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Roleplaying has a history of serving diverse aims, including art, entertainment, therapeutic purposes, and political action (Piper 1973; Gygax 1978; Boal 1993; Matthews et al. 2014). This paper presents an interactive narrative called Grayscale that uses roleplaying to provoke critical reflection through modeling workplace gender discrimination. Grayscale's interface resembles a streamlined email system, constituting what Henry Jenkins has termed an “embedded narrative” since the narration is distributed across elements in the space (e.g. emails, notes, draft messages)., O jogo de papéis tem servido diversos objetivos, incluindo arte, entretenimento, fins terapêuticos e ação política (Piper 1973; Gygax 1978; Boal 1993; Matthews et al. 2014). Este artigo apresenta uma narrativa interativa chamada Grayscale, que usa o jogo de papéis para provocar a reflexão crítica através da modelagem da discriminação de género no local de trabalho. A interface do Grayscale assemelha-se a um sistema de e-mail simplificado, constituindo o que Henry Jenkins denominou de “narrativa embutida”, já que a narração é distribuída entre os elementos no espaço (por exemplo, e-mails, notas, rascunhos de mensagens).

We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs) - pairs of points in source and target sets that are mutual nearest neighbours, i.e., each point is the nearest neighbour of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset while using different types of features., Israel Science Foundation (Grant 1917/2015), National Science Foundation (U.S.) (1212849), Shell Research

Backscatter requires zero transmission power, making it a compelling technology for in-body communication and localization. It can significantly reduce the battery requirements (and hence the size) of micro-implants and smart capsules, and enable them to be located on-the-move inside the body. The problem however is that the electrical properties of human tissues are very different from air and vacuum. This creates new challenges for both communication and localization. For example, signals no longer travel along straight lines, which destroys the geometric principles underlying many localization algorithms. Furthermore, the human skin backscatters the signal creating strong interference to the weak in-body backscatter transmission. These challenges make deep-tissue backscatter intrinsically different from backscatter in air or vacuum. This paper introduces ReMix, a new backscatter design that is particularly customized for deep tissue devices. It overcomes interference from the body surface, and localizes the in-body backscatter devices even though the signal travels along crooked paths. We have implemented our design and evaluated it in animal tissues and human phantoms. Our results demonstrate that ReMix delivers efficient communication at an average SNR of 15.2 dB at 1 MHz bandwidth, and has an average localization accuracy of 1.4cm in animal tissues.

Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy iUS. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. High-dimensional texture attributes were used instead of image intensities for image registration and the standard difference-based attribute matching was replaced with correlation-based attribute matching. A strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images was proposed. Key parameters were optimized across independent MR-iUS brain tumor datasets acquired at 3 institutions, with a total of 43 tumor patients and 758 reference landmarks for evaluating the accuracy of the proposed algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, the algorithm is able to reduce landmark errors prior to registration in three data sets (5.37±4.27, 4.18±1.97 and 6.18±3.38 mm, respectively) to a consistently low level (2.28±0.71, 2.08±0.37 and 2.24±0.78 mm, respectively). This algorithm was tested against 15 other algorithms and it is competitive with the state-of-the-art on multiple datasets. We show that the algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). Landmark errors were further characterized according to brain regions and tumor types, a topic so far missing in the literature., National Institute of Health (Grants P41-EB015898, P41-EB015902 and R01-NS049251)

The rise in computing hardware choices is driving a reevaluation of operating systems. The traditional role of an operating system controlling the execution of its own hardware is evolving toward a model whereby the controlling processor is distinct from the compute engines that are performing most of the computations. In this context, an operating system can be viewed as software that brokers and tracks the resources of the compute engines and is akin to a database management system. To explore the idea of using a database in an operating system role, this work defines key operating system functions in terms of rigorous mathematical semantics (associative array algebra) that are directly translatable into database operations. These operations possess a number of mathematical properties that are ideal for parallel operating systems by guaranteeing correctness over a wide range of parallel operations. The resulting operating system equations provide a mathematical specification for a Tabular Operating System Architecture (TabulaROSA) that can be implemented on any platform. Simulations of forking in TabularROSA are performed using an associative array implementation and compared to Linux on a 32,000+ core supercomputer. Using over 262,000 forkers managing over 68,000,000,000 processes, the simulations show that TabulaROSA has the potential to perform operating system functions on a massively parallel scale. The TabulaROSA simulations show 20x higher performance as compared to Linux while managing 2000x more processes in fully searchable tables., United States. Department of Defense. Assistant Secretary of Defense for Research & Engineering (Air Force Contract No. FA8721-05-C-0002), United States. Department of Defense. Assistant Secretary of Defense for Research & Engineering (Air Force Contract No. FA8702-15-D-0001)

