Miracle-dz/UniFAR-FTUs huggingface dataset:
Alert Classification for the ALeRCE Broker System: The Light Curve Classifier

We present the first version of the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker light curve classifier. ALeRCE is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory. The ALeRCE light curve classifier uses variability features computed from the ZTF alert stream and colors obtained from AllWISE and ZTF photometry. We apply a balanced random forest algorithm with a two-level scheme where the top level classifies each source as periodic, stochastic, or transient, and the bottom level further resolves each of these hierarchical classes among 15 total classes. This classifier corresponds to the first attempt to classify multiple classes of stochastic variables (including core- and host-dominated active galactic nuclei, blazars, young stellar objects, and cataclysmic variables) in addition to different classes of periodic and transient sources, using real data. We created a labeled set using various public catalogs (such as the Catalina Surveys and Gaia DR2 variable stars catalogs, and the Million Quasars catalog), and we classify all objects with ≥6 g-band or ≥6 r-band detections in ZTF (868,371 sources as of 2020 June 9), providing updated classifications for sources with new alerts every day. For the top level we obtain macro-averaged precision and recall scores of 0.96 and 0.99, respectively, and for the bottom level we obtain macro-averaged precision and recall scores of 0.57 and 0.76, respectively. Updated classifications from the light curve classifier can be found at the ALeRCE Explorer website (http://alerce.online).
Electronic and magnetic properties of stoichiometric CeAuBi2

We report the electronic and magnetic properties of stoichiometric CeAuBi2 single crystals. At ambient pressure, CeAuBi2 orders antiferromagnetically below a Néel temperature (TN) of 19 K. Neutron diffraction experiments revealed an antiferromagnetic propagation vector τ^=[0,0,1∕2], which doubles the paramagnetic unit cell along the c axis. At low temperatures several metamagnetic transitions are induced by the application of fields parallel to the c axis, suggesting that the magnetic structure of CeAuBi2 changes as a function of field. At low temperatures, a linear positive magnetoresistance may indicate the presence of band crossings near the Fermi level. Finally, the application of external pressure favors the antiferromagnetic state, indicating that the 4f electrons become more localized.
Towards rotating noncircular black holes in string-inspired gravity

We study stationary slowly rotating black holes, up to quadratic order in the spin angular momentum, in dynamical Chern-Simons gravity and shift symmetric Einstein scalar Gauss-Bonnet gravity, as models of string-inspired gravities. These gravity theories modify general relativity by introducing dynamical scalar fields coupled with curvature invariants. We show that the linear time dependence of the scalar fields is allowed from the stationarity of the effective stress energy tensors. However, these time dependent scalar fields yield singular behavior of the metric functions at the black hole horizons, or they are incompatible with the stationarity of the spacetimes. Thus, these gravity theories admit only known solutions as regular stationary solutions. Our results suggest the non-existence of rotating non-circular black holes in these gravity theories.
Proof-Carrying Plans: a Resource Logic for AI Planning

Planning languages have been used successfully in AI for several decades. Recent trends in AI verification and Explainable AI have raised the question of whether AI planning techniques can be verified. In this paper, we present a novel resource logic, the Proof Carrying Plans (PCP) logic that can be used to verify plans produced by AI planners. The PCP logic takes inspiration from existing resource logics (such as Linear logic and Separation logic) as well as Hoare logic when it comes to modelling states and resource-aware plan execution. It also capitalises on the Curry-Howard approach to logics, in its treatment of plans as functions and plan pre- and post-conditions as types. This paper presents two main results. From the theoretical perspective, we show that the PCP logic is sound relative to the standard possible world semantics used in AI planning. From the practical perspective, we present a complete Agda formalisation of the PCP logic and of its soundness proof. Moreover, we showcase the Curry-Howard, or functional, value of this implementation by supplementing it with the library that parses AI plans into Agda’s proofs automatically. We provide evaluation of this library and the resulting Agda functions. Keywords: AI planning, Verification, Resource Logics, Theorem Proving, Dependent Types.
Engineering the spectral profile of photon pairs by using multi-stage nonlinear interferometers

