OpenRaiser/Intern-Atlas huggingface dataset:
Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data
Venue: arXiv cs.CL 2021
Year: 2021

Predicting stock prices from textual information is a challenging task due to the uncertainty of the market and the difficulty understanding the natural language from a machine's perspective. Previous researches focus mostly on sentiment extraction based on single news. However, the stocks on the financial market can be highly correlated, one news regarding one stock can quickly impact the prices of other stocks. To take this effect into account, we propose a new stock movement prediction framework: Multi-Graph Recurrent Network for Stock Forecasting (MGRN). This architecture allows to combine the textual sentiment from financial news and multiple relational information extracted from other financial data. Through an accuracy test and a trading simulation on the stocks in the STOXX Europe 600 index, we demonstrate a better performance from our model than other benchmarks.
Architecture-Guided Test Resource Allocation Via Logic
Venue: arXiv cs.SE 2021
Year: 2021

We introduce a new logic named Quantitative Confidence Logic (QCL) that quantifies the level of confidence one has in the conclusion of a proof. By translating a fault tree representing a system's architecture to a proof, we show how to use QCL to give a solution to the test resource allocation problem that takes the given architecture into account. We implemented a tool called Astrahl and compared our results to other testing resource allocation strategies.
Reciprocal Multi-Robot Collision Avoidance with Asymmetric State Uncertainty
Venue: arXiv cs.RO 2021
Year: 2021

We present a general decentralized formulation for a large class of collision avoidance methods and show that all collision avoidance methods of this form are guaranteed to be collision free. This class includes several existing algorithms in the literature as special cases. We then present a particular instance of this collision avoidance method, CARP (Collision Avoidance by Reciprocal Projections), that is effective even when the estimates of other agents' positions and velocities are noisy. The method's main computational step involves the solution of a small convex optimization problem, which can be quickly solved in practice, even on embedded platforms, making it practical to use on computationally-constrained robots such as quadrotors. This method can be extended to find smooth polynomial trajectories for higher dynamic systems such at quadrotors. We demonstrate this algorithm's performance in simulations and on a team of physical quadrotors. Our method finds optimal projections in a median time of 17.12ms for 285 instances of 100 randomly generated obstacles, and produces safe polynomial trajectories at over 60hz on-board quadrotors. Our paper is accompanied by an open source Julia implementation and ROS package.
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks
Venue: arXiv cs.LG 2021
Year: 2021

Conditional computation and modular networks have been recently proposed for multitask learning and other problems as a way to decompose problem solving into multiple reusable computational blocks. We propose a new approach for learning modular networks based on the isometric version of ResNet with all residual blocks having the same configuration and the same number of parameters. This architectural choice allows adding, removing and changing the order of residual blocks. In our method, the modules can be invoked repeatedly and allow knowledge transfer to novel tasks by adjusting the order of computation. This allows soft weight sharing between tasks with only a small increase in the number of parameters. We show that our method leads to interpretable self-organization of modules in case of multi-task learning, transfer learning and domain adaptation while achieving competitive results on those tasks. From practical perspective, our approach allows to: (a) reuse existing modules for learning new task by adjusting the computation order, (b) use it for unsupervised multi-source domain adaptation to illustrate that adaptation to unseen data can be achieved by only manipulating the order of pretrained modules, (c) show how our approach can be used to increase accuracy of existing architectures for image classification tasks such as ImageNet, without any parameter increase, by reusing the same block multiple times.
Federated Learning Versus Classical Machine Learning: A Convergence Comparison
Venue: arXiv cs.LG 2021
Year: 2021

In the past few decades, machine learning has revolutionized data processing for large scale applications. Simultaneously, increasing privacy threats in trending applications led to the redesign of classical data training models. In particular, classical machine learning involves centralized data training, where the data is gathered, and the entire training process executes at the central server. Despite significant convergence, this training involves several privacy threats on participants' data when shared with the central cloud server. To this end, federated learning has achieved significant importance over distributed data training. In particular, the federated learning allows participants to collaboratively train the local models on local data without revealing their sensitive information to the central cloud server. In this paper, we perform a convergence comparison between classical machine learning and federated learning on two publicly available datasets, namely, logistic-regression-MNIST dataset and image-classification-CIFAR-10 dataset. The simulation results demonstrate that federated learning achieves higher convergence within limited communication rounds while maintaining participants' anonymity. We hope that this research will show the benefits and help federated learning to be implemented widely.
Octo-Tiger's New Hydro Module and Performance Using HPX+CUDA on ORNL's Summit
Venue: arXiv cs.DC 2021
Year: 2021

