nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Generation of Atomic Consistency Preserving Search Operators for Search-Based Model Engineering

Recently there has been increased interest in combining the fields of Model-Driven Engineering (MDE) and Search-Based Software Engineering (SBSE). Such approaches use meta-heuristic search guided by search operators (model mutators and sometimes breeders) implemented as model transformations. The design of these operators can substantially impact the effectiveness and efficiency of the meta-heuristic search. Currently, designing search operators is left to the person specifying the optimisation problem. However, developing consistent and efficient search-operator rules requires not only domain expertise but also in-depth knowledge about optimisation, which makes the use of model-based meta-heuristic search challenging and expensive. In this paper, we propose a generalised approach to automatically generate atomic consistency preserving search operators (aCPSOs) for a given optimisation problem. This reduces the effort required to specify an optimisation problem and shields optimisation users from the complexity of implementing efficient meta-heuristic search mutation operators. We evaluate our approach with a set of case studies, and show that the automatically generated rules are comparable to, and in some cases better than, manually created rules at guiding evolutionary search towards near-optimal solutions.
A semi-holographic hyperdimensional representation system for hardware-friendly cognitive computing

One of the main, long-term objectives of artificial intelligence is the creation of thinking machines. To that end, substantial effort has been placed into designing cognitive systems; i.e. systems that can manipulate semantic-level information. A substantial part of that effort is oriented towards designing the mathematical machinery underlying cognition in a way that is very efficiently implementable in hardware. In this work, we propose a ‘semi-holographic’ representation system that can be implemented in hardware using only multiplexing and addition operations, thus avoiding the need for expensive multiplication. The resulting architecture can be readily constructed by recycling standard microprocessor elements and is capable of performing two key mathematical operations frequently used in cognition, superposition and binding, within a budget of below 6 pJ for 64-bit operands. Our proposed ‘cognitive processing unit’ is intended as just one (albeit crucial) part of much larger cognitive systems where artificial neural networks of all kinds and associative memories work in concord to give rise to intelligence. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
Gittins’ theorem under uncertainty

We study dynamic allocation problems for discrete time multi-armed bandits under uncertainty, based on the the theory of nonlinear expectations. We show that, under strong independence of the bandits and with some relaxation in the definition of optimality, a Gittins allocation index gives optimal choices. This involves studying the interaction of our uncertainty with controls which determine the filtration. We also run a simple numerical example which illustrates the interaction between the willingness to explore and uncertainty aversion of the agent when making decisions.
Shapley Q-value: A Local Reward Approach to Solve Global Reward Games

Cooperative game is a critical research area in the multi-agent reinforcement learning (MARL). Global reward game is a subclass of cooperative games, where all agents aim to maximize the global reward. Credit assignment is an important problem studied in the global reward game. Most of previous works stood by the view of non-cooperative-game theoretical framework with the shared reward approach, i.e., each agent being assigned a shared global reward directly. This, however, may give each agent an inaccurate reward on its contribution to the group, which could cause inefficient learning. To deal with this problem, we i) introduce a cooperative-game theoretical framework called extended convex game (ECG) that is a superset of global reward game, and ii) propose a local reward approach called Shapley Q-value. Shapley Q-value is able to distribute the global reward, reflecting each agent's own contribution in contrast to the shared reward approach. Moreover, we derive an MARL algorithm called Shapley Q-value deep deterministic policy gradient (SQDDPG), using Shapley Q-value as the critic for each agent. We evaluate SQDDPG on Cooperative Navigation, Prey-and-Predator and Traffic Junction, compared with the state-of-the-art algorithms, e.g., MADDPG, COMA, Independent DDPG and Independent A2C. In the experiments, SQDDPG shows a significant improvement on the convergence rate. Finally, we plot Shapley Q-value and validate the property of fair credit assignment.
Freeze and Chaos for DNNs: an NTK view of Batch Normalization, Checkerboard and Boundary Effects