We study the joint spectral function of photon pairs generated by nonlinear interferometers (NLIs) in which the nonlinear fibers are 2, 3 and 4 pieces, respectively. Although the photon pairs produced by single piece nonlinear fiber via four wave mixing are frequency anti-corelated, their mode profile can be flexibility modified by active filtering effect originated from the quantum interference in NLIs. We perform two series of experiments. One is that all the nonlinear fibers in pulse pumped NLIs are identical; the other is that the lengths of N pieces nonlinear fibers follow binomial distribution. We find the latter provides a better active filtering function: the separation between adjacent islands in the contour of joint spectral function increases with N, and there is no mini-maxima between two primary maxima. This feature of the uneven multi-stage NLI, which is experimentally demonstrated for the first time, is useful for efficiently engineering the factorable two-photon state-a desirable resources of quantum information processing. Our investigation shows that the multi-stage NLI is a useful tool of engineering the mode structure of quantum states.
Entanglement enhanced and one-way steering in PT -symmetric cavity magnomechanics

We study creation of entanglement and quantum steering in a parity-time- (PT -) symmetric cavity magnomechanical system. There is magnetic dipole interaction between the cavity and photon-magnon, and there is also magnetostrictive interaction which is induced by the phononmagnon coupling in this system. By introducing blue-detuned driving microwave field to the system, the bipartite entanglement of the system with PT -symmetry is significantly enhanced versus the case in the conventional cavity magnomechanical systems (loss-loss systems). Moreover, the one-way quantum steering between magnon-phonon and photon-phonon modes can be obtained in the unbroken-PT -symmetric regime. The boundary of stability is demonstrated and this show that the steady-state solutions are more stable in the gain and loss systems. This work opens up a route to explore the characteristics of quantum entanglement and steering in magnomechanical systems, which might have potential applications in quantum state engineering and quantum information.
The effects of spatial variability of nitrous oxide emissions from grazed pastures on the sampling distribution of chamber measurements

SUMMARY Chamber sampling is a common method for measuring nitrous oxide (N2O) emissions from agricultural soils. However, for grazed pastures, the patchy nature of urine deposition results in very high levels of spatial variability in N2O emissions. In the present study, the behaviour of the sample mean was examined by simulating a large number (9999) of random N2O chamber samples under different assumptions regarding the underlying N2O distribution. Using sample sizes of up to 100 chambers, the Central Limit Theorem did not apply. The distribution of the sample mean was always right-skewed with a standard deviation varying between 12·5 and 135% of the true mean. However, the arithmetic mean was an unbiased estimator and the mean of the sample mean distribution was close to the true mean of the simulated N2O distribution. The properties of the sample mean distribution (variance, skewness) were affected significantly by the assumed distribution of the emission factor, but not by distribution of the urine patch concentration. The geometric mean was also investigated as a potential alternative estimator. However, although its distribution had lower variance, it was also biased. Two methods for bias correcting the mean were investigated. These methods reduced the bias, but at the cost of increasing the variance. Neither of the bias-corrected estimators were consistently better than the arithmetic mean in terms of skewness and variance. To improve the estimation of N2O emissions from a grazed pasture using chambers, techniques need to be developed to identify urine patch and non-urine patch areas before sampling.
Safe and efficient collision avoidance control for autonomous vehicles

We study a novel principle for safe and efficient collision avoidance that adopts a mathematically elegant and general framework making as much as possible abstraction of the controlled vehicle’s dynamics and of its environment. Vehicle dynamics is characterized by pre-computed functions for accelerating and braking to a given speed. Environment is modeled by a function of time giving the free distance ahead of the controlled vehicle under the assumption that the obstacles are either fixed or are moving in the same direction. The main result is a control policy enforcing the vehicle’s speed so as to avoid collision and efficiently use the free distance ahead, provided some initial safety condition holds.The studied principle is applied to the design of a synchronous controller. We show that the controller is safe by construction. Furthermore, we show that the efficiency strictly increases for decreasing granularity of discretization. We present the implementation and experimental evaluations in the Carla autonomous driving simulator and investigate various performance issues.
Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning

Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multidimensional and complex design space. Data-driven approaches for CAS design could introduce new insights enabling system engineers to manage the CAS complexity more cost-effectively at the design-phase. This paper introduces a systematic approach to reason about design choices and patterns of learning-based CAS. Using data from a systematic literature review, reasoning is performed with a novel application of data-driven methodologies such as clustering, multiple correspondence analysis and decision trees. The reasoning based on past experience as well as supporting novel and innovative design choices are demonstrated.
Global Optimum Search in Quantum Deep Learning

This paper aims to solve machine learning optimization problem by using quantum circuit. Two approaches, namely the average approach and the Partial Swap Test Cut-off method (PSTC) was proposed to search for the global minimum/maximum of two different objective functions. The current cost is $O(\sqrt{|\Theta|} N)$, but there is potential to improve PSTC further to $O(\sqrt{|\Theta|} \cdot sublinear \ N)$ by enhancing the checking process.
A Comparison of Machine Learning Algorithms Applied to American Legislature Polarization