Octo-Tiger is a code for modeling three-dimensional self-gravitating astrophysical fluids. It was particularly designed for the study of dynamical mass transfer between interacting binary stars. Octo-Tiger is parallelized for distributed systems using the asynchronous many-task runtime system, the C++ standard library for parallelism and concurrency (HPX) and utilizes CUDA for its gravity solver. Recently, we have remodeled Octo-Tiger's hydro solver to use a three-dimensional reconstruction scheme. In addition, we have ported the hydro solver to GPU using CUDA kernels. We present scaling results for the new hydro kernels on ORNL's Summit machine using a Sedov-Taylor blast wave problem. We also compare Octo-Tiger's new hydro scheme with its old hydro scheme, using a rotating star as a test problem.
SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications
Venue: arXiv cs.GR 2021
Year: 2021

Transparent objects are a very challenging problem in computer vision. They are hard to segment or classify due to their lack of precise boundaries, and there is limited data available for training deep neural networks. As such, current solutions for this problem employ rigid synthetic datasets, which lack flexibility and lead to severe performance degradation when deployed on real-world scenarios. In particular, these synthetic datasets omit features such as refraction, dispersion and caustics due to limitations in the rendering pipeline. To address this issue, we present SuperCaustics, a real-time, open-source simulation of transparent objects designed for deep learning applications. SuperCaustics features extensive modules for stochastic environment creation; uses hardware ray-tracing to support caustics, dispersion, and refraction; and enables generating massive datasets with multi-modal, pixel-perfect ground truth annotations. To validate our proposed system, we trained a deep neural network from scratch to segment transparent objects in difficult lighting scenarios. Our neural network achieved performance comparable to the state-of-the-art on a real-world dataset using only 10% of the training data and in a fraction of the training time. Further experiments show that a model trained with SuperCaustics can segment different types of caustics, even in images with multiple overlapping transparent objects. To the best of our knowledge, this is the first such result for a model trained on synthetic data. Both our open-source code and experimental data are freely available online.
Beamforming Design and Power Allocation for Transmissive RMS-based Transmitter Architectures
Venue: arXiv cs.IT 2021
Year: 2021

This letter investigates a downlink multiple input single output (MISO) system based on transmissive reconfigurable metasurface (RMS) transmitter. Specifically, a transmitter design based on a transmissive RMS equipped with a feed antenna is first proposed. Then, in order to maximize the achievable sum-rate of the system, the beamforming design and power allocation are jointly optimized. Since the optimization variables are coupled, this formulated optimization problem is non-convex, so it is difficult to solve it directly. To solve this problem, we propose an alternating optimization (AO) technique based on difference-of-convex (DC) programming and successive convex approximation (SCA). Simulation results verify that the proposed algorithm can achieve convergence and improve the achievable sum-rate of the system.
An Adaptive State Aggregation Algorithm for Markov Decision Processes
Venue: arXiv cs.LG 2021
Year: 2021

Value iteration is a well-known method of solving Markov Decision Processes (MDPs) that is simple to implement and boasts strong theoretical convergence guarantees. However, the computational cost of value iteration quickly becomes infeasible as the size of the state space increases. Various methods have been proposed to overcome this issue for value iteration in large state and action space MDPs, often at the price, however, of generalizability and algorithmic simplicity. In this paper, we propose an intuitive algorithm for solving MDPs that reduces the cost of value iteration updates by dynamically grouping together states with similar cost-to-go values. We also prove that our algorithm converges almost surely to within \(2\varepsilon / (1 - γ)\) of the true optimal value in the \(\ell^\infty\) norm, where \(γ\) is the discount factor and aggregated states differ by at most \(\varepsilon\). Numerical experiments on a variety of simulated environments confirm the robustness of our algorithm and its ability to solve MDPs with much cheaper updates especially as the scale of the MDP problem increases.
Data-driven deep density estimation
Venue: arXiv cs.LG 2021
Year: 2021

Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.
A Flexible Exoskeleton for Collision Resilience
Venue: arXiv cs.RO 2021
Year: 2021

