Miracle-dz/UniFAR-FTUs huggingface dataset:
Event-Driven Architecture and REST: An Exploratory Study on Modularity

Event-driven architecture has been widely adopted in the software industry, emerging as an alternative to the development of enterprise applications based on the REST architectural style. However, little is known about the effects of event-driven architecture on modularity while enterprise applications evolve. Consequently, practitioners end up adopting it without any empirical evidence about its impacts on essential indicators, including separation of concerns, coupling, cohesion, complexity and size. This article, therefore, reports an exploratory study comparing event-driven architecture and REST style in terms of modularity. A real-world application was developed using an event-driven architecture and REST through five evolution scenarios. In each scenario, a feature was added. The generated versions were compared using ten metrics. The initial results suggest that the event-driven architecture improved the separation of concerns, but was outperformed considering the metrics of coupling, cohesion, complexity and size. The findings are encouraging and can be seen as a first step in a more ambitious agenda to empirically evaluate the benefits of event-driven architecture against the REST style.
Memory-triggered supernova neutrino detection

We demonstrate that observations of the gravitational memory from core collapse supernovae at future Deci-Hz interferometers enable time-triggered searches of supernova neutrinos at Mt-scale detectors. Achieving a sensitivity to characteristic strains of at least ∼ 10 − 25 at f (cid:39) 0 . 3 Hz – e.g., by improving the noise of DECIGO by one order of magnitude – will allow robust time triggers for supernovae at distances D ∼ 40 − 300 Mpc, resulting in a nearly background-free sample of ∼ 3 − 70 neutrino events per Mt per decade of operation. This sample would bridge the sensitivity gap between rare galactic supernova bursts and the cosmological diﬀuse supernova neutrino background, allowing detailed studies of the neutrino emission of supernovae in the local Universe.
Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration

Learning from Demonstration (LfD) algorithms enable humans to teach new skills to robots through demonstrations. The learned skills can be robustly reproduced from the identical or near boundary conditions (e.g., initial point). However, when generalizing a learned skill over boundary conditions with higher variance, the similarity of the reproductions changes from one boundary condition to another, and a single LfD representation cannot preserve a consistent similarity across a generalization region. We propose a novel similarity-aware framework including multiple LfD representations and a similarity metric that can improve skill generalization by finding reproductions with the highest similarity values for a given boundary condition. Given a demonstration of the skill, our framework constructs a similarity region around a point of interest (e.g., initial point) by evaluating individual LfD representations using the similarity metric. Any point within this volume corresponds to a representation that reproduces the skill with the greatest similarity. We validate our multi-representational framework in three simulated and four sets of real-world experiments using a physical 6-DOF robot. We also evaluate 11 different similarity metrics and categorize them according to their biases in 286 simulated experiments.
Iterative time-domain method for resolving multiple gravitational wave sources in pulsar timing array data

The sensitivity of ongoing searches for gravitational wave (GW) sources in the ultra-low frequency regime (10 − 9 Hz to 10 − 7 Hz) using Pulsar Timing Arrays (PTAs) will continue to increase in the future as more well-timed pulsars are added to the arrays. It is expected that next-generation radio telescopes, namely, the Five-hundred-meter Aperture Spherical radio Telescope (FAST) and the Square Kilometer Array (SKA), will grow the number of well-timed pulsars to O (10 3 ). The higher sensitivity will result in greater distance reach for GW sources, uncovering multiple resolvable GW sources in addition to an unresolved population. Data analysis techniques are, therefore, required that can search for and resolve multiple signals present simultaneously in PTA data. The multisource resolution problem in PTA data analysis poses a unique set of challenges such as non-uniformly sampled data, a large number of so-called pulsar phase parameters that arise from the inaccurately measured distances to the pulsars, and poor separation of signals in the Fourier domain due to a small number of cycles in the observed waveforms. We present a method that can address these challenges and demonstrate its performance on simulated data from PTAs with 10 2 to 10 3 pulsars. The method estimates and subtracts sources from the data iteratively using multiple stages of reﬁnement, followed by a step that mitigates spurious identiﬁed sources by comparing the outputs from two diﬀerent algorithms. The performance of the method compares favorably with the global ﬁt approaches that have been proposed so far. In all the cases tested in this work, the fraction of sources found by the method that correspond to true sources in the simulated data exceeds 78% and 93% for a large-scale (with 10 3 pulsars and 200 sources) and a mid-scale (with 10 2 pulsars and 100 sources) PTA, respectively. The network signal to noise ratio of the recovered true sources reaches down to 16 . 43 for the large-scale and 9 . 07 for the mid-scale PTA.
Coulomb gas integrals, classical conformal blocks and Richardson–Gaudin models