We present a novel approach to the measurement of American state legislature polarization with an experimental comparison of three different machine learning algorithms. Our approach strictly relies on public data sources and open source software. The results suggest that artificial neural network regression has the best outcome compared to both support vector machine and ordinary least squares regression in the prediction of both state House and state Senate legislature polarization. In addition to the technical outcomes of our study, broader implications are assessed as a means of highlighting the importance of accessible information for the higher purpose of promoting civic responsibility.
Scission: Context-aware and Performance-driven Edge-based Distributed Deep Neural Networks

Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data, and reducing the ingress bandwidth demand beyond the edge. However, for a given DNN, identifying the optimal partition configuration for distributing the DNN that maximizes performance is a significant challenge since: (i) the combination of potential target hardware resources that maximizes performance and (ii) the sequence of layers of the DNN that should be distributed across the target resources needs to be determined, while accounting for (iii) user-defined objectives/constraints for partitioning. This paper presents Scission, a tool for automated benchmarking of DNNs on a given set of target device, edge and cloud resources for determining optimal partitions that maximize DNN performance. The decision-making approach is context-aware by capitalizing on hardware capabilities of the target resources, their locality, the characteristics of DNN layers, and the network condition. Experimental studies are carried out on 18 DNNs. The decisions made by Scission cannot be manually made by a human given the complexity and the number of dimensions affecting the search space. The results obtained validate that Scission is a valuable tool for achieving performance-driven and context-aware distributed DNNs that leverage the edge. Scission is available for public download at this https URL.
Double Blind T-Private Information Retrieval

Double blind <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>-private information retrieval (DB-TPIR) enables two users, each of whom specifies an index (<inline-formula> <tex-math notation="LaTeX">$\theta _{1}, \theta _{2}$ </tex-math></inline-formula>, resp.), to efficiently retrieve a message <inline-formula> <tex-math notation="LaTeX">$W(\theta _{1},\theta _{2})$ </tex-math></inline-formula> labeled by the two indices, from a set of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> servers that store all messages <inline-formula> <tex-math notation="LaTeX">$W(k_{1},k_{2}), k_{1}\in \{1,2,\ldots, K_{1}\}, k_{2}\in \{1,2,\ldots, K_{2}\}$ </tex-math></inline-formula>, such that the two users’ indices are kept private from any set of up to <inline-formula> <tex-math notation="LaTeX">$T_{1},T_{2}$ </tex-math></inline-formula> colluding servers, respectively, as well as from each other. A DB-TPIR scheme based on cross-subspace alignment is proposed in this paper, and shown to be capacity-achieving in the asymptotic setting of large number of messages and bounded latency. The scheme is then extended to <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-way blind <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-secure <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>-private information retrieval (MB-XS-TPIR) with multiple (<inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>) indices, each belonging to a different user, arbitrary privacy levels for each index (<inline-formula> <tex-math notation="LaTeX">$T_{1}, T_{2},\ldots, T_{M}$ </tex-math></inline-formula>), and arbitrary level of security (<inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>) of data storage, so that the message <inline-formula> <tex-math notation="LaTeX">$W(\theta _{1},\theta _{2},\ldots, \theta _{M})$ </tex-math></inline-formula> can be efficiently retrieved while the stored data is held secure against collusion among up to <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> colluding servers, the <inline-formula> <tex-math notation="LaTeX">$m^{th}$ </tex-math></inline-formula> user’s index is private against collusion among up to <inline-formula> <tex-math notation="LaTeX">$T_{m}$ </tex-math></inline-formula> servers, and each user’s index <inline-formula> <tex-math notation="LaTeX">$\theta _{m}$ </tex-math></inline-formula> is private from all other users. The general scheme relies on a tensor-product based extension of cross-subspace alignment and retrieves <inline-formula> <tex-math notation="LaTeX">$1-(X+T_{1}+\cdots +T_{M})/N$ </tex-math></inline-formula> bits of desired message per bit of download.
Unsupervised Feature Learning by Cross-Level Discrimination between Instances and Groups

