nils-herrmann/clean_scirepeval huggingface dataset:
Semantic Role Labeling with Associated Memory Network

Semantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a sentence, which has been in a performance improvement bottleneck after a series of latest works were presented. This paper proposes a novel syntax-agnostic SRL model enhanced by the proposed associated memory network (AMN), which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. In detail, we use sentences and their labels from train dataset as an associated memory cue to help label the target sentence. Furthermore, we compare several associated sentences selecting strategies and label merging methods in AMN to find and utilize the label of associated sentences while attending them. By leveraging the attentive memory from known training data, Our full model reaches state-of-the-art on CoNLL-2009 benchmark datasets for syntax-agnostic setting, showing a new effective research line of SRL enhancement other than exploiting external resources such as well pre-trained language models.
A Search for Variable Stars in the Globular Cluster M4 with K2

We extract light curves for 4554 objects with 9 < G < 19 in the K2 superstamp observations of the globular cluster M4, including 3784 cluster members, and search for variability. Among cluster-member objects, we detect 66 variables, of which 52 are new discoveries. Among objects not belonging to the cluster, we detect 24 variables, of which 20 are new discoveries. We additionally discover 57 cluster-member suspected variables, 10 cluster-non-member suspected variables, and four variables with ambiguous cluster membership. Our light curves reach sub-millimagnitude precision for the cluster horizontal branch, permitting us to detect asteroseismic activity in six horizontal branch stars outside the instability strip and one inside the strip but with only ∼1 mmag amplitude variability. Nineteen additional stars along the red giant branch also have detected asteroseismic variability. Several eclipsing binaries are found in the cluster, including a 4.6 day detached eclipsing binary and an EW-class eclipsing binary, as well as an EW with uncertain cluster membership and three other candidate EWs. A 22 day detached eclipsing binary is also found outside the cluster. We identify a candidate X-ray binary that is a cluster member with quiescent and periodic ∼20 mmag optical variability. We also obtain high-precision light curves for 10 of the previously known RR Lyrae variables in the cluster and identify one as a candidate Blazhko variable with a Blazhko period in excess of 78 days. We make our light curves publicly available.
Explaining Deep Neural Networks Using Spectrum-Based Fault Localization

Deep neural networks (DNNs) increasingly replace traditionally developed software in a broad range of applications. However, in stark contrast to traditional software, the blackbox nature of DNNs makes it impossible to understand their outputs, creating demand for “Explainable AI”. Explanations of the outputs of the DNN are essential for the training process and are supporting evidence of the adequacy of the DNN. In this paper, we show that spectrum-based fault localization delivers good explanations of the outputs of DNNs. We present an algorithm and a tool PROTOZOA, which synthesizes a ranking of the parts of the inputs using several spectrum-based fault localization measures. We show that the highest-ranked parts provide explanations that are consistent with the standard definitions of explanations in the literature. Our experimental results on ImageNet show that the explanations we generate are useful visual indicators for the progress of the training of the DNN. We compare the results of PROTOZOA with SHAP and show that the explanations generated by PROTOZOA are on par or superior. We also generate adversarial examples using our explanations; the efficiency of this process can serve as a proxy metric for the quality of the explanations. Our measurements show that PROTOZOA’s explanations yield a higher number of adversarial examples than those produced by SHAP.
Detecting individual internal displacements following a sudden-onset disaster using time series analysis of call detail records

We use mobile phone call detail records to estimate the resettlement times of a subset of individuals that have been previously identified to be internally displaced persons (IDPs) following a sudden-onset disaster. Four different mobility metrics - two versions of radius of gyration and two versions of entropy - are used to study the behaviour of populations during three disasters - the 2010 earthquake in Haiti, the 2015 Gorkha earthquake in Nepal, and Hurricane Matthew in Haiti in 2016. We characterise the rate at which a disrupted population resettles by the fraction of individuals who remain disrupted each week after the disaster. We find that this rate can be modelled very well as the sum of two exponential decays and observe that the resettling rate for all three disasters is similar, with half the original number of displaced persons having resettled within four to five weeks of the disaster. If the study of further disasters leads to the observation of similar exponential decay rates, then it would imply that the number of IDPs at any time can be inferred from an estimate of the initial number of IDPs immediately following the disaster. Alternatively, the method provides a way to monitor disaster resilience and compare recovery rates across disasters. The method has the advantage that no assumptions need to be made regarding the location or time of resettlement. Our results indicate that CDRs can significantly contribute to measuring and predicting displacement durations, distances, and locations of IDPs in post-disaster scenarios. We believe that information and estimates provided by specifically developed CDR analytics, coupled with field data collection and traditional survey methods, can assist the humanitarian response to natural disasters and the subsequent resettlement efforts.
Estimating the resilience to natural disasters by using call detail records to analyse the mobility of internally displaced persons

