Miracle-dz/UniFAR-FTUs huggingface dataset:
Mention-centered Graph Neural Network for Document-level Relation Extraction

Document-level relation extraction aims to discover relations between entities across a whole document. How to build the dependency of entities from different sentences in a document remains to be a great challenge. Current approaches either leverage syntactic trees to construct document-level graphs or aggregate inference information from different sentences. In this paper, we build cross-sentence dependencies by inferring compositional relations between inter-sentence mentions. Adopting aggressive linking strategy, intermediate relations are reasoned on the document-level graphs by mention convolution. We further notice the generalization problem of NA instances, which is caused by incomplete annotation and worsened by fully-connected mention pairs. An improved ranking loss is proposed to attend this problem. Experiments show the connections between different mentions are crucial to document-level relation extraction, which enables the model to extract more meaningful higher-level compositional relations.
Learning robust driving policies without online exploration

We propose a multi-time-scale predictive representation learning method to efficiently learn robust driving policies in an offline manner that generalize well to novel road geometries, and damaged and distracting lane conditions which are not covered in the offline training data. We show that our proposed representation learning method can be applied easily in an offline (batch) reinforcement learning setting demonstrating the ability to generalize well and efficiently under novel conditions compared to standard batch RL methods. Our proposed method utilizes training data collected entirely offline in the real-world which removes the need of intensive online explorations that impede applying deep reinforcement learning on real-world robot training. Various experiments were conducted in both simulator and real-world scenarios for the purpose of evaluation and analysis of our proposed claims.
Diffusion of Innovation In Competitive Markets-A Study on the Global Smartphone Diffusion

In this work, the aim is to study the diffusion of innovation of two competing products. The main focus has been to understand the effects of the competitive dynamic market on the diffusion of innovation. The global smartphone operating system sales are chosen as an example. The availability of the sales and the number of users data, as well as the predictions for the future number of users, make the smartphone diffusion a new laboratory to test the innovation of diffusion models for the competitive markets. In this work, the Bass model and its extensions which incorporate the competition between the brands are used. The diffusion of smartphones can be considered on two levels: The product level and the brand level. The diffusion of the smartphone as a category is studied by using the Bass equation (Category-level diffusion). The diffusion of each competing operating system (iOS and Android) are considered as the competition of the brands, and it is studied in the context of competitive market models (Product-level diffusion). It is shown that the effects of personal interactions play the dominant role in the diffusion process. Moreover, the volume of near future sales can be predicted by introducing appropriate dynamic market potential which helps to extrapolate the model results for the future.
To Not Miss the Forest for the Trees - A Holistic Approach for Explaining Missing Answers over Nested Data

Query-based explanations for missing answers identify which operators of a query are responsible for the failure to return a missing answer of interest. This type of explanations has proven useful, e.g., to debug complex analytical queries. Such queries are frequent in big data systems such as Apache Spark. We present a novel approach to produce query-based explanations. It is the first to support nested data and to consider operators that modify the schema and structure of the data (e.g., nesting, projections) as potential causes of missing answers. To efficiently compute explanations, we propose a heuristic algorithm that applies two novel techniques: (i) reasoning about multiple schema alternatives for a query and (ii) re-validating at each step whether an intermediate result can contribute to the missing answer. Using an implementation on Spark, we demonstrate that our approach is the first to scale to large datasets while often finding explanations that existing techniques fail to identify.
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning

Recommendation algorithms typically build models based on historical user-item interactions (e.g., clicks, likes, or ratings) to provide a personalized ranked list of items. These interactions are often distributed unevenly over different groups of items due to varying user preferences. However, we show that recommendation algorithms can inherit or even amplify this imbalanced distribution, leading to unfair recommendations to item groups. Concretely, we formalize the concepts of ranking-based statistical parity and equal opportunity as two measures of fairness in personalized ranking recommendation for item groups. Then, we empirically show that one of the most widely adopted algorithms -- Bayesian Personalized Ranking -- produces unfair recommendations, which motivates our effort to propose the novel fairness-aware personalized ranking model. The debiased model is able to improve the two proposed fairness metrics while preserving recommendation performance. Experiments on three public datasets show strong fairness improvement of the proposed model versus state-of-the-art alternatives. This is paper is an extended and reorganized version of our SIGIR 2020~\cite{zhu2020measuring} paper. In this paper, we re-frame the studied problem as `item recommendation fairness' in personalized ranking recommendation systems, and provide more details about the training process of the proposed model and details of experiment setup.
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance

With the enrichment of smartphones, driving distractions caused by phone usages have become a threat to driving safety. A promising way to mitigate driving distractions is to detect them and give real-time safety warnings. However, existing detection algorithms face two major challenges, low user acceptance caused by in-vehicle camera sensors, and uncertain accuracy of pre-trained models due to drivers individual differences. Therefore, this study proposes a domain-specific automated machine learning (AutoML) to self-learn the optimal models to detect distraction based on lane-keeping performance data. The AutoML integrates the key modeling steps into an auto-optimizable pipeline, including knowledge-based feature extraction, feature selection by recursive feature elimination (RFE), algorithm selection, and hyperparameter auto-tuning by Bayesian optimization. An AutoML method based on XGBoost, termed AutoGBM, is built as the classifier for prediction and feature ranking. The model is tested based on driving simulator experiments of three driving distractions caused by phone usage: browsing short messages, browsing long messages, and answering a phone call. The proposed AutoGBM method is found to be reliable and promising to predict phone-related driving distractions, which achieves satisfactory results prediction, with a predictive power of 80\% on group level and 90\% on individual level accuracy. Moreover, the results also evoke the fact that each distraction types and drivers require different optimized hyperparameters values, which reconfirm the necessity of utilizing AutoML to detect driving distractions. The purposed AutoGBM not only produces better performance with fewer features; but also provides data-driven insights about system design.
Pushing the limits of time beyond the Big Bang singularity: Scenarios for the branch cut universe

1Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil 2International Center for Relativistic Astrophysics Network (ICRANet), Pescara, Italy 3Universidad Nacional Autónoma de Mexico (UNAM), México City, México 4Frankfurt Institute for Advanced Studies (FIAS), J.W. von Goethe University (JWGU), Hessen, Germany 5Unversidade Federal de Santa Maria (UFSM), Santa Maria, Brazil 6Laboratório de Geociências Espaciais e Astrofísica (LaGEA), Universidade Federal do PAMPA (UNIPAMPA), Caçapava do Sul, Brazil
Tykhonov well-posedness of a heat transfer problem with unilateral constraints

We consider an elliptic boundary value problem with unilateral constraints and subdifferential boundary conditions. The problem describes the heat transfer in a domain D ⊂ ℝ d and its weak formulation is in the form of a hemivariational inequality for the temperature field, denoted by $${\cal P}$$ P . We associate to Problem $${\cal P}$$ P an optimal control problem, denoted by $${\cal Q}$$ Q . Then, using appropriate Tykhonov triples, governed by a nonlinear operator G and a convex $$\tilde K$$ K ˜ , we provide results concerning the well-posedness of problems $${\cal P}$$ P and $${\cal Q}$$ Q . Our main results are Theorems 4.2 and 5.2, together with their corollaries. Their proofs are based on arguments of compactness, lower semicontinuity and pseudomonotonicity. Moreover, we consider three relevant perturbations of the heat transfer boundary valued problem which lead to penalty versions of Problem $${\cal P}$$ P , constructed with particular choices of G and $$\tilde K$$ K ˜ . We prove that Theorems 4.2 and 5.2 as well as their corollaries can be applied in the study of these problems, in order to obtain various convergence results.
Pseudodeterministic algorithms and the structure of probabilistic time

We connect the study of pseudodeterministic algorithms to two major open problems about the structural complexity of BPTIME: proving hierarchy theorems and showing the existence of complete problems. Our main contributions can be summarised as follows. A new pseudorandom generator and its consequences. We build on techniques developed to prove hierarchy theorems for probabilistic time with advice (Fortnow and Santhanam, FOCS 2004) to construct the first unconditional pseudorandom generator of polynomial stretch computable in pseudodeterministic polynomial time (with one bit of advice) that is secure infinitely often against polynomial-time computations. As an application of this construction, we obtain new results about the complexity of generating and representing prime numbers. For instance, we show unconditionally for each ε > 0 that infinitely many primes pn have a succinct representation in the following sense: there is a fixed probabilistic polynomial time algorithm that generates pn with high probability from its succinct representation of size O(|pn|ε). This offers an exponential improvement over the running time of previous results, and shows that infinitely many primes have succinct and efficient representations. Structural results for probabilistic time from pseudodeterministic algorithms. Oliveira and Santhanam (STOC 2017) established unconditionally that there is a pseudodeterministic algorithm for the Circuit Acceptance Probability Problem (CAPP) that runs in sub-exponential time and is correct with high probability over any samplable distribution on circuits on infinitely many input lengths. We show that improving this running time or obtaining a result that holds for every large input length would imply new time hierarchy theorems for probabilistic time. In addition, we prove that a worst-case polynomial-time pseudodeterministic algorithm for CAPP would imply that BPP has complete problems. Equivalence between pseudodeterministic constructions and hierarchies. We establish an equivalence between a certain explicit pseudodeterministic construction problem and the existence of strong hierarchy theorems for probabilistic time. More precisely, we show that pseudodeterministically constructing in exponential time strings of large rKt complexity (Oliveira, ICALP 2019) is possible if and only if for every constructive function T(n) ≤ exp(o(exp(n))) we have BPTIME[poly(T)] ⊈ i.o. BPTIME[T]/logT. More generally, these results suggest new approaches for designing pseudodeterministic algorithms for search problems and for unveiling the structure of probabilistic time.
A Grid of Core-collapse Supernova Remnant Models. I. The Effect of Wind-driven Mass Loss

