OpenRaiser/Intern-Atlas huggingface dataset:
TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks
Venue: arXiv cs.MS 2021
Year: 2021

Physics-Informed Neural Networks promise to revolutionize science and engineering practice, by introducing domain-aware deep machine learning models into scientific computation. Several software suites have emerged to make the implementation and usage of these architectures available to the research and industry communities. Here we introduce\linebreak TensorDiffEq, built on Tensorflow 2.x, which presents an intuitive Keras-like interface for problem domain definition, model definition, and solution of forward and inverse problems using physics-aware deep learning methods. TensorDiffEq takes full advantage of Tensorflow 2.x infrastructure for deployment on multiple GPUs, allowing the implementation of large high-dimensional and complex models. Simultaneously, TensorDiffEq supports the Keras API for custom neural network architecture definitions. In the case of smaller or simpler models, the package allows for rapid deployment on smaller-scale CPU platforms with negligible changes to the implementation scripts. We demonstrate the basic usage and capabilities of TensorDiffEq in solving forward, inverse, and data assimilation problems of varying sizes and levels of complexity. The source code is available at https://github.com/tensordiffeq.
Inferring the Geometric Nullspace of Robot Skills from Human Demonstrations
Venue: arXiv cs.RO 2021
Year: 2021

In this paper we present a framework to learn skills from human demonstrations in the form of geometric nullspaces, which can be executed using a robot. We collect data of human demonstrations, fit geometric nullspaces to them, and also infer their corresponding geometric constraint models. These geometric constraints provide a powerful mathematical model as well as an intuitive representation of the skill in terms of the involved objects. To execute the skill using a robot, we combine this geometric skill description with the robot's kinematics and other environmental constraints, from which poses can be sampled for the robot's execution. The result of our framework is a system that takes the human demonstrations as input, learns the underlying skill model, and executes the learnt skill with different robots in different dynamic environments. We evaluate our approach on a simulated industrial robot, and execute the final task on the iCub humanoid robot.
Deep Learning and Machine Vision for Food Processing: A Survey
Venue: arXiv cs.CV 2021
Year: 2021

The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. However, these processes are often labour-intensive. Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. As a result, machine vision has been widely used in all aspects of food processing. At the same time, image processing is an important component of machine vision. Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. Subsequently, follow-up design in the machine vision system can address tasks such as food grading, detecting locations of defective spots or foreign objects, and removing impurities. In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. We present the current approaches and challenges, and the future trends.
A resource-efficient method for repeated HPO and NAS problems
Venue: arXiv cs.LG 2021
Year: 2021

In this work we consider the problem of repeated hyperparameter and neural architecture search (HNAS). We propose an extension of Successive Halving that is able to leverage information gained in previous HNAS problems with the goal of saving computational resources. We empirically demonstrate that our solution is able to drastically decrease costs while maintaining accuracy and being robust to negative transfer. Our method is significantly simpler than competing transfer learning approaches, setting a new baseline for transfer learning in HNAS.
Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool
Venue: arXiv cs.CV 2021
Year: 2021

Monte Carlo path tracer renders noisy image sequences at low sampling counts. Although great progress has been made on denoising such sequences, existing methods still suffer from spatial and temporary artifacts. In this paper, we tackle the problems in Monte Carlo rendering by proposing a two-stage denoiser based on the adaptive sampling strategy. In the first stage, concurrent to adjusting samples per pixel (spp) on-the-fly, we reuse the computations to generate extra denoising kernels applying on the adaptively rendered image. Rather than a direct prediction of pixel-wise kernels, we save the overhead complexity by interpolating such kernels from a public kernel pool, which can be dynamically updated to fit input signals. In the second stage, we design the position-aware pooling and semantic alignment operators to improve spatial-temporal stability. Our method was first benchmarked on 10 synthesized scenes rendered from the Mitsuba renderer and then validated on 3 additional scenes rendered from our self-built RTX-based renderer. Our method outperforms state-of-the-art counterparts in terms of both numerical error and visual quality.
Synch: A framework for concurrent data-structures and benchmarks
Venue: arXiv cs.DC 2021
Year: 2021

