Miracle-dz/UniFAR-FTUs huggingface dataset:
Transition of an X-ray binary to the hard ultraluminous state in the blue compact dwarf galaxy VII Zw 403

We examine the X-ray spectra of VII Zw 403, a nearby low-metallicity blue compact dwarf (BCD) galaxy. The galaxy has been observed to contain an X-ray source, likely a high mass X-ray binary (HMXB), with a luminosity of 1.3-23x10^38 erg s^-1 in the 0.3-8 keV energy range. A new Suzaku observation shows a transition to a luminosity of 1.7x10^40 erg s^-1 [0.3-8 keV], higher by a factor of 7-130. The spectra from the high flux state are hard, best described by a disk plus Comptonization model, and exhibit curvature at energies above 5 keV. This is consistent with many high-quality ultraluminous X-ray source spectra which have been interpreted as stellar mass black holes (StMBH) accreting at super-Eddington rates. However, this lies in contrast to another HMXB in a low-metallicity BCD, I Zw 18, that exhibits a soft spectrum at high flux, similar to Galactic black hole binaries and has been interpreted as a possible intermediate mass black hole. Determining the spectral properties of HMXBs in BCDs has important implications for models of the Epoch of Reionization. It is thought that the main component of X-ray heating in the early universe was dominated by HMXBs within the first galaxies. Early galaxies were small, metal-deficient, star forming galaxies with large H I mass fractions --- properties shared by local BCDs we see today. Understanding the spectral evolution of HMXBs in early universe analogue galaxies, such as BCDs, is an important step in estimating their contribution to the heating of the intergalactic medium during the Epoch of Reionization. The strong contrast between the properties of the only two spectroscopically studied HMXBs within BCDs motivates further study on larger samples of HMXBs in low metallicity environments in order to properly estimate the X-ray heating in the early universe.
HiHi fMRI: a data-reordering method for measuring the hemodynamic response of the brain with high temporal resolution and high SNR

Abstract There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with high temporal resolution opens up new avenues to study the in vivo functioning of the human brain with functional magnetic resonance imaging. Because the speed of sampling and the signal level are intrinsically connected in magnetic resonance imaging via the T1 relaxation time, optimization efforts usually must make a trade-off to increase the temporal sampling rate at the cost of the signal level. We present a method, which combines a sparse event-related stimulus paradigm with subsequent data reshuffling to achieve high temporal resolution while maintaining high signal levels (HiHi). The proof-of-principle is presented by separately measuring the single-voxel time course of the BOLD response in both the primary visual and primary motor cortices with 100-ms temporal resolution.
A CLOSE COMPARISON BETWEEN OBSERVED AND MODELED Lyα LINES FOR z ∼ 2.2 Lyα EMITTERS

We present the results of an Lyα profile analysis of 12 Lyα emitters (LAEs) at z ∼ 2.2 ? > with high-resolution Lyα spectra. We find that all 12 objects have an Lyα profile with the main peak redward of the systemic redshift defined by nebular lines, and five have a weak, secondary peak blueward of the systemic redshift (blue bump). The average velocity offset of the red main peak (the blue bump, if any) with respect to the systemic redshift is Δ v Ly &agr; , r = 174 ± 19 ? > km s−1 ( Δ v Ly &agr; , b = − 316 ± 45 ? > km s−1), which is smaller than (comparable to) that of Lyman break galaxies (LBGs). The outflow velocities inferred from metal absorption lines in three individual and one stacked spectra are comparable to those of LBGs. The uniform expanding shell model constructed by Verhamme et al. reproduces not only the Lyα profiles but also other observed quantities, including the outflow velocity and the FWHM of nebular lines for the non-blue-bump objects. On the other hand, the model predicts too high FWHMs of nebular lines for the blue bump objects, although this discrepancy may disappear if we introduce additional Lyα photons produced by gravitational cooling. We show that the small Δ v Ly &agr; , r ? > values of our sample can be explained by low neutral hydrogen column densities of log( N H I ? > ) = 18.9 cm−2 on average. This value is more than one order of magnitude lower than those of LBGs but is consistent with recent findings that LAEs have high ionization parameters and low H i gas masses. This result suggests that low N H I ? > values, giving reduced numbers of resonant scattering of Lyα photons, are the key to the strong Lyα emission of LAEs.
Tnpo3 enables EBF1 function in conditions of antagonistic Notch signaling