Virasoro conformal blocks are universal ingredients of correlation functions of two-dimensional CFTs with Virasoro symmetry. It is acknowledged that in the (classical) limit of large central charge of the Virasoro algebra and large external, and intermediate conformal weights with fixed ratios of these parameters Virasoro blocks exponentiate to functions known as Zamolodchikovs’ classical blocks. The latter are special functions which have a wide spectrum of awesome mathematical and physical applications. Uniformization, monodromy problems, black holes physics, quantum gravity, entanglement, quantum chaos, holography, N = 2 gauge theory and quantum integrable systems (QIS) are just some of contexts and topics, where classical Virasoro blocks are in use. In this paper, exploiting known connections between power series and integral representations of (quantum) Virasoro blocks, we propose new finite closed formulae for certain multi-point classical Virasoro blocks on the sphere. Combining classical limit of Virasoro blocks expansions with a saddle point asymptotics of Dotsenko–Fateev (DF) integrals one can relate classical Virasoro blocks with a critical value of the “Dotsenko–Fateev matrix model action”. The latter is the “DF action” evaluated on a solution of saddle point equations which take the form of Bethe equations for certain QIS (Gaudin spin models). The relationship with integrable models is our main motivation for this research line. Analogous quantities as the “DF on-shell action” appear in the so-called Richardson’s solution of the reduced BCS model describing physics of ultra-small superconducting grains. The Richardson solution and its particular limit describing the rational Gaudin model have known implementation in 2d CFT in terms of free field realization of certain (perturbed) WZW block. The WZW analogue of the “DF on-shell action” appears here and play a crucial role, it is a generating function for eigenvalues of quantum integrals of motion. Hence, the study of relationships between power expansions and Coulomb gas representations of WZW (and parturbed WZW) blocks could pave a way for new analytical tools useful in the study of models of Richardson–Gaudin type. e-mail: marcin.piatek@usz.edu. pl, radekpiatek@gmail.com e-mail: rashid@theor.jinr. ru e-mail: puente.toni@gmail.com e-mail: hearthie@gmail.com ar X iv :2 11 0. 15 00 9v 1 [ he pth ] 2 8 O ct 2 02 1
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples

Learning controllers that reproduce legged locomotion in nature has been a longtime goal in robotics and computer graphics. While yielding promising results, recent approaches are not yet flexible enough to be applicable to legged systems of different morphologies. This is partly because they often rely on precise motion capture references or elaborate learning environments that ensure the naturality of the emergent locomotion gaits but prevent generalization. This work proposes a generic approach for ensuring realism in locomotion by guiding the learning process with the spring-loaded inverted pendulum model as a reference. Leveraging on the exploration capacities of Reinforcement Learning (RL), we learn a control policy that fills in the information gap between the template model and full-body dynamics required to maintain stable and periodic locomotion. The proposed approach can be applied to robots of different sizes and morphologies and adapted to any RL technique and control architecture. We present experimental results showing that even in a model-free setup and with a simple reactive control architecture, the learned policies can generate realistic and energy-efficient locomotion gaits for a bipedal and a quadrupedal robot. And most importantly, this is achieved without using motion capture, strong constraints in the dynamics or kinematics of the robot, nor prescribing limb coordination. We provide supplemental videos for qualitative analysis of the naturality of the learned gaits4.
Running head : COGNITIVE ADVANTAGES OF TNS SYNAESTHESIA 1 Better Together ? The Cognitive Advantages of Synaesthesia for Time

