pwc-archive/papers-with-abstracts huggingface dataset:
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition
Authors: Kleanthis Avramidis, Athanasia Zlatintsi, Christos Garoufis, Petros Maragos
Date: 2020-10-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Classification, Emotion Recognition

Emotion Recognition from EEG signals has long been researched as it can assist numerous medical and rehabilitative applications. However, their complex and noisy structure has proven to be a serious barrier for traditional modeling methods. In this paper, we employ multifractal analysis to examine the behavior of EEG signals in terms of presence of fluctuations and the degree of fragmentation along their major frequency bands, for the task of emotion recognition. In order to extract emotion-related features we utilize two novel algorithms for EEG analysis, based on Multiscale Fractal Dimension and Multifractal Detrended Fluctuation Analysis. The proposed feature extraction methods perform efficiently, surpassing some widely used baseline features on the competitive DEAP dataset, indicating that multifractal analysis could serve as basis for the development of robust models for affective state recognition.
Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning
Authors: Rayson Laroca, Alessander Cidral Boslooper, David Menotti
Date: 2020-10-30 00:00:00

In this work, we present a robust and efficient solution for counting and identifying train wagons using computer vision and deep learning. The proposed solution is cost-effective and can easily replace solutions based on radiofrequency identification (RFID), which are known to have high installation and maintenance costs. According to our experiments, our two-stage methodology achieves impressive results on real-world scenarios, i.e., 100% accuracy in the counting stage and 99.7% recognition rate in the identification one. Moreover, the system is able to automatically reject some of the train wagons successfully counted, as they have damaged identification codes. The results achieved were surprising considering that the proposed system requires low processing power (i.e., it can run in low-end setups) and that we used a relatively small number of images to train our Convolutional Neural Network (CNN) for character recognition. The proposed method is registered, under number BR512020000808-9, with the National Institute of Industrial Property (Brazil).
Learning Vision-based Reactive Policies for Obstacle Avoidance
Authors: Elie Aljalbout, Ji Chen, Konstantin Ritt, Maximilian Ulmer, Sami Haddadin
Date: 2020-10-30 00:00:00
Tasks: Motion Generation

In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators. This topic poses challenges for both perception and motion generation. While most work in the field aims at improving one of those aspects, we provide a unified framework for approaching this problem. The main goal of this framework is to connect perception and motion by identifying the relationship between the visual input and the corresponding motion representation. To this end, we propose a method for learning reactive obstacle avoidance policies. We evaluate our method on goal-reaching tasks for single and multiple obstacles scenarios. We show the ability of the proposed method to efficiently learn stable obstacle avoidance strategies at a high success rate, while maintaining closed-loop responsiveness required for critical applications like human-robot interaction.
Sharp threshold for alignment of graph databases with Gaussian weights
Authors: Luca Ganassali
Date: 2020-10-30 00:00:00

We study the fundamental limits for reconstruction in weighted graph (or matrix) database alignment. We consider a model of two graphs where $\pi^*$ is a planted uniform permutation and all pairs of edge weights $(A_{i,j}, B_{\pi^*(i),\pi^*(j)})_{1 \leq i<j \leq n}$ are i.i.d. pairs of Gaussian variables with zero mean, unit variance and correlation parameter $\rho \in [0,1]$. We prove that there is a sharp threshold for exact recovery of $\pi^*$: if $n \rho^2 \geq (4+\epsilon) \log n + \omega(1)$ for some $\epsilon>0$, there is an estimator $\hat{\pi}$ -- namely the MAP estimator -- based on the observation of databases $A,B$ that achieves exact reconstruction with high probability. Conversely, if $n \rho^2 \leq 4 \log n - \log \log n - \omega(1)$, then any estimator $\hat{\pi}$ verifies $\hat{\pi}=\pi$ with probability $o(1)$. This result shows that the information-theoretic threshold for exact recovery is the same as the one obtained for detection in a recent work by Wu et al. (2020): in other words, for Gaussian weighted graph alignment, the problem of reconstruction is not more difficult than that of detection. Though the reconstruction task was already well understood for vector-shaped database alignment (that is taking signal of the form $(u_i, v_{\pi^*(i)})_{1 \leq i\leq n}$ where $(u_i, v_{\pi^*(i)})$ are i.i.d. pairs in $\mathbb{R}^{d_u} \times \mathbb{R}^{d_v}$), its formulation for graph (or matrix) databases brings a drastically different problem for which the hard phase is conjectured to be wide. The proofs build upon the analysis of the MAP estimator and the second moment method, together with the study of the correlation structure of energies of permutations.
All-Weather Object Recognition Using Radar and Infrared Sensing
Authors: Marcel Sheeny
Date: 2020-10-30 00:00:00
Tasks: All, Data Augmentation, Object Recognition

