Papers.

Simplicitly: foundations and applications of implicit function types

Understanding a program entails understanding its context; dependencies, configurations and even implementations are all forms of contexts. Modern programming languages and theorem provers offer an array of constructs to define contexts, implicitly. Scala offers implicit parameters which are used pervasively, but which cannot be abstracted over. This paper describes a generalization of implicit parameters to implicit function types , a powerful way to abstract over the context in which some piece of code is run. We provide a formalization based on bidirectional type-checking that closely follows the semantics implemented by the Scala compiler. To demonstrate their range of abstraction capabilities, we present several applications that make use of implicit function types. We show how to encode the builder pattern, tagless interpreters, reader and free monads and we assess the performance of the monadic structures presented.

Low Latency Stream Processing: Apache Heron with Infiniband & Intel Omni-Path

Spark and Scala (keynote)

The limitations of type classes as subtyped implicits (short paper)

Type classes in Scala are encoded with implicit parameters and subtyping. This short paper describes limitations that arise from encoding type classes as subtyped implicits, in particular around coherence and ambiguity.

Interactive Development Using the Dotty Compiler

Typesafe abstractions for tensor operations (short paper)

We propose a typesafe abstraction to tensors (i.e. multidimensional arrays) exploiting the type-level programming capabilities of Scala through heterogeneous lists (HList), and showcase typesafe abstractions of common tensor operations and various neural layers such as convolution or recurrent neural networks. This abstraction could lay the foundation of future typesafe deep learning frameworks that runs on Scala/JVM.

XML and JSON Are Like Cardboard

Cardboard is what you wrap things in for shipping; likewise semi-structured formats are packaging for data in motion.

FA*IR

Research for practice

Self-Regulating Streaming Systems: Challenges and Opportunities

Mitigating Poisoning Attacks on Machine Learning Models: A Data Provenance Based Approach

SLAQ: quality-driven scheduling for distributed machine learning

Training machine learning (ML) models with large datasets can incur significant resource contention on shared clusters. This training typically involves many iterations that continually improve the quality of the model. Yet in exploratory settings, better models can be obtained faster by directing resources to jobs with the most potential for improvement. We describe SLAQ, a cluster scheduling system for approximate ML training jobs that aims to maximize the overall job quality. When allocating cluster resources, SLAQ explores the quality-runtime trade-offs across multiple jobs to maximize system-wide quality improvement. To do so, SLAQ leverages the iterative nature of ML training algorithms, by collecting quality and resource usage information from concurrent jobs, and then generating highly-tailored quality-improvement predictions for future iterations. Experiments show that SLAQ achieves an average quality improvement of up to 73% and an average delay reduction of up to 44% on a large set of ML training jobs, compared to resource fairness schedulers.

Haskell a language for modern times

A metaprogramming framework for formal verification

Déjà Vu: The Importance of Time and Causality in Recommender Systems

Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the important challenges of handling time in recommendation algorithms at Netflix. We will focus on challenges related to how our users, items, and systems all change over time. We will then discuss some strategies for tackling these challenges, which revolves around proper treatment of causality in our systems.

Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?

More than the Sum of its Parts: Building Domino Data Lab

Automated Fault Tree Analysis from AADL Models

Cyber-physical systems, used in domains such as avionics or medical devices, perform critical functions where a fault might have catastrophic consequences (mission failure, severe injuries, etc.). Their development is guided by rigorous practice standards that prescribe safety analysis methods in order to verify that failure have been correctly evaluated and/or mitigated. This laborintensive practice typically focuses system safety analysis on system engineering activities. As reliance on software for system operation grows, embedded software systems have become a major source of hazard contributors. Studies show that late discovery of errors in embedded software system have resulted in costly rework, making up as much as 50% of the total software system cost. Automation of the safety analysis process is key to extending safety analysis to the software system and to accommodate system evolution. In this paper we discuss three elements that are key to safety analysis automation in the context of fault tree analysis (FTA). First, generation of fault trees from annotated architecture models consistently reflects architecture changes in safety analysis results. Second, use of a taxonomy of failure effects ensures coverage of potential hazard contributors is achieved. Third, common cause failures are identified based on architecture information and reflected appropriately in probabilistic fault tree analysis. The approach utilizes the SAE Architecture Analysis & Design Language (AADL) standard and the recently published revised Error Model Annex V2 (EMV2) standard to represent annotated architecture models of systems and embedded software systems. The approach takes into account error sources specified with an EMV2 error propagation type taxonomy and occurrence probabilities as well as direct and indirect propagation paths between system components identified in the architecture model to generate a fault graph and apply transformations into a fault tree representatio

Side Effects, Front and Center!

