pwc-archive/papers-with-abstracts huggingface dataset:
Quantum algorithms for spectral sums
Authors: Alessandro Luongo, Changpeng Shao
Date: 2020-11-12 00:00:00

We propose new quantum algorithms for estimating spectral sums of positive semi-definite (PSD) matrices. The spectral sum of an PSD matrix $A$, for a function $f$, is defined as $ \text{Tr}[f(A)] = \sum_j f(\lambda_j)$, where $\lambda_j$ are the eigenvalues of $A$. Typical examples of spectral sums are the von Neumann entropy, the trace of $A^{-1}$, the log-determinant, and the Schatten $p$-norm, where the latter does not require the matrix to be PSD. The current best classical randomized algorithms estimating these quantities have a runtime that is at least linearly in the number of nonzero entries of the matrix and quadratic in the estimation error. Assuming access to a block-encoding of a matrix, our algorithms are sub-linear in the matrix size, and depend at most quadratically on other parameters, like the condition number and the approximation error, and thus can compete with most of the randomized and distributed classical algorithms proposed in the literature, and polynomially improve the runtime of other quantum algorithms proposed for the same problems. We show how the algorithms and techniques used in this work can be applied to three problems in spectral graph theory: approximating the number of triangles, the effective resistance, and the number of spanning trees within a graph.
Exploiting Cross-Dialectal Gold Syntax for Low-Resource Historical Languages: Towards a Generic Parser for Pre-Modern Slavic
Authors: Nilo Pedrazzini
Date: 2020-11-12 00:00:00
Tasks: Dependency Parsing, Part-Of-Speech Tagging, POS, POS Tagging

This paper explores the possibility of improving the performance of specialized parsers for pre-modern Slavic by training them on data from different related varieties. Because of their linguistic heterogeneity, pre-modern Slavic varieties are treated as low-resource historical languages, whereby cross-dialectal treebank data may be exploited to overcome data scarcity and attempt the training of a variety-agnostic parser. Previous experiments on early Slavic dependency parsing are discussed, particularly with regard to their ability to tackle different orthographic, regional and stylistic features. A generic pre-modern Slavic parser and two specialized parsers -- one for East Slavic and one for South Slavic -- are trained using jPTDP (Nguyen & Verspoor 2018), a neural network model for joint part-of-speech (POS) tagging and dependency parsing which had shown promising results on a number of Universal Dependency (UD) treebanks, including Old Church Slavonic (OCS). With these experiments, a new state of the art is obtained for both OCS (83.79\% unlabelled attachment score (UAS) and 78.43\% labelled attachement score (LAS)) and Old East Slavic (OES) (85.7\% UAS and 80.16\% LAS).
Hierarchical Prosody Modeling for Non-Autoregressive Speech Synthesis
Authors: Chung-Ming Chien, Hung-Yi Lee
Date: 2020-11-12 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

Prosody modeling is an essential component in modern text-to-speech (TTS) frameworks. By explicitly providing prosody features to the TTS model, the style of synthesized utterances can thus be controlled. However, predicting natural and reasonable prosody at inference time is challenging. In this work, we analyzed the behavior of non-autoregressive TTS models under different prosody-modeling settings and proposed a hierarchical architecture, in which the prediction of phoneme-level prosody features are conditioned on the word-level prosody features. The proposed method outperforms other competitors in terms of audio quality and prosody naturalness in our objective and subjective evaluation.
3D-OES: Viewpoint-Invariant Object-Factorized Environment Simulators
Authors: Hsiao-Yu Fish Tung, Zhou Xian, Mihir Prabhudesai, Shamit Lal, Katerina Fragkiadaki
Date: 2020-11-12 00:00:00
Tasks: Graph Neural Network, Object

We propose an action-conditioned dynamics model that predicts scene changes caused by object and agent interactions in a viewpoint-invariant 3D neural scene representation space, inferred from RGB-D videos. In this 3D feature space, objects do not interfere with one another and their appearance persists over time and across viewpoints. This permits our model to predict future scenes long in the future by simply "moving" 3D object features based on cumulative object motion predictions. Object motion predictions are computed by a graph neural network that operates over the object features extracted from the 3D neural scene representation. Our model's simulations can be decoded by a neural renderer into2D image views from any desired viewpoint, which aids the interpretability of our latent 3D simulation space. We show our model generalizes well its predictions across varying number and appearances of interacting objects as well as across camera viewpoints, outperforming existing 2D and 3D dynamics models. We further demonstrate sim-to-real transfer of the learnt dynamics by applying our model trained solely in simulation to model-based control for pushing objects to desired locations under clutter on a real robotic setup
Kernel k-Means, By All Means: Algorithms and Strong Consistency
Authors: Debolina Paul, Saptarshi Chakraborty, Swagatam Das, Jason Xu
Date: 2020-11-12 00:00:00
Tasks: All, Clustering