With inspiration from arthropods' exoskeletons, we designed a simple, easily manufactured, semi-rigid structure with flexible joints that can passively damp impact energy. This exoskeleton fuses the protective shell to the main robot structure, thereby minimizing its loss in payload capacity. Our design is simple to build and customize using cheap components and consumer-grade 3D printers. Our results show we can build a sub-250g, autonomous quadcopter with visual navigation that can survive multiple collisions, shows a five-fold increase in the passive energy absorption, that is also suitable for automated battery swapping, and with enough computing power to run deep neural network models. This structure makes for an ideal platform for high-risk activities (such as flying in a cluttered environment or reinforcement learning training) without damage to the hardware or the environment.
Making Reads in BFT State Machine Replication Fast, Linearizable, and Live
Venue: arXiv cs.DC 2021
Year: 2021

Practical Byzantine Fault Tolerance (PBFT) is a seminal state machine replication protocol that achieves a performance comparable to non-replicated systems in realistic environments. A reason for such high performance is the set of optimizations introduced in the protocol. One of these optimizations is read-only requests, a particular type of client request which avoids running the three-step agreement protocol and allows replicas to respond directly, thus reducing the latency of reads from five to two communication steps. Given PBFT's broad influence, its design and optimizations influenced many BFT protocols and systems that followed, e.g., BFT-SMaRt. We show, for the first time, that the read-only request optimization introduced in PBFT more than 20 years ago can violate its liveness. Notably, the problem affects not only the optimized read-only operations but also standard, totally-ordered operations. We show this weakness by presenting an attack in which a malicious leader blocks correct clients and present two solutions for patching the protocol, making read-only operations fast and correct. The two solutions were implemented on BFT-SMaRt and evaluated in different scenarios, showing their effectiveness in preventing the identified attack.
User Preferences and the Shortest Path
Venue: arXiv cs.AI 2021
Year: 2021

Indoor navigation systems leverage shortest path algorithms to calculate routes. In order to define the "shortest path", a cost function has to be specified based on theories and heuristics in the application domain. For the domain of indoor routing, we survey theories and criteria identified in the literature as essential for human path planning. We drive quantitative definitions and integrate them into a cost function that weights each of the criteria separately. We then apply an exhaustive grid search to find weights that lead to an ideal cost function. "Ideal" here is defined as guiding the algorithm to plan routes that are most similar to those chosen by humans. To explore which criteria should be taken into account in an improved pathfinding algorithm, eleven different factors whose favorable impact on route selection has been established in past research were considered. Each factor was included separately in the Dijkstra algorithm and the similarity of thus calculated routes to the actual routes chosen by students at the University of Regensburg was determined. This allows for a quantitative assessment of the factors' impact and further constitutes a way to directly compare them. A reduction of the number of turns, streets, revolving doors, entryways, elevators as well as the combination of the aforementioned factors was found to have a positive effect and generate paths that were favored over the shortest path. Turns and the combination of criteria turned out to be most impactful.
Finite Element Formulations for Beam-to-Solid Interaction -- From Embedded Fibers Towards Contact
Venue: arXiv cs.CE 2021
Year: 2021

Contact and related phenomena, such as friction, wear or elastohydrodynamic lubrication, remain as one of the most challenging problem classes in nonlinear solid and structural mechanics. In the context of their computational treatment with finite element methods (FEM) or isogeometric analysis (IGA), the inherent non-smoothness of contact conditions, the design of robust discretization approaches as well as the implementation of efficient solution schemes seem to provide a never ending source of hard nuts to crack. This is particularly true for the case of beam-to-solid interaction with its mixed-dimensional 1D-3D contact models. Therefore, this contribution gives an overview of current steps being taken, starting from state-of-the-art beam-to-beam (1D) and solid-to-solid (3D) contact algorithms, towards a truly general 1D-3D beam-to-solid contact formulation.
Towards an understanding of how humans perceive stiffness during bimanual exploration
Venue: arXiv cs.HC 2021
Year: 2021

In this paper, an experimental testbed and associated psychophysical paradigm are presented for understanding how people discriminate torsional stiffness using wrist rotation about their forearm. Featured in the testbed are two 1-DoF rotary kinesthetic haptic devices. An adaptive staircase was used to evaluate JNDs for a stiffness discrimination task where participants explored virtual torsion springs by rotating their forearms. The JNDs were evaluated across seven different conditions, under four different exploration modes: bimanual, unimanual, bimanual feedback for unimanual displacement, and unimanual feedback for bimanual displacement. The discrimination results will inform future investigation into understanding how stiffness percepts vary.
Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks
Venue: arXiv cs.CV 2021
Year: 2021

