Miracle-dz/UniFAR-FTUs huggingface dataset:
The Tiny-Tasks Granularity Trade-Off: Balancing Overhead Versus Performance in Parallel Systems

Models of parallel processing systems typically assume that one has <inline-formula><tex-math notation="LaTeX">$l$</tex-math><alternatives><mml:math><mml:mi>l</mml:mi></mml:math><inline-graphic xlink:href="walker-ieq1-3233712.gif"/></alternatives></inline-formula> workers and jobs are split into an equal number of <inline-formula><tex-math notation="LaTeX">$k=l$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mi>l</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="walker-ieq2-3233712.gif"/></alternatives></inline-formula> tasks. Splitting jobs into <inline-formula><tex-math notation="LaTeX">$k > l$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mo>></mml:mo><mml:mi>l</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="walker-ieq3-3233712.gif"/></alternatives></inline-formula> smaller tasks, i.e. using “tiny tasks”, can yield performance and stability improvements because it reduces the variance in the amount of work assigned to each worker, but as <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="walker-ieq4-3233712.gif"/></alternatives></inline-formula> increases, the overhead involved in scheduling and managing the tasks begins to overtake the performance benefit. We perform extensive experiments on the effects of task granularity on an Apache Spark cluster, and based on these, develop a four-parameter model for task and job overhead that, in simulation, produces sojourn time distributions that match those of the real system. We also present analytical results which illustrate how using tiny tasks improves the stability region of split-merge systems, and analytical bounds on the sojourn and waiting time distributions of both split-merge and single-queue fork-join systems with tiny tasks. Finally we combine the overhead model with the analytical models to produce an analytical approximation to the sojourn and waiting time distributions of systems with tiny tasks which include overhead. We also perform analogous tiny-tasks experiments on a hybrid multi-processor shared memory system based on MPI and OpenMP which has no load-balancing between nodes. Though no longer strict analytical bounds, our analytical approximations with overhead match both the Spark and MPI/OpenMP experimental results very well.
Amodal Panoptic Segmentation

Humans have the remarkable ability to perceive objects as a whole, even when parts of them are occluded. This ability of amodal perception forms the basis of our perceptual and cognitive understanding of our world. To enable robots to reason with this capability, we formulate and propose a novel task that we name amodal panoptic segmentation. The goal of this task is to simultaneously predict the pixel-wise semantic segmentation labels of the visible regions of stuff classes and the instance segmentation labels of both the visible and occluded regions of thing classes. To facilitate research on this new task, we extend two established benchmark datasets with pixel-level amodal panoptic segmentation labels that we make publicly available as KITTI-360-APS and BDD100K-APS. We present several strong baselines, along with the amodal panoptic quality (APQ) and amodal parsing coverage (APC) metrics to quantify the performance in an interpretable manner. Furthermore, we propose the novel amodal panoptic segmentation network (APSNet), as a first step towards addressing this task by explicitly modeling the complex relationships between the occluders and occludes. Extensive experimental evaluations demonstrate that APSNet achieves state-of-the-art performance on both benchmarks and more importantly exemplifies the utility of amodal recognition. The datasets are available at http://amodal-panoptic.cs. uni-freiburg.de.
On Gauge Consistency In Gauge-Fixed Yang-Mills Theory

We investigate BRST invariance in Landau gauge Yang-Mills theory with functional methods. To that end, we solve the coupled system of functional renormalisation group equations for the momentum-dependent ghost and gluon propagator, ghost-gluon, and three- and four-gluon vertex dressings. The equations for both, transverse and longitudinal correlation functions are solved self-consistently: all correlation functions are fed back into the loops. Additionally, we also use the Slavnov-Taylor identities for computing the longitudinal correlation functions on the basis of the above results. Then, the gauge consistency of the solutions is checked by comparing the respective longitudinal correlation functions. We ﬁnd good agreement of these results, hinting at the gauge consistency of our setup.
Technology to scale up diversity in astronomy education

