OpenRaiser/Intern-Atlas huggingface dataset:
Good-case and Bad-case Latency of Unauthenticated Byzantine Broadcast: A Complete Categorization
Venue: arXiv cs.DC 2021
Year: 2021

This paper studies the {\em good-case latency} of {\em unauthenticated} Byzantine fault-tolerant broadcast, which measures the time it takes for all non-faulty parties to commit given a non-faulty broadcaster. For both asynchrony and synchrony, we show that $n\geq 4f$ is the tight resilience threshold that separates good-case 2 rounds and 3 rounds. For asynchronous Byzantine reliable broadcast (BRB), we also investigate the {\em bad-case latency} for all non-faulty parties to commit when the broadcaster is faulty but some non-faulty party commits. We provide matching upper and lower bounds on the resilience threshold of bad-case latency for BRB protocols with optimal good-case latency of 2 rounds. In particular, we show 2 impossibility results and propose 4 asynchronous BRB protocols.
Rendering Spatial Sound for Interoperable Experiences in the Audio Metaverse
Venue: arXiv cs.SD 2021
Year: 2021

Interactive audio spatialization technology previously developed for video game authoring and rendering has evolved into an essential component of platforms enabling shared immersive virtual experiences for future co-presence, remote collaboration and entertainment applications. New wearable virtual and augmented reality displays employ real-time binaural audio computing engines rendering multiple digital objects and supporting the free navigation of networked participants or their avatars through a juxtaposition of environments, real and virtual, often referred to as the Metaverse. These applications require a parametric audio scene programming interface to facilitate the creation and deployment of shared, dynamic and realistic virtual 3D worlds on mobile computing platforms and remote servers. We propose a practical approach for designing parametric 6-degree-of-freedom object-based interactive audio engines to deliver the perceptually relevant binaural cues necessary for audio/visual and virtual/real congruence in Metaverse experiences. We address the effects of room reverberation, acoustic reflectors, and obstacles in both the virtual and real environments, and discuss how such effects may be driven by combinations of pre-computed and real-time acoustic propagation solvers. We envision an open scene description model distilled to facilitate the development of interoperable applications distributed across multiple platforms, where each audio object represents, to the user, a natural sound source having controllable distance, size, orientation, and acoustic radiation properties.
Parallel Refinements for Lexically Constrained Text Generation with BART
Venue: arXiv cs.CL 2021
Year: 2021

Lexically constrained text generation aims to control the generated text by incorporating some pre-specified keywords into the output. Previous work injects lexical constraints into the output by controlling the decoding process or refining the candidate output iteratively, which tends to generate generic or ungrammatical sentences, and has high computational complexity. To address these challenges, we propose Constrained BART (CBART) for lexically constrained text generation. CBART leverages the pre-trained model BART and transfers part of the generation burden from the decoder to the encoder by decomposing this task into two sub-tasks, thereby improving the sentence quality. Concretely, we extend BART by adding a token-level classifier over the encoder, aiming at instructing the decoder where to replace and insert. Guided by the encoder, the decoder refines multiple tokens of the input in one step by inserting tokens before specific positions and re-predicting tokens with low confidence. To further reduce the inference latency, the decoder predicts all tokens in parallel. Experiment results on One-Billion-Word and Yelp show that CBART can generate plausible text with high quality and diversity while significantly accelerating inference.
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling
Venue: arXiv cs.LG 2021
Year: 2021

The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popular in the last years. Although this representation has advanced our understanding of brain function, it represents a simplified model of brain connectivity that has a complex dynamic spatio-temporal nature. Oversimplification of the data may hinder the merits of applying advanced non-linear feature extraction algorithms. To this end, we propose a dynamic adaptive spatio-temporal graph convolution (DAST-GCN) model to overcome the shortcomings of pre-defined static correlation-based graph structures. The proposed approach allows end-to-end inference of dynamic connections between brain regions via layer-wise graph structure learning module while mapping brain connectivity to a phenotype in a supervised learning framework. This leverages the computational power of the model, data and targets to represent brain connectivity, and could enable the identification of potential biomarkers for the supervised target in question. We evaluate our pipeline on the UKBiobank dataset for age and gender classification tasks from resting-state functional scans and show that it outperforms currently adapted linear and non-linear methods in neuroimaging. Further, we assess the generalizability of the inferred graph structure by transferring the pre-trained graph to an independent dataset for the same task. Our results demonstrate the task-robustness of the graph against different scanning parameters and demographics.
Efficient Non-linear Calculators
Venue: arXiv cs.LG 2021
Year: 2021