We present four different robust transfer learning and data augmentation strategies for robust mobile scene recognition. By training three mobile-ready (EfficientNetB0, MobileNetV2, MobileNetV3) and two large-scale baseline (VGG16, ResNet50) convolutional neural network architectures on the widely available Event8, Scene15, Stanford40, and MIT67 datasets, we show the generalization ability of our transfer learning strategies. Furthermore, we tested the robustness of our transfer learning strategies under viewpoint and lighting changes using the KTH-Idol2 database. Also, the impact of inference optimization techniques on the general performance and the robustness under different transfer learning strategies is evaluated. Experimental results show that when employing transfer learning, Fine-Tuning in combination with extensive data augmentation improves the general accuracy and robustness in mobile scene recognition. We achieved state-of-the-art results using various baseline convolutional neural networks and showed the robustness against lighting and viewpoint changes in challenging mobile robot place recognition.
Multi-Modal Pedestrian Detection with Large Misalignment Based on Modal-Wise Regression and Multi-Modal IoU
Venue: arXiv cs.CV 2021
Year: 2021

The combined use of multiple modalities enables accurate pedestrian detection under poor lighting conditions by using the high visibility areas from these modalities together. The vital assumption for the combination use is that there is no or only a weak misalignment between the two modalities. In general, however, this assumption often breaks in actual situations. Due to this assumption's breakdown, the position of the bounding boxes does not match between the two modalities, resulting in a significant decrease in detection accuracy, especially in regions where the amount of misalignment is large. In this paper, we propose a multi-modal Faster-RCNN that is robust against large misalignment. The keys are 1) modal-wise regression and 2) multi-modal IoU for mini-batch sampling. To deal with large misalignment, we perform bounding box regression for both the RPN and detection-head with both modalities. We also propose a new sampling strategy called "multi-modal mini-batch sampling" that integrates the IoU for both modalities. We demonstrate that the proposed method's performance is much better than that of the state-of-the-art methods for data with large misalignment through actual image experiments.
Multi-channel Speech Enhancement with 2-D Convolutional Time-frequency Domain Features and a Pre-trained Acoustic Model
Venue: arXiv cs.SD 2021
Year: 2021

We propose a multi-channel speech enhancement approach with a novel two-stage feature fusion method and a pre-trained acoustic model in a multi-task learning paradigm. In the first fusion stage, the time-domain and frequency-domain features are extracted separately. In the time domain, the multi-channel convolution sum (MCS) and the inter-channel convolution differences (ICDs) features are computed and then integrated with the first 2-D convolutional layer, while in the frequency domain, the log-power spectra (LPS) features from both original channels and super-directive beamforming outputs are combined with a second 2-D convolutional layer. To fully integrate the rich information of multi-channel speech, i.e. time-frequency domain features and the array geometry, we apply a third 2-D convolutional layer in the second fusion stage to obtain the final convolutional features. Furthermore, we propose to use a fixed clean acoustic model trained with the end-to-end lattice-free maximum mutual information criterion to enforce the enhanced output to have the same distribution as the clean waveform to alleviate the over-estimation problem of the enhancement task and constrain distortion. On the Task1 development dataset of ConferencingSpeech 2021 challenge, a PESQ improvement of 0.24 and 0.19 is attained compared to the official baseline and a recently proposed multi-channel separation method.
Wavelet Design in a Learning Framework
Venue: arXiv cs.LG 2021
Year: 2021

Wavelets have proven to be highly successful in several signal and image processing applications. Wavelet design has been an active field of research for over two decades, with the problem often being approached from an analytical perspective. In this paper, we introduce a learning based approach to wavelet design. We draw a parallel between convolutional autoencoders and wavelet multiresolution approximation, and show how the learning angle provides a coherent computational framework for addressing the design problem. We aim at designing data-independent wavelets by training filterbank autoencoders, which precludes the need for customized datasets. In fact, we use high-dimensional Gaussian vectors for training filterbank autoencoders, and show that a near-zero training loss implies that the learnt filters satisfy the perfect reconstruction property with very high probability. Properties of a wavelet such as orthogonality, compact support, smoothness, symmetry, and vanishing moments can be incorporated by designing the autoencoder architecture appropriately and with a suitable regularization term added to the mean-squared error cost used in the learning process. Our approach not only recovers the well known Daubechies family of orthogonal wavelets and the Cohen-Daubechies-Feauveau family of symmetric biorthogonal wavelets, but also learns wavelets outside these families.