-duration gamma-ray bursts and superluminous SN host galaxies (〈12 + log (O/H)〉 ∼ 9.4). There are no clear relationships between mass or SFR of the host galaxies and the peak magnitudes or decline rates of the transients themselves.
Characterization of phosphatase in marigold roots infected with vesicular-arbuscular mycorrhizal fungi

Abstract The phosphatase specific to the infection of vesicular-arbuscular mycorrhizal fungi (Glomus etunicatum Becker & Gerdemann) in marigold (Tagetes patula L.) roots was investigated in relation to the mycorrhizal growth promotion in host plant. Mycorrhizal and non-mycorrhizal marigold plants were cultured in a growth chamber, and the enzyme solution was extracted from 2- to 10-week-old plant roots. The infection-specific phosphatase (ISPase) was detected in the mycorrhizal root extract from the beginning of the infection (4-week-old plant) by an electrophoretical technique. The activity increased as the infection rate increased, and decreased at the stationary phase of the host growth when the infection rate was still high. The effect of phosphorus (P) fertilization on the ISPase activity and the mycorrhizal growth promotion in host was investigated in 4- to 8-week-old plants using two kinds of liquid fertilizers with different P concentrations. The plants without P-fertilization showed more increase.
Localization of rung pairs in a hard-core Bose-Hubbard ladder

Quantum simulation experimentally of many-body systems may bring new phenomena which are not well studied theoretically. Motivated by a recent work of quantum simulation on a superconducting ladder circuit, we investigate the rung-pair localization of the Bose-Hubbard ladder model without quenched disorder. Our results show that, in the hard-core limit, the rung-pair localization can exist both at the edges and in the bulk. Using center-of-mass frame, the two-particle system can be mapped to an effective single-particle system with an approximate sub-lattice symmetry. Under the condition of hard-core limit, the effective system is forced to have a defect at the left edge leading to a zero-energy mode, which is the origin of the rung-pair localization. In addition, we also study the dynamics of the Bose-Hubbard ladder model with multiple rung pairs. Using time evolving block decimation method, we demonstrate that the system can display a localization similar to the many-body localization. The entanglement entropies exhibit a long-time logarithmic growth, and the corresponding saturated values satisfy a volume law.
Unbiased Learning to Rank via Propensity Ratio Scoring

Implicit feedback, such as user clicks, is a major source of supervision for learning to rank (LTR) model estimation in modern retrieval systems. However, the inherent bias in such feedback greatly restricts the quality of the learnt ranker. Recent advances in unbiased LTR leverage Inverse Propensity Scoring (IPS) to tackle the bias issue. Though effective, it only corrects the bias introduced by treating clicked documents as relevant, but cannot handle the bias caused by treating unclicked ones as irrelevant. Because non-clicks do not necessarily stand for irrelevance (they might not be examined), IPS-based methods inevitably include loss from comparisons on relevant-relevant document pairs. This directly limits the effectiveness of ranking model learning. In this work, we first prove that in a LTR algorithm that is based on pairwise comparisons, only pairs with different labels (e.g., relevant-irrelevant pairs in binary case) should contribute to the loss function. The proof asserts sub-optimal results of the existing IPS-based methods in practice. We then derive a new weighting scheme called Propensity Ratio Scoring (PRS) that takes a holistic treatment on both clicks and non-clicks. Besides correcting the bias in clicked documents, PRS avoids relevant-relevant comparisons in LTR training in expectation and enjoys a lower variability. Our empirical study confirms that PRS ensures a more effective use of click data in various situations, which leads to its superior performance in an extensive set of LTR benchmarks.
Tracking the Evolutionary Stage of Protostars through the Abundances of Astrophysical Ices

The physical evolution of young stellar objects (YSOs) is accompanied by an enrichment of the molecular complexity, mainly triggered by the heating and energetic processing of astrophysical ices. In this paper, a study of how the ice column density varies across the protostellar evolution has been performed. Tabulated data of H2O, CO2, CH3OH, and HCOOH observed by ground- and space-based telescopes toward 27 early-stage YSOs were taken from the literature. The observational data show that ice column density and spectral index (α), used to classify the evolutionary stage, are well correlated. A 2D continuum radiative transfer simulation containing bare and ice-covered grains at different levels of cosmic-ray processing were used to calculate the spectral energy distributions in different angle inclinations between face-on and edge-on configurations. The H2O:CO2 ice mixture was used to address the H2O and CO2 column density variation, whereas CH3OH and HCOOH are by-products of the virgin ice after energetic processing. The simulated spectra were used to calculate the ice column densities of YSOs in an evolutionary sequence. As a result, the models show that the ice column density variation of HCOOH with α can be justified by envelope dissipation and energetic processing of ice. On the other hand, the ice column densities are mostly overestimated in the cases of H2O, CO2 and CH3OH, even though the physical and cosmic-ray processing effects are taken into account.
Establishment of Fabaceae Tree Species in a Tropical Pasture: Influence of Seed Size and Weeding Methods