Massive stars can shed material via steady, line-driven winds, eruptive outflows, or mass transfer onto a binary companion. In the case of single stars, the mass is deposited by the stellar wind into the nearby environment. After the massive star explodes, the stellar ejecta interact with this circumstellar material (CSM), oftentimes resulting in bright X-ray line emission from both the shock-heated CSM and ejecta. The amount of material lost by the progenitor, the mass of ejecta, and its energetics all impact the bulk spectral characteristics of this X-ray emission. Here we present a grid of core-collapse supernova remnant models derived from models for massive stars with zero-age main-sequence masses of ∼10–30 M ☉ evolved from the pre-main-sequence stage with wind-driven mass loss. Evolution is handled by a multistage pipeline of software packages. First, we use mesa (Modules for Experiments in Stellar Astrophysics) to evolve the progenitors from pre-main-sequence to iron core collapse. We then use the Supernova Explosion Code (snec) to explode the mesa models, and we follow them for the first 100 days following core collapse. Finally, we couple the snec output, along with the CSM generated from mesa mass-loss rates, into the cosmic-ray hydrodynamics code to model the remnant phase to 7000 yr after core collapse. At the end of each stage, we compare our outputs with those found in the literature, and we examine any qualitative and quantitative differences in the bulk properties of the remnants and their spectra based on the initial progenitor mass, as well as mass-loss history.
Interfacial Tension between CO2, Freshwater, and Brine in the Range of Pressure from (2 to 27) MPa, Temperature from (20 to 125) °C, and Water Salinity from (0 to 334 000) mg·L−1

An extensive laboratory program was conducted for the measurement of the interfacial tension between CO2 and water or brine covering the ranges of (2 to 27) MPa pressure, (20 to 125) °C temperature, and (0 to 334 010) mg·L−1 water salinity. The laboratory experiments were conducted using the pendant drop method combined with the solution of the Laplace equation for capillarity for the profile of the brine drop in the CO2−brine equilibrium environment. The analysis of the resulting set of 378 IFT measurements reveals that: (1) under conditions of constant temperature and water salinity, IFT steeply decreases with increasing pressure in the range P Pc with an asymptotic trend toward a constant value at higher pressures; (2) under the same conditions of constant pressure and temperature, IFT increases with increasing water salinity, reflecting decreasing CO2 solubility in brine as salinity increases; (3) the dependence of IFT on temperature is more complex than that on eithe.
Tomlinson-Harashima Precoding with Stream Combiners for MU-MIMO with Rate-Splitting

This paper introduces multiuser multiple-input multiple-output (MU-MIMO) architectures based on non-linear precoding and stream combining techniques using rate-splitting (RS), where the transmitter often has only partial knowledge of the channel state information (CSI). In contrast to existing works, we consider deployments where the receivers may be equipped with multiple antennas. This allows us to employ linear combining techniques based on the Min-Max, the maximum ratio and the minimum mean-square error criteria along with TomlinsonHarashima precoders (THP) for RS-based MU-MIMO systems to enhance the sum-rate performance. Moreover, we incorporate the Multi-Branch (MB) concept into the RS architecture to further improve the sum-rate performance. Closed-form expressions for the signal-to-interference-plus-noise ratio and the sum-rate at the receiver end are devised through statistical analysis. Simulation results show that the proposed RS-THP schemes achieve better performance than conventional linear and THP precoders.
Impact of within-voxel heterogeneity in fibre geometry on spherical deconvolution

Axons in white matter have been shown to have varying geometries within a bundle using ex vivo imaging techniques, but what does this mean for diffusion MRI (dMRI) based spherical deconvolution (SD)? SD attempts to estimate the fibre orientation distribution function (fODF) by assuming a single dMRI fibre response function (FRF) for all white matter populations and deconvolving this FRF from the dMRI signal at each voxel to estimate the fODF. Variable fibre geometry within a bundle however suggests the FRF might not be constant even within a single voxel. We test what impact realistic fibre geometry has on SD by simulating the dMRI signal in a range of realistic white matter numerical phantoms, including synthetic phantoms and real axons segmented from electron microscopy. We demonstrate that variable fibre geometry leads to a variable FRF across axons and that in general no single FRF is effective to recover the underlying fibre orientation distribution function (fODF). This finding suggests that assuming a single FRF can lead to misestimation of the fODF, causing further downstream errors in techniques such as tractography.
Bounding the Zeros of Polynomials Using the Frobenius Companion Matrix Partitioned by the Cartesian Decomposition

