OpenRaiser/Intern-Atlas huggingface dataset:
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
Venue: arXiv cs.LG 2020
Year: 2020

The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering -- generally denoted as inverse design -- was based massively on human intuition and high-throughput virtual screening. The last few years have seen the emergence of significant interest in computer-inspired designs based on evolutionary or deep learning methods. The major challenge here is that the standard strings molecular representation SMILES shows substantial weaknesses in that task because large fractions of strings do not correspond to valid molecules. Here, we solve this problem at a fundamental level and introduce SELFIES (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100\% robust. Every SELFIES string corresponds to a valid molecule, and SELFIES can represent every molecule. SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid. In our experiments, the model's internal memory stores two orders of magnitude more diverse molecules than a similar test with SMILES. Furthermore, as all molecules are valid, it allows for explanation and interpretation of the internal working of the generative models.
Multivariate, Multistep Forecasting, Reconstruction and Feature Selection of Ocean Waves via Recurrent and Sequence-to-Sequence Networks
Venue: arXiv cs.LG 2020
Year: 2020

This article explores the concepts of ocean wave multivariate multistep forecasting, reconstruction and feature selection. We introduce recurrent neural network frameworks, integrated with Bayesian hyperparameter optimization and Elastic Net methods. We consider both short- and long-term forecasts and reconstruction, for significant wave height and output power of the ocean waves. Sequence-to-sequence neural networks are being developed for the first time to reconstruct the missing characteristics of ocean waves based on information from nearby wave sensors. Our results indicate that the Adam and AMSGrad optimization algorithms are the most robust ones to optimize the sequence-to-sequence network. For the case of significant wave height reconstruction, we compare the proposed methods with alternatives on a well-studied dataset. We show the superiority of the proposed methods considering several error metrics. We design a new case study based on measurement stations along the east coast of the United States and investigate the feature selection concept. Comparisons substantiate the benefit of utilizing Elastic Net. Moreover, case study results indicate that when the number of features is considerable, having deeper structures improves the performance.
Enhancing Transformation-based Defenses using a Distribution Classifier
Venue: arXiv cs.LG 2020
Year: 2020

Adversarial attacks on convolutional neural networks (CNN) have gained significant attention and there have been active research efforts on defense mechanisms. Stochastic input transformation methods have been proposed, where the idea is to recover the image from adversarial attack by random transformation, and to take the majority vote as consensus among the random samples. However, the transformation improves the accuracy on adversarial images at the expense of the accuracy on clean images. While it is intuitive that the accuracy on clean images would deteriorate, the exact mechanism in which how this occurs is unclear. In this paper, we study the distribution of softmax induced by stochastic transformations. We observe that with random transformations on the clean images, although the mass of the softmax distribution could shift to the wrong class, the resulting distribution of softmax could be used to correct the prediction. Furthermore, on the adversarial counterparts, with the image transformation, the resulting shapes of the distribution of softmax are similar to the distributions from the clean images. With these observations, we propose a method to improve existing transformation-based defenses. We train a separate lightweight distribution classifier to recognize distinct features in the distributions of softmax outputs of transformed images. Our empirical studies show that our distribution classifier, by training on distributions obtained from clean images only, outperforms majority voting for both clean and adversarial images. Our method is generic and can be integrated with existing transformation-based defenses.
On Atomic Registers and Randomized Consensus in M&M Systems
Venue: arXiv cs.DC 2020
Year: 2020

Motivated by recent distributed systems technology, Aguilera et al. introduced a hybrid model of distributed computing, called message-and-memory model or m&m model for short [1]. In this model, processes can communicate by message passing and also by accessing some shared memory (e.g., through some RDMA connections). We first consider the basic problem of implementing an atomic single-writer multi-reader (SWMR) register shared by all the processes in m&m systems. Specifically, we give an algorithm that implements such a register in m&m systems and show that it is optimal in the number of process crashes that it can tolerate. This generalizes the well-known implementation of an atomic SWMR register in a pure message-passing system [5]. We then combine our register implementation for m&m systems with the well-known randomized consensus algorithm of Aspnes and Herlihy [4], and obtain a randomized consensus algorithm for m&m systems that is also optimal in the number of process crashes that it can tolerate. Finally, we determine the minimum number of RDMA connections that is sufficient to implement a SWMR register, or solve randomized consensus, in an m&m system with t process crashes, for any given t.
Generating Question Relevant Captions to Aid Visual Question Answering
Venue: arXiv cs.CV 2020
Year: 2020