The recent advancements in multicore machines highlight the need to simplify concurrent programming in order to leverage their computational power. One way to achieve this is by designing efficient concurrent data structures (e.g. stacks, queues, hash-tables, etc.) and synchronization techniques (e.g. locks, combining techniques, etc.) that perform well in machines with large amounts of cores. In contrast to ordinary, sequential data-structures, the concurrent data-structures allow multiple threads to simultaneously access and/or modify them. Synch is an open-source framework that not only provides some common high-performant concurrent data-structures, but it also provides researchers with the tools for designing and benchmarking high performant concurrent data-structures. The Synch framework contains a substantial set of concurrent data-structures such as queues, stacks, combining-objects, hash-tables, locks, etc. and it provides a user-friendly runtime for developing and benchmarking concurrent data-structures. Among other features, the provided runtime provides functionality for creating threads easily (both POSIX and user-level threads), tools for measuring performance, etc. Moreover, the provided concurrent data-structures and the runtime are highly optimized for contemporary NUMA multiprocessors such as AMD Epyc and Intel Xeon.
Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, the currently existing neural machine translation delivers unsatisfying results. In this paper, we conduct a deep analysis of a dialogue corpus and summarize three major issues on dialogue translation, including pronoun dropping (\droppro), punctuation dropping (\droppun), and typos (\typo). In response to these challenges, we propose a joint learning method to identify omission and typo, and utilize context to translate dialogue utterances. To properly evaluate the performance, we propose a manually annotated dataset with 1,931 Chinese-English parallel utterances from 300 dialogues as a benchmark testbed for dialogue translation. Our experiments show that the proposed method improves translation quality by 3.2 BLEU over the baselines. It also elevates the recovery rate of omitted pronouns from 26.09% to 47.16%. We will publish the code and dataset publicly at https://github.com/rgwt123/DialogueMT.
Product Inspection Methodology via Deep Learning: An Overview
Venue: arXiv cs.LG 2021
Year: 2021

In this work, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. Also we explain entire steps for building a deep learning-based inspection system in great detail. Second, we address connection schemes that efficiently link the deep learning models to the product inspection systems. Finally, we propose an effective method that can maintain and enhance the deep learning models of the product inspection system. It has good system maintenance and stability due to the proposed methods. All the proposed methods are integrated in a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compared and analyzed the performance of methods in various test scenarios.
The randomized local computation complexity of the Lovász local lemma
Venue: arXiv cs.DS 2021
Year: 2021

The Local Computation Algorithm (LCA) model is a popular model in the field of sublinear-time algorithms that measures the complexity of an algorithm by the number of probes the algorithm makes in the neighborhood of one node to determine that node's output. In this paper we show that the randomized LCA complexity of the Lovász Local Lemma (LLL) on constant degree graphs is $Θ(\log n)$. The lower bound follows by proving an $Ω(\log n)$ lower bound for the Sinkless Orientation problem introduced in [Brandt et al. STOC 2016]. This answers a question of [Rosenbaum, Suomela PODC 2020]. Additionally, we show that every randomized LCA algorithm for a locally checkable problem with a probe complexity of $o(\sqrt{\log{n}})$ can be turned into a deterministic LCA algorithm with a probe complexity of $O(\log^* n)$. This improves exponentially upon the currently best known speed-up result from $o(\log \log n)$ to $O(\log^* n)$ implied by the result of [Chang, Pettie FOCS 2017] in the LOCAL model. Finally, we show that for every fixed constant $c \geq 2$, the deterministic VOLUME complexity of $c$-coloring a bounded degree tree is $Θ(n)$, where the VOLUME model is a close relative of the LCA model that was recently introduced by [Rosenbaum, Suomela PODC 2020].
Conditional Meta-Learning of Linear Representations
Venue: arXiv cs.LG 2021
Year: 2021

Standard meta-learning for representation learning aims to find a common representation to be shared across multiple tasks. The effectiveness of these methods is often limited when the nuances of the tasks' distribution cannot be captured by a single representation. In this work we overcome this issue by inferring a conditioning function, mapping the tasks' side information (such as the tasks' training dataset itself) into a representation tailored to the task at hand. We study environments in which our conditional strategy outperforms standard meta-learning, such as those in which tasks can be organized in separate clusters according to the representation they share. We then propose a meta-algorithm capable of leveraging this advantage in practice. In the unconditional setting, our method yields a new estimator enjoying faster learning rates and requiring less hyper-parameters to tune than current state-of-the-art methods. Our results are supported by preliminary experiments.
Leveraging a Joint of Phenotypic and Genetic Features on Cancer Patient Subgrouping
Venue: arXiv cs.LG 2021
Year: 2021

