nils-herrmann/clean_scirepeval huggingface dataset:
The MicroBooNE continuous readout stream for detection of supernova neutrinos

Since the original detection of core-collapse supernova neutrinos in 1987, all large neutrino experiments seek to detect the neutrinos from the next nearby supernova. Among them, liquid argon time projection chambers (LArTPCs) offer a unique sensitivity to the electron neutrino flux of a supernova. However, the low energy of these events (scale of MeVs), and the fact that all large (multi-tonne) LArTPCs operating at the moment are located near the Earth’s surface, and are therefore subject to an intense cosmic ray flux, makes triggering on the supernova neutrinos very challenging. Instead, MicroBooNE has pioneered a novel approach for detecting supernova neutrinos based on a continuous readout stream and a delayed trigger generated by other neutrino detectors (the Supernova Early Warning System, or SNEWS). MicroBooNE’s data is stored temporarily for a few days, awaiting a SNEWS alert to prompt the permanent recording of the data. In order to cope with the large data rates produced by the continuous readout of the TPC and the PMT systems of MicroBooNE, FPGA-based zero-suppression algorithms have been developed.
Hadron resonance gas with repulsive mean field interaction: Thermodynamics and transport properties

We discuss the interacting hadron resonance gas model to describe the thermodynamics of hadronic matter. While the attractive interaction between hadrons is taken care of by including all the resonances with zero width, the repulsive interactions are included by considering density-dependent mean field potentials. The bulk thermodynamic quantities are confronted with the lattice quantum chromodynamics simulation results at zero as well as at finite baryon chemical potential. We further estimate the shear and bulk viscosity coefficients of hot and dense hadronic matter within the ambit of this interacting hadron resonance gas model.
SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering

Sentiment analysis has various application scenarios in software engineering (SE), such as detecting developers' emotions in commit messages and identifying their opinions on Q&A forums. However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason. Then, researchers have to utilize labeled SE-related texts to customize sentiment analysis for SE tasks via a variety of algorithms. However, the scarce labeled data can cover only very limited expressions and thus cannot guarantee the analysis quality. To address such a problem, we turn to the easily available emoji usage data for help. More specifically, we employ emotional emojis as noisy labels of sentiments and propose a representation learning approach that uses both Tweets and GitHub posts containing emojis to learn sentiment-aware representations for SE-related texts. These emoji-labeled posts can not only supply the technical jargon, but also incorporate more general sentiment patterns shared across domains. They as well as labeled data are used to learn the final sentiment classifier. Compared to the existing sentiment analysis methods used in SE, the proposed approach can achieve significant improvement on representative benchmark datasets. By further contrast experiments, we find that the Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource, but try to transform knowledge from the open domain through ubiquitous signals such as emojis.
An Item Recommendation Approach by Fusing Images based on Neural Networks

There are rich formats of information in the network, such as rating, text, image, and so on, which represent different aspects of user preferences. In the field of recommendation, how to use those data effectively has become a difficult subject. With the rapid development of neural network, researching on multi-modal method for recommendation has become one of the major directions. In the existing recommender systems, numerical rating, item description and review are main information to be considered by researchers. However, the characteristics of the item may affect the user's preferences, which are rarely used for recommendation models. In this work, we propose a novel model to incorporate visual factors into predictors of people's preferences, namely MF-VMLP, based on the recent developments of neural collaborative filtering (NCF). Our experiments conduct Amazon's public dataset for experimental validation and root mean square error (RMSE) as evaluation metrics. To some extent, experimental result on a real-world dataset demonstrates that our model can boost the recommendation performance.
Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation

Graph Neural Networks (GNNs) are powerful for the representation learning of graph-structured data. Most of the GNNs use a message-passing scheme, where the embedding of a node is iteratively updated by aggregating the information from its neighbors. To achieve a better expressive capability of node influences, attention mechanism has grown to be popular to assign trainable weights to the nodes in aggregation. Though the attention-based GNNs have achieved remarkable results in various tasks, a clear understanding of their discriminative capacities is missing. In this work, we present a theoretical analysis of the representational properties of the GNN that adopts the attention mechanism as an aggregator. Our analysis determines all cases when those attention-based GNNs can always fail to distinguish certain distinct structures. Those cases appear due to the ignorance of cardinality information in attention-based aggregation. To improve the performance of attention-based GNNs, we propose cardinality preserved attention (CPA) models that can be applied to any kind of attention mechanisms. Our experiments on node and graph classification confirm our theoretical analysis and show the competitive performance of our CPA models. The code is available online: https://github.com/zetayue/CPA.
An External Knowledge Enhanced Multi-label Charge Prediction Approach with Label Number Learning