How best to evaluate a saliency model's ability to predict where humans look in images is an open research question. The choice of evaluation metric depends on how saliency is defined and how the ground truth is represented. Metrics differ in how they rank saliency models, and this results from how false positives and false negatives are treated, whether viewing biases are accounted for, whether spatial deviations are factored in, and how the saliency maps are pre-processed. In this paper, we provide an analysis of 8 different evaluation metrics and their properties. With the help of systematic experiments and visualizations of metric computations, we add interpretability to saliency scores and more transparency to the evaluation of saliency models. Building off the differences in metric properties and behaviors, we make recommendations for metric selections under specific assumptions and for specific applications.

We envision an intelligent agent that analyzes conversations during human team meetings in order to infer the team’s plan, with the purpose of providing decision support to strengthen that plan. We present a novel learning technique to infer teams' final plans directly from a processed form of their planning conversation. Our method employs reinforcement learning to train a model that maps features of the discussed plan and patterns of dialogue exchange among participants to a final, agreed-upon plan. We employ planning domain models to efficiently search the large space of possible plans, and the costs of candidate plans serve as the reinforcement signal. We demonstrate that our technique successfully infers plans within a variety of challenging domains, with higher accuracy than prior art. With our domain-independent feature set, we empirically demonstrate that our model trained on one planning domain can be applied to successfully infer team plans within a novel planning domain.

We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan. We then derive admissible abstractions for two motion planning domains with continuous state. We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain. These bounds guide the search for a plan while maintaining performance guarantees. We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner. Using our abstractions, we find near-optimal motion plans in planning problems involving $10^{13}$ states without using a separate task planner., Comment: This document is an extended version of a paper to appear in the "27th International Joint Conference on Artificial Intelligence" in July 2018

Coherent uncertainty quantification is a key strength of Bayesian methods. But modern algorithms for approximate Bayesian posterior inference often sacrifice accurate posterior uncertainty estimation in the pursuit of scalability. This work shows that previous Bayesian coreset construction algorithms-which build a small, weighted subset of the data that approximates the full dataset-are no exception. We demonstrate that these algorithms scale the coreset log-likelihood suboptimally, resulting in underestimated posterior uncertainty. To address this shortcoming, we develop greedy iterative geodesic ascent (GIGA), a novel algorithm for Bayesian coreset construction that scales the coreset log-likelihood optimally. GIGA provides geometric decay in posterior approximation error as a function of coreset size, and maintains the fast running time of its predecessors. The paper concludes with validation of GIGA on both synthetic and real datasets, demonstrating that it reduces posterior approximation error by orders of magnitude compared with previous coreset constructions., United States. Office of Naval Research (Grant N00014-17-1-2072)

The red-blue pebble game was formulated in the 1980s [14] to model the I/O complexity of algorithms on a two-level memory hierarchy. Given a directed acyclic graph representing computations (vertices) and their dependencies (edges), the red-blue pebble game allows sequentially adding, removing, and recoloring red or blue pebbles according to a few rules, where red pebbles represent data in cache (fast memory) and blue pebbles represent data on disk (slow, external memory). Specifically, a vertex can be newly pebbled red if and only if all of its predecessors currently have a red pebble; pebbles can always be removed; and pebbles can be recolored between red and blue (corresponding to reading or writing data between disk and cache, also called I/Os or memory transfers). Given an upper bound on the number of red pebbles at any time (the cache size), the goal is to compute a game execution with the fewest pebble recolorings (memory transfers) that finish with pebbles on a specified subset of nodes (outputs get computed). In this paper, we investigate the complexity of computing this trade-off between red-pebble limit (cache size) and number of recolorings (memory transfers) in general DAGs. First we prove this problem PSPACE-complete through an extension of the proof PSPACE-hardness of black pebbling complexity [13]. Second, we consider a natural restriction on the red-blue pebble game to forbid pebble deletions, or equivalently, forbid discarding data from cache without first writing it to disk. This assumption both simplifies the model and immediately places the trade-off computation problem within NP. Unfortunately, we show that even this restricted version is NP-complete. Finally, we show that the trade-off problem parameterized by the number of transitions is W[1]-hard, meaning that there is likely no algorithm running in a fixed polynomial for constant number of transitions., NSF (Grant 1122374)

