nils-herrmann/clean_scirepeval huggingface dataset:
The Importance of Social and Government Learning in Ex Ante Policy Evaluation

We provide two methodological insights on \emph{ex ante} policy evaluation for macro models of economic development. First, we show that the problems of parameter instability and lack of behavioral constancy can be overcome by considering learning dynamics. Hence, instead of defining social constructs as fixed exogenous parameters, we represent them through stable functional relationships such as social norms. Second, we demonstrate how agent computing can be used for this purpose. By deploying a model of policy prioritization with endogenous government behavior, we estimate the performance of different policy regimes. We find that, while strictly adhering to policy recommendations increases efficiency, the nature of such recipes has a bigger effect. In other words, while it is true that lack of discipline is detrimental to prescription outcomes (a common defense of failed recommendations), it is more important that such prescriptions consider the systemic and adaptive nature of the policymaking process (something neglected by traditional technocratic advice).
Quantifying the coherence of development policy priorities

Over the last 30 years, the concept of policy coherence for development has received especial attention among academics, practitioners and international organizations. However, its quantification and measurement remain elusive. To address this challenge, we develop a theoretical and empirical framework to measure the coherence of policy priorities for development. Our procedure takes into account the country-specific constraints that governments face when trying to reach specific development goals. Hence, we put forward a new definition of policy coherence where context-specific efficient resource allocations are employed as the baseline to construct an index. To demonstrate the usefulness and validity of our index, we analyze the cases of Mexico, Korea and Estonia, three developing countries that, arguably, joined the OECD with the aim of coherently establishing policies that could enable a catch-up process. We find that Korea shows significant signs of policy coherence, Estonia seems to be in the process of achieving it, and Mexico has unequivocally failed. Furthermore, our results highlight the limitations of assessing coherence in terms of naive benchmark comparisons using development-indicator data. Altogether, our framework sheds new light in a promising direction to develop bespoke analytic tools to meet the 2030 agenda.
Generalized Sliced Wasserstein Distances

The Wasserstein distance and its variations, e.g., the sliced-Wasserstein (SW) distance, have recently drawn attention from the machine learning community. The SW distance, specifically, was shown to have similar properties to the Wasserstein distance, while being much simpler to compute, and is therefore used in various applications including generative modeling and general supervised/unsupervised learning. In this paper, we first clarify the mathematical connection between the SW distance and the Radon transform. We then utilize the generalized Radon transform to define a new family of distances for probability measures, which we call generalized sliced-Wasserstein (GSW) distances. We also show that, similar to the SW distance, the GSW distance can be extended to a maximum GSW (max-GSW) distance. We then provide the conditions under which GSW and max-GSW distances are indeed distances. Finally, we compare the numerical performance of the proposed distances on several generative modeling tasks, including SW flows and SW auto-encoders.
Molecular docking with Gaussian Boson Sampling

Photonic quantum devices called Gaussian Boson Samplers can be programmed to predict molecular docking configurations. Gaussian Boson Samplers are photonic quantum devices with the potential to perform intractable tasks for classical systems. As with other near-term quantum technologies, an outstanding challenge is to identify specific problems of practical interest where these devices can prove useful. Here, we show that Gaussian Boson Samplers can be used to predict molecular docking configurations, a central problem for pharmaceutical drug design. We develop an approach where the problem is reduced to finding the maximum weighted clique in a graph, and show that Gaussian Boson Samplers can be programmed to sample large-weight cliques, i.e., stable docking configurations, with high probability, even with photon losses. We also describe how outputs from the device can be used to enhance the performance of classical algorithms. To benchmark our approach, we predict the binding mode of a ligand to the tumor necrosis factor-α converting enzyme, a target linked to immune system diseases and cancer.
TF-Replicator: Distributed Machine Learning for Researchers

We describe TF-Replicator, a framework for distributed machine learning designed for DeepMind researchers and implemented as an abstraction over TensorFlow. TF-Replicator simplifies writing data-parallel and model-parallel research code. The same models can be effortlessly deployed to different cluster architectures (i.e. one or many machines containing CPUs, GPUs or TPU accelerators) using synchronous or asynchronous training regimes. To demonstrate the generality and scalability of TF-Replicator, we implement and benchmark three very different models: (1) A ResNet-50 for ImageNet classification, (2) a SN-GAN for class-conditional ImageNet image generation, and (3) a D4PG reinforcement learning agent for continuous control. Our results show strong scalability performance without demanding any distributed systems expertise of the user. The TF-Replicator programming model will be open-sourced as part of TensorFlow 2.0 (see this https URL).
Multi-level Monte Carlo Variational Inference

