Miracle-dz/UniFAR-FTUs huggingface dataset:
Poq: Projection-based Runtime Assertions for Debugging on a Quantum Computer

In this paper, we propose Proq, a runtime assertion scheme for testing and debugging quantum programs on a quantum computer. The predicates in Proq are represented by projections (or equivalently, closed subspaces of the state space), following Birkhoff-von Neumann quantum logic. The satisfaction of a projection by a quantum state can be directly checked upon a small number of projective measurements rather than a large number of repeated executions. On the theory side, we rigorously prove that checking projection-based assertions can help locate bugs or statistically assure that the semantic function of the tested program is close to what we expect, for both exact and approximate quantum programs. On the practice side, we consider hardware constraints and introduce several techniques to transform the assertions, making them directly executable on the measurement-restricted quantum computers. We also propose to achieve simplified assertion implementation using local projection technique with soundness guaranteed. We compare Proq with existing quantum program assertions and demonstrate the effectiveness and efficiency of Proq by its applications to assert two ingenious quantum algorithms, the Harrow-Hassidim-Lloyd algorithm and Shor's algorithm.
Richer priors for infinitely wide multi-layer perceptrons

It is well-known that the distribution over functions induced through a zero-mean iid prior distribution over the parameters of a multi-layer perceptron (MLP) converges to a Gaussian process (GP), under mild conditions. We extend this result firstly to independent priors with general zero or non-zero means, and secondly to a family of partially exchangeable priors which generalise iid priors. We discuss how the second prior arises naturally when considering an equivalence class of functions in an MLP and through training processes such as stochastic gradient descent. The model resulting from partially exchangeable priors is a GP, with an additional level of inference in the sense that the prior and posterior predictive distributions require marginalisation over hyperparameters. We derive the kernels of the limiting GP in deep MLPs, and show empirically that these kernels avoid certain pathologies present in previously studied priors. We empirically evaluate our claims of convergence by measuring the maximum mean discrepancy between finite width models and limiting models. We compare the performance of our new limiting model to some previously discussed models on synthetic regression problems. We observe increasing ill-conditioning of the marginal likelihood and hyper-posterior as the depth of the model increases, drawing parallels with finite width networks which require notoriously involved optimisation tricks.
KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents

Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https:// github.com/ygorg/KPTimes.
Data-Driven Spectrum Cartography via Deep Completion Autoencoders

Spectrum maps, which provide RF spectrum metrics such as power spectral density for every location in a geographic area, find numerous applications in wireless communications such as interference control, spectrum management, resource allocation, and network planning to name a few. Spectrum cartography techniques construct these maps from a collection of measurements collected by spatially distributed sensors. Due to the nature of the propagation of electromagnetic waves, spectrum maps are complicated functions of the spatial coordinates. For this reason, model-free approaches have been preferred. However, all existing schemes rely on some interpolation algorithm unable to learn from data. This paper proposes a novel approach to spectrum cartography where propagation phenomena are learned from data. The resulting algorithms can therefore construct a spectrum map from a significantly smaller number of measurements than existing schemes since the spatial structure of shadowing and other phenomena is previously learned from maps in other environments. Besides the aforementioned new paradigm, this is also the first work to perform spectrum cartography with deep neural networks. To exploit the manifold structure of spectrum maps, a deep network architecture is proposed based on completion autoencoders.
Macroscopic band width inequalities

Inspired by Gromov’s work on Metric inequalities with scalar curvature we establish band width inequalities for Riemannian bands of the form ( V = M × [0 , 1] ,g ), where M n − 1 is a closed manifold. We introduce a new class of orientable manifolds we call ﬁlling enlargeable and prove: If M is ﬁlling enlargeable and all unit balls in the universal cover of ( V,g ) have volume less than a constant 12 ε n , then width( V,g ) ≤ 1. We show that if a closed orientable manifold is enlargeable or aspherical then it is ﬁlling enlargeable. Furthermore we establish that whether a closed orientable manifold is ﬁlling enlargeable or not only depends on the image of the fundamental class under the classifying map of the universal cover.
Measuring gravitational-wave memory in the first LIGO/Virgo gravitational-wave transient catalog

