OpenRaiser/Intern-Atlas huggingface dataset:
Input Dependent Sparse Gaussian Processes
Venue: arXiv cs.LG 2021
Year: 2021

Gaussian Processes (GPs) are Bayesian models that provide uncertainty estimates associated to the predictions made. They are also very flexible due to their non-parametric nature. Nevertheless, GPs suffer from poor scalability as the number of training instances N increases. More precisely, they have a cubic cost with respect to $N$. To overcome this problem, sparse GP approximations are often used, where a set of $M \ll N$ inducing points is introduced during training. The location of the inducing points is learned by considering them as parameters of an approximate posterior distribution $q$. Sparse GPs, combined with variational inference for inferring $q$, reduce the training cost of GPs to $\mathcal{O}(M^3)$. Critically, the inducing points determine the flexibility of the model and they are often located in regions of the input space where the latent function changes. A limitation is, however, that for some learning tasks a large number of inducing points may be required to obtain a good prediction performance. To address this limitation, we propose here to amortize the computation of the inducing points locations, as well as the parameters of the variational posterior approximation q. For this, we use a neural network that receives the observed data as an input and outputs the inducing points locations and the parameters of $q$. We evaluate our method in several experiments, showing that it performs similar or better than other state-of-the-art sparse variational GP approaches. However, with our method the number of inducing points is reduced drastically due to their dependency on the input data. This makes our method scale to larger datasets and have faster training and prediction times.
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks
Venue: arXiv cs.HC 2021
Year: 2021

Human-robot collaboration has the potential to maximize the efficiency of the operation of autonomous robots. Brain-machine interface (BMI) would be a desirable technology to collaborate with robots since the intention or state of users can be translated from the neural activities. However, the electroencephalogram (EEG), which is one of the most popularly used non-invasive BMI modalities, has low accuracy and a limited degree of freedom (DoF) due to a low signal-to-noise ratio. Thus, improving the performance of multi-class EEG classification is crucial to develop more flexible BMI-based human-robot collaboration. In this study, we investigated the possibility for inter-paradigm classification of multiple endogenous BMI paradigms, such as motor imagery (MI), visual imagery (VI), and speech imagery (SI), to enhance the limited DoF while maintaining robust accuracy. We conducted the statistical and neurophysiological analyses on MI, VI, and SI and classified three paradigms using the proposed temporal information-based neural network (TINN). We confirmed that statistically significant features could be extracted on different brain regions when classifying three endogenous paradigms. Moreover, our proposed TINN showed the highest accuracy of 0.93 compared to the previous methods for classifying three different types of mental imagery tasks (MI, VI, and SI).
Probabilistic analysis of solar cell optical performance using Gaussian processes
Venue: arXiv cs.LG 2021
Year: 2021

This work investigates application of different machine learning based prediction methodologies to estimate the performance of silicon based textured cells. Concept of confidence bound regions is introduced and advantages of this concept are discussed in detail. Results show that reflection profiles and depth dependent optical generation profiles can be accurately estimated using Gaussian processes with exact knowledge of uncertainty in the prediction values.It is also shown that cell design parameters can be estimated for a desired performance metric.
Mutation is all you need
Venue: arXiv cs.LG 2021
Year: 2021

Neural architecture search (NAS) promises to make deep learning accessible to non-experts by automating architecture engineering of deep neural networks. BANANAS is one state-of-the-art NAS method that is embedded within the Bayesian optimization framework. Recent experimental findings have demonstrated the strong performance of BANANAS on the NAS-Bench-101 benchmark being determined by its path encoding and not its choice of surrogate model. We present experimental results suggesting that the performance of BANANAS on the NAS-Bench-301 benchmark is determined by its acquisition function optimizer, which minimally mutates the incumbent.
Inferring the Structure of Ordinary Differential Equations
Venue: arXiv cs.LG 2021
Year: 2021

Understanding physical phenomena oftentimes means understanding the underlying dynamical system that governs observational measurements. While accurate prediction can be achieved with black box systems, they often lack interpretability and are less amenable for further expert investigation. Alternatively, the dynamics can be analysed via symbolic regression. In this paper, we extend the approach by (Udrescu et al., 2020) called AIFeynman to the dynamic setting to perform symbolic regression on ODE systems based on observations from the resulting trajectories. We compare this extension to state-of-the-art approaches for symbolic regression empirically on several dynamical systems for which the ground truth equations of increasing complexity are available. Although the proposed approach performs best on this benchmark, we observed difficulties of all the compared symbolic regression approaches on more complex systems, such as Cart-Pole.
Copula-Based Normalizing Flows
Venue: arXiv cs.LG 2021
Year: 2021

Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations. But their expressive power is limited by this choice of the base distribution. We, therefore, propose to generalize the base distribution to a more elaborate copula distribution to capture the properties of the target distribution more accurately. In a first empirical analysis, we demonstrate that this replacement can dramatically improve the vanilla normalizing flows in terms of flexibility, stability, and effectivity for heavy-tailed data. Our results suggest that the improvements are related to an increased local Lipschitz-stability of the learned flow.
One Thousand and One Stories: A Large-Scale Survey of Software Refactoring
Venue: arXiv cs.SE 2021
Year: 2021

Despite the availability of refactoring as a feature in popular IDEs, recent studies revealed that developers are reluctant to use them, and still prefer the manual refactoring of their code. At JetBrains, our goal is to fully support refactoring features in IntelliJ-based IDEs and improve their adoption in practice. Therefore, we start by raising the following main questions. How exactly do people refactor code? What refactorings are the most popular? Why do some developers tend not to use convenient IDE refactoring tools? In this paper, we investigate the raised questions through the design and implementation of a survey targeting 1,183 users of IntelliJ-based IDEs. Our quantitative and qualitative analysis of the survey results shows that almost two-thirds of developers spend more than one hour in a single session refactoring their code; that refactoring types vary greatly in popularity; and that a lot of developers would like to know more about IDE refactoring features but lack the means to do so. These results serve us internally to support the next generation of refactoring features, as well as can help our research community to establish new directions in the refactoring usability research.
Sketching sounds: an exploratory study on sound-shape associations
Venue: arXiv cs.MM 2021
Year: 2021

Sound synthesiser controls typically correspond to technical parameters of signal processing algorithms rather than intuitive sound descriptors that relate to human perception of sound. This makes it difficult to realise sound ideas in a straightforward way. Cross-modal mappings, for example between gestures and sound, have been suggested as a more intuitive control mechanism. A large body of research shows consistency in human associations between sounds and shapes. However, the use of drawings to drive sound synthesis has not been explored to its full extent. This paper presents an exploratory study that asked participants to sketch visual imagery of sounds with a monochromatic digital drawing interface, with the aim to identify different representational approaches and determine whether timbral sound characteristics can be communicated reliably through visual sketches. Results imply that the development of a synthesiser exploiting sound-shape associations is feasible, but a larger and more focused dataset is needed in followup studies.
A Reinforcement Learning Environment for Mathematical Reasoning via Program Synthesis
Venue: arXiv cs.LG 2021
Year: 2021

We convert the DeepMind Mathematics Dataset into a reinforcement learning environment by interpreting it as a program synthesis problem. Each action taken in the environment adds an operator or an input into a discrete compute graph. Graphs which compute correct answers yield positive reward, enabling the optimization of a policy to construct compute graphs conditioned on problem statements. Baseline models are trained using Double DQN on various subsets of problem types, demonstrating the capability to learn to correctly construct graphs despite the challenges of combinatorial explosion and noisy rewards.
Toward quantifying trust dynamics: How people adjust their trust after moment-to-moment interaction with automation
Venue: arXiv cs.HC 2021
Year: 2021

Objective: We examine how human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Background: Most existing studies measured trust by administering questionnaires at the end of an experiment. Only a limited number of studies viewed trust as a dynamic variable that can strengthen or decay over time. Method: Seventy-five participants took part in an aided memory recognition task. In the task, participants viewed a series of images and later on performed 40 trials of the recognition task to identify a target image when it was presented with a distractor. In each trial, participants performed the initial recognition by themselves, received a recommendation from an automated decision aid, and performed the final recognition. After each trial, participants reported their trust on a visual analog scale. Results: Outcome bias and contrast effect significantly influence human operators' trust adjustments. An automation failure leads to a larger trust decrement if the final outcome is undesirable, and a marginally larger trust decrement if the human operator succeeds the task by him-/her-self. An automation success engenders a greater trust increment if the human operator fails the task. Additionally, automation failures have a larger effect on trust adjustment than automation successes. Conclusion: Human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Their trust adjustments are significantly influenced by decision-making heuristics/biases. Application: Understanding the trust adjustment process enables accurate prediction of the operators' moment-to-moment trust in automation and informs the design of trust-aware adaptive automation.
Objective Metrics to Evaluate Residual-Echo Suppression During Double-Talk
Venue: arXiv cs.SD 2021
Year: 2021