Synaesthesia for time, numbers and space (TNS synaesthesia) is thought to have costs and benefits for recalling and manipulating time and number. There are two competing theories about how TNS synaesthesia affects cognition. The ‘magnitude’ account predicts TNS synaesthesia may affect cardinal magnitude judgements, whereas the ‘sequence’ account suggests it may affect ordinal sequence judgements and could rely on visuospatial working memory. We aimed to comprehensively assess the cognitive consequences of TNS synaesthesia and distinguish between these two accounts. TNS synaesthetes, graphemecolour synaesthetes and non-synaesthetes completed a behavioural task battery. Three tasks involved cardinal and ordinal comparisons of temporal, numerical and spatial stimuli; we also examined visuospatial working memory. TNS synaesthetes were significantly more accurate than non-synaesthetes in making ordinal judgements about space. This difference was explained by significantly higher visuospatial working memory accuracy. Our findings demonstrate an advantage of TNS synaesthesia which is more in line with the sequence account.
Re-examining the quantum volume test: Ideal distributions, compiler optimizations, confidence intervals, and scalable resource estimations

The quantum volume test is a full-system benchmark for quantum computers that is sensitive to qubit number, fidelity, connectivity, and other quantities believed to be important in building useful devices. The test was designed to produce a single-number measure of a quantum computer's general capability, but a complete understanding of its limitations and operational meaning is still missing. We explore the quantum volume test to better understand its design aspects, sensitivity to errors, passing criteria, and what passing implies about a quantum computer. We elucidate some transient behaviors the test exhibits for small qubit number including the ideal measurement output distributions and the efficacy of common compiler optimizations. We then present an efficient algorithm for estimating the expected heavy output probability under different error models and compiler optimization options, which predicts performance goals for future systems. Additionally, we explore the original confidence interval construction and show that it underachieves the desired coverage level for single shot experiments and overachieves for more typical number of shots. We propose a new confidence interval construction that reaches the specified coverage for typical number of shots and is more efficient in the number of circuits needed to pass the test. We demonstrate these savings with a QV=210 experimental dataset collected from Quantinuum System Model H1-1. Finally, we discuss what the quantum volume test implies about a quantum computer's practical or operational abilities especially in terms of quantum error correction.
IMDB-WIKI-SbS: An Evaluation Dataset for Crowdsourced Pairwise Comparisons

Today, comprehensive evaluation of large-scale machine learning models is possible thanks to the open datasets produced using crowdsourcing, such as SQuAD, MS COCO, ImageNet, SuperGLUE, etc. These datasets capture objective responses, assuming the single correct answer, which does not allow to capture the subjective human perception. In turn, pairwise comparison tasks, in which one has to choose between only two options, allow taking peoples' preferences into account for very challenging artificial intelligence tasks, such as information retrieval and recommender system evaluation. Unfortunately, the available datasets are either small or proprietary, slowing down progress in gathering better feedback from human users. In this paper, we present IMDB-WIKI-SbS, a new large-scale dataset for evaluating pairwise comparisons. It contains 9,150 images appearing in 250,249 pairs annotated on a crowdsourcing platform. Our dataset has balanced distributions of age and gender using the well-known IMDB-WIKI dataset as ground truth. We describe how our dataset is built and then compare several baseline methods, indicating its suitability for model evaluation.
Amplification of primordial perturbations from the rise or fall of the inflaton