Multi-label charge prediction is a task to predict the corresponding accusations for legal cases, and recently becomes a hot topic. However, current studies use rough methods to deal with the label number. These methods manually set parameters to select label numbers, which has an effect in final prediction quality. We propose an external knowledge enhanced multi-label charge prediction approach that has two phases. One is charge label prediction phase with external knowledge from law provisions, the other one is number learning phase with a number learning network (NLN) designed. Our approach enhanced by external knowledge can automatically adjust the threshold to get label number of law cases. It combines the output probabilities of samples and their corresponding label numbers to get final prediction results. In experiments, our approach is connected to some state of-the art deep learning models. By testing on the biggest published Chinese law dataset, we find that our approach has improvements on these models. We future conduct experiments on multi-label samples from the dataset. In items of macro-F1, the improvement of baselines with our approach is 3%-5%; In items of micro-F1, the significant improvement of our approach is 5%-15%. The experiment results show the effectiveness our approach for multi-label charge prediction.
Online Mixed-Integer Optimization in Milliseconds

We propose a method to approximate the solution of online mixed-integer optimization (MIO) problems at very high speed using machine learning. By exploiting the repetitive nature of online optimization, we can greatly speed up the solution time. Our approach encodes the optimal solution into a small amount of information denoted as strategy using the voice of optimization framework. In this way, the core part of the optimization routine becomes a multiclass classification problem that can be solved very quickly. In this work, we extend that framework to real-time and high-speed applications focusing on parametric mixed-integer quadratic optimization. We propose an extremely fast online optimization method consisting of a feedforward neural network evaluation and a linear system solution where the matrix has already been factorized. Therefore, this online approach does not require any solver or iterative algorithm. We show the speed of the proposed method both in terms of total computations required and measured execution time. We estimate the number of floating point operations required to completely recover the optimal solution as a function of the problem dimensions. Compared with state-of-the-art MIO routines, the online running time of our method is very predictable and can be lower than a single matrix factorization time. We benchmark our method against the state-of-the-art solver Gurobi obtaining up to two to three orders of magnitude speedups on examples from fuel cell energy management, sparse portfolio optimization, and motion planning with obstacle avoidance.  We propose a technique to approximate the solution of online optimization problems at high speed using machine learning. By exploiting the repetitive nature of online optimization, we learn the mapping between the key problem parameters and an encoding of the optimal solution to greatly speed up the solution time. This allows us to significantly improve the computation time and resources needed to solve online mixed-integer optimization problems. We obtain a simple method with a very low computing time variance, which is crucial in online settings.
Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches

Even though it is well known that for most relevant computational problems different algorithms may perform better on different classes of problem instances, most researchers still focus on determining a single best algorithmic configuration based on aggregate results such as the average. In this paper, we propose Integer Programming based approaches to build decision trees for the Algorithm Selection Problem. These techniques allow automate three crucial decisions: (i) discerning the most important problem features to determine problem classes; (ii) grouping the problems into classes and (iii) select the best algorithm configuration for each class. To evaluate this new approach, extensive computational experiments were executed using the linear programming algorithms implemented in the COIN-OR Branch & Cut solver across a comprehensive set of instances, including all MIPLIB benchmark instances. The results exceeded our expectations. While selecting the single best parameter setting across all instances decreased the total running time by 22%, our approach decreased the total running time by 40% on average across 10-fold cross validation experiments. These results indicate that our method generalizes quite well and does not overfit.
Neural Networks, Hypersurfaces, and Radon Transforms

Connections between integration along hypersufaces, Radon transforms, and neural networks are exploited to highlight an integral geometric mathematical interpretation of neural networks. By analyzing the properties of neural networks as operators on probability distributions for observed data, we show that the distribution of outputs for any node in a neural network can be interpreted as a nonlinear projection along hypersurfaces defined by level surfaces over the input data space. We utilize these descriptions to provide new interpretation for phenomena such as nonlinearity, pooling, activation functions, and adversarial examples in neural network-based learning problems.
Tracking Temporal Evolution of Graphs using Non-Timestamped Data