Cancer is responsible for millions of deaths worldwide every year. Although significant progress has been achieved in cancer medicine, many issues remain to be addressed for improving cancer therapy. Appropriate cancer patient stratification is the prerequisite for selecting appropriate treatment plan, as cancer patients are of known heterogeneous genetic make-ups and phenotypic differences. In this study, built upon deep phenotypic characterizations extractable from Mayo Clinic electronic health records (EHRs) and genetic test reports for a collection of cancer patients, we developed a system leveraging a joint of phenotypic and genetic features for cancer patient subgrouping. The workflow is roughly divided into three parts: feature preprocessing, cancer patient classification, and cancer patient clustering based. In feature preprocessing step, we performed filtering, retaining the most relevant features. In cancer patient classification, we utilized joint categorical features to build a patient-feature matrix and applied nine different machine learning models, Random Forests (RF), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), Multilayer Perceptron (MLP), Gradient Boosting (GB), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN), for classification purposes. Finally, in the cancer patient clustering step, we leveraged joint embeddings features and patient-feature associations to build an undirected feature graph and then trained the cancer feature node embeddings.
Wave based damage detection in solid structures using artificial neural networks
Venue: arXiv cs.LG 2021
Year: 2021

The identification of structural damages takes a more and more important role within the modern economy, where often the monitoring of an infrastructure is the last approach to keep it under public use. Conventional monitoring methods require specialized engineers and are mainly time consuming. This research paper considers the ability of neural networks to recognize the initial or alteration of structural properties based on the training processes. The presented work here is based on Convolutional Neural Networks (CNN) for wave field pattern recognition, or more specifically the wave field change recognition. The CNN model is used to identify the change within propagating wave fields after a crack initiation within the structure. The paper describes the implemented method and the required training procedure to get a successful crack detection accuracy, where the training data are based on the dynamic lattice model. Although the training of the model is still time consuming, the proposed new method has an enormous potential to become a new crack detection or structural health monitoring approach within the conventional monitoring methods.
Multi-planner intervention in network games with community structures
Venue: arXiv cs.GT 2021
Year: 2021

Network games study the strategic interaction of agents connected through a network. Interventions in such a game -- actions a coordinator or planner may take that change the utility of the agents and thus shift the equilibrium action profile -- are introduced to improve the planner's objective. We study the problem of intervention in network games where the network has a group structure with local planners, each associated with a group. The agents play a non-cooperative game while the planners may or may not have the same optimization objective. We model this problem using a sequential move game where planners make interventions followed by agents playing the intervened game. We provide equilibrium analysis and algorithms that find the subgame perfect equilibrium. We also propose a two-level efficiency definition to study the efficiency loss of equilibrium actions in this type of game.
The Unfolding Structure of Arguments in Online Debates: The case of a No-Deal Brexit
Venue: arXiv cs.CL 2021
Year: 2021

In the last decade, political debates have progressively shifted to social media. Rhetorical devices employed by online actors and factions that operate in these debating arenas can be captured and analysed to conduct a statistical reading of societal controversies and their argumentation dynamics. In this paper, we propose a five-step methodology, to extract, categorize and explore the latent argumentation structures of online debates. Using Twitter data about a "no-deal" Brexit, we focus on the expected effects in case of materialisation of this event. First, we extract cause-effect claims contained in tweets using RegEx that exploit verbs related to Creation, Destruction and Causation. Second, we categorise extracted "no-deal" effects using a Structural Topic Model estimated on unigrams and bigrams. Third, we select controversial effect topics and explore within-topic argumentation differences between self-declared partisan user factions. We hence type topics using estimated covariate effects on topic propensities, then, using the topics correlation network, we study the topological structure of the debate to identify coherent topical constellations. Finally, we analyse the debate time dynamics and infer lead/follow relations among factions. Results show that the proposed methodology can be employed to perform a statistical rhetorics analysis of debates, and map the architecture of controversies across time. In particular, the "no-deal" Brexit debate is shown to have an assortative argumentation structure heavily characterized by factional constellations of arguments, as well as by polarized narrative frames invoked through verbs related to Creation and Destruction. Our findings highlight the benefits of implementing a systemic approach to the analysis of debates, which allows the unveiling of topical and factional dependencies between arguments employed in online debates.
The Price of Anarchy is Fragile in Single-Selection Coverage Games
Venue: arXiv cs.GT 2021
Year: 2021