We propose a framework for variance reduction using the multi-level Monte Carlo (MLMC) method. The framework is naturally compatible with reparameterized gradient estimators. We also propose a novel stochastic gradient estimation method and optimization algorithm on the MLMC method, which estimates sample size per level adaptively according to the ratio of the variance and computational cost in each iteration. Furthermore, we analyzed the convergence of the gradient in stochastic gradient descent and the quality of the gradient estimator in each optimization step on the basis of the $\textit{signal-to-noise}$ ratio. Finally, we evaluated our method by comparing it with sampling-based benchmark methods in several experiments and found that our method got closer to the optimal value and reduced gradient variance more than the other methods did.
Examining the Presence of Gender Bias in Customer Reviews Using Word Embedding

Humans have entered the age of algorithms. Each minute, algorithms shape countless preferences from suggesting a product to a potential life partner. In the marketplace algorithms are trained to learn consumer preferences from customer reviews because user-generated reviews are considered the voice of customers and a valuable source of information to firms. Insights mined from reviews play an indispensable role in several business activities ranging from product recommendation, targeted advertising, promotions, segmentation etc. In this research, we question whether reviews might hold stereotypic gender bias that algorithms learn and propagate Utilizing data from millions of observations and a word embedding approach, GloVe, we show that algorithms designed to learn from human language output also learn gender bias. We also examine why such biases occur: whether the bias is caused because of a negative bias against females or a positive bias for males. We examine the impact of gender bias in reviews on choice and conclude with policy implications for female consumers, especially when they are unaware of the bias, and the ethical implications for firms.
Quantum Grothendieck rings as quantum cluster algebras

We define and construct a quantum Grothendieck ring for a certain monoidal subcategory of the category O of representations of the quantum loop algebra introduced by Hernandez–Jimbo. We use the cluster algebra structure of the Grothendieck ring of this category to define the quantum Grothendieck ring as a quantum cluster algebra. When the underlying simple Lie algebra is of type A , we prove that this quantum Grothendieck ring contains the quantum Grothendieck ring of the category of finite‐dimensional representations of the associated quantum affine algebra. In type A1 , we identify remarkable relations in this quantum Grothendieck ring.
Differentiable Grammars for Videos

This paper proposes a novel algorithm which learns a formal regular grammar from real-world continuous data, such as videos. Learning latent terminals, non-terminals, and production rules directly from continuous data allows the construction of a generative model capturing sequential structures with multiple possibilities. Our model is fully differentiable, and provides easily interpretable results which are important in order to understand the learned structures. It outperforms the state-of-the-art on several challenging datasets and is more accurate for forecasting future activities in videos. We plan to open-source the
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions

Cross-lingual word embeddings (CLEs) facilitate cross-lingual transfer of NLP models. Despite their ubiquitous downstream usage, increasingly popular projection-based CLE models are almost exclusively evaluated on bilingual lexicon induction (BLI). Even the BLI evaluations vary greatly, hindering our ability to correctly interpret performance and properties of different CLE models. In this work, we take the first step towards a comprehensive evaluation of CLE models: we thoroughly evaluate both supervised and unsupervised CLE models, for a large number of language pairs, on BLI and three downstream tasks, providing new insights concerning the ability of cutting-edge CLE models to support cross-lingual NLP. We empirically demonstrate that the performance of CLE models largely depends on the task at hand and that optimizing CLE models for BLI may hurt downstream performance. We indicate the most robust supervised and unsupervised CLE models and emphasize the need to reassess simple baselines, which still display competitive performance across the board. We hope our work catalyzes further research on CLE evaluation and model analysis.
Generating Entangled Inflationary Quantum States

Entangled inflationary quantum states offer an interesting alternative to the standard Bunch-Davies vacuum. While they can be viewed from the point of view of a theory of effective initial states, it has been unclear exactly how such states might be generated. Using a model involving two scalar fields $\Phi,\Sigma$ we show that if {\em both} fields have non-zero time dependent expectation values $\phi,\sigma$ respectively, and if the interaction potential $V(\Phi,\Sigma)$ when evaluated at these expectation values has $\partial_{\phi}\partial_{\sigma}V(\phi, \sigma)\neq 0$, this can serve as a source term for the entanglement kernel describing the state within the Gaussian approximation around the expectation values. We also show how cubic interactions between the metric perturbation $\zeta$ and a scalar $\Sigma$ can be used to generate a source term for entanglement.
A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics

We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.
Dynamical Analysis of an n−H−T Cosmological Quintessence Real Gas Model with a General Equation of State

The cosmological dynamics of a quintessence model based on real gas with general equation of state is presented within the framework of a three-dimensional dynamical system describing the time evolution of the number density, the Hubble parameter and the temperature. Two global first integrals are found and examples for gas with virial expansion and van der Waals gas are presented. The van der Waals system is completely integrable. In addition to the unbounded trajectories, stemming from the presence of the conserved quantities, stable periodic solutions (closed orbits) also exist under certain conditions and these represent models of a cyclic Universe. The cyclic solutions exhibit regions characterized by inflation and deflation, while the open trajectories are characterized by inflation in a “fly-by” near an unstable critical point.
Neutrino Induced Neutral Current Coherent $\pi^0$ Production in The NOvA Near Detector

The NOvA experiment is a long-baseline neutrino oscillation experiment designed to measure the rate of electron neutrinos appearance in a muon neutrino beam. It consists of two finely segmented, liquid scintillator detectors at 14 mrad off-axis in the NuMI beam. The NOvA Near Detector, located at Fermilab, provides an excellent opportunity to study neutrino-nucleus interactions which are important for neutrino oscillation measurements. This presentation will present one of the first such measurements from NOvA: neutrino-induced coherent-$\pi^0$ production. Neutrinos can coherently interact with the target nucleus via neutral current exchange and produce a single, forward $\pi^0$ , which makes background to the $\nu_e$ appearance measurement. This analysis aims to measure the coherent-$\pi^0$ kinematics and cross-section and compare to model predictions, and thusly also provide a data constraint on $\pi^0$ production in the neutral current resonance and deep-inelastic interaction.
The spatially conscious machine learning model

Successfully predicting gentrification could have many social and commercial applications; however, real estate sales are difficult to predict because they belong to a chaotic system comprised of intrinsic and extrinsic characteristics, perceived value, and market speculation. Using New York City real estate as our subject, we combine modern techniques of data science and machine learning with traditional spatial analysis to create robust real estate prediction models for both classification and regression tasks. We compare several cutting edge machine learning algorithms across spatial, semispatial, and nonspatial feature engineering techniques, and we empirically show that spatially conscious machine learning models outperform nonspatial models when married with advanced prediction techniques such as Random Forests, generalized linear models, gradient boosting machines, and artificial neural networks.
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection

Local outlier factor (LOF) is a state-of-the-art unsupervised machine learning model for anomaly detection in the Internet of Things (IoT). In recent years, there have been many important applications of LOF in large IoT systems, such as wireless transmission device system in high-speed trains, smart city vehicular social networks, industrial conditional monitoring systems, and network infrastructures for cyber security. LOF aims to forecast the imminent faults of an appliance in the IoT system, whose predictive performance greatly depends on the selection of its hyperparameters. The major challenge of hyperparameter tuning for unsupervised machine learning models including LOF in the context of IoT systems is that the incoming data may contain new types of anomaly that are unseen in the training data. In this paper, we propose a novel, heuristic methodology to tune the hyperparameters in LOF that accounts for this challenge. A tuned LOF model that uses the proposed method shows good predictive performance in both simulations and real data applications.
Observation of two excited B$^+_\mathrm{c}$ states and measurement of the B$^+_\mathrm{c}$(2S) mass in pp collisions at $\sqrt{s} =$ 13 TeV

Signals consistent with the Bc(2S) and Bc(2S) states are observed in proton-proton collisions at √ s = 13 TeV, in an event sample corresponding to an integrated luminosity of 143 fb−1, collected by the CMS experiment during the 2015–2018 LHC running periods. These excited bc states are observed in the Bc π+π− invariant mass spectrum, with the ground state Bc reconstructed through its decay to J/ψ π+. The two states are reconstructed as two well-resolved peaks, separated in mass by 29.1 ± 1.5 (stat) ± 0.7 (syst) MeV. The observation of two peaks, rather than one, is established with a significance exceeding five standard deviations. The mass of the Bc(2S) meson is measured to be 6871.0± 1.2 (stat)± 0.8 (syst)± 0.8 (Bc)MeV, where the last term corresponds to the uncertainty in the world-average Bc mass.  CC-BY-4.0 license ∗See Appendix A for the list of collaboration members ar X iv :1 90 2. 00 57 1v 3 [ he pex ] 3 A pr 2 01 9
Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog

This paper presents a new model for visual dialog, Recurrent Dual Attention Network (ReDAN), using multi-step reasoning to answer a series of questions about an image. In each question-answering turn of a dialog, ReDAN infers the answer progressively through multiple reasoning steps. In each step of the reasoning process, the semantic representation of the question is updated based on the image and the previous dialog history, and the recurrently-refined representation is used for further reasoning in the subsequent step. On the VisDial v1.0 dataset, the proposed ReDAN model achieves a new state-of-the-art of 64.47% NDCG score. Visualization on the reasoning process further demonstrates that ReDAN can locate context-relevant visual and textual clues via iterative refinement, which can lead to the correct answer step-by-step.
Incremental Techniques for Large-Scale Dynamic Query Processing

Many applications from various disciplines are now required to analyze fast evolving big data in real time. Various approaches for incremental processing of queries have been proposed over the years. Traditional approaches rely on updating the results of a query when updates are streamed rather than re-computing these queries, and therefore, higher execution performance is expected. However, they do not perform well for large databases that are updated at high frequencies. Therefore, new algorithms and approaches have been proposed in the literature to address these challenges by, for instance, reducing the complexity of processing updates. Moreover, many of these algorithms are now leveraging distributed streaming platforms such as Spark Streaming and Flink. In this tutorial, we briefly discuss legacy approaches for incremental query processing, and then give an overview of the new challenges introduced due to processing big data streams. We then discuss in detail the recently proposed algorithms that address some of these challenges. We emphasize the characteristics and algorithmic analysis of various proposed approaches and conclude by discussing future research directions.
An end-to-end Generative Retrieval Method for Sponsored Search Engine -Decoding Efficiently into a Closed Target Domain

In this paper, we present a generative retrieval method for sponsored search engine, which uses neural machine translation (NMT) to generate keywords directly from query. This method is completely end-to-end, which skips query rewriting and relevance judging phases in traditional retrieval systems. Different from standard machine translation, the target space in the retrieval setting is a constrained closed set, where only committed keywords should be generated. We present a Trie-based pruning technique in beam search to address this problem. The biggest challenge in deploying this method into a real industrial environment is the latency impact of running the decoder. Self-normalized training coupled with Trie-based dynamic pruning dramatically reduces the inference time, yielding a speedup of more than 20 times. We also devise an mixed online-offline serving architecture to reduce the latency and CPU consumption. To encourage the NMT to generate new keywords uncovered by the existing system, training data is carefully selected. This model has been successfully applied in Baidu's commercial search engine as a supplementary retrieval branch, which has brought a remarkable revenue improvement of more than 10 percents.
Measurement of the neutron capture cross section on argon

The use of argon as a detection and shielding medium for neutrino and dark matter experiments has made the precise knowledge of the cross section for neutron capture on argon an important design and operational parameter. Since previous measurements were averaged over thermal spectra and have significant disagreements, a differential measurement has been performed using a Time-Of-Flight neutron beam and a $\sim$4$\pi$ gamma spectrometer. A fit to the differential cross section from $0.015-0.15$\,eV, assuming a $1/v$ energy dependence, yields $\sigma^{2200} = 673 \pm 26 \text{ (stat.)} \pm 59 \text{ (sys.)}$\,mb.
Understanding Composition of Word Embeddings via Tensor Decomposition

Word embedding is a powerful tool in natural language processing. In this paper we consider the problem of word embedding composition \--- given vector representations of two words, compute a vector for the entire phrase. We give a generative model that can capture specific syntactic relations between words. Under our model, we prove that the correlations between three words (measured by their PMI) form a tensor that has an approximate low rank Tucker decomposition. The result of the Tucker decomposition gives the word embeddings as well as a core tensor, which can be used to produce better compositions of the word embeddings. We also complement our theoretical results with experiments that verify our assumptions, and demonstrate the effectiveness of the new composition method.