Datasets to study the temporal evolution of graphs are scarce. To encourage the research of novel dynamic graph learning algorithms we introduce YoutubeGraph-Dyn (available at https://github.com/palash1992/YoutubeGraph-Dyn), an evolving graph dataset generated from YouTube real-world interactions. YoutubeGraph-Dyn provides intra-day time granularity (with 416 snapshots taken every 6 hours for a period of 104 days), multi-modal relationships that capture different aspects of the data, multiple attributes including timestamped, non-timestamped, word embeddings, and integers. Our data collection methodology emphasizes the creation of time evolving graphs from non-timestamped data. In this paper, we provide various graph statistics of YoutubeGraph-Dyn and test state-of-the-art graph clustering algorithms to detect community migration, and time series analysis and recurrent neural network algorithms to forecast non-timestamped data.
Graph-based Knowledge Distillation by Multi-head Attention Network

Knowledge distillation (KD) is a technique to derive optimal performance from a small student network (SN) by distilling knowledge of a large teacher network (TN) and transferring the distilled knowledge to the small SN. Since a role of convolutional neural network (CNN) in KD is to embed a dataset so as to perform a given task well, it is very important to acquire knowledge that considers intra-data relations. Conventional KD methods have concentrated on distilling knowledge in data units. To our knowledge, any KD methods for distilling information in dataset units have not yet been proposed. Therefore, this paper proposes a novel method that enables distillation of dataset-based knowledge from the TN using an attention network. The knowledge of the embedding procedure of the TN is distilled to graph by multi-head attention (MHA), and multi-task learning is performed to give relational inductive bias to the SN. The MHA can provide clear information about the source dataset, which can greatly improves the performance of the SN. Experimental results show that the proposed method is 7.05% higher than the SN alone for CIFAR100, which is 2.46% higher than the state-of-the-art.
Toward fairness in AI for people with disabilities SBG@a research roadmap

AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.
Testing Fifth Forces from the Galactic Dark Matter

Is there an unknown long-range force between dark matter (DM) and ordinary matters? When such a fifth force exists and in the case that it is ignored, the equivalence principle (EP) is violated apparently. The violation of EP was severely constrained by, for examples, the Eöt–Wash laboratory experiments, the lunar laser ranging, the MICROSCOPE satellite, and the long-term observation of binary pulsars. We discuss a recent bound that comes from PSR J1713+0747. When it is combined with the other bounds, a compelling limit on the hypothetical fifth force is derived. For the neutral hydrogen, the strength of such a fifth force should not exceed 1 % of the gravity.
Dimensional Reweighting Graph Convolutional Networks

Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs. Though there exist various developments on sampling and aggregation to accelerate the training process and improve the performances, limited works focus on dealing with the dimensional information imbalance of node representations. To bridge the gap, we propose a method named Dimensional reweighting Graph Convolution Network (DrGCN). We theoretically prove that our DrGCN can guarantee to improve the stability of GCNs via mean field theory. Our dimensional reweighting method is very flexible and can be easily combined with most sampling and aggregation techniques for GCNs. Experimental results demonstrate its superior performances on several challenging transductive and inductive node classification benchmark datasets. Our DrGCN also outperforms existing models on an industrial-sized Alibaba recommendation dataset.
Fair Kernel Regression via Fair Feature Embedding in Kernel Space

In recent years, there have been significant efforts on mitigating unethical demographic biases in machine learning methods. However, very little work is done for kernel methods. In this paper, we propose a novel fair kernel regression method via fair feature embedding (FKR-F^2E) in kernel space. Motivated by prior works feature processing for fair learning and feature selection for kernel methods, we propose to learn fair feature embeddings in kernel space, where the demographic discrepancy of feature distributions is minimized. Through experiments on three public real-world data sets, we show the proposed FKR-F^2E achieves significantly lower prediction disparity compared with the state-of-the-art fair kernel regression method and several other baseline methods.
Searching for Apparel Products from Images in the Wild

In this age of social media, people often look at what others are wearing. In particular, Instagram and Twitter influencers often provide images of themselves wearing different outfits and their followers are often inspired to buy similar clothes.We propose a system to automatically find the closest visually similar clothes in the online Catalog (street-to-shop searching). The problem is challenging since the original images are taken under different pose and lighting conditions. The system initially localizes high-level descriptive regions (top, bottom, wristwear. . . ) using multiple CNN detectors such as YOLO and SSD that are trained specifically for apparel domain. It then classifies these regions into more specific regions such as t-shirts, tunic or dresses. Finally, a feature embedding learned using a multi-task function is recovered for every item and then compared with corresponding items in the online Catalog database and ranked according to distance. We validate our approach component-wise using benchmark datasets and end-to-end using human evaluation.
FPCNet: Fast Pavement Crack Detection Network Based on Encoder-Decoder Architecture