Objective: The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments. Summary Background Data: AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers. Methods: A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed. Results: Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed. Conclusions: Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care.

We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU., National Institutes of Health (U.S.) (Contract HHSN268201000029C)

We consider the problem of sequential manipulationand tool-use planning in domains that include physical interac-tions such as hitting and throwing. The approach integrates aTask And Motion Planning formulation with primitives that ei-ther impose stable kinematic constraints or differentiable dynam-ical and impulse exchange constraints at the path optimizationlevel. We demonstrate our approach on a variety of physicalpuzzles that involve tool use and dynamic interactions. We thencompare manipulation sequences generated by our approach tohuman actions on analogous tasks, suggesting future directionsand illuminating current limitations.

The successful deployment of many autonomous systems in part hinges on providing rigorous guarantees on their performance and safety through a formal verification method, such as reachability analysis. In this work, we present a simple-to-implement, sampling-based algorithm for reachability analysis that is provably optimal up to any desired approximation accuracy. Our method achieves computational efficiency by judiciously sampling a finite subset of the state space and generating an approximate reachable set by conducting reachability analysis on this finite set of states. We prove that the reachable set generated by our algorithm approximates the ground-truth reachable set for any user-specified approximation accuracy. As a corollary to our main method, we introduce an asymptoticallyoptimal, anytime algorithm for reachability analysis. We present simulation results that reaffirm the theoretical properties of our algorithm and demonstrate its effectiveness in real-world inspired scenarios, National Science Foundation (U.S.)

We prove that if every problem in NP has nk-size circuits for a fixed constant k, then for every NP-verifier and every yes-instance x of length n for that verifier, the verifier’s search space has an nO(k3)size witness circuit: a witness for x that can be encoded with a circuit of only nO(k3) size. An analogous statement is proved for nondeterministic quasi-polynomial time, i.e., NQP = NTIME nlogO(1) n . This significantly extends the Easy Witness Lemma of Impagliazzo, Kabanets, and Wigderson [JCSS’02] which only held for larger nondeterministic classes such as NEXP. As a consequence, the connections between circuit-analysis algorithms and circuit lower bounds can be considerably sharpened: algorithms for approximately counting satisfying assignments to given circuits which improve over exhaustive search can imply circuit lower bounds for functions in NQP, or even NP. To illustrate, applying known algorithms for satisfiability of ACC ◦ THR circuits [R. Williams, STOC 2014] we conclude that for every fixed k, NQP does not have nlogk n-size ACC ◦ THR circuits., National Science Foundation (U.S.). Career (Award CCF-1552651)

Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN. We compare our approach with a segmentation method on fifteen cities, and find that at a 5% error rate, RoadTracer correctly captures 45% more junctions across these cities., Comment: CVPR 2018

Control of robots in safety-critical tasks and situations where costly errors may occur is paramount for realizing the vision of pervasive human-robot collaborations. For these cases, the ability to use human cognition in the loop can be key for recuperating safe robot operation. This paper combines two streams of human biosignals, electrical muscle and brain activity via EMG and EEG, respectively, to achieve fast and accurate human intervention in a supervisory control task. In particular, this paper presents an end-to-end system for continuous rolling-window classification of gestures that allows the human to actively correct the robot on demand, discrete classification of Error-Related Potential signals (unconsciously produced by the human supervisor’s brain when observing a robot error), and a framework that integrates these two classification streams for fast and effective human intervention. The system also allows “plug-and-play” operation, demonstrating accurate performance even with new users whose biosignals have not been used for training the classifiers. The resulting hybrid control system for safety-critical situations is evaluated with 7 untrained human subjects in a supervisory control scenario where an autonomous robot performs a multi-target selection task., Boeing Company

