Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Supervised Off Policy Reinforcement Learning

Reinforcement learning (RL) has shown great success in estimating sequential treatment strategies which account for patient heterogeneity. However, health-outcome information is often not well coded but rather embedded in clinical notes. Extracting precise outcome information is a resource intensive task. This translates into only small well-annotated cohorts available. We propose a semi-supervised learning (SSL) approach that can efficiently leverage a small sized labeled data $\mathcal{L}$ with true outcome observed, and a large sized unlabeled data $\mathcal{U}$ with outcome surrogates $\pmb W$. In particular we propose a theoretically justified SSL approach to Q-learning and develop a robust and efficient SSL approach to estimating the value function of the derived optimal STR, defined as the expected counterfactual outcome under the optimal STR. Generalizing SSL to learning STR brings interesting challenges. First, the feature distribution for predicting $Y_t$ is unknown in the $Q$-learning procedure, as it includes unknown $Y_{t-1}$ due to the sequential nature. Our methods for estimating optimal STR and its associated value function, carefully adapts to this sequentially missing data structure. Second, we modify the SSL framework to handle the use of surrogate variables $\pmb W$ which are predictive of the outcome through the joint law $\mathbb{P}_{Y,\pmb O,\pmb W}$, but are not part of the conditional distribution of interest $\mathbb{P}_{Y|\pmb O}$. We provide theoretical results to understand when and to what degree efficiency can be gained from $\pmb W$ and $\pmb O$. Our approach is robust to misspecification of the imputation models. Further, we provide a doubly robust value function estimator for the derived STR. If either the Q functions or the propensity score functions are correctly specified, our value function estimators are consistent for the true value function.
Five percent measurement of the gravitational constant in the Large Magellanic Cloud

We perform a novel test of General Relativity by measuring the gravitational constant in the Large Magellanic Cloud (LMC). The LMC contains six well-studied Cepheid variable stars in detached eclipsing binaries. Radial velocity and photometric observations enable a complete orbital solution, and precise measurements of the Cepheids' periods permit detailed stellar modelling. Both are sensitive to the strength of gravity, the former via Kepler's third law and the latter through the gravitational free-fall time. We jointly fit the observables for stellar parameters and the gravitational constant. Performing a full Markov Chain Monte Carlo analysis of the parameter space including all relevant nuisance parameters, we constrain the gravitational constant in the Large Magellanic Cloud relative to the Solar System to be $G_\text{LMC}/G_\text{SS} = 0.93^{+0.05}_{-0.04}$. We discuss the implications of this 5% measurement of Newton's constant in another galaxy for dark energy and modified gravity theories. This result excludes one Cepheid, CEP-1812, which is an outlier and needs further study: it is either a highly unusual system to which our model does not apply, or it prefers $G_\text{LMC}<G_\text{SS}$ at $2.6\sigma$. We also obtain new bounds on critical parameters that appear in semi-analytic descriptions of stellar processes. In particular, we measure the mixing length parameter to be $\alpha=0.90^{+0.36}_{-0.26}$ (when assumed to be constant across our sample), and obtain constraints on the parameters describing turbulent dissipation and convective flux.
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation

A learning dialogue agent can infer its behaviour from interactions with the users. These interactions can be taken from either human-to-human or human-machine conversations. However, human interactions are scarce and costly, making learning from few interactions essential. One solution to speedup the learning process is to guide the agent's exploration with the help of an expert. We present in this paper several imitation learning strategies for dialogue policy where the guiding expert is a near-optimal handcrafted policy. We incorporate these strategies with state-of-the-art reinforcement learning methods based on Q-learning and actor-critic. We notably propose a randomised exploration policy which allows for a seamless hybridisation of the learned policy and the expert. Our experiments show that our hybridisation strategy outperforms several baselines, and that it can accelerate the learning when facing real humans.
1000 Days of the Lowest-frequency Emission from the Low-luminosity GRB 171205A