In this work, some new inequalities for the numerical radius of block n-by-n matrices are presented. As an application, the bounding of zeros of polynomials using the Frobenius companion matrix partitioned by the Cartesian decomposition method is proved. We highlight several numerical examples showing that our approach to bounding zeros of polynomials could be very effective in comparison with the most famous results as well as some recent results presented in the field. Finally, observations, a discussion, and a conclusion regarding our proposed bound of zeros are considered. Namely, it is proved that our proposed bound is more efficient than any other bound under some conditions; this is supported with many polynomial examples explaining our choice of restrictions.
Performance Versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

We present the results of the comparative performance-versus-complexity analysis for the several types of artificial neural networks (NNs) used for nonlinear channel equalization in coherent optical communication systems. The comparison is carried out using an experimental set-up with the transmission dominated by the Kerr nonlinearity and component imperfections. For the first time, we investigate the application to the channel equalization of the convolution layer (CNN) in combination with a bidirectional long short-term memory (biLSTM) layer and the design combining CNN with a multi-layer perceptron. Their performance is compared with the one delivered by the previously proposed NN-based equalizers: one biLSTM layer, three-dense-layer perceptron, and the echo state network. Importantly, all architectures have been initially optimized by a Bayesian optimizer. First, we present the general expressions for the computational complexity associated with each NN type; these are given in terms of real multiplications per symbol. We demonstrate that in the experimental system considered, the convolutional layer coupled with the biLSTM (CNN+biLSTM) provides the largest Q-factor improvement compared to the reference linear chromatic dispersion compensation (2.9 dB improvement). Then, we examine the trade-off between the computational complexity and performance of all equalizers and demonstrate that the CNN+biLSTM is the best option when the computational complexity is not constrained, while when we restrict the complexity to some lower levels, the three-layer perceptron provides the best performance.
Forecasting reconciliation with a top-down alignment of independent level forecasts

Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. The overall forecasting performance is heavily aﬀected by the forecasting accuracy of intermittent time series at bottom levels. In this paper, we present a forecasting reconciliation approach that treats the bottom level forecast as latent to ensure higher forecasting accuracy on the upper levels of the hierarchy. We employ a pure deep learning forecasting approach N-BEATS for continuous time series on top levels and a widely used tree-based algo-rithm LightGBM for the bottom level intermittent time series. The hierarchical forecasting with alignment approach is simple and straightforward to implement in practice. It sheds light on an orthogonal direction for forecasting reconciliation. When there is diﬃculty ﬁnding an optimal reconciliation, allowing suboptimal forecasts at a lower level could retain a high overall performance. The approach in this empirical study was developed by the ﬁrst author during the M5 Forecasting Accuracy competition ranking second place. The approach is business orientated and could be beneﬁcial for business strategic planning.
The C18O core mass function toward Orion A: Single-dish observations

We have performed an unbiased dense core survey toward the Orion A Giant Molecular Cloud in the CO (J = 1–0) emission line taken with the Nobeyama Radio Observatory (NRO) 45m telescope. The effective angular resolution of the map is 26′′, which corresponds to ∼ 0.05 pc at a distance of 414 pc. By using the Herschel–Planck H2 column density map, we calculate the CO fractional abundance and find that it is roughly constant over the column density range of ∼ 5 × 10 22 cm−3, although a trend of CO depletion is determined toward higher column density. Therefore, CO intensity can follow the cloud structure reasonably well. The mean CO abundance in Orion A is estimated to be 5.7×10−7, which is about 3 times larger than the fiducial value. We identified 746 CO cores with astrodendro and classified 709 cores as starless cores. We compute the core masses by decomposing the Herschel–Planck dust column density using the relative proportions of the CO integrated intensities of line-of-sight components. Applying this procedure, we attempt to remove the contribution of the background emission, i.e., the ambient gas outside the cores. Then, we derived mass function for starless cores and found that it resembles the stellar initial mass function (IMF). The CMF for starless cores, dN/dM , is fitted with a power-law relation of M 1 ar X iv :2 10 3. 08 52 6v 1 [ as tr oph . G A ] 2 5 Fe b 20 21 with a power index of α=−2.25± 0.16 at the high-mass slope (∼ 0.44M ). We also found that the ratio of each core mass to the total mass integrated along the line of sight is significantly large. Therefore, in the previous studies, the core masses derived from the dust image are likely to be overestimated at least by a factor of a few. Accordingly, such previous studies may underestimate the star formation efficiency of individual cores.