Papers.

Model-based design and automated validation of ARINC653 architectures

Safety-Critical Systems as used in avionics systems are now extremely software-reliant. As these systems are life-or mission- critical, software must be carefully designed and certified according to stringent standards. One typical pitfalls of such project is the late detection of safety issues or bugs at integration time that impose to redo development steps. Model-Based Engineering aims at capturing system concerns with a specific notations and use models to drive the development process through all its phases - design, validation, implementation and ultimately, certification. Through a single consistent notation, such an approach would avoid undefined assumption and traditional hurdles due to informal, text-based, specifications. In this paper, we present recent contributions we pushed forward in the AADL architecture description language for the design and validation of Integrated Modular Avionics systems. First, we review modeling patterns to support abstractions for IMA systems. We then introduce capabilities to check all ARINC653 patterns are enforced at model-level. In addition, we review errror modeling and safety analysis capabilities towards the production of safety reports conforming to ARP4761 recommandations.

Big Data: Promises and Problems

Bitmap-Based On-line Analytical Processing of Time Interval Data

Matrix regressor adaptive observers for battery management systems

Models for sustainability

Low-current spin transfer torque MRAM

Deploying clouds in the Guifi community network

Decentralized credit mining in P2P systems

STT-MRAM with double magnetic tunnel junctions

GP-GPIS-OPT: Grasp planning with shape uncertainty using Gaussian process implicit surfaces and Sequential Convex Programming

Computing grasps for an object is challenging when the object geometry is not known precisely. In this paper, we explore the use of Gaussian process implicit surfaces (GPISs) to represent shape uncertainty from RGBD point cloud observations of objects. We study the use of GPIS representations to select grasps on previously unknown objects, measuring grasp quality by the probability of force closure. Our main contribution is GP-GPIS-OPT, an algorithm for computing grasps for parallel-jaw grippers on 2D GPIS object representations. Specifically, our method optimizes an approximation to the probability of force closure subject to antipodal constraints on the parallel jaws using Sequential Convex Programming (SCP). We also introduce GPIS-Blur, a method for visualizing 2D GPIS models based on blending shape samples from a GPIS. We test the algorithm on a set of 8 planar objects with transparency, translucency, and specularity. Our experiments suggest that GP-GPIS-OPT computes grasps with higher probability of force closure than a planner that does not consider shape uncertainty on our test objects and may converge to a grasp plan up to 5.7×faster than using Monte-Carlo integration, a common method for grasp planning under shape uncertainty. Furthermore, initial experiments on the Willow Garage PR2 robot suggest that grasps selected with GP-GPIS-OPT are up to 90% more successful than those planned assuming a deterministic shape. Our dataset, code, and videos of our experiments are available at http://rll.berkeley.edu/icra2015grasping/.

Toward asymptotically optimal motion planning for kinodynamic systems using a two-point boundary value problem solver

We present an approach for asymptotically optimal motion planning for kinodynamic systems with arbitrary nonlinear dynamics amid obstacles. Optimal sampling-based planners like RRT*, FMT*, and BIT* when applied to kinodynamic systems require solving a two-point boundary value problem (BVP) to perform exact connections between nodes in the tree. Two-point BVPs are non-trivial to solve, hence the prevalence of alternative approaches that focus on specific instances of kinodynamic systems, use approximate solutions to the two-point BVP, or use random propagation of controls. In this work, we explore the feasibility of exploiting recent advances in numerical optimal control and optimization to solve these two-point BVPs for arbitrary kinodynamic systems and how they can be integrated with existing optimal planning algorithms. We combine BIT* with a two-point BVP solver that uses sequential quadratic programming (SQP). We consider the problem of computing minimum-time trajectories. Since the duration of trajectories is not known a-priori, we include the time-step as part of the optimization to allow SQP to optimize over the duration of the trajectory while keeping the number of discrete steps fixed for every connection attempted. Our experiments indicate that using a two-point BVP solver in the inner-loop of BIT* is competitive with the state-of-the-art in sampling-based optimal planning that explicitly avoids the use of two-point BVP solvers.