We report the lowest-frequency measurements of gamma-ray burst (GRB) 171205A with the upgraded Giant Metrewave Radio Telescope (uGMRT) covering a frequency range of 250–1450 MHz and a period of 4–937 days. It is the first GRB afterglow detected in the 250–500 MHz frequency range and the second brightest GRB detected with the uGMRT. Even though the GRB was observed for nearly 1000 days, there is no evidence of a transition to a nonrelativistic regime. We also analyzed the archival Chandra X-ray data on day ∼70 and day ∼200. We also found no evidence of a jet break from the analysis of combined data. We fit synchrotron afterglow emission arising from a relativistic, isotropic, self-similar deceleration as well as from a shock breakout of a wide-angle cocoon. Our data also allowed us to discern the nature and the density of the circumburst medium. We found that the density profile deviates from a standard constant density medium and suggests that the GRB exploded in a stratified wind-like medium. Our analysis shows that the lowest-frequency measurements covering the absorbed part of the light curves are critical to unraveling the GRB environment. Our data combined with other published measurements indicate that the radio afterglow has a contribution from two components: a weak, possibly slightly off-axis jet and a surrounding wider cocoon, consistent with the results of Izzo et al. The cocoon emission likely dominates at early epochs, whereas the jet starts to dominate at later epochs, resulting in flatter radio light curves.
Route Reconstruction from Traffic Flow via Representative Trajectories

Understanding human mobility patterns is an important aspect of traffic analysis and urban planning. Trajectory data provide detailed views on specific routes, but typically do not capture all traffic. On the other hand, loop detectors built into the road network capture all traffic flow at specific locations, but provide no information on the individual routes. Given a set of loop-detector measurements as well as a (small) set of representative trajectories, our goal is to investigate how one can effectively combine these two partial data sources to create a more complete picture of the underlying mobility patterns. Specifically, we want to reconstruct a realistic set of routes from the loop-detector data, using the given trajectories as representatives of typical behavior. We model the loop-detector data as a network flow field that needs to be covered by the reconstructed routes and we capture the realism of the routes via the strong Fréchet distance to the representative trajectories. We prove that several forms of the resulting algorithmic problem are NP-hard. Hence we explore heuristic approaches which decompose the flow well while following the representative trajectories to varying degrees. We propose an iterative Fréchet Routes (FR) heuristic which generates candidates routes with bounded Fréchet distance to the representative trajectories. We also describe a variant of multi-commodity min-cost flow (MCMCF) which is only loosely coupled to the trajectories. We perform an extensive experimental evaluation of our two proposed approaches in comparison to a global min-cost flow (GMCF), which is essentially agnostic to the representative trajectories. To make meaningful claims in terms of quality, we derive a ground truth by map-matching real-world trajectories. We find that GMCF explains the flow best, but produces a large number of often nonsensical routes (significantly more than the ground truth). MCMCF produces a large number of mostly realistic routes which explain the flow reasonably well. In contrast, FR produces much smaller sets of realistic routes which still explain the flow well, at the cost of a higher running time. Finally, we report on the results of a case study which combines real-world loop detector data and representative trajectories for the region around The Hague, the Netherlands.
Euclid: Forecasts for k-cut 3×2 Point Statistics

Modelling uncertainties at small scales, i.e. high $k$ in the power spectrum $P(k)$, due to baryonic feedback, nonlinear structure growth and the fact that galaxies are biased tracers poses a significant obstacle to fully leverage the constraining power of the Euclid wide-field survey. $k$-cut cosmic shear has recently been proposed as a method to optimally remove sensitivity to these scales while preserving usable information. In this paper we generalise the $k$-cut cosmic shear formalism to $3 \times 2$ point statistics and estimate the loss of information for different $k$-cuts in a $3 \times 2$ point analysis of the {\it Euclid} data. Extending the Fisher matrix analysis of Euclid Collaboration: Blanchard et al. (2019), we assess the degradation in constraining power for different $k$-cuts. We find that taking a $k$-cut at $2.6 \ h \ {\rm Mpc} ^{-1}$ yields a dark energy Figure of Merit (FOM) of 1018. This is comparable to taking a weak lensing cut at $\ell = 5000$ and a galaxy clustering and galaxy-galaxy lensing cut at $\ell = 3000$ in a traditional $3 \times 2$ point analysis. We also find that the fraction of the observed galaxies used in the photometric clustering part of the analysis is one of the main drivers of the FOM. Removing $50 \% \ (90 \%)$ of the clustering galaxies decreases the FOM by $19 \% \ (62 \%)$. Given that the FOM depends so heavily on the fraction of galaxies used in the clustering analysis, extensive efforts should be made to handle the real-world systematics present when extending the analysis beyond the luminous red galaxy (LRG) sample.
NSL: Hybrid Interpretable Learning From Noisy Raw Data