Gravitational-wave memory, a strong-field effect of general relativity, manifests itself as a permanent displacement in spacetime. We develop a Bayesian framework to detect gravitational-wave memory with the Advanced LIGO/Virgo detector network. We apply this algorithm on the ten binary black hole mergers in LIGO/Virgo’s first transient gravitational-wave catalog. We find no evidence of memory, which is consistent with expectations. In order to estimate when memory will be detected, we use the best current population estimates to construct a realistic sample of binary black hole observations for LIGO/Virgo at design sensitivity. We show that an ensemble of O(2000) binary black hole observations can be used to find definitive evidence for gravitational-wave memory. We conclude that memory is likely to be detected in the early A+/Virgo+era.
Quality of Life Measurement of Patients Receiving Treatments for Gallstone Disease: Options, Issues and Results

The paper discusses the methods for determining health state values in the context of a study of treatment options for gallstone disease. * The study design is a cost utility analysis (CUA) and the technique used to measure quality of life had to meet the specific requirements of a CUA, viz that the units of measurement were appropriate for the calculation of quality adjusted life years. The paper examines the selection of a measurement technique and the related issue of how the measurement task was controlled to ensure the eliciting of valid responses. It also discusses the psychometric properties of the two measurement techniques used, namely the time-trade off technique (TTO) and the rating scale (RS). It supports the findings of other studies that the psychological quantity measured by the two scaling techniques is either quite different or measured in different units. Consequently, the magnitude of the measured change in QALYs would depend upon the choice of a scaling technique. It argues for the adoption of a de facto gold standard for measuring health state values for CUA via a generic multi attribute utility index. * In two companion papers Street (1993) presents the methods and results from the analysis of treatment costs, and Cook and Richardson (1993) discuss the method adopted in the construction of health state scenarios used for the quantification of treatment outcomes. An overview of the study, its methods, results and the sensitivity of these to various assumed values is presented in Cook, Richardson and Street (1993).
Voter models on subcritical inhomogeneous random graphs

The voter model is a classical interacting particle system modelling how consensus is formed across a network. We analyse the time to consensus for the voter model when the underlying graph is a subcritical scale-free random graph. Moreover, we generalise the model to include a `temperature' parameter. The interplay between the temperature and the structure of the random graph leads to a very rich phase diagram, where in the different phases different parts of the underlying geometry dominate the time to consensus. Finally, we also consider a discursive voter model, where voters discuss their opinions with their neighbours. Our proofs rely on the well-known duality to coalescing random walks and a detailed understanding of the structure of the random graphs.
Detection of Na, K, and H2O in the hazy atmosphere of WASP-6b

We present new observations of the transmission spectrum of the hot Jupiter WASP-6b both from the ground with the Very Large Telescope FOcal Reducer and Spectrograph (FORS2) from 0.45 to 0.83 μm, and space with the Transiting Exoplanet Survey Satellite from 0.6 to 1.0 μm and the Hubble Space Telescope (HST) Wide Field Camera 3 from 1.12 to 1.65 μm. Archival data from the HST Space Telescope Imaging Spectrograph (STIS) and Spitzer are also re-analysed on a common Gaussian process framework, of which the STIS data show a good overall agreement with the overlapping FORS2 data. We also explore the effects of stellar heterogeneity on our observations and its resulting implications towards determining the atmospheric characteristics of WASP-6b. Independent of our assumptions for the level of stellar heterogeneity we detect Na i, K i, and H2O absorption features and constrain the elemental oxygen abundance to a value of [O/H] ≃ −0.9 ± 0.3 relative to solar. In contrast, we find that the stellar heterogeneity correction can have significant effects on the retrieved distributions of the [Na/H] and [K/H] abundances, primarily through its degeneracy with the sloping optical opacity of scattering haze species within the atmosphere. Our results also show that despite this presence of haze, WASP-6b remains a favourable object for future atmospheric characterization with upcoming missions such as the James Webb Space Telescope.
Type Safety with JSON Subschema

JSON is a popular data format used pervasively in web APIs, cloud computing, NoSQL databases, and increasingly also machine learning. JSON Schema is a language for declaring the structure of valid JSON data. There are validators that can decide whether a JSON document is valid with respect to a schema. Unfortunately, like all instance-based testing, these validators can only show the presence and never the absence of a bug. This paper presents a complementary technique: JSON subschema checking, which can be used for static type checking with JSON Schema. Deciding whether one schema is a subschema of another is non-trivial because of the richness of the JSON Schema specification language. Given a pair of schemas, our approach first canonicalizes and simplifies both schemas, then decides the subschema question on the canonical forms, dispatching simpler subschema queries to type-specific checkers. We apply an implementation of our subschema checking algorithm to 8,548 pairs of real-world JSON schemas from different domains, demonstrating that it can decide the subschema question for most schema pairs and is always correct for schema pairs that it can decide. We hope that our work will bring more static guarantees to hard-to-debug domains, such as cloud computing and artificial intelligence.
An Efficient Multi-Domain Framework for Image-to-Image Translation

