Given Myanmar’s historical and socio-political context, hate speech spread on social media have escalated into offline unrest and violence. This paper presents findings from our remote study on the automatic detection of hate speech online in Myanmar. We argue that effectively addressing this problem will require community-based approaches that combine the knowledge of context experts with machine learning tools that can analyze the vast amount of data produced. To this end, we develop a systematic process to facilitate this collaboration covering key aspects of data collection, annotation, and model validation strategies. We highlight challenges in this area stemming from small and imbalanced datasets, the need to balance non-glamorous data work and stakeholder priorities, and closed data-sharing practices. Stemming from these findings, we discuss avenues for further work in developing and deploying hate speech detection systems for low-resource languages.

Internet video providers rely on alerting workflows to identify and remedy incidents that can impact users (e.g., outages or buggy players). There is growing evidence for the need for viewership-based analytics---detecting and diagnosing incidents that manifest through changes in viewership patterns but not in other (e.g., QoE) metrics. However, both detection and diagnosis of viewership anomalies is challenging due to the contextual nature of anomalies, non-stationarity of viewership, and complex dependencies between the structure of events and how they impact different subpopulations of viewers. We present Proteas, an alerting framework for video viewership anomalies that tackles these challenges. Proteas builds on key spatiotemporal structural insights. First, across different sub-populations of viewers and days of the week, we find that the shape of the viewership curve remains invariant over multiple weeks, thus enabling anomaly detection. Second, we use the hierarchy of viewership groups to produce compact alerts. Finally, we find that common anomalies manifest with spatiotemporal signatures, which enables us to classify anomalies to produce actionable alerts. We evaluate Proteas using 3 months of real viewership data (including the onset of the COVID-19 pandemic) and show that Proteas is accurate with over 80% True Positive Rate, average precision of over 86% (i.e., few false positives) and doesn't miss any major events. In addition, we find that approximately half of Proteas's alerts refer to events not caught by other alerting workflows, thus adding value to operators' existing toolkit.

Summary Planning for large‐scale epidemiological outbreaks in livestock populations often involves executing compute‐intensive disease spread simulations. To capture the probabilities of various outcomes, these simulations are executed several times over a collection of representative input scenarios, producing voluminous data. The resulting datasets contain valuable insights, including sequences of events that lead to extreme outbreaks. However, discovering and leveraging such information is also computationally expensive. In this study, we set out to achieve two goals, ie, (1) providing a distributed framework for modeling disease transmission at scale using Spark, including improvements to the default GraphX partitioning strategy, and (2) giving planners and epidemiologists a means to analyze interactions between entities (herds) during simulated disease outbreaks. Using our disease transmission network (DTN), planners or analysts can isolate herds that have a disproportionate effect on epidemiological outcomes, enabling effective allocation of limited resources such as vaccinations and field personnel. We use a representative dataset to verify our approach and optimized the underlying graph partitioning algorithm to ensure the system will scale with increases in the dataset size or number of participating machines. Our analysis includes identification of influential herds as well as the creation of machine learning models for accurate classifications that generalize to other datasets.

We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word level generative model for natural language. It uses a recurrent neural network with a dynamic attention and canvas memory mechanism to iteratively construct sentences. By viewing the state of the memory at intermediate stages and where the model is placing its attention, we gain insight into how it constructs sentences. We demonstrate that AUTR learns a meaningful latent representation for each sentence, and achieves competitive log-likelihood lower bounds whilst being computationally efficient. It is effective at generating and reconstructing sentences, as well as imputing missing words.

We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning of the sentence. GNMT achieves competitive BLEU scores on pure translation tasks, and is superior when there are missing words in the source sentence. We augment the model to facilitate multilingual translation and semi-supervised learning without adding parameters. This framework significantly reduces overfitting when there is limited paired data available, and is effective for translating between pairs of languages not seen during training.

The implementation of 2D-3v (2D in space and 3D in velocity space) PIC-MCC (Particle-In-Cell Monte Carlo Collision) method described in this paper involves the computational solution of Vlasov-Poisson equations, which provides the spatial and temporal evolution of the charged-particle velocity distribution functions in plasmas under the effect of self-consistent electromagnetic (EM) fields and collisions. Stringent numerical constraints associated with a PIC code makes it computationally prohibitive on CPUs in case of large problem sizes (total number of particles, number of grid points and simulation time-scale). We present the design and implementation of a Graphics Processing Unit (GPU) based 2D-3v PIC code using the CUDA C APIs for Kepler architecture. Several parallelization and optimization techniques have been presented in this paper with special emphasis on shuffle intrinsic specific to Nvidia Kepler architecture (or later), which significantly improves the performance compared to existing GPU implementations in the literature. On a test bed comprising of a serial implementation on Xeon E5 CPU and parallel implementations on Nvidia Tesla K40 graphics card, we have achieved a speedup of up to 60x in double precision mode. Effect of important numerical parameters on speedup has been investigated. Finally, we compare the performance of our best parallel implementation on different GPUs (Kepler as well as Maxwell) and analyze the effect of hardware architecture on the performance of the PIC code.

The ability to fit complex generative probabilistic models to data is a key challenge in AI. Currently, variational methods are popular, but remain difficult to train due to high variance of the sampling methods employed. We introduce the overdispersed variational autoencoder and overdispersed importance weighted autoencoder, which combine overdispersed black box variational inference with the variational autoencoder and importance weighted autoencoder respectively. We use the log likelihood lower bounds and reparametrisation trick from the variational and importance weighted autoencoders, but rather than drawing samples from the variational distribution itself, we use importance sampling to draw samples from an overdispersed (i.e. heavier-tailed) proposal in the same family as the variational distribution. We run experiments on two different datasets, and show that this technique produces a lower variance estimate of the gradients, and reaches a higher bound on the log likelihood of the observed data.

Planning for large-scale epidemiological outbreaks in livestock populations often involves executing compute-intensive disease spread simulations. To capture the probabilities of various outcomes, these simulations are executed several times over a collection of representative input scenarios, producing voluminous data. The resulting datasets contain valuable insights, including sequences of events that lead to extreme outbreaks. However, discovering and leveraging such information is also computationally expensive. In this study, we propose a distributed approach for analyzing voluminous epidemiology data to locate and classify the most influential entities in a disease outbreak. Using our disease transmission network (DTN), planners or analysts can isolate entities that have a disproportionate effect on epidemiological outcomes, enabling effective allocation of limited resources such as vaccinations and field personnel. We use a representative dataset to verify our approach, including identification of influential entities and creation of machine learning models for accurate classifications that generalize to other datasets.

MOTIVATION: We introduce Census 2, an update of a mass spectrometry data analysis tool for peptide/protein quantification. New features for analysis of isobaric labeling, such as Tandem Mass Tag (TMT) or Isobaric Tags for Relative and Absolute Quantification (iTRAQ), have been added in this version, including a reporter ion impurity correction, a reporter ion intensity threshold filter and an option for weighted normalization to correct mixing errors. TMT/iTRAQ analysis can be performed on experiments using HCD (High Energy Collision Dissociation) only, CID (Collision Induced Dissociation)/HCD (High Energy Collision Dissociation) dual scans or HCD triple-stage mass spectrometry data. To improve measurement accuracy, we implemented weighted normalization, multiple tandem spectral approach, impurity correction and dynamic intensity threshold features. AVAILABILITY AND IMPLEMENTATION: Census 2 supports multiple input file formats including MS1/MS2, DTASelect, mzXML and pepXML. It requires JAVA version 6 or later to run. Free download of Census 2 for academic users is available at http://fields.scripps.edu/census/index.php. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.