Inductive Logic Programming (ILP) systems learn generalised, interpretable rules in a data-efficient manner utilising existing background knowledge. However, current ILP systems require training examples to be specified in a structured logical format. Neural networks learn from unstructured data, although their learned models may be difficult to interpret and are vulnerable to data perturbations at run-time. This paper introduces a hybrid neural-symbolic learning framework, called NSL, that learns interpretable rules from labelled unstructured data. NSL combines pre-trained neural networks for feature extraction with FastLAS, a state-of-the-art ILP system for rule learning under the answer set semantics. Features extracted by the neural components define the structured context of labelled examples and the confidence of the neural predictions determines the level of noise of the examples. Using the scoring function of FastLAS, NSL searches for short, interpretable rules that generalise over such noisy examples. We evaluate our framework on propositional and first-order classification tasks using the MNIST dataset as raw data. Specifically, we demonstrate that NSL is able to learn robust rules from perturbed MNIST data and achieve comparable or superior accuracy when compared to neural network and random forest baselines whilst being more general and interpretable.
Dust reverberation mapping of Z229–15

We report results of the dust reverberation mapping (DRM) on the Seyfert 1 galaxy Z229–15 at z = 0.0273. Quasi-simultaneous photometric observations for a total of 48 epochs were acquired during the period 2017 July to 2018 December in B, V, J, H and Ks bands. The calculated spectral index (α) between B and V bands for each epoch was used to correct for the accretion disc (AD) component present in the infrared light curves. The observed α ranges between −0.99 and 1.03. Using cross-correlation function analysis we found significant time delays between the optical V and the AD corrected J, H and Ks light curves. The lags in the rest frame of the source are $12.52^{+10.00}_{-9.55}$ d (between V and J), $15.63^{+5.05}_{-5.11}$ d (between V and H) and $20.36^{+5.82}_{-5.68}$ d (between V and Ks). Given the large error bars, these lags are consistent with each other. However, considering the lag between V and Ks bands to represent the inner edge of the dust torus, the torus in Z229–15 lies at a distance of 0.017 pc from the central ionizing continuum. This is smaller than that expected from the radius luminosity (R−L) relationship known from DRM. Using a constant α = 0.1 to account for the AD component, as is normally done in DRM, the deduced radius (0.025 pc) lies close to the expected R−L relation. However, usage of constant α in DRM is disfavoured as the α of the ionizing continuum changes with the flux of the source.
Com-DDPG: A Multiagent Reinforcement Learning-based Offloading Strategy for Mobile Edge Computing

The development of mobile services has impacted a variety of computation-intensive and time-sensitive applications, such as recommendation systems and daily payment methods. However, computing task competition involving limited resources increases the task processing latency and energy consumption of mobile devices, as well as time constraints. Mobile edge computing (MEC) has been widely used to address these problems. However, there are limitations to existing methods used during computation offloading. On the one hand, they focus on independent tasks rather than dependent tasks. The challenges of task dependency in the real world, especially task segmentation and integration, remain to be addressed. On the other hand, the multiuser scenarios related to resource allocation and the mutex access problem must be considered. In this paper, we propose a novel offloading approach, Com-DDPG, for MEC using multiagent reinforcement learning to enhance the offloading performance. First, we discuss the task dependency model, task priority model, energy consumption model, and average latency from the perspective of server clusters and multidependence on mobile tasks. Our method based on these models is introduced to formalize communication behavior among multiple agents; then, reinforcement learning is executed as an offloading strategy to obtain the results. Because of the incomplete state information, long short-term memory (LSTM) is employed as a decision-making tool to assess the internal state. Moreover, to optimize and support effective action, we consider using a bidirectional recurrent neural network (BRNN) to learn and enhance features obtained from agents' communication. Finally, we simulate experiments on the Alibaba cluster dataset. The results show that our method is better than other baselines in terms of energy consumption, load status and latency.
MusE GAs FLOw and wind (MEGAFLOW) VII. A NOEMA pilot program to probe molecular gas in galaxies with measured circumgalactic gas flows