A novel algorithm for producing smooth nonlinearities on digital hardware is presented. The non-linearities are inherently quadratic and have both symmetrical and asymmetrical variants. The integer (and fixed point) implementation is highly amenable for use with digital gates on an ASIC or FPGA. The implementations are multiplier-less. Scaling of the non-linear output, as required in an LSTM cell, is integrated into the implementation. This too does not require a multiplier. The non-linearities are useful as activation functions in a variety of ANN architectures. The floating point mappings have been compared with other non-linearities and have been benchmarked. Results show that these functions should be considered in the ANN design phase. The hardware resource usage of the implementations have been thoroughly investigated. Our results make a strong case for implementions in edge applications. This document summarizes the findings and serves to give a quick overview of the outcomes of our research\footnote{The authors peer-reviewed manuscripts (available at https://doi.org/10.1016/j.neucom.2021.02.030) offer more detail and may be better suited for a thorough consideration}.
Soundata: A Python library for reproducible use of audio datasets
Venue: arXiv cs.SD 2021
Year: 2021

Soundata is a Python library for loading and working with audio datasets in a standardized way, removing the need for writing custom loaders in every project, and improving reproducibility by providing tools to validate data against a canonical version. It speeds up research pipelines by allowing users to quickly download a dataset, load it into memory in a standardized and reproducible way, validate that the dataset is complete and correct, and more. Soundata is based and inspired on mirdata and design to complement mirdata by working with environmental sound, bioacoustic and speech datasets, among others. Soundata was created to be easy to use, easy to contribute to, and to increase reproducibility and standardize usage of sound datasets in a flexible way.
Equilibria and learning dynamics in mixed network coordination/anti-coordination games
Venue: arXiv cs.GT 2021
Year: 2021

Whilst network coordination games and network anti-coordination games have received a considerable amount of attention in the literature, network games with coexisting coordinating and anti-coordinating players are known to exhibit more complex behaviors. In fact, depending on the network structure, such games may even fail to have pure-strategy Nash equilibria. An example is represented by the well-known matching pennies (discoordination) game. In this work, we first provide graph-theoretic conditions for the existence of pure-strategy Nash equilibria in mixed network coordination/anti-coordination games of arbitrary size. For the case where such conditions are met, we then study the asymptotic behavior of best-response dynamics and provide sufficient conditions for finite-time convergence to the set of Nash equilibria. Our results build on an extension and refinement of the notion of network cohesiveness and on the formulation of the new concept of network indecomposibility.
The Colombian Scientific Elite -- Science Mapping and Bibliometric Outlook
Venue: arXiv cs.DL 2021
Year: 2021

A well established agenda on the research output, impact, and structure of global scientific elites such as Nobel Prize laureates has generated interest in the scientific elites from developing countries. This study deploys science mapping techniques to provide a comprehensive analysis of the output, impact, and structure of the Colombian scientific elite, i.e., researchers awarded with the Alejandro Angel Escobar Foundation National Prize 1990 2020, known locally as the Colombian Nobel. Findings showed that the Colombian scientific elite has a broader agenda than indexing titles in internationally renowned bibliographic databases. The Colombian scientific elite also showed positive growth, which is an inverse trend compared with Nobel laureate productivity. There were no noticeable changes in productivity and impact before and after receiving the prize. Institutional collaboration within the Colombian scientific elite displayed the highest betweenness (brokerage) role of world and local top-tier universities. However, only two Colombian scientific elite members published an article with two Nobel Prize laureates. Most of the research profiles reflected the national output priorities, but were found to diverge from the national focus in respect of strategic research capacities. This study also conducted a productivity and impact comparison with Nobel Prize laureates in science and economics by means of a stratified random sample 1990-2020 via the composite indicator proposed by Ioannidis et al. The interleaving of the Colombian scientific elite and Nobel Prize laureates, particularly between the 3rd and 2nd quartiles, enabled a more nuanced analysis of the local impact in the global scientific landscape.
Learning Multimodal Rewards from Rankings
Venue: arXiv cs.LG 2021
Year: 2021