In this paper, we analyze a number of architectural features of Deep Neural Networks (DNNs), using the so-called Neural Tangent Kernel (NTK). The NTK describes the training trajectory and generalization of DNNs in the infinite-width limit. In this limit, we show that for (fully-connected) DNNs, as the depth grows, two regimes appear: “freeze” (also known as “order”), where the (scaled) NTK converges to a constant (slowing convergence), and “chaos”, where it converges to a Kronecker delta (limiting generalization). We show that when using the scaled ReLU as a nonlinearity, we naturally end up in the “freeze”. We show that Batch Normalization (BN) avoids the freeze regime by reducing the importance of the constant mode in the NTK. A similar effect is obtained by normalizing the nonlinearity which moves the network to the chaotic regime. We uncover the same “freeze” and “chaos” modes in Deep Deconvolutional Networks (DC-NNs). The “freeze” regime is characterized by checkerboard patterns in the image space in addition to the constant modes in input space. Finally, we introduce a new NTK-based parametrization to eliminate border artifacts and we propose a layer-dependent learning rate to improve the convergence of DC-NNs. We illustrate our findings by training DCGANs using our setup. When trained in the “freeze” regime, we see that the generator collapses to a checkerboard mode. We also demonstrate numerically that the generator collapse can be avoided and that good quality samples can be obtained, by tuning the nonlinearity to reach the “chaos” regime (without using batch normalization).
Why Blocking Targeted Adversarial Perturbations Impairs the Ability to Learn

Despite their accuracy, neural network-based classifiers are still prone to manipulation through adversarial perturbations. Those perturbations are designed to be misclassified by the neural network, while being perceptually identical to some valid input. The vast majority of attack methods rely on white-box conditions, where the attacker has full knowledge of the attacked network's parameters. This allows the attacker to calculate the network's loss gradient with respect to some valid input and use this gradient in order to create an adversarial example. The task of blocking white-box attacks has proven difficult to solve. While a large number of defense methods have been suggested, they have had limited success. In this work we examine this difficulty and try to understand it. We systematically explore the abilities and limitations of defensive distillation, one of the most promising defense mechanisms against adversarial perturbations suggested so far in order to understand the defense challenge. We show that contrary to commonly held belief, the ability to bypass defensive distillation is not dependent on an attack's level of sophistication. In fact, simple approaches, such as the Targeted Gradient Sign Method, are capable of effectively bypassing defensive distillation. We prove that defensive distillation is highly effective against non-targeted attacks but is unsuitable for targeted attacks. This discovery leads us to realize that targeted attacks leverage the same input gradient that allows a network to be trained. This implies that blocking them will require losing the network's ability to learn, presenting an impossible tradeoff to the research community.
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search

Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-net and searching for an optimal architecture. In this paper, we present a novel approach, namely Partially-Connected DARTS, by sampling a small part of super-net to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance. In particular, we perform operation search in a subset of channels while bypassing the held out part in a shortcut. This strategy may suffer from an undesired inconsistency on selecting the edges of super-net caused by sampling different channels. We solve it by introducing edge normalization, which adds a new set of edge-level hyper-parameters to reduce uncertainty in search. Thanks to the reduced memory cost, PC-DARTS can be trained with a larger batch size and, consequently, enjoy both faster speed and higher training stability. Experiment results demonstrate the effectiveness of the proposed method. Specifically, we achieve an error rate of 2.57% on CIFAR10 within merely 0.1 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile setting) within 3.8 GPU-days for search. Our code has been made available at https://www.dropbox.com/sh/on9lg3rpx1r6dkf/AABG5mt0sMHjnEJyoRnLEYW4a?dl=0.
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems

Data scarcity is a long-standing and crucial challenge that hinders quick development of task-oriented dialogue systems across multiple domains: task-oriented dialogue models are expected to learn grammar, syntax, dialogue reasoning, decision making, and language generation from absurdly small amounts of task-specific data. In this paper, we demonstrate that recent progress in language modeling pre-training and transfer learning shows promise to overcome this problem. We propose a task-oriented dialogue model that operates solely on text input: it effectively bypasses explicit policy and language generation modules. Building on top of the TransferTransfo framework (Wolf et al., 2019) and generative model pre-training (Radford et al., 2019), we validate the approach on complex multi-domain task-oriented dialogues from the MultiWOZ dataset. Our automatic and human evaluations show that the proposed model is on par with a strong task-specific neural baseline. In the long run, our approach holds promise to mitigate the data scarcity problem, and to support the construction of more engaging and more eloquent task-oriented conversational agents.
Generating Sentences from Disentangled Syntactic and Semantic Spaces

Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous latent space does not explicitly model the syntactic information. In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces. Our proposed method explicitly models syntactic information in the VAE’s latent space by using the linearized tree sequence, leading to better performance of language generation. Additionally, the advantage of sampling in the disentangled syntactic and semantic latent spaces enables us to perform novel applications, such as the unsupervised paraphrase generation and syntax transfer generation. Experimental results show that our proposed model achieves similar or better performance in various tasks, compared with state-of-the-art related work.
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 96 languages, including several dialects or low-resource languages. We do not limit the extraction process to alignments with English, but we systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences for 16720 different language pairs, out of which only 34M are aligned with English. This corpus is freely available. To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English.
Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems

Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e.g. flight booking, hotel reservation, technical support, student advising etc. These dialog systems must learn to interact with external domain knowledge to achieve the desired goal e.g. recommending courses to a student, booking a table at a restaurant etc. This paper presents extended Enhanced Sequential Inference Model (ESIM) models: a) K-ESIM (Knowledge-ESIM), which incorporates the external domain knowledge and b) T-ESIM (Targeted-ESIM), which leverages information from similar conversations to improve the prediction accuracy. Our proposed models and the baseline ESIM model are evaluated on the Ubuntu and Advising datasets in the Sentence Selection track of the latest Dialog System Technology Challenge (DSTC7), where the goal is to find the correct next utterance, given a partial conversation, from a set of candidates. Our preliminary results suggest that incorporating external knowledge sources and leveraging information from similar dialogs leads to performance improvements for predicting the next utterance.
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks

Detection of anomalous trajectories is an important problem with potential applications to various domains, such as video surveillance, risk assessment, vessel monitoring and high-energy physics. Modeling the distribution of trajectories with statistical approaches has been a challenging task due to the fact that such time series are usually non stationary and highly dimensional. However, modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction. In this paper, we propose a Sequence to Sequence architecture for real-time detection of anomalies in human trajectories, in the context of risk-based security. Our detection scheme is tested on a synthetic dataset of diverse and realistic trajectories generated by the ISL iCrowd simulator [11], . The experimental results indicate that our scheme accurately detects motions that deviate from normal behaviors and is promising for future real-world applications.
Self-Supervised Learning With Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera

We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video -- addressing the difficulty of acquiring realistic ground-truth for such tasks. We propose three contributions: 1) we design new loss functions that capture multiple geometric constraints (eg. epipolar geometry) as well as adaptive photometric loss that supports multiple moving objects, rigid and non-rigid, 2) we extend the model such that it predicts camera intrinsics, making it applicable to uncalibrated video, and 3) we propose several online refinement strategies that rely on the symmetry of our self-supervised loss in training and testing, in particular optimizing model parameters and/or the output of different tasks, leveraging their mutual interactions. The idea of jointly optimizing the system output, under all geometric and photometric constraints can be viewed as a dense generalization of classical bundle adjustment. We demonstrate the effectiveness of our method on KITTI and Cityscapes, where we outperform previous self-supervised approaches on multiple tasks. We also show good generalization for transfer learning.
DisCoRL: Continual Reinforcement Learning via Policy Distillation

In multi-task reinforcement learning there are two main challenges: at training time, the ability to learn different policies with a single model; at test time, inferring which of those policies applying without an external signal. In the case of continual reinforcement learning a third challenge arises: learning tasks sequentially without forgetting the previous ones. In this paper, we tackle these challenges by proposing DisCoRL, an approach combining state representation learning and policy distillation. We experiment on a sequence of three simulated 2D navigation tasks with a 3 wheel omni-directional robot. Moreover, we tested our approach's robustness by transferring the final policy into a real life setting. The policy can solve all tasks and automatically infer which one to run.
Constraints on dark matter-photon coupling in the presence of time-varying dark energy

