nils-herrmann/clean_scirepeval huggingface dataset:
Transformer for 3D Point Clouds

Deep neural networks are widely used for understanding 3D point clouds. At each point convolution layer, features are computed from local neighbourhoods of 3D points and combined for subsequent processing in order to extract semantic information. Existing methods adopt the same individual point neighborhoods throughout the network layers, defined by the same metric on the fixed input point coordinates. This common practice is easy to implement but not necessarily optimal. Ideally, local neighborhoods should be different at different layers, as more latent information is extracted at deeper layers. We propose a novel end-to-end approach to learn different non-rigid transformations of the input point cloud so that optimal local neighborhoods can be adopted at each layer. We propose both linear (affine) and non-linear (projective and deformable) spatial transformers for 3D point clouds. With spatial transformers on the ShapeNet part segmentation dataset, the network achieves higher accuracy for all categories, with 8 percent gain on earphones and rockets in particular. Our method also outperforms the state-of-the-art on other point cloud tasks such as classification, detection, and semantic segmentation. Visualizations show that spatial transformers can learn features more efficiently by dynamically altering local neighborhoods according to the geometry and semantics of 3D shapes in spite of their within-category variations.
Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess

Machine learning plays an increasing role in intelligent tutoring systems as both the amount of data available and specialization among students grow. Nowadays, these systems are frequently deployed on mobile applications. Users on such mobile education platforms are dynamic, frequently being added, accessing the application with varying levels of focus, and changing while using the service. The education material itself, on the other hand, is often static and is an exhaustible resource whose use in tasks such as problem recommendation must be optimized. The ability to update user models with respect to educational material in real-time is thus essential; however, existing approaches require time-consuming re-training of user features whenever new data is added. In this paper, we introduce a neural pedagogical agent for real-time user modeling in the task of predicting user response correctness, a central task for mobile education applications. Our model, inspired by work in natural language processing on sequence modeling and machine translation, updates user features in real-time via bidirectional recurrent neural networks with an attention mechanism over embedded question-response pairs. We experiment on the mobile education application SantaTOEIC, which has 559k users, 66M response data points as well as a set of 10k study problems each expert-annotated with topic tags and gathered since 2016. Our model outperforms existing approaches over several metrics in predicting user response correctness, notably out-performing other methods on new users without large question-response histories. Additionally, our attention mechanism and annotated tag set allow us to create an interpretable education platform, with a smart review system that addresses the aforementioned issue of varied user attention and problem exhaustion.
Towards Empathic Deep Q-Learning

As reinforcement learning (RL) scales to solve increasingly complex tasks, interest continues to grow in the fields of AI safety and machine ethics. As a contribution to these fields, this paper introduces an extension to Deep Q-Networks (DQNs), called Empathic DQN, that is loosely inspired both by empathy and the golden rule ("Do unto others as you would have them do unto you"). Empathic DQN aims to help mitigate negative side effects to other agents resulting from myopic goal-directed behavior. We assume a setting where a learning agent coexists with other independent agents (who receive unknown rewards), where some types of reward (e.g. negative rewards from physical harm) may generalize across agents. Empathic DQN combines the typical (self-centered) value with the estimated value of other agents, by imagining (by its own standards) the value of it being in the other's situation (by considering constructed states where both agents are swapped). Proof-of-concept results in two gridworld environments highlight the approach's potential to decrease collateral harms. While extending Empathic DQN to complex environments is non-trivial, we believe that this first step highlights the potential of bridge-work between machine ethics and RL to contribute useful priors for norm-abiding RL agents.
Interpretable Question Answering on Knowledge Bases and Text

Interpretability of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination of knowledge bases (KB) and text documents. We adapt post hoc explanation methods such as LIME and input perturbation (IP) and compare them with the self-explanatory attention mechanism of the model. For this purpose, we propose an automatic evaluation paradigm for explanation methods in the context of QA. We also conduct a study with human annotators to evaluate whether explanations help them identify better QA models. Our results suggest that IP provides better explanations than LIME or attention, according to both automatic and human evaluation. We obtain the same ranking of methods in both experiments, which supports the validity of our automatic evaluation paradigm.
A simple approach to dual representations of systemic risk measures