Visual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to improve VQA performance that exploits this connection by jointly generating captions that are targeted to help answer a specific visual question. The model is trained using an existing caption dataset by automatically determining question-relevant captions using an online gradient-based method. Experimental results on the VQA v2 challenge demonstrates that our approach obtains state-of-the-art VQA performance (e.g. 68.4% on the Test-standard set using a single model) by simultaneously generating question-relevant captions.
What should 6G be?
Venue: arXiv cs.NI 2020
Year: 2020

The standardization of fifth generation (5G) communications has been completed, and the 5G network should be commercially launched in 2020. As a result, the visioning and planning of sixth generation (6G) communications has begun, with an aim to provide communication services for the future demands of the 2030s. Here we provide a vision for 6G that could serve a research guide in the post-5G era. We suggest that human-centric mobile communications will still be the most important application of 6G and the 6G network should be human centric. Thus, high security, secrecy, and privacy should be key features of 6G and should be given particular attention by the wireless research community. To support this vision, we provide a systematic framework in which potential application scenarios of 6G are anticipated and subdivided. We subsequently define key potential features of 6G and discuss the required communication technologies. We also explore the issues beyond communication technologies that could hamper research and deployment of 6G.
Sea of Genes: Combining Animation and Narrative Strategies to Visualize Metagenomic Data for Museums
Venue: arXiv cs.GR 2020
Year: 2020

We examine the application of narrative strategies to present a complex and unfamiliar metagenomics dataset to the public in a science museum. Our dataset contains information about microbial gene expressions that scientists use to infer the behavior of microbes. This exhibit had three goals: to inform (the) public about microbes' behavior, cycles, and patterns; to link their behavior to the concept of gene expression; and to highlight scientists' use of gene expression data to understand the role of microbes. To address these three goals, we created a visualization with three narrative layers, each layer corresponding to a goal. This study presented us with an opportunity to assess existing frameworks for narrative visualization in a naturalistic setting. We present three successive rounds of design and evaluation of our attempts to engage visitors with complex data through narrative visualization. We highlight our design choices and their underlying rationale based on extant theories. We conclude that a central animation based on a curated dataset could successfully achieve our first goal, i.e., to communicate the aggregate behavior and interactions of microbes. We failed to achieve our second goal and had limited success with the third goal. Overall, this study highlights the challenges of telling multi-layered stories and the need for new frameworks for communicating layered stories in public settings.
Options as responses: Grounding behavioural hierarchies in multi-agent RL
Venue: arXiv cs.LG 2020
Year: 2020

This paper investigates generalisation in multi-agent games, where the generality of the agent can be evaluated by playing against opponents it hasn't seen during training. We propose two new games with concealed information and complex, non-transitive reward structure (think rock/paper/scissors). It turns out that most current deep reinforcement learning methods fail to efficiently explore the strategy space, thus learning policies that generalise poorly to unseen opponents. We then propose a novel hierarchical agent architecture, where the hierarchy is grounded in the game-theoretic structure of the game -- the top level chooses strategic responses to opponents, while the low level implements them into policy over primitive actions. This grounding facilitates credit assignment across the levels of hierarchy. Our experiments show that the proposed hierarchical agent is capable of generalisation to unseen opponents, while conventional baselines fail to generalise whatsoever.
Multi-way Encoding for Robustness
Venue: arXiv cs.CV 2020
Year: 2020

Deep models are state-of-the-art for many computer vision tasks including image classification and object detection. However, it has been shown that deep models are vulnerable to adversarial examples. We highlight how one-hot encoding directly contributes to this vulnerability and propose breaking away from this widely-used, but highly-vulnerable mapping. We demonstrate that by leveraging a different output encoding, multi-way encoding, we decorrelate source and target models, making target models more secure. Our approach makes it more difficult for adversaries to find useful gradients for generating adversarial attacks. We present robustness for black-box and white-box attacks on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN. The strength of our approach is also presented in the form of an attack for model watermarking, raising challenges in detecting stolen models.
OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features
Venue: arXiv cs.CV 2020
Year: 2020