Here, Bayer et al. investigated the regulation of EBF1 function and, using mass spectrometric analysis of proteins associated with endogenous EBF1 in pro-B cells, identified the nuclear import receptor Transportin-3 (Tnpo3) and found that it interacts with the immunoglobulin-like fold domain of EBF1. Altogether, their observations suggest that Tnpo3 ensures B cell programming by EBF1 in nonpermissive conditions.
DN200434 Inhibits Vascular Smooth Muscle Cell Proliferation and Prevents Neointima Formation in Mice after Carotid Artery Ligation

Background Excessive proliferation and migration of vascular smooth muscle cells (VSMCs), which contributes to the development of occlusive vascular diseases, requires elevated mitochondrial oxidative phosphorylation to meet the increased requirements for energy and anabolic precursors. Therefore, therapeutic strategies based on blockade of mitochondrial oxidative phosphorylation are considered promising for treatment of occlusive vascular diseases. Here, we investigated whether DN200434, an orally available estrogen receptor-related gamma inverse agonist, inhibits proliferation and migration of VSMCs and neointima formation by suppressing mitochondrial oxidative phosphorylation. Methods VSMCs were isolated from the thoracic aortas of 4-week-old Sprague-Dawley rats. Oxidative phosphorylation and the cell cycle were analyzed in fetal bovine serum (FBS)- or platelet-derived growth factor (PDGF)-stimulated VSMCs using a Seahorse XF-24 analyzer and flow cytometry, respectively. A model of neointimal hyperplasia was generated by ligating the left common carotid artery in male C57BL/6J mice. Results DN200434 inhibited mitochondrial respiration and mammalian target of rapamycin complex 1 activity and consequently suppressed FBS- or PDGF-stimulated proliferation and migration of VSMCs and cell cycle progression. Furthermore, DN200434 reduced carotid artery ligation-induced neointima formation in mice. Conclusion Our data suggest that DN200434 is a therapeutic option to prevent the progression of atherosclerosis.
Rainbow subgraphs in edge‐colored complete graphs: Answering two questions by Erdős and Tuza

An edge‐coloring of a complete graph with a set of colors is called completely balanced if any vertex is incident to the same number of edges of each color from . Erdős and Tuza asked in 1993 whether for any graph on edges and any completely balanced coloring of any sufficiently large complete graph using colors contains a rainbow copy of . This question was restated by Erdős in his list of “Some of my favourite problems on cycles and colourings. ” We answer this question in the negative for most cliques by giving explicit constructions of respective completely balanced colorings. Further, we answer a related question concerning completely balanced colorings of complete graphs with more colors than the number of edges in the graph .
Accelerating netty-based applications through transparent InfiniBand support

—Many big-data frameworks are written in Java, e.g. Apache Spark, Flink and Cassandra. These systems use the networking framework netty which is based on Java NIO. While this allows for fast networking on traditional Ethernet networks, it cannot fully exploit the whole performance of modern interconnects, like InﬁniBand, providing bandwidths of 100 Gbit/s and more. In this paper we propose netty support for hadroNIO , a Java library, providing transparent InﬁniBand support for Java ap- plications based on NIO. hadroNIO is based on UCX , which supports several interconnects, including InﬁniBand. We present hadroNIO extensions and optimizations for supporting netty. The evaluations with microbenchmarks, covering single- and multi- threaded scenarios, show that it is possible for netty applications to reach round-trip times as low as 5 µs and fully utilize the 100 Gbit/s bandwidth of high-speed NICs, without changing the application’s source code. We also compare hadroNIO with traditional sockets, as well as libvma and the results show, that hadroNIO offers a substantial improvement over plain sockets and can outperform libvma in several scenarios.
Spectral clustering and model reduction for weakly-connected coherent network systems

We propose a novel model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. Our approach is theoretically justified under a random graph setting. Finally, numerical experiments align with and validate our theoretical findings.
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: A tantalum-rich superconductor with a honeycomb network structure

The crystal structure of solid-state materials with unique lattices is considered one of the main factors determining physical properties, including superconductivity. Materials with honeycomb lattices have inspired intense research interest for their novel properties. A previously unknown compound, Ta4CoSi, are discussed herein and its crystal structure and superconducting properties. It crystallizes in a CuAl2-type structure (space group P4/mcm, No. 124) with the lattice parameters a = b = 6.17755(7){\AA} and c = 4.98443(8) {\AA}, featuring honeycomb networks of Ta-Ta in (110) plane. Superconductivity is observed below the critical temperature of 2.45 K, while lower and upper critical magnetic fields are 9.86 mT and 0.84 T, respectively. Experiments and theoretical calculations show that the honeycomb lattices significantly influence the superconducting properties. This material may thus provide a new platform for investigating the exotic superconductivity of honeycomb networks.
Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task