Timely, accurate and automatic detection of pavement cracks is necessary for making cost-effective decisions concerning road maintenance. Conventional crack detection algorithms focus on the design of single or multiple crack features and classifiers. However, complicated topological structures, varying degrees of damage and oil stains make the design of crack features difficult. In addition, the contextual information around a crack is not investigated extensively in the design process. Accordingly, these design features have limited discriminative adaptability and cannot fuse effectively with the classifiers. To solve these problems, this paper proposes a deep learning network for pavement crack detection. Using the Encoder-Decoder structure, crack characteristics with multiple contexts are automatically learned, and end-to-end crack detection is achieved. Specifically, we first propose the Multi-Dilation (MD) module, which can synthesize the crack features of multiple context sizes via dilated convolution with multiple rates. The crack MD features obtained in this module can describe cracks of different widths and topologies. Next, we propose the SE-Upsampling (SEU) module, which uses the Squeeze-and-Excitation learning operation to optimize the MD features. Finally, the above two modules are integrated to develop the fast crack detection network, namely, FPCNet. This network continuously optimizes the MD features step-by-step to realize fast pixel-level crack detection. Experiments are conducted on challenging public CFD datasets and G45 crack datasets involving various crack types under different shooting conditions. The distinct performance and speed improvements over all the datasets demonstrate that the proposed method outperforms other state-of-the-art crack detection methods.
Lepton Flavor Universality tests through angular observables of $\overline{B}\to D^{(\ast)}\ell\overline{\nu}$ decay modes

We discuss the possibility of using the observables deduced from the angular distribution of the $B\to D^{(\ast)} \ell\bar{\nu}$ decays to test the effects of lepton flavor universality violation (LFUV). We show that the measurement of even a subset of these observables could be very helpful in distinguishing the Lorentz structure of the New Physics contributions to these decays. To do so we use the low energy effective theory in which besides the Standard Model contribution we add all possible Lorentz structures with the couplings (Wilson coefficients) that are determined by matching theory with the measured ratios $R{(D^{(\ast)})}^\mathrm{exp}$. We argue that even in the situation in which the measured $R{(D^{(\ast)})}^\mathrm{exp}$ becomes fully compatible with the Standard Model, one can still have significant New Physics contributions the size of which could be probed by measuring the observables discussed in this paper and comparing them with their Standard Model predictions.
Supervised Symbolic Music Style Translation Using Synthetic Data

Research on style transfer and domain translation has clearly demonstrated the ability of deep learning-based algorithms to manipulate images in terms of artistic style. More recently, several attempts have been made to extend such approaches to music (both symbolic and audio) in order to enable transforming musical style in a similar manner. In this study, we focus on symbolic music with the goal of altering the 'style' of a piece while keeping its original 'content'. As opposed to the current methods, which are inherently restricted to be unsupervised due to the lack of 'aligned' data (i.e. the same musical piece played in multiple styles), we develop the first fully supervised algorithm for this task. At the core of our approach lies a synthetic data generation scheme which allows us to produce virtually unlimited amounts of aligned data, and hence avoid the above issue. In view of this data generation scheme, we propose an encoder-decoder model for translating symbolic music accompaniments between a number of different styles. Our experiments show that our models, although trained entirely on synthetic data, are capable of producing musically meaningful accompaniments even for real (non-synthetic) MIDI recordings.
Conservation laws and stability of higher derivative extended Chern-Simons

The higher derivative field theories are notorious for the stability problems both at classical and quantum level. Classical instability is connected with unboundedness of the canonical energy, while the unbounded energy spectrum leads to the quantum instability. For a wide class of higher derivative theories, including the extended Chern-Simons, other bounded conserved quantities which provide the stability can exist. The most general gauge invariant extended Chern-Simons theory of arbitrary finite order n admits (n − 1)-parameter series of conserved energy-momentum tensors. If the 00-component of the most general representative of this series is bounded, the theory is stable. The stability condition requires from the free extended Chern-Simons theory to describe the unitary reducible representation of the Poincaré group. The unstable theory corresponds to nonunitary representation.