We describe a general approach to obtain dual representations for systemic risk measures of the "allocate first, then aggregate"-type, which have recently received significant attention in the literature. Our method is based on the possibility to express this type of multivariate risk measures as special cases of risk measures with multiple eligible assets. This allows us to apply standard Fenchel-Moreau techniques to tackle duality also for systemic risk measures. The same approach can be also successfully employed to obtain an elementary proof of the dual representation of "first aggregate, then allocate"-type systemic risk measures. As a final application, we apply our results to derive a simple proof of the dual representation of univariate utility-based risk measures.
Neural-network based general method for statistical mechanics on sparse systems

We propose a general method for solving statistical mechanics problems defined on sparse graphs, such as random graphs, real-world networks, and low-dimensional lattices. Our approach extract a small feedback vertex set of the sparse graph, converting the sparse system to a strongly correlated system with many-body and dense interactions on the feedback set, then solve it using variational method based on neural networks to estimate free energy, observables, and generate unbiased samples via direct sampling. Extensive experiments show that our approach is more accurate than existing approaches for sparse spin glass systems. On random graphs and real-world networks, our approach significantly outperforms the standard methods for sparse systems such as belief-propagation; on structured sparse systems such as two-dimensional lattices our approach is significantly faster and more accurate than recently proposed variational autoregressive networks using convolution neural networks.
Direct Quantification of Water Surface Charge by Phase-Sensitive Second Harmonic Spectroscopy.

We develop and verify a phase-sensitive second harmonic generation spectroscopic scheme that allows for direct determination of the absolute surface charge density and surface potential of a water interface without need of prior interfacial information. The method relies on selective probing of surface-field-induced reorientation order of water molecules in the electrical double layer and is, hence, independent of the interfacial molecular bonding structure. Application of this technique to a mixed surfactant monolayer on water suggests the manifest effect of the chain-chain interactions among the monolayer on adsorption of soluble ionic surfactants. We also deduce the third-order nonlinear susceptibility of bulk water and prove its applicability to analysis of charges of various water interfaces. In addition, we show that the Debye-Hückle theory should be avoided in the spectroscopic analysis for its potential significant error, as evidenced experimentally and theoretically.
End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving

3D semantic scene labeling is a fundamental task for Autonomous Driving. Recent work shows the capability of Deep Neural Networks in labeling 3D point sets provided by sensors like LiDAR, and Radar. Imbalanced distribution of classes in the dataset is one of the challenges that face 3D semantic scene labeling task. This leads to misclassifying for the non-dominant classes which suffer from two main problems: a) rare appearance in the dataset, and b) few sensor points reflected from one object of these classes. This paper proposes a Weighted Self-Incremental Transfer Learning as a generalized methodology that solves the imbalanced training dataset problems. It re-weights the components of the loss function computed from individual classes based on their frequencies in the training dataset, and applies Self-Incremental Transfer Learning by running the Neural Network model on non-dominant classes first, then dominant classes one-by-one are added. The experimental results introduce a new 3D point cloud semantic segmentation benchmark for KITTI dataset.
The UN Security Council debates 1995-2017

This paper presents a new dataset containing 65,393 speeches held in the public meetings of the UN Security Council (UNSC) between 1995 and 2017. The dataset is based on publicly available meeting transcripts with the S/PV document symbol and includes the full substance of individual speeches as well as automatically extracted and manually corrected metadata on the speaker, the position of the speech in the sequence of speeches of a meeting, and the date of the speech. After contextualizing the dataset in recent research on the UNSC, the paper presents descriptive statistics on UNSC meetings and speeches that characterize the period covered by the dataset. Data highlight the extensive presence of the UN bureaucracy in UNSC meetings as well as an emerging trend towards more lengthy open UNSC debates. These open debates cover key issues that have emerged only during the period that is covered by the dataset, for example the debates relating to Women, Peace and Security or Climate-related Disasters.
Defending Adversarial Attacks by Correcting logits

Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning. While previous research verified that adversarial attacks are often fragile and can be defended via image-level processing, it remains unclear how high-level features are perturbed by such attacks. We investigate this issue from a new perspective, which purely relies on logits, the class scores before softmax, to detect and defend adversarial attacks. Our defender is a two-layer network trained on a mixed set of clean and perturbed logits, with the goal being recovering the original prediction. Upon a wide range of adversarial attacks, our simple approach shows promising results with relatively high accuracy in defense, and the defender can transfer across attackers with similar properties. More importantly, our defender can work in the scenarios that image data are unavailable, and enjoys high interpretability especially at the semantic level.
Formation threshold of rotating primordial black holes

