Miracle-dz/UniFAR-FTUs huggingface dataset:
X-Ray Binary Luminosity Function Scaling Relations in Elliptical Galaxies: Evidence for Globular Cluster Seeding of Low-mass X-Ray Binaries in Galactic Fields

We investigate X-ray binary (XRB) luminosity function (XLF) scaling relations for Chandra-detected populations of low-mass XRBs (LMXBs) within the footprints of 24 early-type galaxies. Our sample includes Chandra and Hubble Space Telescope observed galaxies at Mpc that have estimates of the globular cluster (GC) specific frequency (SN) reported in the literature. As such, we are able to directly classify X-ray-detected sources as being coincident with unrelated background/foreground objects, GCs, or sources that are within the fields of the galaxy targets. We model the GC and field LMXB population XLFs for all galaxies separately and then construct global models characterizing how the LMXB XLFs vary with galaxy stellar mass and SN. We find that our field LMXB XLF models require a component that scales with SN and has a shape consistent with that found for the GC LMXB XLF. We take this to indicate that GCs are “seeding” the galactic field LMXB population, through the ejection of GC LMXBs and/or the diffusion of the GCs in the galactic fields themselves. However, we also find that an important LMXB XLF component is required for all galaxies that scales with stellar mass, implying that a substantial population of LMXBs are formed “in situ,” which dominates the LMXB population emission for galaxies with SN ≲ 2. For the first time, we provide a framework quantifying how directly associated GC LMXBs, GC-seeded LMXBs, and in situ LMXBs contribute to LMXB XLFs in the broader early-type galaxy population.
From a stochastic maximal inequality to infinite-dimensional martingales.

As an alternative to the well-known methods of "chaining" and "bracketing" that have been developed in the study of random fields, a new method, which is based on a stochastic maximal inequality derived by using the Taylor expansion, is presented. The inequality dealing with finite-dimensional discrete-time martingales is pulled up to infinite-dimensional ones by using the monotone convergence arguments. The main results are some weak convergence theorems for sequences of separable random fields of discrete-time martingales under the uniform topology with the help also of entropy methods. As special cases, some new results for i.i. d. random sequences, including a new Donsker theorem and a moment bound for suprema of empirical processes indexed by classes of sets or functions, are obtained.
Emergent Geometries from the BMN Matrix Model

We review recent results of emergent geometries in the BMN matrix model, a one-dimensional gauge theory considered as a non-perturbative formulation of M-theory on the plane-wave geometry. A key to understand the emergent geometries is the eigenvalue distribution of a BPS operator. Gauge-theory calculation shows that the BPS operator reproduces the corresponding supergravity solutions in the gauge/gravity duality and also brane geometries in the M-brane picture. At finite temperatures, these geometries should be realised in a non-trivial way. Monte Carlo simulations of this gauge theory revealed two types of phase transitions: the confinement/deconfinement transition and the Myers transition, which provide insights into the emergence of the geometries. Especially, the numerical results qualitatively agree with the critical temperature of the confinement/deconfinement transition predicted on the gravity side.
Magnetotransport and Berry phase in magnetically doped 
Bi0.97−xSb0.03
 single crystals