Opinion mining in outdoor images posted by users during different activities can provide valuable information to better understand urban areas. In this regard, we propose a framework to classify the sentiment of outdoor images shared by users on social networks. We compare the performance of state-of-the-art ConvNet architectures, and one specifically designed for sentiment analysis. We also evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification and cross-dataset generalization performance. The evaluation explores a novel dataset, namely OutdoorSent, and other datasets publicly available. We observe that the incorporation of knowledge about semantic attributes improves the accuracy of all ConvNet architectures studied. Besides, we found that exploring only images related to the context of the study, outdoor in our case, is recommended, i.e., indoor images were not significantly helpful. Furthermore, we demonstrated the applicability of our results in the city of Chicago, USA, showing that they can help to improve the knowledge of subjective characteristics of different areas of the city. For instance, particular areas of the city tend to concentrate more images of a specific class of sentiment, which are also correlated with median income, opening up opportunities in different fields.
On the Convergence of SARAH and Beyond
Venue: arXiv cs.LG 2020
Year: 2020

The main theme of this work is a unifying algorithm, \textbf{L}oop\textbf{L}ess \textbf{S}ARAH (L2S) for problems formulated as summation of $n$ individual loss functions. L2S broadens a recently developed variance reduction method known as SARAH. To find an $ε$-accurate solution, L2S enjoys a complexity of ${\cal O}\big( (n+κ) \ln (1/ε)\big)$ for strongly convex problems. For convex problems, when adopting an $n$-dependent step size, the complexity of L2S is ${\cal O}(n+ \sqrt{n}/ε)$; while for more frequently adopted $n$-independent step size, the complexity is ${\cal O}(n+ n/ε)$. Distinct from SARAH, our theoretical findings support an $n$-independent step size in convex problems without extra assumptions. For nonconvex problems, the complexity of L2S is ${\cal O}(n+ \sqrt{n}/ε)$. Our numerical tests on neural networks suggest that L2S can have better generalization properties than SARAH. Along with L2S, our side results include the linear convergence of the last iteration for SARAH in strongly convex problems.
3D-RelNet: Joint Object and Relational Network for 3D Prediction
Venue: arXiv cs.CV 2020
Year: 2020

We propose an approach to predict the 3D shape and pose for the objects present in a scene. Existing learning based methods that pursue this goal make independent predictions per object, and do not leverage the relationships amongst them. We argue that reasoning about these relationships is crucial, and present an approach to incorporate these in a 3D prediction framework. In addition to independent per-object predictions, we predict pairwise relations in the form of relative 3D pose, and demonstrate that these can be easily incorporated to improve object level estimates. We report performance across different datasets (SUNCG, NYUv2), and show that our approach significantly improves over independent prediction approaches while also outperforming alternate implicit reasoning methods.
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series
Venue: arXiv cs.LG 2020
Year: 2020

Recurrent neural networks (RNNs) are commonly applied to clinical time-series data with the goal of learning patient risk stratification models. Their effectiveness is due, in part, to their use of parameter sharing over time (i.e., cells are repeated hence the name recurrent). We hypothesize, however, that this trait also contributes to the increased difficulty such models have with learning relationships that change over time. Conditional shift, i.e., changes in the relationship between the input X and the output y, arises when risk factors associated with the event of interest change over the course of a patient admission. While in theory, RNNs and gated RNNs (e.g., LSTMs) in particular should be capable of learning time-varying relationships, when training data are limited, such models often fail to accurately capture these dynamics. We illustrate the advantages and disadvantages of complete parameter sharing (RNNs) by comparing an LSTM with shared parameters to a sequential architecture with time-varying parameters on prediction tasks involving three clinically-relevant outcomes: acute respiratory failure (ARF), shock, and in-hospital mortality. In experiments using synthetic data, we demonstrate how parameter sharing in LSTMs leads to worse performance in the presence of conditional shift. To improve upon the dichotomy between complete parameter sharing and no parameter sharing, we propose a novel RNN formulation based on a mixture model in which we relax parameter sharing over time. The proposed method outperforms standard LSTMs and other state-of-the-art baselines across all tasks. In settings with limited data, relaxed parameter sharing can lead to improved patient risk stratification performance.
Preventive Model-based Verification and Repairing for SDN Requests
Venue: arXiv cs.NI 2020
Year: 2020

Software Defined Networking (SDN) is a novel network management technology, which currently attracts a lot of attention due to the provided capabilities. Recently, different works have been devoted to testing / verifying the (correct) configurations of SDN data planes. In general, SDN forwarding devices (e.g., switches) route (steer) traffic according to the configured flow rules; the latter identifies the set of virtual paths implemented in the data plane. In this paper, we propose a novel preventive approach for verifying that no misconfigurations (e.g., infinite loops), can occur given the requested set of paths. We discuss why such verification is essential, namely, how, when synthesizing a set of data paths, other not requested and undesired data paths (including loops) may be unintentionally configured. Furthermore, we show that for some cases the requested set of paths cannot be implemented without adding such undesired behavior, i.e., only a superset of the requested set can be implemented. Correspondingly, we present a verification technique for detecting such issues of potential misconfigurations and estimate the complexity of the proposed method; its polynomial complexity highlights the applicability of the obtained results. Finally, we propose a technique for debugging and repairing a set of paths in such a way that the corrected set does not induce undesired paths into the data plane, if the latter is possible.
Planning With Uncertain Specifications (PUnS)
Venue: arXiv cs.RO 2020
Year: 2020