Kernel $k$-means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from non-convexity of the underlying objective function. In this paper, we generalize recent results leveraging a general family of means to combat sub-optimal local solutions to the kernel and multi-kernel settings. Called Kernel Power $k$-Means, our algorithm makes use of majorization-minimization (MM) to better solve this non-convex problem. We show the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates, and we rigorously characterize its convergence properties both from computational and statistical points of view. In particular, we characterize the large sample behavior of the proposed method by establishing strong consistency guarantees. Its merits are thoroughly validated on a suite of simulated datasets and real data benchmarks that feature non-linear and multi-view separation.
World Trade Center responders in their own words: Predicting PTSD symptom trajectories with AI-based language analyses of interviews
Authors: Youngseo Son, Sean A. P. Clouston, Roman Kotov, Johannes C. Eichstaedt, Evelyn J. Bromet, Benjamin J. Luft, H Andrew Schwartz
Date: 2020-11-12 00:00:00

Background: Oral histories from 9/11 responders to the World Trade Center (WTC) attacks provide rich narratives about distress and resilience. Artificial Intelligence (AI) models promise to detect psychopathology in natural language, but they have been evaluated primarily in non-clinical settings using social media. This study sought to test the ability of AI-based language assessments to predict PTSD symptom trajectories among responders. Methods: Participants were 124 responders whose health was monitored at the Stony Brook WTC Health and Wellness Program who completed oral history interviews about their initial WTC experiences. PTSD symptom severity was measured longitudinally using the PTSD Checklist (PCL) for up to 7 years post-interview. AI-based indicators were computed for depression, anxiety, neuroticism, and extraversion along with dictionary-based measures of linguistic and interpersonal style. Linear regression and multilevel models estimated associations of AI indicators with concurrent and subsequent PTSD symptom severity (significance adjusted by false discovery rate). Results: Cross-sectionally, greater depressive language (beta=0.32; p=0.043) and first-person singular usage (beta=0.31; p=0.044) were associated with increased symptom severity. Longitudinally, anxious language predicted future worsening in PCL scores (beta=0.31; p=0.031), whereas first-person plural usage (beta=-0.37; p=0.007) and longer words usage (beta=-0.36; p=0.007) predicted improvement. Conclusions: This is the first study to demonstrate the value of AI in understanding PTSD in a vulnerable population. Future studies should extend this application to other trauma exposures and to other demographic groups, especially under-represented minorities.
A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments
Authors: Manish Bhattarai, Manel Martınez-Ramon
Date: 2020-11-12 00:00:00
Tasks: Q-Learning

Live fire creates a dynamic, rapidly changing environment that presents a worthy challenge for deep learning and artificial intelligence methodologies to assist firefighters with scene comprehension in maintaining their situational awareness, tracking and relay of important features necessary for key decisions as they tackle these catastrophic events. We propose a deep Q-learning based agent who is immune to stress induced disorientation and anxiety and thus able to make clear decisions for navigation based on the observed and stored facts in live fire environments. As a proof of concept, we imitate structural fire in a gaming engine called Unreal Engine which enables the interaction of the agent with the environment. The agent is trained with a deep Q-learning algorithm based on a set of rewards and penalties as per its actions on the environment. We exploit experience replay to accelerate the learning process and augment the learning of the agent with human-derived experiences. The agent trained under this deep Q-learning approach outperforms agents trained through alternative path planning systems and demonstrates this methodology as a promising foundation on which to build a path planning navigation assistant capable of safely guiding fire fighters through live fire environments.
How to Measure Gender Bias in Machine Translation: Optimal Translators, Multiple Reference Points
Authors: Anna Farkas, Renáta Németh
Date: 2020-11-12 00:00:00
Tasks: Machine Translation, Translation

In this paper, as a case study, we present a systematic study of gender bias in machine translation with Google Translate. We translated sentences containing names of occupations from Hungarian, a language with gender-neutral pronouns, into English. Our aim was to present a fair measure for bias by comparing the translations to an optimal non-biased translator. When assessing bias, we used the following reference points: (1) the distribution of men and women among occupations in both the source and the target language countries, as well as (2) the results of a Hungarian survey that examined if certain jobs are generally perceived as feminine or masculine. We also studied how expanding sentences with adjectives referring to occupations effect the gender of the translated pronouns. As a result, we found bias against both genders, but biased results against women are much more frequent. Translations are closer to our perception of occupations than to objective occupational statistics. Finally, occupations have a greater effect on translation than adjectives.
Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping
Authors: Adithyavairavan Murali, Weiyu Liu, Kenneth Marino, Sonia Chernova, Abhinav Gupta
Date: 2020-11-12 00:00:00
Tasks: Diversity, Robotic Grasping