We report large magnetoresistance (MR) and Shubnikov-de Haas (SdH) oscillations in single crystals of magnetically (M= Ni and Fe) doped ${\mathrm{M}}_{x}{\mathrm{Bi}}_{0.97\ensuremath{-}x}{\mathrm{Sb}}_{0.03}$ $(x=0, 0.02)$ topological insulators. The $R\overline{3}m$ symmetry and phase have been confirmed by the Rietveld refinement of x-ray diffraction data. Interestingly, a magnetic field induced phase transition from semimetallic to semiconducting type is found with the energy gap around 80 meV at 15 T in the $x=0$ sample. Moreover, we observe linear behavior of MR up to 15 T in transverse mode and SdH oscillations in longitudinal mode where the field direction is parallel with respect to the current and crystal plane. For the parent sample, we found the coherence length ${L}_{\ensuremath{\phi}}=12.7\phantom{\rule{0.28em}{0ex}}\mathrm{nm}$ through the fitting of MR data in transverse mode with modified H-L-N equation. The extracted frequencies of SdH oscillations using the fast Fourier transform method and Landau level (LL) fan diagram are found to be consistent for the parent and Ni doped samples. The determined Fermi surface area is found to be slightly larger in Ni doped as compared to the parent sample possibly due to change in the Fermi energy. The Kohler's plots indicate a single scattering mechanism below 100 K. More importantly, the analysis with the help of the LL fan diagram reveals the nonzero Berry phase ${\ensuremath{\phi}}_{\mathrm{B}}=\ensuremath{-}(1\ifmmode\pm\else\textpm\fi{}0.1)\ensuremath{\pi}$, which demonstrates the nontrivial topological states near the Dirac point in the parent and Ni doped samples.
The GLEAM 4-Jy (G4Jy) Sample: II. Host galaxy identification for individual sources

Abstract The entire southern sky (Declination, $\delta< 30^{\circ}$ ) has been observed using the Murchison Widefield Array (MWA), which provides radio imaging of $\sim$ 2 arcmin resolution at low frequencies (72–231 MHz). This is the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, and we have previously used a combination of visual inspection, cross-checks against the literature, and internal matching to identify the ‘brightest’ radio-sources ( $S_{\mathrm{151\,MHz}}>4$ Jy) in the extragalactic catalogue (Galactic latitude, $|b| >10^{\circ}$ ). We refer to these 1 863 sources as the GLEAM 4-Jy (G4Jy) Sample, and use radio images (of ${\leq}45$ arcsec resolution), and multi-wavelength information, to assess their morphology and identify the galaxy that is hosting the radio emission (where appropriate). Details of how to access all of the overlays used for this work are available at https://github.com/svw26/G4Jy. Alongside this we conduct further checks against the literature, which we document here for individual sources. Whilst the vast majority of the G4Jy Sample are active galactic nuclei with powerful radio-jets, we highlight that it also contains a nebula, two nearby, star-forming galaxies, a cluster relic, and a cluster halo. There are also three extended sources for which we are unable to infer the mechanism that gives rise to the low-frequency emission. In the G4Jy catalogue we provide mid-infrared identifications for 86% of the sources, and flag the remainder as: having an uncertain identification (129 sources), having a faint/uncharacterised mid-infrared host (126 sources), or it being inappropriate to specify a host (2 sources). For the subset of 129 sources, there is ambiguity concerning candidate host-galaxies, and this includes four sources (B0424–728, B0703–451, 3C 198, and 3C 403.1) where we question the existing identification.
A Branch and Bound Algorithm for Coalition Structure Generation over Graphs

We give a column generation based branch and bound algorithm for coalition structure generation over graphs problem using valuation functions for which this problem is proven to be NP-complete. For a given graph G = (V;E) and a valuation function w : 2^V -> R, the problem is to find the most valuable coalition structure (or partition) of V. We consider two cases: first when the value of a coalition is the sum of the weights of its edges which can be positive or negative, second when the value of a coalition takes account of both inter- and intra-coalitional disagreements and agreements, respectively. For both valuations we give experimental results which cover for the first time sets of more than forty agents. For another valuation function (coordination) we give only the theoretical considerations in the appendix.
VIGN: Variational Integrator Graph Networks

Rich, physically-informed inductive biases play an imperative role in accurately modelling the time dynamics of physical systems. In this paper, we introduce Variational Integrator Graph Networks (VIGNs), the first approach to combine a Variational Integrator (VI) inductive bias with a Graph Network (GN) and demonstrate an order of magnitude improvement in performance, both in terms of data-efficient learning and predictive accuracy, over existing methods. We show that this improvement arises because VIs induce coupled learning of generalized position and momentum updates which can be formulated as a Partitioned Runge-Kutta (PRK) method. We empirically establish that VIGN outperforms numerous methods in learning from existing datasets with noise.
Showing Your Work Doesn’t Always Work