We present a pilot program using IRAM’s NOrthern Extended Millimeter Array (NOEMA) to probe the molecular gas reservoirs of six $z$ = 0.6–1.1 star-forming galaxies whose circumgalactic medium has been observed in absorption along quasar lines of sight as part of the MusE GAs FLOw and Wind (MEGAFLOW) survey and for which we have estimates of either the accretion or the outflow rate. This program is motivated by testing the quasi-equilibrium model and the compaction scenario describing the evolution of galaxies along the main sequence of star formation, which imply tight relations between the gas content, the star formation activity, and the amount of gas flowing in and out. We report individual carbon monoxide CO(4-3), CO(3-2), and dust continuum upper limits, as well as stacked CO detections over the whole sample and the three galaxies identified with outflows. The resulting molecular gas fractions and depletion times are compatible with published scaling relations established within a mass-selected sample, indicating that galaxies selected through their absorption follow similar relations on average. We further detect the dust continuum of three of the quasars and a strong emission line in one of them, which we identify as CO(4-3). Extending the sample to more galaxies and deeper observations will enable us to quantify how the molecular gas fraction and depletion time depend on the inflow and ouflow rates.
Using the method of moments to analyze three-dimensional diffusion-limited solute transport from tem

A swimming system for maximum efficiency in water has a single foot fin with a large fluke and two foot openings leading to foot pockets separated by a cushion. A series of water directed openings extend rearward and outward from a line above the toe portions of the pockets diagonally through the fluke to a line near a tip of the fluke on a rearward portion of the fin. A fastening surrounds the fin near instep areas of the foot-receiving pockets. A leg sheath has a corresponding lower fastening, a cushioning divider between legs and an achilles cushion above a heel portion to streamline the sheath. A reinforced upper waist band fastens to a jacket portion with hand openings which overlie hand fins formed of flat circular plates with finger and palm cutouts mounted between two pieces of synthetic dolphin skin. The helmet with an annular neck encircling cushion completes the streamlined entire body covering with a synthetic dolphin skin exterior. A blow-hole rearward of a neck portion of the helmet has a raised forward edge to promote air entrapment in a snorkle which leads through the annular cushion to a mouthpiece. A water-tight snap seal covers a forward opening in the mouthpiece. Nostril blocks are provided in a nose cavity. Eyes are encircled by seals which support curved lens for removing water distance distortions. Tubes lead from eye areas to the mouthpiece for pressure equalization. Screened ear holes permit pressure equalization and hearing.
Protosappanin A, a novel biphenyl compound from Sappan Lignum.

Protosappanin A (I), C15H12O5, mp 250-251°C, was isolated from the heart-wood of Caesalpinia sappan L. (appan Lignum). It yielded a triacetate (II) on acetylation, and an alcohol (III) on reduction with sodium borohybride. On alkali fusion, I afforded sappanin (IV) along with small amounts of catechol and resorcinol. On the basis of chemical and spectroscopic evidence, the structure of I was established as 3, 10, 11-trihydroxy-7, 8-dihydro-6H-dibenz[b, d]oxocin-7-one. The result of an X-ray crystal structure analysis supported this conclusion. Protosappanin A (I) seems to be the precursor of sappanin, 2, 3', 4, 4'-tetrahydroxybiphenyl, which has been known as an artificial component of Sappan Lignum since 1872, and may be a metabolite of sappanchalcone. A possible biogenetic relationship of sappanchalcone, brazilin, and protosappanin A is discussed. Protosappanin A (I) has a weak sedative effect in mice.
Inorganic anion sorption and interactions with phosphate sorption by hydrous ferric oxide gel