Despite the enormous progress and generalization in robotic grasping in recent years, existing methods have yet to scale and generalize task-oriented grasping to the same extent. This is largely due to the scale of the datasets both in terms of the number of objects and tasks studied. We address these concerns with the TaskGrasp dataset which is more diverse both in terms of objects and tasks, and an order of magnitude larger than previous datasets. The dataset contains 250K task-oriented grasps for 56 tasks and 191 objects along with their RGB-D information. We take advantage of this new breadth and diversity in the data and present the GCNGrasp framework which uses the semantic knowledge of objects and tasks encoded in a knowledge graph to generalize to new object instances, classes and even new tasks. Our framework shows a significant improvement of around 12% on held-out settings compared to baseline methods which do not use semantics. We demonstrate that our dataset and model are applicable for the real world by executing task-oriented grasps on a real robot on unknown objects. Code, data and supplementary video could be found at https://sites.google.com/view/taskgrasp
Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning
Authors: Yuan Jin, Wray Buntine, Francois Petitjean, Geoffrey I. Webb
Date: 2020-11-12 00:00:00
Tasks: Missing Values, Self-Supervised Learning

Supervised learning, characterized by both discriminative and generative learning, seeks to predict the values of single (or sometimes multiple) predefined target attributes based on a predefined set of predictor attributes. For applications where the information available and predictions to be made may vary from instance to instance, we propose the task of target-agnostic learning where arbitrary disjoint sets of attributes can be used for each of predictors and targets for each to-be-predicted instance. For this task, we survey a wide range of techniques available for handling missing values, self-supervised training and pseudo-likelihood training, and adapt them to a suite of algorithms that are suitable for the task. We conduct extensive experiments on this suite of algorithms on a large collection of categorical, continuous and discretized datasets, and report their performance in terms of both classification and regression errors. We also report the training and prediction time of these algorithms when handling large-scale datasets. Both generative and self-supervised learning models are shown to perform well at the task, although their characteristics towards the different types of data are quite different. Nevertheless, our derived theorem for the pseudo-likelihood theory also shows that they are related for inferring a joint distribution model based on the pseudo-likelihood training.
Realization of Stochastic Neural Networks and Its Potential Applications
Authors: S. Rahimi Kari
Date: 2020-11-12 00:00:00
Tasks: Decoder

Successive Cancellation Decoders have come a long way since the implementation of traditional SC decoders, but there still is a potential for improvement. The main struggle over the years was to find an optimal algorithm to implement them. Most of the proposed algorithms are not practical enough to be implemented in real-life. In this research, we aim to introduce the Efficiency of stochastic neural networks as an SC decoder and Find the possible ways of improving its performance and practicality. In this paper, after a brief introduction to stochastic neurons and SNNs, we introduce methods to realize Stochastic NNs on both deterministic and stochastic platforms.
StrObe: Streaming Object Detection from LiDAR Packets
Authors: Davi Frossard, Simon Suo, Sergio Casas, James Tu, Rui Hu, Raquel Urtasun
Date: 2020-11-12 00:00:00
Tasks: Object, object-detection, Object Detection

Many modern robotics systems employ LiDAR as their main sensing modality due to its geometrical richness. Rolling shutter LiDARs are particularly common, in which an array of lasers scans the scene from a rotating base. Points are emitted as a stream of packets, each covering a sector of the 360{\deg} coverage. Modern perception algorithms wait for the full sweep to be built before processing the data, which introduces an additional latency. For typical 10Hz LiDARs this will be 100ms. As a consequence, by the time an output is produced, it no longer accurately reflects the state of the world. This poses a challenge, as robotics applications require minimal reaction times, such that maneuvers can be quickly planned in the event of a safety-critical situation. In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built. StrObe reuses computations from previous packets and iteratively updates a latent spatial representation of the scene, which acts as a memory, as new evidence comes in, resulting in accurate low-latency perception. We demonstrate the effectiveness of our approach on a large scale real-world dataset, showing that StrObe far outperforms the state-of-the-art when latency is taken into account, and matches the performance in the traditional setting.
Turning Transport Data to Comply with EU Standards while Enabling a Multimodal Transport Knowledge Graph
Authors: Mario Scrocca, Marco Comerio, Alessio Carenini, Irene Celino
Date: 2020-11-12 00:00:00
Tasks: Translation