This paper considers coverage games in which a group of agents are tasked with identifying the highest-value subset of resources; in this context, game-theoretic approaches are known to yield Nash equilibria within a factor of 2 of optimal. We consider the case that some of the agents suffer a communication failure and cannot observe the actions of other agents; in this case, recent work has shown that if there are k>0 compromised agents, Nash equilibria are only guaranteed to be within a factor of k+1 of optimal. However, the present paper shows that this worst-case guarantee is fragile; in a sense which we make precise, we show that if a problem instance has a very poor worst-case guarantee, then it is necessarily very "close" to a problem instance with an optimal Nash equilibrium. Conversely, an instance that is far from one with an optimal Nash equilibrium necessarily has relatively good worst-case performance guarantees. To contextualize this fragility, we perform simulations using the log-linear learning algorithm and show that average performance on worst-case instances is considerably better even than our improved analytical guarantees. This suggests that the fragility of the price of anarchy can be exploited algorithmically to compensate for online communication failures.
Generalized Linear Tree Space Nearest Neighbor
Venue: arXiv cs.LG 2021
Year: 2021

We present a novel method of stacking decision trees by projection into an ordered time split out-of-fold (OOF) one nearest neighbor (1NN) space. The predictions of these one nearest neighbors are combined through a linear model. This process is repeated many times and averaged to reduce variance. Generalized Linear Tree Space Nearest Neighbor (GLTSNN) is competitive with respect to Mean Squared Error (MSE) compared to Random Forest (RF) on several publicly available datasets. Some of the theoretical and applied advantages of GLTSNN are discussed. We conjecture a classifier based upon the GLTSNN would have an error that is asymptotically bounded by twice the Bayes error rate like k = 1 Nearest Neighbor.
EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models
Venue: arXiv cs.LG 2021
Year: 2021

The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language. As ML models continue increasing in size, so does their respective energy consumption and computational requirements. However, the methods for tracking, reporting, and comparing energy consumption remain limited. We presentEnergyVis, an interactive energy consumption tracker for ML models. Consisting of multiple coordinated views, EnergyVis enables researchers to interactively track, visualize and compare model energy consumption across key energy consumption and carbon footprint metrics (kWh and CO2), helping users explore alternative deployment locations and hardware that may reduce carbon footprints. EnergyVis aims to raise awareness concerning computational sustainability by interactively highlighting excessive energy usage during model training; and by providing alternative training options to reduce energy usage.
Order P4-66: Characterizing and mitigating surreptitious programmable network device exploitation
Venue: arXiv cs.CR 2021
Year: 2021

Substantial efforts are invested in improving network security, but the threat landscape is rapidly evolving, particularly with the recent interest in programmable network hardware. We explore a new security threat, from an attacker who has gained control of such devices. While it should be obvious that such attackers can trivially cause substantial damage, the challenge and novelty are in doing so while preventing quick diagnosis by the operator. We find that compromised programmable devices can easily degrade networked applications by orders of magnitude, while evading diagnosis by even the most sophisticated network diagnosis methods in deployment. Two key observations yield this result: (a) targeting a small number of packets is often enough to cause disproportionate performance degradation; and (b) new programmable hardware is an effective enabler of careful, selective targeting of packets. Our results also point to recommendations for minimizing the damage from such attacks, ranging from known, easy to implement techniques like encryption and redundant requests, to more complex considerations that would potentially limit some intended uses of programmable hardware. For data center contexts, we also discuss application-aware monitoring and response as a potential mitigation.
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques
Venue: arXiv cs.LG 2021
Year: 2021

Human Activity Recognition (HAR) is considered a valuable research topic in the last few decades. Different types of machine learning models are used for this purpose, and this is a part of analyzing human behavior through machines. It is not a trivial task to analyze the data from wearable sensors for complex and high dimensions. Nowadays, researchers mostly use smartphones or smart home sensors to capture these data. In our paper, we analyze these data using machine learning models to recognize human activities, which are now widely used for many purposes such as physical and mental health monitoring. We apply different machine learning models and compare performances. We use Logistic Regression (LR) as the benchmark model for its simplicity and excellent performance on a dataset, and to compare, we take Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). Additionally, we select the best set of parameters for each model by grid search. We use the HAR dataset from the UCI Machine Learning Repository as a standard dataset to train and test the models. Throughout the analysis, we can see that the Support Vector Machine performed (average accuracy 96.33%) far better than the other methods. We also prove that the results are statistically significant by employing statistical significance test methods.