Research for practice

Detection of Trending Topic Communities

Diagnosing Machine Learning Pipelines with Fine-grained Lineage

Semantic Query Understanding

The Lucene for Information Access and Retrieval Research (LIARR) Workshop at SIGIR 2017

Towards the Prediction of Dyslexia by a Web-based Game with Musical Elements

Provable learning of noisy-OR networks.

Finding approximate local minima faster than gradient descent.

Too Big NOT to Fail

Embracing failure as normal: how hyperscale services are engineered so that constant partial failure is survivable.

Research for practice

Global Entity Ranking Across Multiple Languages

We present work on building a global long-tailed ranking of entities across multiple languages using Wikipedia and Freebase knowledge bases. We identify multiple features and build a model to rank entities using a ground-truth dataset of more than 10 thousand labels. The final system ranks 27 million entities with 75% precision and 48% F1 score. We provide performance evaluation and empirical evidence of the quality of ranking across languages, and open the final ranked lists for future research.

DAWT: Densely Annotated Wikipedia Texts Across Multiple Languages

In this work, we open up the DAWT dataset - Densely Annotated Wikipedia Texts across multiple languages. The annotations include labeled text mentions mapping to entities (represented by their Freebase machine ids) as well as the type of the entity. The data set contains total of 13.6M articles, 5.0B tokens, 13.8M mention entity co-occurrences. DAWT contains 4.8 times more anchor text to entity links than originally present in the Wikipedia markup. Moreover, it spans several languages including English, Spanish, Italian, German, French and Arabic. We also present the methodology used to generate the dataset which enriches Wikipedia markup in order to increase number of links. In addition to the main dataset, we open up several derived datasets including mention entity co-occurrence counts and entity embeddings, as well as mappings between Freebase ids and Wikidata item ids. We also discuss two applications of these datasets and hope that opening them up would prove useful for the Natural Language Processing and Information Retrieval communities, as well as facilitate multi-lingual research.

Exploring Query Auto-Completion and Click Logs for Contextual-Aware Web Search and Query Suggestion

An Architecture Supporting Formal and Compositional Binary Analysis

ACIDRain

Scalable Kernel Density Classification via Threshold-Based Pruning

ZipG: A Memory-efficient Graph Store for Interactive Queries

Demonstration

MacroBase

Differentially-Private Big Data Analytics for High-Speed Research Network Traffic Measurement

Subcontracting Microwork

Research for practice

Next generation JDBC database drivers for performance, transparent caching, load balancing, and scale-out.

Ten Years of Wisdom

Research for practice

What happens when f0 movements and prosodic units were randomly aligned?

The Interdisciplinarity of Collaborations in Cognitive Science

Low-current Spin Transfer Torque MRAM

Low-current Spin Transfer Torque MRAM

Domain randomization for transferring deep neural networks from simulation to the real world

Bridging the ‘reality gap’ that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability. This paper explores domain randomization, a simple technique for training models on simulated images that transfer to real images by randomizing rendering in the simulator. With enough variability in the simulator, the real world may appear to the model as just another variation. We focus on the task of object localization, which is a stepping stone to general robotic manipulation skills. We find that it is possible to train a real-world object detector that is accurate to 1.5 cm and robust to distractors and partial occlusions using only data from a simulator with non-realistic random textures. To demonstrate the capabilities of our detectors, we show they can be used to perform grasping in a cluttered environment. To our knowledge, this is the first successful transfer of a deep neural network trained only on simulated RGB images (without pre-training on real images) to the real world for the purpose of robotic control.

Finding Bottlenecks: Predicting Student Attrition with Unsupervised Classifier

With pressure to increase graduation rates and reduce time to degree in higher education, it is important to identify at-risk students early. Automated early warning systems are therefore highly desirable. In this paper, we use unsupervised clustering techniques to predict the graduation status of declared majors in five departments at California State University Northridge (CSUN), based on a minimal number of lower division courses in each major. In addition, we use the detected clusters to identify hidden bottleneck courses.