SUMMARY The sorption of inorganic anions by hydrous ferric oxide gel (Fe gel) from 10 −1 M NaClO4 at pH 6.5 decreased in the order: orthophosphate (H2PO4)>arsenate (H2AsO4) = selenite (HseO3) > silicate (H4SiO4) > molybdate (MoO24−) > sulphate (SO24−) > selenate(SeO24−)>chloride (Cl−) = nitrate (NO−3). When each anion was added to Fe gel with an equimolar quantity of H2PO−4, there was no detectable effect of SO24−, SeO24−, Cl−, and NO−3 on the amount of H2PO−4 sorbed. Other anions depressed H2PO4 sorption in the order H2AsO4 >HseO3 > H4SiO4 > MoO24. At the lowest level of anion addition (500 mmol kg −1), H2PO4 sorption was depressed by no more than 6% of the sorption level in the absence of a competing anion. In contrast, at the highest level of anion addition (1700 mmol kg-1 of each), H2AsO4 decreased H2PO4 sorption by 44%. The sorption of SO4− was completely eliminated when this anion was added with equimolar amounts of H2PO4. The ability of anions to compete with H2PO4 for sorption sites could not be explained solely by the results obtained for the sorption of each anion alone. Thus, H2AsO4 was more competitive than H2PO4 when added together, even though more H2PO4 than H2AsO4 was sorbed when each anion was added alone. Although H2PO4 was sorbed in larger amounts, there is no evidence to suggest that H2PO4. H2AsO4, and HseO3 were sorbed on different sites.
Dyeing Properties of Capsules Containing Disperse Dyes into Polyester Fabrics.

Dyeing properties of micro-capsulated disperse dyes into poly (ethylene terephthalate)(PET) fabrics was investigated in terms of color depth K/S. Microcapsule dyes (MC-dye) were prepared by encapsulating both disperse dyes and ferromagnetic substance with acrylate resin. MC-dye deposited PET fabrics was dyed by heat-treatment ranging 170 - 210°C. It was found that (1) K/S values were greatly higher when heated from both sides of fabrics, (2) thickness of MC-dye layer on PET frabrics gave practically no difference in K/S values, (3) higher temperature treatment resulted in the effective color evenness in a short time, and (4) control of K/S was possible by adjusting heat-treatment time.In addition, the comparison between the proposed MC-dye method and conventional high-pressure dyeing method revealed no difference in the light color fastness and the Munsell hue.On the basis of the present results a heat-treating apparatus was constructed.
Diet composition and preferences of Bohor reedbuck (Redunca redunca ) in the compound of Alage College, Central Rift Valley of Ethiopia

Abstract Numerous indices have been developed to compare use and availability of foods in field diets of wild ungulates. However, little attention has been given to laboratory analysis for comparing food preferences. To this end, a study aimed at investigating the diet composition and preference of Bohor reedbuck was conducted in the compound of Alage Agricultural College, Central Rift Valley of Ethiopia from 2017 to 2018 encompassing both dry and wet seasons. Bohor reedbuck is a medium sized horned antelope species endemic to Africa. Continuous focal animal observation was used to collect the data on plant species included in the diet of Bohor reedbuck. Focal individuals’ observation was carried out for 30 min in 10 min sampling interval during their active feeding period (early morning and late afternoon) over four different habitat types. The nutrient composition of plants consumed was determined using wet chemistry laboratory analysis. Bohor reedbucks consumed 15 species of plants; herbs comprised 94.3% of the foods they consumed. Digitaria abyssinica was the most preferred plant species with highest crude protein (23.75%) and less fiber (61.8% nitrogen detergent fiber and 27.8% acid detergent fiber). These findings suggest that food preference of Bohor reedbuck is determined by the nutritional content of the plant it consumed, since the area is more or less natural habitat in terms of plant species composition. For sustainable conservation of the species, there is a need to actively promote management of the plant species most preferred by the reedbuck to feed on.