Papers.

Natural Language Processing Using R Pocket Primer

Lenguaje de dominio especifico embebido para programacion estocastica multietapa en Scala

Kafka in Action

Practical guide to Apache Kafka as a high-performance event-streaming platform: core concepts, producers and consumers, Kafka Connect ETL, schema management, Java clients, and administration and operations for data pipelines.

Java for Developers Pocket Primer

Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments.

Hands-on Reinforcement Learning for Recommender Systems: From Bandits to SlateQ to Offline RL with Ray RLlib

Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization.

DevOps Tools for Java Developers: Best Practices from Source Code to Production Containers

Decoupled Transactions: Low Tail Latency Online Transactions Atop Jittery Servers

Proposes decoupling transaction execution from the servers running it so that online transactions keep low tail latency even when individual servers are jittery.

Dealing with Data Pocket Primer

Data Wrangling Using Pandas, SQL, and Java

Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation.

Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift.

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations.

Bash for Data Scientists

An Explanation of In-context Learning as Implicit Bayesian Inference.

All aboard the GPT-3 Express! A LegalTech AI Hype Train

Advanced Analytics with PySpark: Patterns for Learning from Data at Scale Using Python and Spark

Technical Debt in Practice

"A look at technical debt in software that focuses on the practical implications of technical debt for the entire software lifecycle"--

Achieving Sybil-Proofness in Distributed Work Systems

First DIHARD Challenge -- System Submissions and Scores

Responsible AI for Social Media Governance

Responsible AI for Social Media Governance

Continual Learning In Environments With Polynomial Mixing Times

The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios. Yet, the effect of mixing times on learning in continual reinforcement learning (RL) remains underexplored. In this paper, we characterize problems that are of long-term interest to the development of continual RL, which we call scalable MDPs, through the lens of mixing times. In particular, we theoretically establish that scalable MDPs have mixing times that scale polynomially with the size of the problem. We go on to demonstrate that polynomial mixing times present significant difficulties for existing approaches, which suffer from myopic bias and stale bootstrapped estimates. To validate our theory, we study the empirical scaling behavior of mixing times with respect to the number of tasks and task duration for high performing policies deployed across multiple Atari games. Our analysis demonstrates both that polynomial mixing times do emerge in practice and how their existence may lead to unstable learning behavior like catastrophic forgetting in continual learning settings.

Evaluating a Session-based Recommender System using Prod2vec in a Commercial Application

Practical Precision Agriculture with LoRa based Wireless Sensor Networks.

Efficient Flash Indexing for Time Series Data on Memory-constrained Embedded Sensor Devices.

Polarimetric Properties of Event Horizon Telescope Targets from ALMA

First M87 Event Horizon Telescope Results. VII. Polarization of the Ring

First M87 Event Horizon Telescope Results. VIII. Magnetic Field Structure near the Event Horizon

Global TIMIT Mandarin Chinese

Random Forest Similarity Maps: A Scalable Visual Representation for Global and Local Interpretation

Machine Learning prediction algorithms have made significant contributions in today's world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in addressing such an issue, allowing users to grasp models' inner workings. Despite their popularity, visualization techniques still present visual scalability limitations, mainly when applied to analyze popular and complex models, such as Random Forests (RF). In this work, we propose Random Forest Similarity Map (RFMap), a scalable interactive visual analytics tool designed to analyze RF ensemble models. RFMap focuses on explaining the inner working mechanism of models through different views describing individual data instance predictions, providing an overview of the entire forest of trees, and highlighting instance input feature values.

Gender Bias in the News: A Scalable Topic Modelling and Visualization Framework

Gender bias in the news is examined through a scalable topic modelling and visualization framework applied to a large corpus of Canadian English-language news. The authors combine named-entity-based gender annotation with topic models to surface which topics quote and mention women versus men, and provide an interactive visualization for exploring the results over time and across outlets.