Existing approaches have been proposed to tackle unsupervised image-to-image translation in recent years. However, they mainly focus on one-to-one mappings, making it difficult to handle more general and practical problems such as multi-domain translations. To address issues like large cost of training time and resources in translation between any number of domains, we propose a general framework called multi-domain translator (MDT), which is extended from bi-directional image-to-image translation. MDT is designed to have only one domain-shared encoder for the consideration of efficiency, together with several domain-specified decoders to transform an image into multiple domains without knowing the input domain label. Moreover, we propose to employ two constraints, namely reconstruction loss and identity loss to further improve the generation. Experiments are conducted on different databases for several multi-domain translation tasks. Both qualitative and quantitative results demonstrate the effectiveness and efficiency performed by the proposed MDT against the state-of-the-art models.
Qini-based uplift regression

Uplift models provide a solution to the problem of isolating the marketing effect of a campaign. For customer churn reduction, uplift models are used to identify the customers who are likely to respond positively to a retention activity only if targeted, and to avoid wasting resources on customers that are very likely to switch to another company. We introduce a Qini-based uplift regression model to analyze a large insurance company's retention marketing campaign. Our approach is based on logistic regression models. We show that a Qini-optimized uplift model acts as a regularizing factor for uplift, much as a penalized likelihood model does for regression. This results in interpretable parsimonious models with few relevant xplanatory variables. Our results show that performing Qini-based parameters estimation significantly improves the uplift models performance.
Spin flop and crystalline anisotropic magnetoresistance in CuMnAs

Recent research works have shown that the magnetic order in some antiferromagnetic materials can be manipulated and detected electrically, due to two physical mechanisms: Neel-order spin-orbit torques and anisotropic magnetoresistance. While these observations open up opportunities to use antiferromagnets for magnetic memory devices, different physical characterization methods are required for a better understanding of those mechanisms. Here we report a magnetic field induced rotation of the antiferromagnetic Neel vector in epitaxial tetragonal CuMnAs thin films. Using soft x-ray magnetic linear dichroism spectroscopy, x-ray photoemission electron microscopy, integral magnetometry and magneto-transport methods, we demonstrate spin-flop switching and continuous spin reorientation in antiferromagnetic films with uniaxial and biaxial magnetic anisotropies, respectively. From field-dependent measurements of the magnetization and magnetoresistance, we obtain key material parameters including the anisotropic magnetoresistance coefficients, magnetocrystalline anisotropy, spin-flop and exchange fields.
Link Prediction in the Stochastic Block Model with Outliers

The Stochastic Block Model is a popular model for network analysis in the presence of community structure. However, in numerous examples, the assumptions underlying this classical model are put in default by the behaviour of a small number of outlier nodes such as hubs, nodes with mixed membership profiles, or corrupted nodes. In addition, real-life networks are likely to be incomplete, due to non-response or machine failures. We introduce a new algorithm to estimate the connection probabilities in a network, which is robust to both outlier nodes and missing observations. Under fairly general assumptions, this method detects the outliers, and achieves the best known error for the estimation of connection probabilities with polynomial computation cost. In addition, we prove sub-linear convergence of our algorithm. We provide a simulation study which demonstrates the good behaviour of the method in terms of outliers selection and prediction of the missing links.
The Detection of Dust Gap-ring Structure in the Outer Region of the CR Cha Protoplanetary Disk