We use mobile phone call detail records to estimate the resettlement times of a subset of individuals that have been previously identified to be internally displaced persons (IDPs) following a sudden-onset disaster . Four different mobility metrics two versions of radius of gyration and two versions of entropy are used to study the behaviour of populations during three disasters the 2010 earthquake in Haiti, the 2015 Gorkha earthquake in Nepal, and Hurricane Matthew in Haiti in 2016. We characterise the rate at which a disrupted population resettles by the fraction of individuals who remain disrupted each week after the disaster. We find that this rate can be modelled very well as the sum of two exponential decays and observe that the resettling rate for all three disasters is similar, with half the original number of displaced persons having resettled within four to five weeks of the disaster. If the study of further disasters leads to the observation of similar exponential decay rates, then it would imply that the number of IDPs at any time can be inferred from an estimate of the initial number of IDPs immediately following the disaster. Alternatively, the method provides a way to monitor disaster resilience and compare recovery rates across disasters. The method has the advantage that no assumptions need to be made regarding the location or time of resettlement. We also find that metrics that are skewed by long distances, such as the radius of gyration, may not be ideal in this specific context because post-disaster disruption manifests in many individuals as a decrease in long-distance travel and an increase in short-distance travel. A metric that is skewed by long distances can therefore make it appear as though these individuals have recovered earlier than they really have, resulting in an underestimate of the number of people requiring support at a given time. Our results indicate that CDRs can significantly contribute to measuring and predicting displacement durations, distances, and locations of IDPs in post-disaster scenarios. We believe that information and estimates provided by specifically developed CDR analytics, coupled with field data collection and traditional survey ∗tracey.li@flowminder.org †veronique.lefebvre@flowminder.org ar X iv :1 90 8. 02 38 1v 1 [ ph ys ic s. so cph ] 6 A ug 2 01 9 methods, can assist the humanitarian response to natural disasters and the subsequent resettlement efforts.
Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge

Low-precision DNNs have been extensively explored in order to reduce the size of DNN models for edge devices. Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision in [5..8]-bits. However, previous studies were limited to studying posit for DNN inference only. In this paper, we propose the Cheetah framework, which supports both DNN training and inference using posits, as well as other commonly used formats. Additionally, the framework is amenable for different quantization approaches and supports mixed-precision floating point and fixed-point numerical formats. Cheetah is evaluated on three datasets: MNIST, Fashion MNIST, and CIFAR-10. Results indicate that 16-bit posits outperform 16-bit floating point in DNN training. Furthermore, performing inference with [5..8]-bit posits improves the trade-off between performance and energy-delay-product over both [5..8]-bit float and fixed-point.
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment

This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses that incentives exploration in reinforcement learning. We do so by fixing the learning algorithm used and focusing only on the impact of the different exploration bonuses in the agent's performance. We use Rainbow, the state-of-the-art algorithm for value-based agents, and focus on some of the bonuses proposed in the last few years. We consider the impact these algorithms have on performance within the popular game Montezuma's Revenge which has gathered a lot of interest from the exploration community, across the the set of seven games identified by Bellemare et al. (2016) as challenging for exploration, and easier games where exploration is not an issue. We find that, in our setting, recently developed bonuses do not provide significantly improved performance on Montezuma's Revenge or hard exploration games. We also find that existing bonus-based methods may negatively impact performance on games in which exploration is not an issue and may even perform worse than $\epsilon$-greedy exploration.
Bag of Negatives for Siamese Architectures

Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy.
Refining the Structure of Neural Networks Using Matrix Conditioning