Human subjective evaluation is optimal to assess speech quality for human perception. The recently introduced deep noise suppression mean opinion score (DNSMOS) metric was shown to estimate human ratings with great accuracy. The signal-to-distortion ratio (SDR) metric is widely used to evaluate residual-echo suppression (RES) systems by estimating speech quality during double-talk. However, since the SDR is affected by both speech distortion and residual-echo presence, it does not correlate well with human ratings according to the DNSMOS. To address that, we introduce two objective metrics to separately quantify the desired-speech maintained level (DSML) and residual-echo suppression level (RESL) during double-talk. These metrics are evaluated using a deep learning-based RES-system with a tunable design parameter. Using 280 hours of real and simulated recordings, we show that the DSML and RESL correlate well with the DNSMOS with high generalization to various setups. Also, we empirically investigate the relation between tuning the RES-system design parameter and the DSML-RESL tradeoff it creates and offer a practical design scheme for dynamic system requirements.
A Hybrid Simulation Model for Open Software Development Processes
Venue: arXiv cs.SE 2021
Year: 2021

Open software development provides software organizations access to infinite online resource supply. The resource supply is a pool of unknown workers who work from different location and time zone and are interested in performing various type of tasks. Improper task execution in such dynamic and competitive environment leads to zero task registration, zero task submissions or low qualified submissions due to unforeseen reasons such as uncertainty in workers' behavior and performance. Therefore, to ensure effectiveness of open software development, there is a need for improved understanding and visibility into characteristics associated with attracting reliable workers in making qualified submissions and reducing task failure.
Algorithmic Concept-based Explainable Reasoning
Venue: arXiv cs.LG 2021
Year: 2021

Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems. This has numerous benefits, such as allowing applications of algorithms when preconditions are not satisfied, or reusing learned models when sufficient training data is not available or can't be generated. Unfortunately, a key hindrance of these approaches is their lack of explainability, since GNNs are black-box models that cannot be interpreted directly. In this work, we address this limitation by applying existing work on concept-based explanations to GNN models. We introduce concept-bottleneck GNNs, which rely on a modification to the GNN readout mechanism. Using three case studies we demonstrate that: (i) our proposed model is capable of accurately learning concepts and extracting propositional formulas based on the learned concepts for each target class; (ii) our concept-based GNN models achieve comparative performance with state-of-the-art models; (iii) we can derive global graph concepts, without explicitly providing any supervision on graph-level concepts.
SMLtoCoq: Automated Generation of Coq Specifications and Proof Obligations from SML Programs with Contracts
Venue: arXiv cs.LO 2021
Year: 2021

Formally reasoning about functional programs is supposed to be straightforward and elegant, however, it is not typically done as a matter of course. Reasoning in a proof assistant requires "reimplementing" the code in those tools, which is far from trivial. SMLtoCoq provides an automatic translation of SML programs and function contracts into Coq. Programs are translated into Coq specifications, and function contracts into theorems, which can then be formally proved. Using the Equations plugin and other well established Coq libraries, SMLtoCoq is able to translate SML programs without side-effects containing partial functions, structures, functors, records, among others. Additionally, we provide a Coq version of many parts of SML's basis library, so that calls to these libraries are kept almost as is.
CutDepth:Edge-aware Data Augmentation in Depth Estimation
Venue: arXiv cs.CV 2021
Year: 2021

It is difficult to collect data on a large scale in a monocular depth estimation because the task requires the simultaneous acquisition of RGB images and depths. Data augmentation is thus important to this task. However, there has been little research on data augmentation for tasks such as monocular depth estimation, where the transformation is performed pixel by pixel. In this paper, we propose a data augmentation method, called CutDepth. In CutDepth, part of the depth is pasted onto an input image during training. The method extends variations data without destroying edge features. Experiments objectively and subjectively show that the proposed method outperforms conventional methods of data augmentation. The estimation accuracy is improved with CutDepth even though there are few training data at long distances.
Applying Declarative Analysis to Software Product Line Models: An Industrial Study
Venue: arXiv cs.SE 2021
Year: 2021