Text segmentation aims to divide text into contiguous, semantically coherent segments, while segment labeling deals with producing labels for each segment. Past work has shown success in tackling segmentation and labeling for documents and conversations. This has been possible with a combination of task-specific pipelines, supervised and unsupervised learning objectives. In this work, we propose a single encoder-decoder neural network that can handle long documents and conversations, trained simultaneously for both segmentation and segment labeling using only standard supervision. We successfully show a way to solve the combined task as a pure generation task, which we refer to as structured summarization. We apply the same technique to both document and conversational data, and we show state of the art performance across datasets for both segmentation and labeling, under both high- and low-resource settings. Our results establish a strong case for considering text segmentation and segment labeling as a whole, and moving towards general-purpose techniques that don't depend on domain expertise or task-specific components.
Emulating Human Kinematic Behavior on Lower-Limb Prostheses via Multi-Contact Models and Force-Based Nonlinear Control

Active lower-limb prostheses could enable more natural assisted locomotion by contributing net positive work through important gait events, such as ankle push-off. This paper uses multi-contact models of locomotion together with force-based nonlinear optimization-based controllers to achieve human-like kinematic behavior, including ankle push-off, on a powered transfemoral prosthesis. In particular, we leverage model-based control approaches for dynamic bipedal robotic walking to develop a systematic method to realize human-like walking on a powered prosthesis that does not require subject- specific tuning. The proposed controller is implemented on a prosthesis for 2 subjects without tuning between subjects, emulating subject-specific human kinematic trends on the prosthesis joints. These experimental results demonstrate that our force- based nonlinear control approach achieves better tracking of human-like kinematic trajectories, with an average RMSE of 0.0223 during weight-bearing, compared to 2 non-force-sensing methods with an average RMSE of 0.0411 and 0.0430.
PRISM: Sparse recovery of the primordial spectrum from WMAP9 and Planck datasets

Abstract The primordial power spectrum is an indirect probe of inflation or other structure-formation mechanisms. We introduce a new method, named PRISM, to estimate this spectrum from the empirical cosmic microwave background (CMB) power spectrum. This is a sparsity-based inversion method, which leverages a sparsity prior on features in the primordial spectrum in a wavelet dictionary to regularise the inverse problem. This non-parametric approach is able to reconstruct the global shape as well as localised features of the primordial spectrum accurately and proves to be robust for detecting deviations from the currently favoured scale-invariant spectrum. We investigate the strength of this method on a set of WMAP nine-year simulated data for three types of primordial spectra and then process the WMAP nine-year data as well as the Planck PR1 data. We find no significant departures from a near scale-invariant spectrum.
Predicting cell stress and strain during extrusion bioprinting

Bioprinting of living cells can cause major shape deformations, which may severely affect cell survival and functionality. While the shear stresses occurring during cell flow through the printer nozzle have been quantified to some extent, the extensional stresses occurring as cells leave the nozzle into the free printing strand have been mostly ignored. Here we use Lattice-Boltzmann simulations together with a finite-element based cell model to study cell deformation at the nozzle exit. Our simulation results are in good qualitative agreement with experimental microscopy images. We show that for cells flowing in the center of the nozzle extensional stresses can be significant, while for cells flowing off-center their deformation is dominated by the shear flow inside the nozzle. From the results of these simulations, we develop two simple methods that only require the printing parameters (nozzle diameter, flow rate, bioink rheology) to (i) accurately predict the maximum cell stress occurring during the 3D bioprinting process and (ii) approximately predict the cell strains caused by the elongational flow at the nozzle exit.
High-precision (p,t) reaction measurement to determine Ne-18(alpha,p)Na-21 reaction rates

x-ray bursts are identified as thermonuclear explosions in the outer atmosphere of accreting neutron stars. The thermonuclear runaway is fueled by the alpha p process that describes a sequence of (alpha,p) reactions triggered by the Ne-18(alpha,p)Na-21 breakout reaction from the hot CNO cycles. We studied the level structure of the compound nucleus Mg-22 by measuring the Mg-24(p,t)Mg-22 reaction at the Grand Raiden spectrometer at Research Center for Nuclear Physics, Osaka. A large number of alpha-unbound states was identified and precise excitation energies were determined. Based on shell model and alpha-cluster model calculations we predict the level parameters for determining the stellar reaction rate of Ne-18(alpha,p)Na-21 for a wide temperature range. x-ray burst simulations have been performed to study the impact of the reaction on the x-ray burst luminosity.
Online Subset Selection using $\alpha$-Core with no Augmented Regret