The Future of Computational Linguistics: On Beyond Alchemy

Automated Analysis of Digitized Letter Fluency Data

Digital Speech Analysis in Progressive Supranuclear Palsy and Corticobasal Syndromes

Natural Language Processing with Pandas DataFrames

Most areas of Python data science have standardized on using Pandas DataFrames for representing and manipulating structured data in memory. Natural Language Processing (NLP), not so much. We believe that Pandas has the potential to serve as a universal data structure for NLP data. DataFrames could make every phase of NLP easier, from creating new models, to evaluating their effectiveness, to building applications that integrate those models. However, Pandas currently lacks important data types and operations for representing and manipulating crucial types of data in many of these NLP tasks. This paper describes Text Extensions for Pandas, a library of extensions to Pandas that make it possible to build end-to-end NLP applications while representing all of the applications’ internal data with DataFrames. We leverage the extension points built into Pandas library to add new data types, and we provide important NLP-specfific operations over these data types and and integrations with popular NLP libraries and data formats.

Improved Identification of Portraiture of the Julio-Claudian Period with Mobile Apps

We present the results of our image analysis of portrait art from the Roman Empire’s Julio-Claudian dynastic period. Our novel approach involves processing pictures of ancient statues, cameos, altar friezes, bas-reliefs, frescoes, and coins using modern mobile apps, such as Reface and FaceApp, to improve identification of the historical subjects depicted. In particular, we have discovered that the Reface app has limited, but useful capability to restore the approximate appearance of damaged noses of the statues. We confirm many traditional identifications, propose a few identification corrections for items located in museums and private collections around the world, and discuss the advantages and limitations of our approach. For example, Reface may make aquiline noses appear wider or shorter than they should be. This deficiency can be partially corrected if multiple views are available. We demonstrate that our approach can be extended to analyze portraiture from other cultures and historical periods. The article is intended for a broad section of the readers interested in how the modern AI-based solutions for mobile imaging merge with humanities to help improve our understanding of the modern civilization’s ancient past and increase appreciation of our diverse cultural heritage.

Grover Adaptive Search for Constrained Polynomial Binary Optimization

Correction: Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis (Preprint)

Correction: Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis

Bots and Misinformation Spread on Social Media: Implications for COVID-19

As of March 2021, the SARS-CoV-2 virus has been responsible for over 115 million cases of COVID-19 worldwide, resulting in over 2.5 million deaths. As the virus spread exponentially, so did its media coverage, resulting in a proliferation of conflicting information on social media platforms-a so-called "infodemic." In this viewpoint, we survey past literature investigating the role of automated accounts, or "bots," in spreading such misinformation, drawing connections to the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and examine the potential origins of bots. We conclude by conducting and presenting a secondary analysis of data sets of known bots in which we find that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic.

Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis

Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia.

Comparing Supervised Models and Learned Speech Representations for Classifying Intelligibility of Disordered Speech on Selected Phrases.

Automatic Speech Recognition of Disordered Speech: Personalized Models Outperforming Human Listeners on Short Phrases.

The Third DIHARD Diarization Challenge

Variational Quantum Optimization with Multi-Basis Encodings

Despite extensive research efforts, few quantum algorithms for classical optimization demonstrate realizable quantum advantage. The utility of many quantum algorithms is limited by high requisite circuit depth and nonconvex optimization landscapes. We tackle these challenges by introducing a new variational quantum algorithm that utilizes multi-basis graph encodings and nonlinear activation functions. Our technique results in increased optimization performance, a factor of two increase in effective quantum resources, and a quadratic reduction in measurement complexity. While the classical simulation of many qubits with traditional quantum formalism is impossible due to its exponential scaling, we mitigate this limitation with exact circuit representations using factorized tensor rings. In particular, the shallow circuits permitted by our technique, combined with efficient factorized tensor-based simulation, enable us to successfully optimize the MaxCut of the nonlocal 512-vertex DIMACS library graphs on a single GPU. By improving the performance of quantum optimization algorithms while requiring fewer quantum resources and utilizing shallower, more error-resistant circuits, we offer tangible progress for variational quantum optimization.

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