Autonomous cars are an emergent technology which has the capacity to change human lives. The current sensor systems which are most capable of perception are based on optical sensors. For example, deep neural networks show outstanding results in recognising objects when used to process data from cameras and Light Detection And Ranging (LiDAR) sensors. However these sensors perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This thesis explores new sensing developments based on long wave polarised infrared (IR) imagery and imaging radar to recognise objects. First, we developed a methodology based on Stokes parameters using polarised infrared data to recognise vehicles using deep neural networks. Second, we explored the potential of using only the power spectrum captured by low-THz radar sensors to perform object recognition in a controlled scenario. This latter work is based on a data-driven approach together with the development of a data augmentation method based on attenuation, range and speckle noise. Last, we created a new large-scale dataset in the "wild" with many different weather scenarios (sunny, overcast, night, fog, rain and snow) showing radar robustness to detect vehicles in adverse weather. High resolution radar and polarised IR imagery, combined with a deep learning approach, are shown as a potential alternative to current automotive sensing systems based on visible spectrum optical technology as they are more robust in severe weather and adverse light conditions.
DeepRx MIMO: Convolutional MIMO Detection with Learned Multiplicative Transformations
Authors: Dani Korpi, Mikko Honkala, Janne M. J. Huttunen, Vesa Starck
Date: 2020-10-30 00:00:00

Recently, deep learning has been proposed as a potential technique for improving the physical layer performance of radio receivers. Despite the large amount of encouraging results, most works have not considered spatial multiplexing in the context of multiple-input and multiple-output (MIMO) receivers. In this paper, we present a deep learning-based MIMO receiver architecture that consists of a ResNet-based convolutional neural network, also known as DeepRx, combined with a so-called transformation layer, all trained together. We propose two novel alternatives for the transformation layer: a maximal ratio combining-based transformation, or a fully learned transformation. The former relies more on expert knowledge, while the latter utilizes learned multiplicative layers. Both proposed transformation layers are shown to clearly outperform the conventional baseline receiver, especially with sparse pilot configurations. To the best of our knowledge, these are some of the first results showing such high performance for a fully learned MIMO receiver.
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations
Authors: Mihir Prabhudesai, Shamit Lal, Hsiao-Yu Fish Tung, Adam W. Harley, Shubhankar Potdar, Katerina Fragkiadaki
Date: 2020-10-30 00:00:00
Tasks: 3D Object Recognition, Object, Object Recognition, Pose Estimation, Retrieval

We propose a system that learns to detect objects and infer their 3D poses in RGB-D images. Many existing systems can identify objects and infer 3D poses, but they heavily rely on human labels and 3D annotations. The challenge here is to achieve this without relying on strong supervision signals. To address this challenge, we propose a model that maps RGB-D images to a set of 3D visual feature maps in a differentiable fully-convolutional manner, supervised by predicting views. The 3D feature maps correspond to a featurization of the 3D world scene depicted in the images. The object 3D feature representations are invariant to camera viewpoint changes or zooms, which means feature matching can identify similar objects under different camera viewpoints. We can compare the 3D feature maps of two objects by searching alignment across scales and 3D rotations, and, as a result of the operation, we can estimate pose and scale changes without the need for 3D pose annotations. We cluster object feature maps into a set of 3D prototypes that represent familiar objects in canonical scales and orientations. We then parse images by inferring the prototype identity and 3D pose for each detected object. We compare our method to numerous baselines that do not learn 3D feature visual representations or do not attempt to correspond features across scenes, and outperform them by a large margin in the tasks of object retrieval and object pose estimation. Thanks to the 3D nature of the object-centric feature maps, the visual similarity cues are invariant to 3D pose changes or small scale changes, which gives our method an advantage over 2D and 1D methods.
View selection in multi-view stacking: Choosing the meta-learner
Authors: Wouter van Loon, Marjolein Fokkema, Botond Szabo, Mark de Rooij
Date: 2020-10-30 00:00:00
Tasks: regression

Multi-view stacking is a framework for combining information from different views (i.e. different feature sets) describing the same set of objects. In this framework, a base-learner algorithm is trained on each view separately, and their predictions are then combined by a meta-learner algorithm. In a previous study, stacked penalized logistic regression, a special case of multi-view stacking, has been shown to be useful in identifying which views are most important for prediction. In this article we expand this research by considering seven different algorithms to use as the meta-learner, and evaluating their view selection and classification performance in simulations and two applications on real gene-expression data sets. Our results suggest that if both view selection and classification accuracy are important to the research at hand, then the nonnegative lasso, nonnegative adaptive lasso and nonnegative elastic net are suitable meta-learners. Exactly which among these three is to be preferred depends on the research context. The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three.
The Combinatorial Multi-Bandit Problem and its Application to Energy Management
Authors: Tobias Jacobs, Mischa Schmidt, Sébastien Nicolas, Anett Schülke
Date: 2020-10-30 00:00:00
Tasks: energy management, Management