The successful establishment of trees in pastures may be related to species, seed size, and weed control treatments. This study experimentally evaluated the emergence, establishment after 2 years, and growth of individuals of 7 tree species with different seed sizes sowed under three weeding methods (no weeding, grasses clipped, and grasses removed). The experiment was carried out in a tropical abandoned pasture in Midwestern Brazil, dominated by the exotic invasive grass Urochloa brizantha. The effect of seed size on seedling emergence was significant and was more intense in the no weeding and weeding treatment plots than in grasses clipped. Furthermore, an increase in seed size resulted in an increase in the probability of establishment of tree species, although this pattern also differed among weeding treatments. The increase in seed size reflected negatively on tree growth in the absence of grasses, whereas in the presence of grasses there was an inverse relationship. The experimental results suggested that the use of seeds of different sizes for direct seeding in pastures is a possible strategy of Fabaceae tree species reintroduction in pastures within the Cerrado. Fabaceae species with seeds larger than 100 mg can establish in areas with no weeding, whereas species with seeds smaller than 50 mg can establish preferentially in areas with weeding, growing faster than larger ones. Thus, weeding strategies can determine the success of direct seeding of different species in restoration projects of tropical pastures.
C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation

Estimation of information theoretic quantities such as mutual information and its conditional variant has drawn interest in recent times owing to their multifaceted applications. Newly proposed neural estimators for these quantities have overcome severe drawbacks of classical $k$NN-based estimators in high dimensions. In this work, we focus on conditional mutual information (CMI) estimation by utilizing its formulation as a minmax optimization problem. Such a formulation leads to a joint training procedure similar to that of generative adversarial networks. We find that our proposed estimator provides better estimates than the existing approaches on a variety of simulated data sets comprising linear and non-linear relations between variables. As an application of CMI estimation, we deploy our estimator for conditional independence (CI) testing on real data and obtain better results than state-of-the-art CI testers.
On the strength of general polynomials

A slice decomposition is an expression of a homogeneous polynomial as a sum of forms with a linear factor. A strength decomposition is an expression of a homogeneous polynomial as a sum of reducible forms. The slice rank and strength of a polynomial are the minimal lengths of such decompositions, respectively. The slice rank is an upper bound for the strength and the gap between these two values can be arbitrary large. However, in line with a conjecture by Catalisano et al. on the dimensions of secant varieties of the varieties of reducible forms, we conjecture that equality holds for general forms. By using a weaker version of Fröberg's Conjecture on the Hilbert series of ideals generated by general forms, we show that our conjecture holds up to degree 7 and in degree 9.
Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations

We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task. This formulation allows for a simple integration of conversational knowledge coded in large pretrained conversational models such as ConveRT (Henderson et al., 2019). We show that leveraging such knowledge in Span-ConveRT is especially useful for few-shot learning scenarios: we report consistent gains over 1) a span extractor that trains representations from scratch in the target domain, and 2) a BERT-based span extractor. In order to inspire more work on span extraction for the slot-filling task, we also release RESTAURANTS-8K, a new challenging data set of 8,198 utterances, compiled from actual conversations in the restaurant booking domain.
Optimal execution strategy with an uncertain volume target

In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss (2001) define the optimal trading strategy to liquidate a fixed volume of a single security under price uncertainty. Yet there exist situations, such as in the power market, in which the volume to be traded can only be estimated and becomes more accurate when approaching a specified delivery time. During the course of execution, a trader should then constantly adapt their trading strategy to meet their fluctuating volume target. In this paper, we develop a model that accounts for volume uncertainty and we show that a risk-averse trader has benefit in delaying their trades. More precisely, we argue that the optimal strategy is a trade-off between early and late trades in order to balance risk associated with both price and volume. By incorporating a risk term related to the volume to trade, the static optimal strategies suggested by our model avoid the explosion in the algorithmic complexity usually associated with dynamic programming solutions, all the while yielding competitive performance.
Inferring astrophysical X-ray polarization with deep learning

We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021. In particular, we propose two models that can be used to estimate the impact point as well as the polarization direction of the incoming radiation. The results obtained show that data-driven approaches depict a promising alternative to the existing analytical approaches. We also discuss problems and challenges to be addressed in the near future.
Modeling extra-deep electromagnetic logs using a deep neural network

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is used to generate a training data set. The data set size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training data set that embraces the geologic rules and geosteering specifics supported by the forward model. We use this data set to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite using a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multilayer synthetic case and a section of a published historical operation from the Goliat field. The observed average evaluation time of 0.15 ms per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte Carlo inversion algorithms within geosteering workflows.