Reward engineering is crucial to high performance in reinforcement learning systems. Prior research into reward design has largely focused on Markovian functions representing the reward. While there has been research into expressing non-Markov rewards as linear temporal logic (LTL) formulas, this has focused on task specifications directly defined by the user. However, in many real-world applications, task specifications are ambiguous, and can only be expressed as a belief over LTL formulas. In this paper, we introduce planning with uncertain specifications (PUnS), a novel formulation that addresses the challenge posed by non-Markovian specifications expressed as beliefs over LTL formulas. We present four criteria that capture the semantics of satisfying a belief over specifications for different applications, and analyze the qualitative implications of these criteria within a synthetic domain. We demonstrate the existence of an equivalent Markov decision process (MDP) for any instance of PUnS. Finally, we demonstrate our approach on the real-world task of setting a dinner table automatically with a robot that inferred task specifications from human demonstrations.
Detecting the Starting Frame of Actions in Video
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we address the problem of precisely localizing key frames of an action, for example, the precise time that a pitcher releases a baseball, or the precise time that a crowd begins to applaud. Key frame localization is a largely overlooked and important action-recognition problem, for example in the field of neuroscience, in which we would like to understand the neural activity that produces the start of a bout of an action. To address this problem, we introduce a novel structured loss function that properly weights the types of errors that matter in such applications: it more heavily penalizes extra and missed action start detections over small misalignments. Our structured loss is based on the best matching between predicted and labeled action starts. We train recurrent neural networks (RNNs) to minimize differentiable approximations of this loss. To evaluate these methods, we introduce the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was collected and labeled by experts for the purpose of neuroscience research. On this dataset, we demonstrate that our method outperforms related approaches and baseline methods using an unstructured loss.
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Venue: arXiv cs.LG 2020
Year: 2020

Imitation learning allows agents to learn complex behaviors from demonstrations. However, learning a complex vision-based task may require an impractical number of demonstrations. Meta-imitation learning is a promising approach towards enabling agents to learn a new task from one or a few demonstrations by leveraging experience from learning similar tasks. In the presence of task ambiguity or unobserved dynamics, demonstrations alone may not provide enough information; an agent must also try the task to successfully infer a policy. In this work, we propose a method that can learn to learn from both demonstrations and trial-and-error experience with sparse reward feedback. In comparison to meta-imitation, this approach enables the agent to effectively and efficiently improve itself autonomously beyond the demonstration data. In comparison to meta-reinforcement learning, we can scale to substantially broader distributions of tasks, as the demonstration reduces the burden of exploration. Our experiments show that our method significantly outperforms prior approaches on a set of challenging, vision-based control tasks.
Joint Visual Grounding with Language Scene Graphs
Venue: arXiv cs.CV 2020
Year: 2020

Visual grounding is a task to ground referring expressions in images, e.g., localize "the white truck in front of the yellow one". To resolve this task fundamentally, the model should first find out the contextual objects (e.g., the "yellow" truck) and then exploit them to disambiguate the referent from other similar objects by using the attributes and relationships (e.g., "white", "yellow", "in front of"). However, due to the lack of annotations on contextual objects and their relationships, existing methods degenerate the above joint grounding process into a holistic association between the expression and regions, thus suffering from unsatisfactory performance and limited interpretability. In this paper, we alleviate the missing-annotation problem and enable the joint reasoning by leveraging the language scene graph which covers both labeled referent and unlabeled contexts (other objects, attributes, and relationships). Specifically, the language scene graph is a graphical representation where the nodes are objects with attributes and the edges are relationships. We construct a factor graph based on it and then perform marginalization over the graph, such that we can ground both referent and contexts on corresponding image regions to achieve the joint visual grounding (JVG). Experimental results demonstrate that the proposed approach is effective and interpretable, e.g., on three benchmarks, it outperforms the state-of-the-art methods while offers a complete grounding of all the objects mentioned in the referring expression.