In natural language processing, a recently popular line of work explores how to best report the experimental results of neural networks. One exemplar publication, titled “Show Your Work: Improved Reporting of Experimental Results” (Dodge et al., 2019), advocates for reporting the expected validation effectiveness of the best-tuned model, with respect to the computational budget. In the present work, we critically examine this paper. As far as statistical generalizability is concerned, we find unspoken pitfalls and caveats with this approach. We analytically show that their estimator is biased and uses error-prone assumptions. We find that the estimator favors negative errors and yields poor bootstrapped confidence intervals. We derive an unbiased alternative and bolster our claims with empirical evidence from statistical simulation. Our codebase is at https://github.com/castorini/meanmax.
Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users

Document classification is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing feedback-retrain loop with the team that maintain it. We consider this feedback-retrain loop from a multi-agent point of view, considering the end users as autonomous agents that provide feedback on the labelled data provided by the classifier. This allows us to examine the effect on the classifier's performance of unreliable end users who provide incorrect feedback. We demonstrate a classifier that can learn which users tend to be unreliable, filtering their feedback out of the loop, thus improving performance in subsequent iterations.
Tree-depth and the Formula Complexity of Subgraph Isomorphism

For a fixed “pattern” graph <tex>$G$</tex>, the colored <tex>$G$</tex>-subgraph isomorphism problem (denoted <tex>$\text{SUB}(G)$</tex>) asks, given an <tex>$n$</tex>-vertex graph <tex>$H$</tex> and a coloring <tex>$V(H)\rightarrow V(G)$</tex>, whether <tex>$H$</tex> contains a properly colored copy of <tex>$G$</tex>. The complexity of this problem is tied to parameterized versions of <tex>$P=? \ \ NP$</tex> and <tex>$L=? \ \ NL$</tex>, among other questions. An overarching goal is to understand the complexity of <tex>$\text{SUB}(G)$</tex>, under different computational models, in terms of natural invariants of the pattern graph <tex>$G$</tex>. In this paper, we establish a close relationship between the formula complexity of <tex>$\text{SUB}(G)$</tex> and an invariant known as tree-depth (denoted td (<tex>$G$</tex>)). <tex>$\text{SUB}(G)$</tex> is known to be solvable by monotone <tex>$AC^{0}$</tex> formulas of size <tex>$O(n^{\text{td}(G)})$</tex>. Our main result is an <tex>$n^{\widetilde{\Omega}(\text{td}(G)^{1/3})}$</tex> lower bound for formulas that are monotone or have sub-logarithmic depth. This complements a lower bound of Li, Razborov and Rossman [8] relating tree-width and AC° circuit size. As a corollary, it implies a stronger homomorphism preservation theorem for first-order logic on finite structures [14]. The technical core of this result is an <tex>$n^{\Omega(k)}$</tex> lower bound in the special case where <tex>$G$</tex> is a complete binary tree of height <tex>$k$</tex>, which we establish using the pathset framework introduced in [15]. (The lower bound for general patterns follows via a recent excluded-minor characterization of tree-depth [4], [6].) Additional results of this paper extend the pathset framework and improve upon both, the best known upper and lower bounds on the average-case formula size of <tex>$\text{SUB}(G)$</tex> when <tex>$G$</tex> is a path.
Estimating End-to-End Latencies in Automotive Cyber-physical Systems

Controller networks in today's automotive systems consist of more than 100 ECUs connected by various bus protocols. Seamless operation of the entire system requires a well-orchestrated interaction of these ECUs. Consequently, to ensure safety and comfort, a performance analysis is an inherent part of the engineering process. Conducting such an analysis manually is expensive, slow, and error prone. Tool support is therefore crucial, and a number of approaches have been presented. However, most work is limited to either network latencies or software latencies which results in an analysis gap at the transition between different layers of the communication stack. The work presented here introduces an approach to close this gap. Furthermore, we discuss the integration of different methods to obtain an end-to-end latency analysis.
EARL: Speedup Transformer-based Rankers with Pre-computed Representation

Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval. However, their performance gains come at a steep computational cost. This paper presents a novel Embed Ahead Rank Later (EARL) framework, which speeds-up Transformer-based rankers by pre-computing representations and keeping online computation shallow. EARL dis-entangles the attention in a typical Transformer-based ranker into three asynchronous tasks and assign each to a dedicated Transformer: query understanding, document understanding, and relevance judging. With such a ranking framework, query and document token representations can be offline computed and reused. We also propose a new judger transformer block that keeps online relevance judging light and shallow. Our experiments demonstrate that EARL can be as effective as previous state-of-the-art BERT rankers in accuracy while substantially faster in evaluation time.
Task Dynamics of Prior Training Influence Visual Force Estimation Ability During Teleoperation

The lack of haptic feedback in teleoperation is a potential barrier to safe handling of soft materials, yet in Robot-assisted Minimally Invasive Surgery (RMIS), haptic feedback is often unavailable. Due to its availability in open and laparoscopic surgery, surgeons with such experience potentially possess learned models of tissue stiffness that might promote good force estimation abilities during RMIS. To test if prior haptic experience leads to improved force estimation ability in teleoperation, 33 naive participants were assigned to one of three training conditions: manual manipulation, teleoperation with force feedback, or teleoperation without force feedback, and learned to tension a silicone sample to a set of forces. They were then asked to perform the tension task, and a previously unencountered palpation task, to a different set of forces under teleoperation without force feedback. Compared to the teleoperation groups, the manual group had higher force error in the tension task outside the range of forces they had trained on, but showed better speed-accuracy functions in the palpation task at low force levels. This suggests that the dynamics of the training modality affect force estimation ability during teleoperation, with the prior haptic experience accessible if formed under the same dynamics as the task.
In operando active learning of interatomic interaction during large-scale simulations

A well-known drawback of state-of-the-art machine-learning interatomic potentials is their poor ability to extrapolate beyond the training domain. For small-scale problems with tens to hundreds of atoms this can be solved by using active learning which is able to select atomic configurations on which a potential attempts extrapolation and add them to the ab initio-computed training set. In this sense an active learning algorithm can be viewed as an on-the-fly interpolation of an ab initio model. For large-scale problems, possibly involving tens of thousands of atoms, this is not feasible because one cannot afford even a single density functional theory (DFT) computation with such a large number of atoms. This work marks a new milestone toward fully automatic ab initio-accurate large-scale atomistic simulations. We develop an active learning algorithm that identifies local subregions of the simulation region where the potential extrapolates. Then the algorithm constructs periodic configurations out of these local, non-periodic subregions, sufficiently small to be computable with plane-wave DFT codes, in order to obtain accurate ab initio energies. We benchmark our algorithm on the problem of screw dislocation motion in bcc tungsten and show that our algorithm reaches ab initio accuracy, down to typical magnitudes of numerical noise in DFT codes. We show that our algorithm reproduces material properties such as core structure, Peierls barrier, and Peierls stress. This unleashes new capabilities for computational materials science toward applications which have currently been out of scope if approached solely by ab initio methods.
Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling

We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with respect to the prospect of deep RL for complex real-world control tasks: first, that current state of the art policy gradient methods seem inappropriate in the domain of high-consequence environments; second, that learning explicit communication actions (an emerging machine-to-machine language, so to speak) might offer a remedy. These hypotheses need to be confirmed by future work. If confirmed, they hold promises with respect to optimizing highly efficient logistics ecosystems like the Swiss Federal Railways railway network.
Uncertainty of Resilience in Complex Networks With Nonlinear Dynamics