We study a Combinatorial Multi-Bandit Problem motivated by applications in energy systems management. Given multiple probabilistic multi-arm bandits with unknown outcome distributions, the task is to optimize the value of a combinatorial objective function mapping the vector of individual bandit outcomes to a single scalar reward. Unlike in single-bandit problems with multi-dimensional action space, the outcomes of the individual bandits are observable in our setting and the objective function is known. Guided by the hypothesis that individual observability enables better trade-offs between exploration and exploitation, we generalize the lower regret bound for single bandits, showing that indeed for multiple bandits it admits parallelized exploration. For our energy management application we propose a range of algorithms that combine exploration principles for multi-arm bandits with mathematical programming. In an experimental study we demonstrate the effectiveness of our approach to learn action assignments for 150 bandits, each having 24 actions, within a horizon of 365 episodes.
Exploring Dynamic Context for Multi-path Trajectory Prediction
Authors: Hao Cheng, Wentong Liao, Xuejiao Tang, Michael Ying Yang, Monika Sester, Bodo Rosenhahn
Date: 2020-10-30 00:00:00
Tasks: Prediction, Trajectory Forecasting, Trajectory Prediction

To accurately predict future positions of different agents in traffic scenarios is crucial for safely deploying intelligent autonomous systems in the real-world environment. However, it remains a challenge due to the behavior of a target agent being affected by other agents dynamically and there being more than one socially possible paths the agent could take. In this paper, we propose a novel framework, named Dynamic Context Encoder Network (DCENet). In our framework, first, the spatial context between agents is explored by using self-attention architectures. Then, the two-stream encoders are trained to learn temporal context between steps by taking the respective observed trajectories and the extracted dynamic spatial context as input. The spatial-temporal context is encoded into a latent space using a Conditional Variational Auto-Encoder (CVAE) module. Finally, a set of future trajectories for each agent is predicted conditioned on the learned spatial-temporal context by sampling from the latent space, repeatedly. DCENet is evaluated on one of the most popular challenging benchmarks for trajectory forecasting Trajnet and reports a new state-of-the-art performance. It also demonstrates superior performance evaluated on the benchmark inD for mixed traffic at intersections. A series of ablation studies is conducted to validate the effectiveness of each proposed module. Our code is available at https://github.com/wtliao/DCENet.
Experimental design for MRI by greedy policy search
Authors: Tim Bakker, Herke van Hoof, Max Welling
Date: 2020-10-30 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Experimental Design, Policy Gradient Methods

In today's clinical practice, magnetic resonance imaging (MRI) is routinely accelerated through subsampling of the associated Fourier domain. Currently, the construction of these subsampling strategies - known as experimental design - relies primarily on heuristics. We propose to learn experimental design strategies for accelerated MRI with policy gradient methods. Unexpectedly, our experiments show that a simple greedy approximation of the objective leads to solutions nearly on-par with the more general non-greedy approach. We offer a partial explanation for this phenomenon rooted in greater variance in the non-greedy objective's gradient estimates, and experimentally verify that this variance hampers non-greedy models in adapting their policies to individual MR images. We empirically show that this adaptivity is key to improving subsampling designs.
SLM: Learning a Discourse Language Representation with Sentence Unshuffling
Authors: Haejun Lee, Drew A. Hudson, Kangwook Lee, Christopher D. Manning
Date: 2020-10-30 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Language Modeling, Language Modelling, Sentence

We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training methods in NLP focus on learning either bottom or top-level language representations: contextualized word representations derived from language model objectives at one extreme and a whole sequence representation learned by order classification of two given textual segments at the other. However, these models are not directly encouraged to capture representations of intermediate-size structures that exist in natural languages such as sentences and the relationships among them. To that end, we propose a new approach to encourage learning of a contextualized sentence-level representation by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering. Through experiments on downstream tasks such as GLUE, SQuAD, and DiscoEval, we show that this feature of our model improves the performance of the original BERT by large margins.
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
Authors: Hussain Hussain, Tomislav Duricic, Elisabeth Lex, Roman Kern, Denis Helic
Date: 2020-10-30 00:00:00
Tasks: Classification, General Classification, Model Selection, Node Classification

Graph Neural Networks (GNNs) are effective in many applications. Still, there is a limited understanding of the effect of common graph structures on the learning process of GNNs. In this work, we systematically study the impact of community structure on the performance of GNNs in semi-supervised node classification on graphs. Following an ablation study on six datasets, we measure the performance of GNNs on the original graphs, and the change in performance in the presence and the absence of community structure. Our results suggest that communities typically have a major impact on the learning process and classification performance. For example, in cases where the majority of nodes from one community share a single classification label, breaking up community structure results in a significant performance drop. On the other hand, for cases where labels show low correlation with communities, we find that the graph structure is rather irrelevant to the learning process, and a feature-only baseline becomes hard to beat. With our work, we provide deeper insights in the abilities and limitations of GNNs, including a set of general guidelines for model selection based on the graph structure.
Interleaving Fast and Slow Decision Making
Authors: Aditya Gulati, Sarthak Soni, Shrisha Rao
Date: 2020-10-30 00:00:00
Tasks: Decision Making