Software Product Lines (SPLs) are families of related software products developed from a common set of artifacts. Most existing analysis tools can be applied to a single product at a time, but not to an entire SPL. Some tools have been redesigned/re-implemented to support the kind of variability exhibited in SPLs, but this usually takes a lot of effort, and is error-prone. Declarative analyses written in languages like Datalog have been collectively lifted to SPLs in prior work, which makes the process of applying an existing declarative analysis to a product line more straightforward. In this paper, we take an existing declarative analysis (behaviour alteration) written in the Grok declarative language, port it to Datalog, and apply it to a set of automotive software product lines from General Motors. We discuss the design of the analysis pipeline used in this process, present its scalability results, and provide a means to visualize the analysis results for a subset of products filtered by feature expression. We also reflect on some of the lessons learned throughout this project.
Analysing Design Approaches for the Power Consumption in Cyber-Physical Systems
Venue: arXiv cs.OH 2021
Year: 2021

The importance of Cyber Physical Systems (CPS) and Internet of Things (IoT) applications is constantly increasing, especially in the context of Industry 4.0. Architectural decisions are crucial not just for performance, security and resilience reasons but also regarding costs and resource usage. In this paper we analyse two of the fundamental approaches to design control loops (i.e. time-driven and event-driven), show how they can be realised and evaluate their power requirements. Through this the design criteria can be extended also considering the optimization of energy related aspects.
Measuring Fairness in Generative Models
Venue: arXiv cs.LG 2021
Year: 2021

Deep generative models have made much progress in improving training stability and quality of generated data. Recently there has been increased interest in the fairness of deep-generated data. Fairness is important in many applications, e.g. law enforcement, as biases will affect efficacy. Central to fair data generation are the fairness metrics for the assessment and evaluation of different generative models. In this paper, we first review fairness metrics proposed in previous works and highlight potential weaknesses. We then discuss a performance benchmark framework along with the assessment of alternative metrics.
More Robust Dense Retrieval with Contrastive Dual Learning
Venue: arXiv cs.IR 2021
Year: 2021

Dense retrieval conducts text retrieval in the embedding space and has shown many advantages compared to sparse retrieval. Existing dense retrievers optimize representations of queries and documents with contrastive training and map them to the embedding space. The embedding space is optimized by aligning the matched query-document pairs and pushing the negative documents away from the query. However, in such training paradigm, the queries are only optimized to align to the documents and are coarsely positioned, leading to an anisotropic query embedding space. In this paper, we analyze the embedding space distributions and propose an effective training paradigm, Contrastive Dual Learning for Approximate Nearest Neighbor (DANCE) to learn fine-grained query representations for dense retrieval. DANCE incorporates an additional dual training object of query retrieval, inspired by the classic information retrieval training axiom, query likelihood. With contrastive learning, the dual training object of DANCE learns more tailored representations for queries and documents to keep the embedding space smooth and uniform, thriving on the ranking performance of DANCE on the MS MARCO document retrieval task. Different from ANCE that only optimized with the document retrieval task, DANCE concentrates the query embeddings closer to document representations while making the document distribution more discriminative. Such concentrated query embedding distribution assigns more uniform negative sampling probabilities to queries and helps to sufficiently optimize query representations in the query retrieval task. Our codes are released at https://github.com/thunlp/DANCE.
Wasserstein Distances, Geodesics and Barycenters of Merge Trees
Venue: arXiv cs.GR 2021
Year: 2021

This paper presents a unified computational framework for the estimation of distances, geodesics and barycenters of merge trees. We extend recent work on the edit distance [106] and introduce a new metric, called the Wasserstein distance between merge trees, which is purposely designed to enable efficient computations of geodesics and barycenters. Specifically, our new distance is strictly equivalent to the L2-Wasserstein distance between extremum persistence diagrams, but it is restricted to a smaller solution space, namely, the space of rooted partial isomorphisms between branch decomposition trees. This enables a simple extension of existing optimization frameworks [112] for geodesics and barycenters from persistence diagrams to merge trees. We introduce a task-based algorithm which can be generically applied to distance, geodesic, barycenter or cluster computation. The task-based nature of our approach enables further accelerations with shared-memory parallelism. Extensive experiments on public ensembles and SciVis contest benchmarks demonstrate the efficiency of our approach -- with barycenter computations in the orders of minutes for the largest examples -- as well as its qualitative ability to generate representative barycenter merge trees, visually summarizing the features of interest found in the ensemble. We show the utility of our contributions with dedicated visualization applications: feature tracking, temporal reduction and ensemble clustering. We provide a lightweight C++ implementation that can be used to reproduce our results.