Several hybrid transactional memory (HyTM) schemes have recently been proposed to complement the fast, but best-effort nature of hardware transactional memory with a slow, reliable software backup. However, the costs of providing concurrency between hardware and software transactions in HyTM are still not well understood. In this paper, we propose a general model for HyTM implementations, which captures the ability of hardware transactions to buffer memory accesses. The model allows us to formally quantify and analyze the amount of overhead (instrumentation) caused by the potential presence of software transactions. We prove that (1) it is impossible to build a strictly serializable HyTM implementation that has both uninstrumented reads and writes, even for very weak progress guarantees, and (2) the instrumentation cost incurred by a hardware transaction in any progressive opaque HyTM is linear in the size of the transaction’s data set. We further describe two implementations which exhibit optimal instrumentation costs for two different progress conditions. In sum, this paper proposes the first formal HyTM model and captures for the first time the trade-off between the degree of hardware-software TM concurrency and the amount of instrumentation overhead. Keywords: hardware transactional memory, Instrumentation, Lower bounds

Model diagnosis is the process of analyzing machine learning (ML) model performance to identify where the model works well and where it doesn’t. It is a key part of the modeling process and helps ML developers iteratively improve model accuracy. Often, model diagnosis is performed by analyzing different datasets or inter- mediates associated with the model such as the input data and hidden representations learned by the model (e.g., [ 4 , 24 , 39 ]). The bottleneck in fast model diagnosis is the creation and storage of model intermediates. Storing these intermediates requires tens to hundreds of GB of storage whereas re-running the model for each diagnostic query slows down model diagnosis. To address this bottleneck, we propose a system called MISTIQUE that can work with traditional ML pipelines as well as deep neural networks to efficiently capture, store, and query model intermediates for diag- nosis. For each diagnostic query, MISTIQUE intelligently chooses whether to re-run the model or read a previously stored intermediate. For intermediates that are stored in MISTIQUE , we propose a range of optimizations to reduce storage footprint including quantization, summarization, and data deduplication. We evaluate our techniques on a range of real-world ML models in scikit-learn and Tensorflow. We demonstrate that our optimizations reduce storage by up to 110X for traditional ML pipelines and up to 6X for deep neural networks. Furthermore, by using MISTIQUE , we can speed up diagnostic queries on traditional ML pipelines by up to 390X and 210X on deep neural networks., Facebook PhD Fellowship, Alfred P. Sloan Foundation. University Centers for Exemplary Mentoring (UCEM) fellowship

Research in human vision suggests that in a single fixation, humans can extract a significant amount of information from a natural scene, e.g. the semantic category, spatial layout, and object identities. This ability is useful, for example, for quickly determining location, navigating around obstacles, detecting threats, and guiding eye movements to gather more information. In this paper, we ask a new question: What can we see at a glance at a web page – an artificial yet complex “real world” stimulus? Is it possible to notice the type of website, or where the relevant elements are, with only a glimpse? We find that observers, fixating at the center of a web page shown for only 120 milliseconds, are well above chance at classifying the page into one of ten categories. Furthermore, this ability is supported in part by text that they can read at a glance. Users can also understand the spatial layout well enough to reliably localize the menu bar and to detect ads, even though the latter are often camouflaged among other graphical elements. We discuss the parallels between web page gist and scene gist, and the implications of our findings for both vision science and human-computer interaction., Google Award

This paper considers the problem of completing assemblies of passive objects in nonconvex environments, cluttered with convex obstacles of unknown position, shape and size that satisfy a specific separation assumption. A differential drive robot equipped with a gripper and a LIDAR sensor, capable of perceiving its environment only locally, is used to position the passive objects in a desired configuration. The method combines the virtues of a deliberative planner generating high-level, symbolic commands, with the formal guarantees of convergence and obstacle avoidance of a reactive planner that requires little onboard computation and is used online. The validity of the proposed method is verified both with formal proofs and numerical simulations., ARL/GDRS RCTA project (agreement no. W911NF-1020016), AFRL (grant no. FA865015D1845)