The "Thinking, Fast and Slow" paradigm of Kahneman proposes that we use two different styles of thinking -- a fast and intuitive System 1 for certain tasks, along with a slower but more analytical System 2 for others. While the idea of using this two-system style of thinking is gaining popularity in AI and robotics, our work considers how to interleave the two styles of decision-making, i.e., how System 1 and System 2 should be used together. For this, we propose a novel and general framework which includes a new System 0 to oversee Systems 1 and 2. At every point when a decision needs to be made, System 0 evaluates the situation and quickly hands over the decision-making process to either System 1 or System 2. We evaluate such a framework on a modified version of the classic Pac-Man game, with an already-trained RL algorithm for System 1, a Monte-Carlo tree search for System 2, and several different possible strategies for System 0. As expected, arbitrary switches between Systems 1 and 2 do not work, but certain strategies do well. With System 0, an agent is able to perform better than one that uses only System 1 or System 2.
"Thy algorithm shalt not bear false witness": An Evaluation of Multiclass Debiasing Methods on Word Embeddings
Authors: Thalea Schlender, Gerasimos Spanakis
Date: 2020-10-30 00:00:00
Tasks: Fairness, Word Embeddings

With the vast development and employment of artificial intelligence applications, research into the fairness of these algorithms has been increased. Specifically, in the natural language processing domain, it has been shown that social biases persist in word embeddings and are thus in danger of amplifying these biases when used. As an example of social bias, religious biases are shown to persist in word embeddings and the need for its removal is highlighted. This paper investigates the state-of-the-art multiclass debiasing techniques: Hard debiasing, SoftWEAT debiasing and Conceptor debiasing. It evaluates their performance when removing religious bias on a common basis by quantifying bias removal via the Word Embedding Association Test (WEAT), Mean Average Cosine Similarity (MAC) and the Relative Negative Sentiment Bias (RNSB). By investigating the religious bias removal on three widely used word embeddings, namely: Word2Vec, GloVe, and ConceptNet, it is shown that the preferred method is ConceptorDebiasing. Specifically, this technique manages to decrease the measured religious bias on average by 82,42%, 96,78% and 54,76% for the three word embedding sets respectively.
Multiplicative Updates for NMF with $β$-Divergences under Disjoint Equality Constraints
Authors: Valentin Leplat, Nicolas Gillis, Jérôme Idier
Date: 2020-10-30 00:00:00

Nonnegative matrix factorization (NMF) is the problem of approximating an input nonnegative matrix, $V$, as the product of two smaller nonnegative matrices, $W$ and $H$. In this paper, we introduce a general framework to design multiplicative updates (MU) for NMF based on $\beta$-divergences ($\beta$-NMF) with disjoint equality constraints, and with penalty terms in the objective function. By disjoint, we mean that each variable appears in at most one equality constraint. Our MU satisfy the set of constraints after each update of the variables during the optimization process, while guaranteeing that the objective function decreases monotonically. We showcase this framework on three NMF models, and show that it competes favorably the state of the art: (1)~$\beta$-NMF with sum-to-one constraints on the columns of $H$, (2) minimum-volume $\beta$-NMF with sum-to-one constraints on the columns of $W$, and (3) sparse $\beta$-NMF with $\ell_2$-norm constraints on the columns of $W$.
HOI Analysis: Integrating and Decomposing Human-Object Interaction
Authors: Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Cewu Lu
Date: 2020-10-30 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Human-Object Interaction Detection, Object

Human-Object Interaction (HOI) consists of human, object and implicit interaction/verb. Different from previous methods that directly map pixels to HOI semantics, we propose a novel perspective for HOI learning in an analytical manner. In analogy to Harmonic Analysis, whose goal is to study how to represent the signals with the superposition of basic waves, we propose the HOI Analysis. We argue that coherent HOI can be decomposed into isolated human and object. Meanwhile, isolated human and object can also be integrated into coherent HOI again. Moreover, transformations between human-object pairs with the same HOI can also be easier approached with integration and decomposition. As a result, the implicit verb will be represented in the transformation function space. In light of this, we propose an Integration-Decomposition Network (IDN) to implement the above transformations and achieve state-of-the-art performance on widely-used HOI detection benchmarks. Code is available at https://github.com/DirtyHarryLYL/HAKE-Action-Torch/tree/IDN-(Integrating-Decomposing-Network).