The First Facial Landmark Tracking in-the-Wild Challenge: Benchmark and Results

Detection and tracking of faces in image sequences is among the most well studied problems in the intersection of statistical machine learning and computer vision. Often, tracking and detection methodologies use a rigid representation to describe the facial region 1, hence they can neither capture nor exploit the non-rigid facial deformations, which are crucial for countless of applications (e.g., facial expression analysis, facial motion capture, high-performance face recognition etc.). Usually, the non-rigid deformations are captured by locating and tracking the position of a set of fiducial facial landmarks (e.g., eyes, nose, mouth etc.). Recently, we witnessed a burst of research in automatic facial landmark localisation in static imagery. This is partly attributed to the availability of large amount of annotated data, many of which have been provided by the first facial landmark localisation challenge (also known as 300-W challenge). Even though now well established benchmarks exist for facial landmark localisation in static imagery, to the best of our knowledge, there is no established benchmark for assessing the performance of facial landmark tracking methodologies, containing an adequate number of annotated face videos. In conjunction with ICCV'2015 we run the first competition/challenge on facial landmark tracking in long-term videos. In this paper, we present the first benchmark for long-term facial landmark tracking, containing currently over 110 annotated videos,

A crosslinguistic study of prosodic focus

A security framework for population-scale genomics analysis

AUDIME: Augmented disaster medicine

Similarity Search of Bounded TIDASETs within Large Time Interval Databases

Control-oriented modeling and adaptive parameter estimation of a Lithium ion intercalation cell

Key-value store implementations for Arduino microcontrollers.

Sentiment Expression via Emoticons on Social Media

Emoticons (e.g., :) and :( ) have been widely used in sentiment analysis and other NLP tasks as features to machine learning algorithms or as entries of sentiment lexicons. In this paper, we argue that while emoticons are strong and common signals of sentiment expression on social media the relationship between emoticons and sentiment polarity are not always clear. Thus, any algorithm that deals with sentiment polarity should take emoticons into account but extreme caution should be exercised in which emoticons to depend on. First, to demonstrate the prevalence of emoticons on social media, we analyzed the frequency of emoticons in a large recent Twitter data set. Then we carried out four analyses to examine the relationship between emoticons and sentiment polarity as well as the contexts in which emoticons are used. The first analysis surveyed a group of participants for their perceived sentiment polarity of the most frequent emoticons. The second analysis examined clustering of words and emoticons to better understand the meaning conveyed by the emoticons. The third analysis compared the sentiment polarity of microblog posts before and after emoticons were removed from the text. The last analysis tested the hypothesis that removing emoticons from text hurts sentiment classification by training two models with and without emoticons in the text, respectively. The results confirms the arguments that: 1) a few emoticons are strong and reliable signals of sentiment polarity and one should take advantage of them in any sentiment analysis; 2) a large group of the emoticons conveys complicated sentiment hence they should be treated with extreme caution.

Klout score: Measuring influence across multiple social networks

In this work, we present the Klout Score, an influence scoring system that assigns scores to 750 million users across 9 different social networks on a daily basis. We propose a hierarchical framework for generating an influence score for each user, by incorporating information for the user from multiple networks and communities. Over 3600 features that capture signals of influential interactions are aggregated across multiple dimensions for each user. The features are scalably generated by processing over 45 billion interactions from social networks every day, as well as by incorporating factors that indicate real world influence. Supervised models trained from labeled data determine the weights for features, and the final Klout Score is obtained by hierarchically combining communities and networks. We validate the correctness of the score by showing that users with higher scores are able to spread information more effectively in a network. Finally, we use several comparisons to other ranking systems to show that highly influential and recognizable users across different domains have high Klout scores.

Identifying actionable messages on social media

Text actionability detection is the problem of classifying user authored natural language text, according to whether it can be acted upon by a responding agent. In this paper, we propose a supervised learning framework for domain-aware, large-scale actionability classification of social media messages. We derive lexicons, perform an in-depth analysis for over 25 text based features, and explore strategies to handle domains that have limited training data. We apply these methods to over 46 million messages spanning 75 companies and 35 languages, from both Facebook and Twitter. The models achieve an aggregate population-weighted F measure of 0.78 and accuracy of 0.74, with values of over 0.9 in some cases.

Scientific computing meets big data technology: An astronomy use case

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, tools from the HPC software stack are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark - a modern big data platform - to parallelize many-task applications. We present Kira, a flexible and distributed astronomy image processing toolkit using Apache Spark. We then use the Kira toolkit to implement a Source Extractor application for astronomy images, called Kira SE. With Kira SE as the use case, we study the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the EC2 cloud. By exploiting data locality, Kira SE achieves a 3.7 χ speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, we show that by leveraging software originally designed for big data infrastructure, Kira SE achieves competitive performance to the C implementation running on the NERSC Edison supercomputer. Our experience with Kira indicates that emerging Big Data platforms such as Apache Spark are a performant alternative for many-task scientific applications.