Deep learning models have proven to be exceptionally useful in performing many machine learning tasks. However, for each new dataset, choosing an effective size and structure of the model can be a time-consuming process of trial and error. While a small network with few neurons might not be able to capture the intricacies of a given task, having too many neurons can lead to overfitting and poor generalization. Here, we propose a practical method that employs matrix conditioning to automatically design the structure of layers of a feed-forward network, by first adjusting the proportion of neurons among the layers of a network and then scaling the size of network up or down. Results on sample image and non-image datasets demonstrate that our method results in small networks with high accuracies. Finally, guided by matrix conditioning, we provide a method to effectively squeeze models that are already trained. Our techniques reduce the human cost of designing deep learning models and can also reduce training time and the expense of using neural networks for applications.
Flexibly-Structured Model for Task-Oriented Dialogues

This paper proposes a novel end-to-end architecture for task-oriented dialogue systems. It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot. The policy engine and language generation tasks are modeled jointly following that. The copy-augmented sequential decoder deals with new or unknown values in the conversation, while the multi-label decoder combined with the sequential decoder ensures the explicit assignment of values to slots. On the generation part, slot binary classifiers are used to improve performance. This architecture is scalable to real-world scenarios and is shown through an empirical evaluation to achieve state-of-the-art performance on both the Cambridge Restaurant dataset and the Stanford in-car assistant dataset.
A Logic for Dually Hemimorphic Semi-Heyting Algebras and Axiomatic Extensions

Semi-Heyting algebras were introduced by the second-named author during 1983-85 as an abstraction of Heyting algebras. The first results on these algebras, however, were published only in 2008 (see [San08]). Three years later, in [San11], he initiated the investigations into the variety DHMSH of dually hemimorphic semi-Heyting algebras obtained by expanding semi-Heyting algebras with a dually hemimorphic operation. His investigations were continued in a series of papers thereafter. He also had raised the problem of finding logics corresponding to subvarieties of DHMSH, such as the variety DMSH of De Morgan semi-Heyting algebras, and DPCSH of dually pseudocomplemented semi-Heyting algebras, as well as logics to 2, 3, and 4-valued DHMSH-matrices. In this paper, we first present a Hilbert-style axiomatization of a new implicative logic called--Dually hemimorphic semi-Heyting logic, (DHMSH, for short)-- as an expansion of semi-intuitionistic logic by the dual hemimorphism as the negation and prove that it is complete with respect to the variety DHMSH of dually hemimorphic semi-Heyting algebras as its equivalent algebraic semantics (in the sense of Abstract Algebraic Logic). Secondly, we characterize the (axiomatic) extensions of DHMSH in which the Deduction Theorem holds. Thirdly, we present several logics, extending the logic DHMSH, corresponding to several important subvarieties of the variety DHMSH, thus solving the problem mentioned earlier. We also provide new axiomatizations for Moisil's logic and the 3-valued Lukasiewicz logic.
Unitarity, stability, and loops of unstable ghosts

We present a new understanding of the unstable ghost-like resonance which appears in theories such as quadratic gravity and Lee-Wick type theories. Quantum corrections make this resonance unstable, such that it does not appear in the asymptotic spectrum. We prove that these theories are unitary to all orders. Unitarity is satisfied by the inclusion of only cuts from stable states in the unitarity sum. This removes the need to consider this as a ghost state in the unitarity sum. However, we often use a narrow-width approximation where we do include cuts through unstable states, and ignore cuts through the stable decay products. If we do this with the unstable ghost resonance at one loop, we get the correct answer only by using a contour which was originally defined by Lee and Wick. The quantum effects also provide damping in both the Feynman and the retarded propagators, leading to stability under perturbations.
Review of the Plan for Integrating Big Data Analytics Program for the Electronic Marketing System and Customer Relationship Management: A Case Study XYZ Institution

This research aims to explore business processes and what the factors have major influence on electronic marketing and CRM systems? Which data needs to be analyzed and integrated in the system, and how to do that? How effective of integration the electronic marketing and CRM with big data enabled to support Marketing and Customer Relation operations. Research based on case studies at XYZ Organization: International Language Education Service in Surabaya. Research is studying secondary data which is supported by qualitative research methods. Using purposive sampling technique with observation and interviewing several respondents who need the system integration. The documentation of interview is coded to keep confidentiality of the informant. Method of extending participation, triangulation of data sources, discussions and the adequacy of the theory are uses to validate data. Miles and Huberman models is uses to do analysis the data interview. Results of the research are expected to become a holistic approach to fully integrate the Big Data Analytics program with electronic marketing and CRM systems.
DronePick: Object Picking and Delivery Teleoperation with the Drone Controlled by a Wearable Tactile Display