The San Antonio Teacher Training Astronomy Academy (SATTAA) completed its fourth annual iteration in June 2021 . While the program began as a face-toface professional development opportunity for future and current school teachers, it transitioned to a fully online opportunity in 2020. In our efforts to offer an astronomy education program that is inclusive and particularly attentive to highly diverse populations, the transition to online programming became a core aspect of scaling up the program. The 2021 iteration featured an international facilitation team, and, for the first time, supported teachers from across the State of Texas. In this paper, we share data on how the facilitation team transitioned from a local to an international group, and on how the participant pool expanded from local to state-wide. 1. Background Astronomy can be perceived by some as esoteric and less relevant to our daily lives than other sciences. This is wholly incorrect, rather it is (1) highly inspirational, (2) arguably the most attractive STEM discipline, (3) closely aligned to astronautics and the space industry, and (4) tackles some of the most fundamental questions humankind has considered. As Carl Sagan once said, science is not a body of knowledge, it is a way of thinking to skeptically interrogate the world around us, including our leaders. At this turbulent time, a society equipped to make smart informed choices and avoid conspiracy theories is more important than ever. 2 Author(s) Last Name(s) This paper describes how we use astronomy’s unique qualities as leverage to serve our local, regional and state-wide community, from K-12 education through to world-class research astrophysics, and especially underrepresented minority (URM) students. It also shows how, over time, the program grew and how the shift to fully online resulted in more diversity amongst facilitators and participants.
Which Galaxy Property is the Best Gauge of the Oxygen Abundance?

We present an extensive exploration of the impact of 29 physical parameters in the oxygen abundance for a sample of 299 star-forming galaxies extracted from the extended Calar Alto Legacy Integral Field Area Survey sample. We corroborate that the stellar mass is the physical parameter that better traces the observed oxygen abundance (i.e., the mass–metallicity relation; MZR), while other physical parameters could play a potential role in shaping this abundance, but with a lower significant impact. We find that the functional form that best describes the MZR is a third-order polynomial function. From the residuals between this best functional form and the MZR, we find that once considered the impact of the mass in the oxygen abundance, the other physical parameters do not play a significant secondary role in shaping the oxygen abundance in these galaxies (including the gas fraction or the star formation rate). Our analysis suggests that the origin of the MZR is related to the chemical enrichment evolution of the interstellar medium due, most likely, to the buildup of stellar mass in these star-forming galaxies.
A local limit theorem for convergent random walks on relatively hyperbolic groups

We study random walks on relatively hyperbolic groups whose law is convergent, in the sense that the derivative of its Green function is finite at the spectral radius.When parabolic subgroups are virtually abelian, we prove that for such a random walk satisfies a local limit theorem of the form $p_n(e, e)\sim CR^{-n}n^{-d/2}$, where $p_n(e, e)$ is the probability of returning to the origin at time $n$, $R$ is the inverse of the spectral radius of the random walk and $d$ is the minimal rank of a parabolic subgroup along which the random walk is spectrally degenerate.This concludes the classification all possible behaviour for $p_n(e, e)$ on such groups.
Unravelling Token Ecosystem of EOSIO Blockchain

Being the largest Initial Coin Offering project, EOSIO has attracted great interest in cryptocurrency markets. Despite its popularity and prosperity (e.g., 26,311,585,008 token transactions occurred from June 8, 2018 to Aug. 5, 2020), there is almost no work investigating the EOSIO token ecosystem. To fill this gap, we are the first to conduct a systematic investigation on the EOSIO token ecosystem by conducting a comprehensive graph analysis on the entire on-chain EOSIO data (nearly 135 million blocks). We construct token creator graphs, token-contract creator graphs, token holder graphs, and token transfer graphs to characterize token creators, holders, and transfer activities. Through graph analysis, we have obtained many insightful findings and observed some abnormal trading patterns. Moreover, we propose a fake-token detection algorithm to identify tokens generated by fake users or fake transactions and analyze their corresponding manipulation behaviors. Evaluation results also demonstrate the effectiveness of our algorithm.
On Bayesian data assimilation for PDEs with ill-posed forward problems

We study Bayesian data assimilation (filtering) for time-evolution Partial differential equations (PDEs), for which the underlying forward problem may be very unstable or ill-posed. Such PDEs, which include the Navier–Stokes equations of fluid dynamics, are characterized by a high sensitivity of solutions to perturbations of the initial data, a lack of rigorous global well-posedness results as well as possible non-convergence of numerical approximations. Under very mild and readily verifiable general hypotheses on the forward solution operator of such PDEs, we prove that the posterior measure expressing the solution of the Bayesian filtering problem is stable with respect to perturbations of the noisy measurements, and we provide quantitative estimates on the convergence of approximate Bayesian filtering distributions computed from numerical approximations. For the Navier–Stokes equations, our results imply uniform stability of the filtering problem even at arbitrarily small viscosity, when the underlying forward problem may become ill-posed, as well as the compactness of numerical approximants in a suitable metric on time-parametrized probability measures.
Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation

Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or biologist expertise), understand sophisticated chemical or biological process mechanisms, and provide heuristic ideas for structure optimization in pharmaceutical industry. For this purpose, we construct a multi-layer self-attention based Graph Neural Network framework, namely Ligandformer, for predicting compound property with interpretation. Ligandformer integrates attention maps on compound structure from different network blocks. The integrated attention map reflects the machine's local interest on compound structure, and indicates the relationship between predicted compound property and its structure. This work mainly contributes to three aspects: 1. Ligandformer directly opens the black-box of deep learning methods, providing local prediction rationales on chemical structures. 2. Ligandformer gives robust prediction in different experimental rounds, overcoming the ubiquitous prediction instability of deep learning methods. 3. Ligandformer can be generalized to predict different chemical or biological properties with high performance. Furthermore, Ligandformer can simultaneously output specific property score and visible attention map on structure, which can support researchers to investigate chemical or biological property and optimize structure efficiently. Our framework outperforms over counterparts in terms of accuracy, robustness and generalization, and can be applied in complex system study.
Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt

Prompt-based tuning has been proven effective for pretrained language models (PLMs). While most of the existing work focuses on the monolingual prompts, we study the multilingual prompts for multilingual PLMs, especially in the zero-shot cross-lingual setting. To alleviate the effort of designing different prompts for multiple languages, we propose a novel model that uses a unified prompt for all languages, called UniPrompt. Different from the discrete prompts and soft prompts, the unified prompt is model-based and language-agnostic. Specifically, the unified prompt is initialized by a multilingual PLM to produce language-independent representation, after which is fused with the text input. During inference, the prompts can be pre-computed so that no extra computation cost is needed. To collocate with the unified prompt, we propose a new initialization method for the target label word to further improve the model’s transferability across languages. Extensive experiments show that our proposed methods can significantly outperform the strong baselines across different languages. We release data and code to facilitate future research.
Numerical Modelling of Wind Turbine Foundations subjected to Combined Loading

With the growing wind industry in Canada, it has become important to optimize wind turbine foundation design. Wind turbine foundations are subjected to combinations of vertical, horizontal and moment loads due to vertical self-weight of the structure and soil surcharge. Additionally, there are signi cant lateral loads and overturning moments attributed to varying wind forces acting at considerable tower heights above the ground level. In this thesis, the undrained bearing capacity response of circular & octagonal foundations subjected to combined loading is calculated using nite element analyses. Previous works have mostly focused on circular foundations. An octagonal foundation of the style typically used in the wind industry forms the focus of this research. Foundations are either surface based or embedded in homogeneous or heterogeneous soils. The results are expressed in terms of a coherent set of bearing capacity factors and failure envelopes in two dimensional planes (VH, VM and HM). This research also presents a parametric study on the e ect of a sur cial crust on the bearing capacity of a foundation. Finally, working and design loads for a typical wind turbine foundation are plotted in two dimensional failure planes to investigate if there is a potential ‘spare’ capacity. The nite element study indicates that an increase in soil strength heterogeneity and embedment leads to increases in the uniaxial limit capacities and size of the failure envelopes. For octagonal foundations, the average increase in uniaxial vertical, horizontal and moment capacities due to increases in the embedment is 15%, 52% and 32% respectively. The average increase in uniaxial vertical and moment capacities due to increase in the soil strength heterogeneity is 7.1% and 6.7% respectively, for octagonal foundations. When the shape of a foundation changes from a circle to an octagon, the ultimate uniaxial vertical and moment capacities slightly increase (by 7.7% and 7.2% respectively). Under combined loading, conventional methods are found to underestimate the combinations of horizontal and moment loads that a foundation can resist safely. During eccentric loading, the e ective area predicted using the method given by DNV (2002) is also under-predicted.
Dual-Resonator Kinetic-Inductance Detector for Distinction between Signal and 1/f Frequency Noise