We revisit the classic problem of optimal subset selection in the online learning set-up. Assume that the set $[N]$ consists of $N$ distinct elements. On the $t$th round, an adversary chooses a monotone reward function $f_t: 2^{[N]} \to \mathbb{R}_+$ that assigns a non-negative reward to each subset of $[N]. $ An online policy selects (perhaps randomly) a subset $S_t \subseteq [N]$ consisting of $k$ elements before the reward function $f_t$ for the $t$th round is revealed to the learner. As a consequence of its choice, the policy receives a reward of $f_t(S_t)$ on the $t$th round. Our goal is to design an online sequential subset selection policy to maximize the expected cumulative reward accumulated over a time horizon. In this connection, we propose an online learning policy called SCore (Subset Selection with Core) that solves the problem for a large class of reward functions. The proposed SCore policy is based on a new polyhedral characterization of the reward functions called $\alpha$-Core - a generalization of Core from the cooperative game theory literature. We establish a learning guarantee for the SCore policy in terms of a new performance metric called $\alpha$-augmented regret. In this new metric, the performance of the online policy is compared with an unrestricted offline benchmark that can select all $N$ elements at every round. We show that a large class of reward functions, including submodular, can be efficiently optimized with the SCore policy. We also extend the proposed policy to the optimistic learning set-up where the learner has access to additional untrusted hints regarding the reward functions. Finally, we conclude the paper with a list of open problems.
Experimental study of time series forecasting methods for groundwater level prediction

Groundwater level prediction is an applied time series forecasting task with important social impacts to optimize water management as well as preventing some natural disasters: for instance, floods or severe droughts. Machine learning methods have been reported in the literature to achieve this task, but they are only focused on the forecast of the groundwater level at a single location. A global forecasting method aims at exploiting the groundwater level time series from a wide range of locations to produce predictions at a single place or at several places at a time. Given the recent success of global forecasting methods in prestigious competitions, it is meaningful to assess them on groundwater level prediction and see how they are compared to local methods. In this work, we created a dataset of 1026 groundwater level time series. Each time series is made of daily measurements of groundwater levels and two exogenous variables, rainfall and evapotranspiration. This dataset is made available to the communities for reproducibility and further evaluation. To identify the best configuration to effectively predict groundwater level for the complete set of time series, we compared different predictors including local and global time series forecasting methods. We assessed the impact of exogenous variables. Our result analysis shows that the best predictions are obtained by training a global method on past groundwater levels and rainfall data.
Disentangling Transfer in Continual Reinforcement Learning

The ability of continual learning systems to transfer knowledge from previously seen tasks in order to maximize performance on new tasks is a significant challenge for the field, limiting the applicability of continual learning solutions to realistic scenarios. Consequently, this study aims to broaden our understanding of transfer and its driving forces in the specific case of continual reinforcement learning. We adopt SAC as the underlying RL algorithm and Continual World as a suite of continuous control tasks. We systematically study how different components of SAC (the actor and the critic, exploration, and data) affect transfer efficacy, and we provide recommendations regarding various modeling options. The best set of choices, dubbed ClonEx-SAC, is evaluated on the recent Continual World benchmark. ClonEx-SAC achieves 87% final success rate compared to 80% of PackNet, the best method in the benchmark. Moreover, the transfer grows from 0.18 to 0.54 according to the metric provided by Continual World.
Derived equivalences for trigonometric double affine Hecke algebras

The trigonometric double affine Hecke algebra $\mathbf{H}_c$ for an irreducible root system depends on a family of complex parameters $c$ Given two families of parameters $c$ and $c'$ which differ by integers, we construct the translation functor from $\mathbf{H}_{c}\operatorname{-Mod}$ to $\mathbf{H}_{c'}\operatorname{-Mod}$ and prove that it induces equivalence of derived categories. This is a trigonometric counterpart of a theorem of Losev on the derived equivalences for rational Cherednik algebras.
Generic classification and asymptotic enumeration of dope matrices

For a complex polynomial of degree and an ‐tuple of distinct complex numbers , the dope matrix is defined as the matrix with if and otherwise. We classify the set of dope matrices when the entries of are algebraically independent, resolving a conjecture of Alon, Kravitz, and O'Bryant. We also provide asymptotic upper and lower bounds on the total number of dope matrices. For much smaller than , these bounds give an asymptotic estimate of the logarithm of the number of dope matrices.