Unsupervised feature learning has made great strides with invariant mapping and instance-level discrimination, as benchmarked by classification on common datasets. However, these datasets are curated to be distinctive and class-balanced, whereas naturally collected data could be highly correlated within the class (with repeats at the extreme) and long-tail distributed across classes. The natural grouping of instances conflicts with the fundamental assumption of instance-level discrimination. Contrastive feature learning is thus unstable without grouping, whereas grouping without contrastive feature learning is easily trapped into degeneracy. We propose to integrate grouping into instance-level discrimination, not by imposing group-level discrimination, but by imposing cross-level discrimination between instances and groups. Our key insight is that grouping results from not just attraction, but also repulsion. While invariant mapping is achieved by local attraction between augmented instances, instance similarity emerges from long-range repulsion against common instance groups. To further avoid the clash between grouping and discrimination objectives, we also impose them on separate features derived from the common feature. Our extensive experimentation demonstrates not only significant gain on datasets with high correlation and long-tail distributions, but also leading performance on multiple self-supervision and semi-supervision benchmarks, bringing unsupervised feature learning closer to real data applications.
A survey of embedding models of entities and relationships for knowledge graph completion

Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.
Shapley Value Confidence Intervals for Attributing Variance Explained

The coefficient of determination, the R 2 , is often used to measure the variance explained by an affine combination of multiple explanatory covariates. An attribution of this explanatory contribution to each of the individual covariates is often sought in order to draw inference regarding the importance of each covariate with respect to the response phenomenon. A recent method for ascertaining such an attribution is via the game theoretic Shapley value decomposition of the coefficient of determination. Such a decomposition has the desirable efficiency, monotonicity, and equal treatment properties. Under a weak assumption that the joint distribution is pseudo-elliptical, we obtain the asymptotic normality of the Shapley values. We then utilize this result in order to construct confidence intervals and hypothesis tests for Shapley values. Monte Carlo studies regarding our results are provided. We found that our asymptotic confidence intervals required less computational time to competing bootstrap methods and are able to exhibit improved coverage, especially on small samples. In an expository application to Australian real estate price modeling, we employ Shapley value confidence intervals to identify significant differences between the explanatory contributions of covariates, between models, which otherwise share approximately the same R 2 value. These different models are based on real estate data from the same periods in 2019 and 2020, the latter covering the early stages of the arrival of the novel coronavirus, COVID-19.
Automated Functional Fuzzing of Android Apps

Android apps are GUI-based event-driven software and have become ubiquitous in recent years. Besides fail-stop errors like app crashes, functional correctness is critical for an app's success. However, functional bugs (e.g., inadvertent function failures, sudden user data lost, and incorrect display information) are prevalent, even in popular, well-tested apps, due to significant challenges in effectively detecting them: (1) current practices heavily rely on expensive, small-scale manual validation (the lack of automation); and (2) modern automated testing has been limited to app crashes (the lack of test oracles). This paper fills this gap by introducing independent view fuzzing, the first automated approach for detecting functional bugs in Android apps. Our key insight is to leverage the commonly-held independent view property of Android apps to manufacture property-preserving mutant tests from a set of seed tests that validate certain app properties. The mutated tests help exercise the tested apps under additional, adverse conditions. Any property violations indicate likely functional bugs. We have realized our approach as a practical, end-to-end functional fuzzing tool, Genie. Given an off-the-shelf app, Genie (1) automatically detects functional bugs without requiring human-provided tests and oracles (thus fully automated), and (2) the detected functional bugs are diverse (thus general and not limited to specific functional properties). We have evaluated Genie on twelve popular, well-maintained Android apps and successfully uncovered 33 previously unknown functional bugs in their latest releases -- all have been confirmed, and 21 have already been fixed. Most of the detected bugs are nontrivial and have escaped developer (and user) testing for at least one year and affected many app releases, thus clearly demonstrating Genie's practical utility.
Neural Light Transport for Relighting and View Synthesis

The light transport (LT) of a scene describes how it appears under different lighting conditions from different viewing directions, and complete knowledge of a scene’s LT enables the synthesis of novel views under arbitrary lighting. In this article, we focus on image-based LT acquisition, primarily for human bodies within a light stage setup. We propose a semi-parametric approach for learning a neural representation of the LT that is embedded in a texture atlas of known but possibly rough geometry. We model all non-diffuse and global LT as residuals added to a physically based diffuse base rendering. In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint. This strategy allows the network to learn complex material effects (such as subsurface scattering) and global illumination (such as diffuse interreflection), while guaranteeing the physical correctness of the diffuse LT (such as hard shadows). With this learned LT, one can relight the scene photorealistically with a directional light or an HDRI map, synthesize novel views with view-dependent effects, or do both simultaneously, all in a unified framework using a set of sparse observations. Qualitative and quantitative experiments demonstrate that our Neural Light Transport (NLT) outperforms state-of-the-art solutions for relighting and view synthesis, without requiring separate treatments for both problems that prior work requires. The code and data are available at http://nlt.csail. mit.edu.