Learning from human feedback has shown to be a useful approach in acquiring robot reward functions. However, expert feedback is often assumed to be drawn from an underlying unimodal reward function. This assumption does not always hold including in settings where multiple experts provide data or when a single expert provides data for different tasks -- we thus go beyond learning a unimodal reward and focus on learning a multimodal reward function. We formulate the multimodal reward learning as a mixture learning problem and develop a novel ranking-based learning approach, where the experts are only required to rank a given set of trajectories. Furthermore, as access to interaction data is often expensive in robotics, we develop an active querying approach to accelerate the learning process. We conduct experiments and user studies using a multi-task variant of OpenAI's LunarLander and a real Fetch robot, where we collect data from multiple users with different preferences. The results suggest that our approach can efficiently learn multimodal reward functions, and improve data-efficiency over benchmark methods that we adapt to our learning problem.
High Frame Rate Video Quality Assessment using VMAF and Entropic Differences
Venue: arXiv cs.MM 2021
Year: 2021

The popularity of streaming videos with live, high-action content has led to an increased interest in High Frame Rate (HFR) videos. In this work we address the problem of frame rate dependent Video Quality Assessment (VQA) when the videos to be compared have different frame rate and compression factor. The current VQA models such as VMAF have superior correlation with perceptual judgments when videos to be compared have same frame rates and contain conventional distortions such as compression, scaling etc. However this framework requires additional pre-processing step when videos with different frame rates need to be compared, which can potentially limit its overall performance. Recently, Generalized Entropic Difference (GREED) VQA model was proposed to account for artifacts that arise due to changes in frame rate, and showed superior performance on the LIVE-YT-HFR database which contains frame rate dependent artifacts such as judder, strobing etc. In this paper we propose a simple extension, where the features from VMAF and GREED are fused in order to exploit the advantages of both models. We show through various experiments that the proposed fusion framework results in more efficient features for predicting frame rate dependent video quality. We also evaluate the fused feature set on standard non-HFR VQA databases and obtain superior performance than both GREED and VMAF, indicating the combined feature set captures complimentary perceptual quality information.
The VMT-LIB Language and Tools
Venue: arXiv cs.LO 2021
Year: 2021

We present VMT-LIB, a language for the representation of verification problems of linear-time temporal properties on infinite-state symbolic transition systems. VMT-LIB is an extension of the standard SMT-LIB language for SMT solvers, developed with the goal of facilitating the interoperability and exchange of benchmark problems among different verification tools. Besides describing its syntax and semantics, we also present a set of open-source tools to work with the language.
Review of Clustering-Based Recommender Systems
Venue: arXiv cs.IR 2021
Year: 2021

Recommender systems are one of the most applied methods in machine learning and find applications in many areas, ranging from economics to the Internet of things. This article provides a general overview of modern approaches to recommender system design using clustering as a preliminary step to improve overall performance. Using clustering can address several known issues in recommendation systems, including increasing the diversity, consistency, and reliability of recommendations; the data sparsity of user-preference matrices; and changes in user preferences over time. This work will be useful for both beginners in the field of recommender systems and specialists in related fields that are interested in examining the applicability of recommender systems. This review is focused on the analysis of the scientific literature on the topics of recommender systems and clustering models that have appeared in recent years and contains a representative list of the literature for the further exploration of this topic. In the first part, a brief introduction to the so-called classic or traditional recommendation algorithms is given, along with an overview of the clustering problem.
News Consumption in Time of Conflict: 2021 Palestinian-Israel War as an Example
Venue: arXiv cs.SI 2021
Year: 2021

This paper examines news consumption in response to a major polarizing event, and we use the May 2021 Israeli-Palestinian conflict as an example. We conduct a detailed analysis of the news consumption of more than eight thousand Twitter users who are either pro-Palestinian or pro-Israeli and authored more than 29 million tweets between January 1 and August 17, 2021. We identified the stance of users using unsupervised stance detection. We observe that users may consume more topically-related content from foreign and less popular sources, because, unlike popular sources, they may reaffirm their views, offer more extreme, hyper-partisan, or sensational content, or provide more in depth coverage of the event. The sudden popularity of such sources may not translate to longer-term or general popularity on other topics.
Bayesian deep learning of affordances from RGB images
Venue: arXiv cs.CV 2021
Year: 2021