Resilience is a system's ability to maintain its function when perturbations and errors occur. Whilst we understand low-dimensional networked systems's behavior well, our understanding of systems consisting of a large number of components is limited. Recent research in predicting the network level resilience pattern has advanced our understanding of the coupling relationship between global network topology and local nonlinear component dynamics. However, when there is uncertainty in the model parameters, our understanding of how this translates to uncertainty in resilience is unclear for a large-scale networked system. Here we develop a polynomial chaos expansion method to estimate the resilience for a wide range of uncertainty distributions. By applying this method to case studies, we not only reveal the general resilience distribution with respect to the topology and dynamics submodels but also identify critical aspects to inform better monitoring to reduce uncertainty.
MAVEN: A Massive General Domain Event Detection Dataset

Event detection (ED), which identifies event trigger words and classifies event types according to contexts, is the first and most fundamental step for extracting event knowledge from plain text. Most existing datasets exhibit the following issues that limit further development of ED: (1) Small scale of existing datasets is not sufficient for training and stably benchmarking increasingly sophisticated modern neural methods. (2) Limited event types of existing datasets lead to the trained models cannot be easily adapted to general-domain scenarios. To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 117,200 event mention instances, and 207 event types. MAVEN alleviates the lack of data problem and covers much more general event types. Besides the dataset, we reproduce the recent state-of-the-art ED models and conduct a thorough evaluation for these models on MAVEN. The experimental results and empirical analyses show that existing ED methods cannot achieve promising results as on the small datasets, which suggests ED in real world remains a challenging task and requires further research efforts. The dataset and baseline code will be released in the future to promote this field.
A new species of Ceratosolen from the Philippines (Hymenoptera: Agaonidae)

A new species of fig wasp, Ceratosolen (Ceratosolen) polyodontos n. sp. , is described from females captured at Los Banos, Luzon, Philippines. The species can be distinguished from its congeners by the possession of a much greater number of ventral mandibular lamellae (22-23), divided into an anterior and posterior area, and posterior metasomal structures associated with the ovipositor.
HOLISMOKES

We present a systematic search for wide-separation (with Einstein radius θE ≳ 1.5″), galaxy-scale strong lenses in the 30 000 deg2 of the Pan-STARRS 3π survey on the Northern sky. With long time delays of a few days to weeks, these types of systems are particularly well-suited for catching strongly lensed supernovae with spatially-resolved multiple images and offer new insights on early-phase supernova spectroscopy and cosmography. We produced a set of realistic simulations by painting lensed COSMOS sources on Pan-STARRS image cutouts of lens luminous red galaxies (LRGs) with redshift and velocity dispersion known from the sloan digital sky survey (SDSS). First, we computed the photometry of mock lenses in gri bands and applied a simple catalog-level neural network to identify a sample of 1 050 207 galaxies with similar colors and magnitudes as the mocks. Second, we trained a convolutional neural network (CNN) on Pan-STARRS gri image cutouts to classify this sample and obtain sets of 105 760 and 12 382 lens candidates with scores of pCNN > 0.5 and > 0.9, respectively. Extensive tests showed that CNN performances rely heavily on the design of lens simulations and the choice of negative examples for training, but little on the network architecture. The CNN correctly classified 14 out of 16 test lenses, which are previously confirmed lens systems above the detection limit of Pan-STARRS. Finally, we visually inspected all galaxies with pCNN > 0.9 to assemble a final set of 330 high-quality newly-discovered lens candidates while recovering 23 published systems. For a subset, SDSS spectroscopy on the lens central regions proves that our method correctly identifies lens LRGs at z ∼ 0.1–0.7. Five spectra also show robust signatures of high-redshift background sources, and Pan-STARRS imaging confirms one of them as a quadruply-imaged red source at zs = 1.185, which is likely a recently quenched galaxy strongly lensed by a foreground LRG at zd = 0.3155. In the future, high-resolution imaging and spectroscopic follow-up will be required to validate Pan-STARRS lens candidates and derive strong lensing models. We also expect that the efficient and automated two-step classification method presented in this paper will be applicable to the ∼4 mag deeper gri stacks from the Rubin Observatory Legacy Survey of Space and Time (LSST) with minor adjustments.