Within the framework that primordial black holes are formed by the direct gravitational collapse of large primordial density perturbations in the radiation dominated stage, we derive the threshold of the density contrast for the formation of rotating primordial black holes based on the simple Jeans criterion. It is found that the threshold value increases in proportion to the square of the angular momentum. We then apply the recently refined analysis on the formation threshold for non-rotating black holes to the case of rotating black holes, and contrast the derived threshold with the former. Caveats and effects ignored in our analysis are also presented, which suggests that the uncertainties of our result can be addressed only by means of numerical relativity.
Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving objects will cause the vehicle to freeze and fail the maneuver. Human drivers leverage the cooperation of other drivers to avoid such deadlock situations and convince others to change their behavior. Decision making algorithms must reason about the interaction with other drivers and anticipate a broad range of driver behaviors. In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels. We enhanced the performance of traditional reinforcement learning algorithms by maintaining a belief over the level of cooperation of other drivers. We show that our agent successfully learns how to navigate a dense merging scenario with less deadlocks than with online planning methods.
The coevolution of banks and corporate securities markets: The financing of Belgium’s industrial take-off in the 1830s

Recent developments in the literature on financial architecture suggest that banks and markets not only coexist, but also coevolve in ways that are non-neutral from the viewpoint of optimality. This article aims to analyse the concrete mechanisms of this coevolution by focussing on a very relevant case study: Belgium (the first Continental country to industrialise) at the time of the very first emergence of a modern financial system (the 1830s). The article shows that intermediaries played a crucial role in developing secondary securities markets (as banks acted as securitisers), but market conditions also had a strong feedback on banks’ balance sheets and activities (as banks also acted as market-makers for the securities they had issued). The findings suggest that not only structural, but also cyclical factors can be important determinants of changes in financial architecture.
Cognitive Systems Approach to Smart Cities

In our connected world, services are expected to be delivered at speed through multiple means with seamless communication. To put it in day to day conversational terms, 'there is an app for it' attitude prevails. Several technologies are needed to meet this growing demand and indeed these technologies are being developed. The first noteworthy is Internet of Things (IoT), which is in itself coupled technologies to deliver seamless communication with 'anywhere, anytime' as an underlying objective. The 'anywhere, anytime' service delivery paradigm requires a new type of smart systems in developing these services with better capabilities to interact with the human user, such as personalisation, affect state recognition, etc. Here enter cognitive systems, where AI meets cognitive sciences (e.g. cognitive psychology, linguistics, social cognition, etc.). In this paper we will examine the requirements imposed by smart cities development, e.g. intelligent logistics, sensor networks and domestic appliances connectivity, data streams and media delivery, to mention but few. Then we will explore how cognitive systems can meet the challenges these requirements present to the development of new systems. Throughout our discussion here, examples from our recent and current projects will be given supplemented by examples from the literature.
Principal component analysis for multivariate extremes

The first order behavior of multivariate heavy-tailed random vectors above large radial thresholds is ruled by a limit measure in a regular variation framework. For a high dimensional vector, a reasonable assumption is that the support of this measure is concentrated on a lower dimensional subspace, meaning that certain linear combinations of the components are much likelier to be large than others. Identifying this subspace and thus reducing the dimension will facilitate a refined statistical analysis. In this work we apply Principal Component Analysis (PCA) to a re-scaled version of radially thresholded observations. Within the statistical learning framework of empirical risk minimization, our main focus is to analyze the squared reconstruction error for the exceedances over large radial thresholds. We prove that the empirical risk converges to the true risk, uniformly over all projection subspaces. As a consequence, the best projection subspace is shown to converge in probability to the optimal one, in terms of the Hausdorff distance between their intersections with the unit sphere. In addition, if the exceedances are re-scaled to the unit ball, we obtain finite sample uniform guarantees to the reconstruction error pertaining to the estimated projection sub-space. Numerical experiments illustrate the relevance of the proposed framework for practical purposes.
Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis

Liquidation is the process of selling a large number of shares of one stock sequentially within a given time frame, taking into consideration the costs arising from market impact and a trader's risk aversion. The main challenge in optimizing liquidation is to find an appropriate modeling system that can incorporate the complexities of the stock market and generate practical trading strategies. In this paper, we propose to use multi-agent deep reinforcement learning model, which better captures high-level complexities comparing to various machine learning methods, such that agents can learn how to make the best selling decisions. First, we theoretically analyze the Almgren and Chriss model and extend its fundamental mechanism so it can be used as the multi-agent trading environment. Our work builds the foundation for future multi-agent environment trading analysis. Secondly, we analyze the cooperative and competitive behaviours between agents by adjusting the reward functions for each agent, which overcomes the limitation of single-agent reinforcement learning algorithms. Finally, we simulate trading and develop an optimal trading strategy with practical constraints by using a reinforcement learning method, which shows the capabilities of reinforcement learning methods in solving realistic liquidation problems.
Expected Sarsa($\lambda$) with Control Variate for Variance Reduction

Off-policy learning is powerful for reinforcement learning. However, the high variance of off-policy evaluation is a critical challenge, which causes off-policy learning with function approximation falls into an uncontrolled instability. In this paper, for reducing the variance, we introduce control variate technique to Expected Sarsa(λ) and propose a tabular ES(λ)-CV algorithm. We prove that if a proper estimator of value function reaches, the proposed ES(λ)-CV enjoys a lower variance than Expected Sarsa(λ). Furthermore, to extend ES(λ)-CV to be a convergent algorithm with linear function approximation, we propose the GES(λ) algorithm under the convex-concave saddle-point formulation. We prove that the convergence rate of GES(λ) achieves O(1/T ), which matches or outperforms several state-of-art gradient-based algorithms, but we use a more relaxed step-size. Numerical experiments show that the proposed algorithm is stable and converges faster with lower variance than several state-of-art gradient-based TD learning algorithms: GQ(λ), GTB(λ) and ABQ(ζ).
Survey of Information Encoding Techniques for DNA

Key to DNA storage is encoding the information to a sequence of nucleotides before it can be synthesised for storage. Definition of such an encoding or mapping must adhere to multiple design restrictions. First, not all possible sequences of nucleotides can be synthesised. Homopolymers, e.g., sequences of the same nucleotide, of a length of more than two, for example, cannot be synthesised without potential errors. Similarly, the G-C content of the resulting sequences should be higher than 50\%. Second, given that synthesis is expensive, the encoding must map as many bits as possible to one nucleotide. Third, the synthesis (as well as the sequencing) is error prone, leading to substitutions, deletions and insertions. An encoding must therefore be designed to be resilient to errors through error correction codes or replication. Fourth, for the purpose of computation and selective retrieval, encodings should result in substantially different sequences across all data, even for very similar data. In the following we discuss the history and evolution of encodings.
A Review of Machine Learning Applications in Fuzzing

Fuzzing has played an important role in improving software development and testing over the course of several decades. Recent research in fuzzing has focused on applications of machine learning (ML), offering useful tools to overcome challenges in the fuzzing process. This review surveys the current research in applying ML to fuzzing. Specifically, this review discusses successful applications of ML to fuzzing, briefly explores challenges encountered, and motivates future research to address fuzzing bottlenecks.
Predicting Confusion from Eye-Tracking Data with Recurrent Neural Networks

Encouraged by the success of deep learning in a variety of domains, we investigate the suitability and effectiveness of Recurrent Neural Networks (RNNs) in a domain where deep learning has not yet been used; namely detecting confusion from eye-tracking data. Through experiments with a dataset of user interactions with ValueChart (an interactive visualization tool), we found that RNNs learn a feature representation from the raw data that allows for a more powerful classifier than previous methods that use engineered features. This is evidenced by the stronger performance of the RNN (0.74/0.71 sensitivity/specificity), as compared to a Random Forest classifier (0.51/0.70 sensitivity/specificity), when both are trained on an un-augmented dataset. However, using engineered features allows for simple data augmentation methods to be used. These same methods are not as effective at augmentation for the feature representation learned from the raw data, likely due to an inability to match the temporal dynamics of the data.
Regularized Hierarchical Policies for Compositional Transfer in Robotics

The successful application of flexible, general learning algorithms -- such as deep reinforcement learning -- to real-world robotics applications is often limited by their poor data-efficiency. Domains with more than a single dominant task of interest encourage algorithms that share partial solutions across tasks to limit the required experiment time. We develop and investigate simple hierarchical inductive biases -- in the form of structured policies -- as a mechanism for knowledge transfer across tasks in reinforcement learning (RL). To leverage the power of these structured policies we design an RL algorithm that enables stable and fast learning. We demonstrate the success of our method both in simulated robot environments (using locomotion and manipulation domains) as well as real robot experiments, demonstrating substantially better data-efficiency than competitive baselines.