We report on the teleoperation system DronePick which provides remote object picking and delivery by a human- controlled quadcopter. The main novelty of the proposed system is that the human user continuously gets the visual and haptic feedback for accurate teleoperation. DronePick consists of a quadcopter equipped with a magnetic grabber, a tactile glove with finger motion tracking sensor, hand tracking system, and the Virtual Reality (VR) application. The human operator teleoperates the quadcopter by changing the position of the hand. The proposed vibrotactile patterns representing the location of the remote object relative to the quadcopter are delivered to the glove. It helps the operator to determine when the quadcopter is right above the object. When the “pick” command is sent by clasping the hand in the glove, the quadcopter decreases its altitude and the magnetic grabber attaches the target object. The whole scenario is in parallel simulated in VR. The air flow from the quadcopter and the relative positions of VR objects help the operator to determine the exact position of the delivered object to be picked. The experiments showed that the vibrotactile patterns were recognized by the users at the high recognition rates: the average 99% recognition rate and the average 2.36s recognition time. The real-life implementation of DronePick featuring object picking and delivering to the human was developed and tested.
Significant reduction of lattice thermal conductivity in suspended graphene by charge doping

Declining the lattice thermal conductivity in graphene is essential for its thermoelectric applications. In high electron density systems, scatterings of phonons by electrons are no less than the phonon scatterings by other phonons. With the aid of first-principle calculations we examine the lattice thermal conductivity in graphene by inducing electron-phonon scattering externally. With experimentally tunable charge carrier density we find $\sim 50\%$ reduction of the lattice thermal conductivity at 200 K. The present study opens up new avenues for potential thermoelectric applications of graphene.
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations

Adversarial examples contain small perturbations that can remain imperceptible to human observers but alter the behavior of even the best performing deep learning models and yield incorrect outputs. Since their discovery, adversarial examples have drawn significant attention in machine learning: researchers try to reveal the reasons for their existence and improve the robustness of machine learning models to adversarial perturbations. The state-of-the-art defense is the computationally expensive and very time consuming adversarial training via projected gradient descent (PGD). We hypothesize that adversarial attacks exploit the open space risk of classic monotonic activation functions. This paper introduces the tent activation function with bounded open space risk and shows that tents make deep learning models more robust to adversarial attacks. We demonstrate on the MNIST dataset that a classifier with tents yields an average accuracy of 91.8% against six white-box adversarial attacks, which is more than 15 percentage points above the state of the art. On the CIFAR-10 dataset, our approach improves the average accuracy against the six white-box adversarial attacks to 73.5% from 41.8% achieved by adversarial training via PGD.
Continuous Graph Flow for Flexible Density Estimation

In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model distributions of graph-structured complex data. The model is formulated as an ordinary differential equation system with shared and reusable functions that operate over the graph structure. This leads to a new type of neural graph message passing scheme that performs continuous message passing over time. This class of models offer several advantages: (1) modeling complex graphical distributions without rigid assumptions on the distributions; (2) not limited to modeling data of fixed dimensions and can generalize probability evaluation and data generation over unseen subset of variables; (3) the underlying continuous graph message passing process is reversible and memory-efficient. We demonstrate the effectiveness of our model on two generation tasks, namely, image puzzle generation, and layout generation from scene graphs. Compared to unstructured and structured latent-space VAE models, we show that our proposed model achieves significant performance improvement (up to 400% in negative log-likelihood).
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning

We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoencoder form. For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture. In order to prevent the numerical instability of the network caused by the Laplacian sharpening introduction, we further propose a new numerically stable form of the Laplacian sharpening by incorporating the signed graphs. In addition, a new cost function which finds a latent representation and a latent affinity matrix simultaneously is devised to boost the performance of image clustering tasks. The experimental results on clustering, link prediction and visualization tasks strongly support that the proposed model is stable and outperforms various state-of-the-art algorithms.