Complying with the EU Regulation on multimodal transportation services requires sharing data on the National Access Points in one of the standards (e.g., NeTEx and SIRI) indicated by the European Commission. These standards are complex and of limited practical adoption. This means that datasets are natively expressed in other formats and require a data translation process for full compliance. This paper describes the solution to turn the authoritative data of three different transport stakeholders from Italy and Spain into a format compliant with EU standards by means of Semantic Web technologies. Our solution addresses the challenge and also contributes to build a multi-modal transport Knowledge Graph of interlinked and interoperable information that enables intelligent querying and exploration, as well as facilitates the design of added-value services.
Heterogeneous Data-Aware Federated Learning
Authors: Lixuan Yang, Cedric Beliard, Dario Rossi
Date: 2020-11-12 00:00:00
Tasks: Federated Learning, Traffic Classification

Federated learning (FL) is an appealing concept to perform distributed training of Neural Networks (NN) while keeping data private. With the industrialization of the FL framework, we identify several problems hampering its successful deployment, such as presence of non i.i.d data, disjoint classes, signal multi-modality across datasets. In this work, we address these problems by proposing a novel method that not only (1) aggregates generic model parameters (e.g. a common set of task generic NN layers) on server (e.g. in traditional FL), but also (2) keeps a set of parameters (e.g, a set of task specific NN layer) specific to each client. We validate our method on the traditionally used public benchmarks (e.g., Femnist) as well as on our proprietary collected dataset (i.e., traffic classification). Results show the benefit of our method, with significant advantage on extreme cases.
Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement
Authors: Hamed Hemati, Damian Borth
Date: 2020-11-12 00:00:00
Tasks: text-to-speech, Text to Speech, Transfer Learning

Recent neural Text-to-Speech (TTS) models have been shown to perform very well when enough data is available. However, fine-tuning them for new speakers or languages is not straightforward in a low-resource setup. In this paper, we show that by applying minor modifications to a Tacotron model, one can transfer an existing TTS model for new speakers from the same or a different language using only 20 minutes of data. For this purpose, we first introduce a base multi-lingual Tacotron with language-agnostic input, then demonstrate how transfer learning is done for different scenarios of speaker adaptation without exploiting any pre-trained speaker encoder or code-switching technique. We evaluate the transferred model in both subjective and objective ways.
Towards A Sentiment Analyzer for Low-Resource Languages
Authors: Dian Indriani, Arbi Haza Nasution, Winda Monika, Salhazan Nasution
Date: 2020-11-12 00:00:00

Twitter is one of the top influenced social media which has a million number of active users. It is commonly used for microblogging that allows users to share messages, ideas, thoughts and many more. Thus, millions interaction such as short messages or tweets are flowing around among the twitter users discussing various topics that has been happening world-wide. This research aims to analyse a sentiment of the users towards a particular trending topic that has been actively and massively discussed at that time. We chose a hashtag \textit{\#kpujangancurang} that was the trending topic during the Indonesia presidential election in 2019. We use the hashtag to obtain a set of data from Twitter to analyse and investigate further the positive or the negative sentiment of the users from their tweets. This research utilizes rapid miner tool to generate the twitter data and comparing Naive Bayes, K-Nearest Neighbor, Decision Tree, and Multi-Layer Perceptron classification methods to classify the sentiment of the twitter data. There are overall 200 labeled data in this experiment. Overall, Naive Bayes and Multi-Layer Perceptron classification outperformed the other two methods on 11 experiments with different size of training-testing data split. The two classifiers are potential to be used in creating sentiment analyzer for low-resource languages with small corpus.
Automatic Neural Lyrics and Melody Composition
Authors: Gurunath Reddy Madhumani, Yi Yu, Florian Harscoët, Simon Canales, Suhua Tang
Date: 2020-11-12 00:00:00
Tasks: Decoder, Sentence

In this paper, we propose a technique to address the most challenging aspect of algorithmic songwriting process, which enables the human community to discover original lyrics, and melodies suitable for the generated lyrics. The proposed songwriting system, Automatic Neural Lyrics and Melody Composition (AutoNLMC) is an attempt to make the whole process of songwriting automatic using artificial neural networks. Our lyric to vector (lyric2vec) model trained on a large set of lyric-melody pairs dataset parsed at syllable, word and sentence levels are large scale embedding models enable us to train data driven model such as recurrent neural networks for popular English songs. AutoNLMC is a encoder-decoder sequential recurrent neural network model consisting of a lyric generator, a lyric encoder and melody decoder trained end-to-end. AutoNLMC is designed to generate both lyrics and corresponding melody automatically for an amateur or a person without music knowledge. It can also take lyrics from professional lyric writer to generate matching melodies. The qualitative and quantitative evaluation measures revealed that the proposed method is indeed capable of generating original lyrics and corresponding melody for composing new songs.