Astronomical Kinetic Inductance Detectors (KIDs), similar to quantum information devices, experience performance limiting noise from materials. In particular, 1/f (frequency) noise can be a dominant noise mechanism, which arises from Two-Level System defects (TLSs) in the circuit dielectrics and material interfaces. Here we present a Dual-Resonator KID (DuRKID), which is designed for improved signal to noise (or noise equivalent power) relative to 1/f-noise limited KIDs. We first show the DuRKID schematic, fabricated circuit, and we follow with a description of the intended operation, first measurements, theory, and discussion. The circuit consists of two superconducting resonators sharing an electrical capacitance bridge of 4 capacitors, each of which hosts TLSs. The device is intended to operate using hybridization of the modes, which causes TLSs to either couple to one mode or the other, depending upon which capacitor they reside in. In contrast, the intended KID signal is directed to an inductor, and due to hybridization this causes correlated frequency changes in both (hybridized) modes. Therefore, one can distinguish photon signal from TLS frequency noise. To achieve hybridization, a TiN inductor is current biased to allow tuning of one bare resonator mode into degeneracy with the other and measurements show that the intended resonator modes frequency tune and hybridize as expected. The interresonator coupling and unintentional coupling of the 2 resonators to transmission lines are also characterized in measurements. In the theory, based on a quantum-information-science modes, we calculate the 4-port S parameters and simulate the 1/f frequency noise of the device. The study reveals that the DuRKID can exhibit a large and fundamental performance advantage over 1/f-noise-limited KID detectors.
Canonical Polyadic Decomposition via the Generalized Schur Decomposition

The canonical decomposition (CPD) is a fundamental tensor decomposition which expresses a tensor as a sum of rank one tensors. In stark contrast to the matrix case, with light assumptions, the CPD of a low rank tensor is (essentially) unique. The essential uniqueness of CPD makes this decomposition a powerful tool in many applications as it allows for extraction of component information from a signal of interest. One popular algorithm for algebraic computation of a CPD is the generalized eigenvalue decomposition (GEVD) which selects a matrix subpencil of a tensor, then computes the generalized eigenvectors of the pencil. In this article, we present a simplification of GEVD which improves the accuracy of the algorithm. Surprisingly, the generalized eigenvector computation in GEVD is in fact unnecessary and can be replaced by a QZ decomposition which factors a pair of matrices as a product of unitary and upper triangular matrices. Computing a QZ decomposition is a standard first step when computing generalized eigenvectors, so our algorithm can been seen as a direct simplification of GEVD.
Training Adaptive Reconstruction Networks for Inverse Problems

Neural networks are full of promises for the resolution of ill-posed inverse problems. In particular, physics informed learning approaches already seem to progressively gradually replace carefully hand-crafted reconstruction algorithms, for their superior quality. The aim of this paper is twofold. First we show a signiﬁcant weakness of these networks: they do not adapt eﬃciently to variations of the forward model. Second, we show that training the network with a family of forward operators allows to solve the adaptivity problem without compromising the reconstruction quality signiﬁcantly. All our experiments are carefully devised on partial Fourier sampling problems arising in magnetic resonance imaging (MRI).
Neural Generalised AutoRegressive Conditional Heteroskedasticity

We propose Neural GARCH, a class of methods to model conditional heteroskedasticity in financial time series. Neural GARCH is a neural network adaptation of the GARCH 1,1 model in the univariate case, and the diagonal BEKK 1,1 model in the multivariate case. We allow the coefficients of a GARCH model to be time varying in order to reflect the constantly changing dynamics of financial markets. The time varying coefficients are parameterised by a recurrent neural network that is trained with stochastic gradient variational Bayes. We propose two variants of our model, one with normal innovations and the other with Students t innovations. We test our models on a wide range of univariate and multivariate financial time series, and we find that the Neural Students t model consistently outperforms the others.
Addition and Differentiation of ZX-Diagrams

The ZX-calculus is a powerful framework for reasoning in quantum computing. It provides in particular a compact representation of matrices of interests. A peculiar property of the ZX-calculus is the absence of a formal sum allowing the linear combinations of arbitrary ZX-diagrams. The universality of the formalism guarantees however that for any two ZX-diagrams, the sum of their interpretations can be represented by a ZX-diagram. We introduce a general, inductive definition of the addition of ZX-diagrams, relying on the construction of controlled diagrams. Based on this addition technique, we provide an inductive differentiation of ZX-diagrams. Indeed, given a ZX-diagram with variables in the description of its angles, one can differentiate the diagram according to one of these variables. Differentiation is ubiquitous in quantum mechanics and quantum computing (e.g. for solving optimization problems). Technically, differentiation of ZX-diagrams is strongly related to summation as witnessed by the product rules. We also introduce an alternative, non inductive, differentiation technique rather based on the isolation of the variables. Finally, we apply our results to deduce a diagram for an Ising Hamiltonian.