Fuzzing the Rust Typechecker Using CLP (T)

Hardware acceleration of Private Information Retrieval protocols using GPUs

Control-oriented modeling of spark assisted compression ignition using a double Wiebe function

Model for Thermal Relic Dark Matter of Strongly Interacting Massive Particles

The NIH BD2K center for big data in translational genomics

Harmony Assumptions in Information Retrieval and Social Networks

Validation Study for Automated Nucleic Acid Extraction From Frozen Tissue, Peripheral Blood, and Formalin-Fixed Paraffin-Embedded Tissue

Placing a Face to a Case: Investigating the Impact of Nonconventional Laboratory Practices on Staff Morale and Job Satisfaction

The Automated Lab: Challenges, Triumphs, Pitfalls, and Considerations

Noninvasive monitoring of infection and rejection after lung transplantation

Materials investigation for thermally-assisted magnetic random access memory robust against 400 °C temperatures

Intrinsic retroviral reactivation in human preimplantation embryos and pluripotent cells

The psychology of intelligence analysis: Drivers of prediction accuracy in world politics.

This article extends psychological methods and concepts into a domain that is as profoundly consequential as it is poorly understood: intelligence analysis. We report findings from a geopolitical forecasting tournament that assessed the accuracy of more than 150,000 forecasts of 743 participants on 199 events occurring over 2 years. Participants were above average in intelligence and political knowledge relative to the general population. Individual differences in performance emerged, and forecasting skills were surprisingly consistent over time. Key predictors were (a) dispositional variables of cognitive ability, political knowledge, and open-mindedness; (b) situational variables of training in probabilistic reasoning and participation in collaborative teams that shared information and discussed rationales (Mellers, Ungar, et al., 2014); and (c) behavioral variables of deliberation time and frequency of belief updating. We developed a profile of the best forecasters; they were better at inductive reasoning, pattern detection, cognitive flexibility, and open-mindedness. They had greater understanding of geopolitics, training in probabilistic reasoning, and opportunities to succeed in cognitively enriched team environments. Last but not least, they viewed forecasting as a skill that required deliberate practice, sustained effort, and constant monitoring of current affairs.

A representation theorem for second-order functionals

CUFP'13 scribe's report

Flow simulation and analysis of high-power flow batteries

Time and information retrieval: Introduction to the special issue

Playing with knowledge: A virtual player for “Who Wants to Be a Millionaire?” that leverages question answering techniques

Effects of a Community-Based Fall Management Program on Medicare Cost Savings

Measuring Inter-site Engagement in a Network of Sites

Continuous Model Selection for Large-Scale Recommender Systems

How to use NodeXL

Robust semantic text similarity using LSA, machine learning, and linguistic resources

How to present more readable text for people with dyslexia

Paying the Guard: An Entry-Guard-Based Payment System for Tor

Firenzina: Porting a Chess Engine to Android

BiobankCloud: A Platform for the Secure Storage, Sharing, and Processing of Large Biomedical Data Sets

Reviewer Integration and Performance Measurement for Malware Detection

We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate that even in small numbers, reviewers can vastly improve the system's ability to keep pace with evolving threats. We conduct our evaluation on a sample of VirusTotal submissions spanning 2.5i?źyears and containing 1.1 million binaries with 778i?źGB of raw feature data. Without reviewer assistance, we achieve 72i?ź% detection at a 0.5i?ź% false positive rate, performing comparable to the best vendors on VirusTotal. Given a budget of 80 accurate reviews daily, we improve detection to 89i?ź% and are able to detect 42i?ź% of malicious binaries undetected upon initial submission to VirusTotal. Additionally, we identify a previously unnoticed temporal inconsistency in the labeling of training datasets. We compare the impact of training labels obtained at the same time training data is first seen with training labels obtained months later. We find that using training labels obtained well after samples appear, and thus unavailable in practice for current training data, inflates measured detection by almost 20i?ź% points. We release our cluster-based implementation, as well as a list of all hashes in our evaluation and 3i?ź% of our entire dataset.