In a recent work [S. Kumar, R. C. Nunes and S. K. Yadav,  (2018)], we have investigated a dark matter (DM)-photon coupling model in which the DM decays into photons in the presence of dark energy (DE) with constant equation of state (EoS) parameter. Here, we study an extension of the DM-photon coupling model by considering a time-varying EoS of DE via Chevalier–Polarski–Linder (CPL) parametrization. We derive observational constraints on the model parameters by using the data from cosmic microwave background (CMB), baryonic acoustic oscillations (BAO), the local value of Hubble constant from Hubble Space Telescope (HST), and large-scale structure (LSS) information from the abundance of galaxy clusters, in four different combinations. We find that in this DM-photon coupling scenario the mean values of  see text] are in quintessence region  see text] whereas they were in the phantom region  see text] in our previous study with all data combinations. The constraints on the DM-photon coupling parameter do not reflect any significant deviation from the previous results. Due to the decay of DM into photons, we obtain higher values of  see text], consistent with the local measurements, similar to our previous study. But, the time-varying DE leads to lower values of  see text] in the DM-photon coupling model with all data combinations, in comparison to the results in our previous study. Thus, allowing time-varying DE in the DM-photon coupling scenario is useful to alleviate the  see text] and  see text] tensions.
Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction

Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of electrocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extreme learning machine (R-HessELM), and feature models; squared, circled, inclined and grid entropy measurement were introduced and used for prediction of CHF. This study proved that inclined entropy measurements features well represent characteristics of ECG signals and together with R-HessELM approach overall accuracy of 98.49% was achieved.
Z′ mediated WIMPs: dead, dying, or soon to be detected?

Although weakly interacting massive particles (WIMPs) have long been among the most studied and theoretically attractive classes of candidates for the dark matter of our universe, the lack of their detection in direct detection and collider experiments has begun to dampen enthusiasm for this paradigm. In this study, we set out to appraise the status of the WIMP paradigm, focusing on the case of dark matter candidates that interact with the Standard Model through a new gauge boson. After considering a wide range of Z′ mediated dark matter models, we quantitatively evaluate the fraction of the parameter space that has been excluded by existing experiments, and that is projected to fall within the reach of future direct detection experiments. Despite the existence of stringent constraints, we find that a sizable fraction of this parameter space remains viable. More specifically, if the dark matter is a Majorana fermion, we find that an order one fraction of the parameter space is in many cases untested by current experiments. Future direct detection experiments with sensitivity near the irreducible neutrino floor will be able to test a significant fraction of the currently viable parameter space, providing considerable motivation for the next generation of direct detection experiments.
On the Diversity of Fallback Rates from Tidal Disruption Events with Accurate Stellar Structure

The tidal disruption of stars by supermassive black holes (SMBHs) can be used to probe the SMBH mass function, the properties of individual stars, and stellar dynamics in galactic nuclei. Upcoming missions will detect thousands of tidal disruption events (TDEs), and accurate theoretical modeling is required to interpret the data with precision. Here we analyze the influence of more realistic stellar structure on the outcome of TDEs; in particular, we compare the fallback rates—being the rate at which tidally disrupted debris returns to the black hole—from progenitors generated with the stellar evolution code mesa to and γ = 5/3 polytropes. We find that mesa-generated density profiles yield qualitatively different fallback rates as compared to polytropic approximations, and that only the fallback curves from low-mass (1 M⊙ or less), zero-age main-sequence stars are well fit by either a or 5/3 polytrope. Stellar age has a strong affect on the shape of the fallback curve, and can produce characteristic timescales (e.g., the time to the peak of the fallback rate) that greatly differ from the polytropic values. We use these differences to assess the degree to which the inferred black hole mass from the observed light curve can deviate from the true value, and find that the discrepancy can be at the order of magnitude level. Accurate stellar structure also leads to a substantial variation in the critical impact parameter at which the star is fully disrupted, and can increase the susceptibility of the debris stream to fragmentation under its own self-gravity. These results suggest that detailed modeling is required to accurately interpret observed light curves of TDEs.