We observe the dust continuum at 225 GHz and CO isotopologue (12CO, 13CO, and C18O) J = 2–1 emission lines toward the CR Cha protoplanetary disk using the Atacama Large Millimeter/submillimeter Array. The dust continuum image shows a dust gap-ring structure in the outer region of the dust disk. A faint dust ring is also detected around 120 au beyond the dust gap. The CO isotopologue lines indicate that the gas disk is more extended than the dust disk. The peak brightness temperature of the 13CO line shows a small bump around 130 au, while 12CO lines do not. We investigate two possible mechanisms for reproducing the observed dust gap-ring structure and a gas temperature bump. First, the observed gap structure can be opened by a Jupiter-mass planet using the relation between the planet mass and the gap depth and width. Meanwhile, the radiative transfer calculations based on the observed dust surface density profile show that the observed dust ring could be formed by dust accumulation at the gas temperature bump, that is, the gas pressure bump produced beyond the outer edge of the dust disk.
The cohomology of left-invariant elliptic involutive structures on compact Lie groups

Inspired by the work of Chevalley and Eilenberg on the de Rham cohomology on compact Lie groups, we prove that, under certain algebraic and topological conditions, the cohomology associated to left-invariant elliptic, and even hypocomplex, involutive structures on compact Lie groups can be computed by using only Lie algebras, thus reducing the analytical problem to a purely algebraic one. The main tool is the Leray spectral sequence that connects the result obtained Chevalley and Eilenberg to a result by Bott on the Dolbeault cohomology of a homogeneous manifold.
Perceive, Transform, and Act: Multi-Modal Attention Networks for Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) is a challenging task in which an agent needs to follow a language-specified path to reach a target destination. In this paper, we strive for the creation of an agent able to tackle three key issues: multi-modality, long-term dependencies, and adaptability towards different locomotive settings. To that end, we devise "Perceive, Transform, and Act" (PTA): a fully-attentive VLN architecture that leaves the recurrent approach behind and the first Transformer-like architecture incorporating three different modalities - natural language, images, and discrete actions for the agent control. In particular, we adopt an early fusion strategy to merge lingual and visual information efficiently in our encoder. We then propose to refine the decoding phase with a late fusion extension between the agent's history of actions and the perception modalities. We experimentally validate our model on two datasets and two different action settings. PTA surpasses previous state-of-the-art architectures for low-level VLN on R2R and achieves the first place for both setups in the recently proposed R4R benchmark. Our code is publicly available at this https URL.
U-CNNpred: A Universal CNN-based Predictor for Stock Markets

The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this paper, we investigate the importance of extracting such general features in stock market prediction domain and show how it can improve the performance of financial market prediction. We present a framework called U-CNNpred, that uses a CNN-based structure. A base model is trained in a specially designed layer-wise training procedure over a pool of historical data from many financial markets, in order to extract the common patterns from different markets. Our experiments, in which we have used hundreds of stocks in S\&P 500 as well as 14 famous indices around the world, show that this model can outperform baseline algorithms when predicting the directional movement of the markets for which it has been trained for. We also show that the base model can be fine-tuned for predicting new markets and achieve a better performance compared to the state of the art baseline algorithms that focus on constructing market-specific models from scratch.
To Trust, or Not to Trust? A Study of Human Bias in Automated Video Interview Assessments

Supervised systems require human labels for training. But, are humans themselves always impartial during the annotation process? We examine this question in the context of automated assessment of human behavioral tasks. Specifically, we investigate whether human ratings themselves can be trusted at their face value when scoring video-based structured interviews, and whether such ratings can impact machine learning models that use them as training data. We present preliminary empirical evidence that indicates there might be biases in such annotations, most of which are visual in nature.
An Iterative Polishing Framework based on Quality Aware Masked Language Model for Chinese Poetry Generation

Owing to its unique literal and aesthetical characteristics, automatic generation of Chinese poetry is still challenging in Artificial Intelligence, which can hardly be straightforwardly realized by end-to-end methods. In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation. In the first stage, an encoder-decoder structure is utilized to generate a poem draft. Afterwards, our proposed Quality-Aware Masked Language Model (QA-MLM) is employed to polish the draft towards higher quality in terms of linguistics and literalness. Based on a multi-task learning scheme, QA-MLM is able to determine whether polishing is needed based on the poem draft. Furthermore, QA-MLM is able to localize improper characters of the poem draft and substitute with newly predicted ones accordingly. Benefited from the masked language model structure, QA-MLM incorporates global context information into the polishing process, which can obtain more appropriate polishing results than the unidirectional sequential decoding. Moreover, the iterative polishing process will be terminated automatically when QA-MLM regards the processed poem as a qualified one. Both human and automatic evaluation have been conducted, and the results demonstrate that our approach is effective to improve the performance of encoder-decoder structure.