Autonomous agents, such as robots or intelligent devices, need to understand how to interact with objects and its environment. Affordances are defined as the relationships between an agent, the objects, and the possible future actions in the environment. In this paper, we present a Bayesian deep learning method to predict the affordances available in the environment directly from RGB images. Based on previous work on socially accepted affordances, our model is based on a multiscale CNN that combines local and global information from the object and the full image. However, previous works assume a deterministic model, but uncertainty quantification is fundamental for robust detection, affordance-based reason, continual learning, etc. Our Bayesian model is able to capture both the aleatoric uncertainty from the scene and the epistemic uncertainty associated with the model and previous learning process. For comparison, we estimate the uncertainty using two state-of-the-art techniques: Monte Carlo dropout and deep ensembles. We also compare different types of CNN encoders for feature extraction. We have performed several experiments on an affordance database on socially acceptable behaviours and we have shown improved performance compared with previous works. Furthermore, the uncertainty estimation is consistent with the the type of objects and scenarios. Our results show a marginal better performance of deep ensembles, compared to MC-dropout on the Brier score and the Expected Calibration Error.
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting
Venue: arXiv cs.LG 2021
Year: 2021

The crime forecasting is an important problem as it greatly contributes to urban safety. Typically, the goal of the problem is to predict different types of crimes for each geographical region (like a neighborhood or censor tract) in the near future. Since nearby regions usually have similar socioeconomic characteristics which indicate similar crime patterns, recent state-of-the-art solutions constructed a distance-based region graph and utilized Graph Neural Network (GNN) techniques for crime forecasting, because the GNN techniques could effectively exploit the latent relationships between neighboring region nodes in the graph. However, this distance-based pre-defined graph cannot fully capture crime correlation between regions that are far from each other but share similar crime patterns. Hence, to make an accurate crime prediction, the main challenge is to learn a better graph that reveals the dependencies between regions in crime occurrences and meanwhile captures the temporal patterns from historical crime records. To address these challenges, we propose an end-to-end graph convolutional recurrent network called HAGEN with several novel designs for crime prediction. Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution Gated Recurrent Unit (DCGRU). Based on the homophily assumption of GNN, we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution. It also incorporates crime embedding to model the interdependencies between regions and crime categories. Empirical experiments and comprehensive analysis on two real-world datasets showcase the effectiveness of HAGEN.
Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
Venue: arXiv cs.RO 2021
Year: 2021

This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a (BNN), and evaluate two practical inference techniques to obtain better uncertainty estimates, namely Concrete Dropout (CDP) and Kronecker-factored Laplace Approximation (LAP). We show a performance increase using more reliable uncertainty estimates as unary potentials within a Conditional Random Field (CRF), which is able to incorporate contextual information as well. Furthermore, the obtained uncertainties are exploited to achieve domain adaptation in a semi-supervised manner, which requires less manual efforts in annotating data. We evaluate our approach on two public benchmark datasets that are relevant for robot perception tasks.
Distributed Computing With the Cloud
Venue: arXiv cs.DC 2021
Year: 2021

We investigate the effect of omnipresent cloud storage on distributed computing. We specify a network model with links of prescribed bandwidth that connect standard processing nodes, and, in addition, passive storage nodes. Each passive node represents a cloud storage system, such as Dropbox, Google Drive etc. We study a few tasks in this model, assuming a single cloud node connected to all other nodes, which are connected to each other arbitrarily. We give implementations for basic tasks of collaboratively writing to and reading from the cloud, and for more advanced applications such as matrix multiplication and federated learning. Our results show that utilizing node-cloud links as well as node-node links can considerably speed up computations, compared to the case where processors communicate either only through the cloud or only through the network links. We provide results for general directed graphs, and for graphs with ``fat'' links between processing nodes. For the general case, we provide optimal algorithms for uploading and downloading files using flow techniques. We use these primitives to derive algorithms for \emph{combining}, where every processor node has an input value and the task is to compute a combined value under some given associative operator. In the case of fat links, we assume that links between processors are bidirectional and have high bandwidth, and we give near-optimal algorithms for any commutative combining operator (such as vector addition). For the task of matrix multiplication (or other non-commutative combining operators), where the inputs are ordered, we present sharp results in the simple ``wheel'' network, where procesing nodes are arranged in a ring, and are all connected to a single cloud node.
Time Series Model Attribution Visualizations as Explanations
Venue: arXiv cs.LG 2021
Year: 2021

Attributions are a common local explanation technique for deep learning models on single samples as they are easily extractable and demonstrate the relevance of input values. In many cases, heatmaps visualize such attributions for samples, for instance, on images. However, heatmaps are not always the ideal visualization to explain certain model decisions for other data types. In this review, we focus on attribution visualizations for time series. We collect attribution heatmap visualizations and some alternatives, discuss the advantages as well as disadvantages and give a short position towards future opportunities for attributions and explanations for time series.