The next generation of cosmic microwave background, gravitational wave, and large scale structure, experiments will provide an unprecedented opportunity to probe the primordial power spectrum on small scales. An exciting possibility for what lurks on small scales is a sharp rise in the primordial power spectrum: this can lead to the formation of primordial black holes, providing a dark matter candidate or the black holes observed by the LIGO-Virgo collaboration. In this work we develop a mechanism for the amplification of the small-scale primordial power spectrum, in the context of single-field inflation with a step-like feature in the inflaton potential. Specifically, we consider both the upward and the downward step in the potential. We also discuss the possibility of the strong coupling between perturbations because the rapid changes of the potential derivatives with the time-dependent field value, caused by the step-like feature, could make the coupling stronger. As a result, we find that the perturbations can remain weakly coupled yet sufficiently enhanced if the step realizes the rapid changes of the potential derivatives in some fraction of an e-fold, 𝒪(𝒫ℛ 1/2) ≲ ΔN < 1, where 𝒫ℛ is the power spectrum of the curvature perturbation at that time. We also discuss the PBH formation rate from the inflaton trapping at the local minimum, which can occur in the potential with an upward step.
A Cosmic Ray Muon Spectrometer Using Pressurized Gaseous Cherenkov Radiators

In this work, we propose a new approach to cosmic ray muon momentum measurement using multiple pressurized gaseous Cherenkov radiators. Knowledge of cosmic ray muon momentum spectrum has the potential to significantly improve and expand the use of a variety of recently developed muon-based radiographic techniques. However, existing muon tomography systems rely only on muon tracking and have no momentum measurement capabilities which reduces the image resolution and requires longer measurement times. A fieldable cosmic ray muon spectrometer with momentum measurement capabilities for use in muon tomography is currently missing. We address this challenge by optimally varying the pressure of multiple gaseous Cherenkov radiators and identifying the radiators that are triggered by muons that have momentum higher than the Cherenkov threshold momentum. We evaluate the proposed concept through Monte Carlo simulations and demonstrate that the sea level cosmic ray muon momentum spectrum can be reconstructed with sufficient accuracy and resolution for two scenarios: (i) a perfect Cherenkov radiator and (ii) a practical Cherenkov radiator where noise was introduced in the form of scintillation and transition photons. To quantify the detector classification accuracy, true and false classifications are introduced. The fraction of true classification is investigated for each momentum level in a practical radiator. The average classification rate for momenta 0.2 to 7.0 GeV/c with uncertainty 1 GeV/c was higher than 85%.
Bayesian OOD detection with aleatoric uncertainty and outlier exposure

Typical Bayesian approaches to OOD detection use epistemic uncertainty. Surprisingly from the Bayesian perspective, there are a number of methods that successfully use aleatoric uncertainty to detect OOD points (e.g. Hendryks et al. 2018). In addition, it is difficult to use outlier exposure to improve a Bayesian OOD detection model, as it is not clear whether it is possible or desirable to increase posterior (epistemic) uncertainty at outlier points. We show that a generative model of data curation provides a principled account of aleatoric uncertainty for OOD detection. In particular, aleatoric uncertainty signals a specific type of OOD point: one without a well-defined class-label, and our model of data curation gives a likelihood for these points, giving us a mechanism for conditioning on outlier points and thus performing principled Bayesian outlier exposure. Our principled Bayesian approach, combining aleatoric and epistemic uncertainty with outlier exposure performs better than methods using aleatoric or epistemic alone.
Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data: With Applications to Massive Biobank Data

Semiparametric joint models of longitudinal and competing risk data are computationally costly, and their current implementations do not scale well to massive biobank data. This paper identifies and addresses some key computational barriers in a semiparametric joint model for longitudinal and competing risk survival data. By developing and implementing customized linear scan algorithms, we reduce the computational complexities from O(n2) or O(n3) to O(n) in various steps including numerical integration, risk set calculation, and standard error estimation, where n is the number of subjects. Using both simulated and real-world biobank data, we demonstrate that these linear scan algorithms can speed up the existing methods by a factor of up to hundreds of thousands when n > 104, often reducing the runtime from days to minutes. We have developed an R package, FastJM, based on the proposed algorithms for joint modeling of longitudinal and competing risk time-to-event data and made it publicly available on the Comprehensive R Archive Network (CRAN).
Towards optimally abstaining from prediction

A common challenge across all areas of machine learning is that training data is not distributed like test data, due to natural shifts,"blind spots,"or adversarial examples; such test examples are referred to as out-of-distribution (OOD) test examples. We consider a model where one may abstain from predicting, at a fixed cost. In particular, our transductive abstention algorithm takes labeled training examples and unlabeled test examples as input, and provides predictions with optimal prediction loss guarantees. The loss bounds match standard generalization bounds when test examples are i.i. d. from the training distribution, but add an additional term that is the cost of abstaining times the statistical distance between the train and test distribution (or the fraction of adversarial examples). For linear regression, we give a polynomial-time algorithm based on Celis-Dennis-Tapia optimization algorithms. For binary classification, we show how to efficiently implement it using a proper agnostic learner (i.e., an Empirical Risk Minimizer) for the class of interest. Our work builds on a recent abstention algorithm of Goldwasser, Kalais, and Montasser (2020) for transductive binary classification.
Towards Model Agnostic Federated Learning Using Knowledge Distillation

Is it possible to design an universal API for federated learning using which an ad-hoc group of data-holders (agents) collaborate with each other and perform federated learning? Such an API would necessarily need to be model-agnostic i.e. make no assumption about the model architecture being used by the agents, and also cannot rely on having representative public data at hand. Knowledge distillation (KD) is the obvious tool of choice to design such protocols. However, surprisingly, we show that most natural KD-based federated learning protocols have poor performance. To investigate this, we propose a new theoretical framework, Federated Kernel ridge regression, which can capture both model heterogeneity as well as data heterogeneity. Our analysis shows that the degradation is largely due to a fundamental limitation of knowledge distillation under data heterogeneity. We further validate our framework by analyzing and designing new protocols based on KD. Their performance on real world experiments using neural networks, though still unsatisfactory, closely matches our theoretical predictions.
Three dimensional simulations of embolic stroke: clinical comparisons and an equation for sizing emboli from imaging

The size and location of ischaemic lesions provides vital clues to the origins of a stroke. This study performs three-dimensional (3D) stroke simulations for comparison with brain images to predict the size and location of emboli. Simulated e mboli were released into a 3D in silico vasculature, supplying grey and white matter brain volumes, to generate individual 3D lesion estimates and probabilistic lesion overlap maps . Computer generated lesions were compared with real world radiological images obtained from the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset by a panel of three clinically qualified experts. S imulations of large single emboli entering the vasculature reproduced similar middle cerebral artery (MCA), posterior cerebral artery (PCA) and anterior cerebral artery (ACA) lesions to those observed clinically. Estimated infarct volume as a percentage of total brain volume was found to be related to embolus diameter according to the following expression: where a= 2.54 +/0.062 mm -b , b=3.380 +/0.030 diameter = [ % infarct volume / a ] 1/ b (with diameter in mm). Maximum embolus sizes observed were consistent with those observed clinically. Probabilistic lesion overlap maps were created, confirming the MCA territory as the most probable resting place of emboli in the computational vasculature, followed by the PCA then ACA. We conclude that 3D stroke simulations are capable of reproducing radiologically observed lesion distributions following embolic stroke. Personalised stroke simulations aimed at rapid diagnosis of the location and source of a stroke, have potential to distinguish cardioembolic sources from other types of stroke, facilitating rapid access to targeted treatment. Future work will focus on matching patient-specific simulation results to real-world brain imaging to inform future predictive diagnostics. Preprint uploaded to arXiv, physics.med-ph. 28th Oct 2021, v1