Deep neural network (DNN) architectures have been shown to outperform traditional pipelines for object segmentation and pose estimation using RGBD data, but the performance of these DNN pipelines is directly tied to how representative the training data is of the true data. Hence a key requirement for employing these methods in practice is to have a large set of labeled data for your specific robotic manipulation task, a requirement that is not generally satisfied by existing datasets. In this paper we develop a pipeline to rapidly generate high quality RGBD data with pixelwise labels and object poses. We use an RGBD camera to collect video of a scene from multiple viewpoints and leverage existing reconstruction techniques to produce a 3D dense reconstruction. We label the 3D reconstruction using a human assisted ICP-fitting of object meshes. By reprojecting the results of labeling the 3D scene we can produce labels for each RGBD image of the scene. This pipeline enabled us to collect over 1,000,000 labeled object instances in just a few days. We use this dataset to answer questions related to how much training data is required, and of what quality the data must be, to achieve high performance from a DNN architecture. Our dataset and annotation pipeline are available at labelfusion.csail.mit.edu.

Deep learning has been successfully applied to “end-to-end” learning of the autonomous driving task, where a deep neural network learns to predict steering control commands from camera data input. While these previous works support reactionary control, the representation learned is not usable for higher-level decision making required for autonomous navigation. This paper tackles the problem of learning a representation to predict a continuous control probability distribution, and thus steering control options and bounds for those options, which can be used for autonomous navigation. Each mode of the distribution encodes a possible macro-action that the system could execute at that instant, and the covariances of the modes place bounds on safe steering control values. Our approach has the added advantage of being trained on unlabeled data collected from inexpensive cameras. The deep neural network based algorithm generates a probability distribution over the space of steering angles, from which we leverage Variational Bayesian methods to extract a mixture model and compute the different possible actions in the environment. A bound, which the autonomous vehicle must respect in our parallel autonomy setting, is then computed for each of these actions. We evaluate our approach on a challenging dataset containing a wide variety of driving conditions, and show that our algorithm is capable of parameterizing Gaussian Mixture Models for possible actions, and extract steering bounds with a mean error of only 2 degrees. Additionally, we demonstrate our system working on a full scale autonomous vehicle and evaluate its ability to successful handle various different parallel autonomy situations., Toyota Research Institute

Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, which is an instance of NER. In this work, we analyze to what extent transfer learning may address this issue. In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification., Comment: The first two authors contributed equally to this work

In this paper we consider a fleet of self-driving cars operating in a road network governed by rules of the road, such as the Vienna Convention on Road Traffic, providing rides to customers to serve their demands with desired deadlines. We focus on the associated motion planning problem that trades-off the demands' delays and level of violation of the rules of the road to achieve social optimum among the vehicles. Due to operating in the same environment, the interaction between the cars must be taken into account, and can induce further delays. We propose an integrated route and motion planning approach that achieves scalability with respect to the number of cars by resolving potential collision situations locally within so-called bubble spaces enclosing the conflict. The algorithms leverage the road geometries, and perform joint planning only for lead vehicles in the conflict and use queue scheduling for the remaining cars. Furthermore, a framework for storing previously resolved conflict situations is proposed, which can be use for quick querying of joint motion plans. We show the mobility-on-demand setup and effectiveness of the proposed approach in simulated case studies involving up to 10 self-driving vehicles.

Glial cells have increasingly been implicated as active participants in the pathogenesis of neurological diseases, but critical pathways and mechanisms controlling glial function and secondary non-cell autonomous neuronal injury remain incompletely defined. Here we use models of Alexander disease, a severe brain disorder caused by gain-of-function mutations in GFAP, to demonstrate that misregulation of GFAP leads to activation of a mechanosensitive signaling cascade characterized by activation of the Hippo pathway and consequent increased expression of A-type lamin. Importantly, we use genetics to verify a functional role for dysregulated mechanotransduction signaling in promoting behavioral abnormalities and non-cell autonomous neurodegeneration. Further, we take cell biological and biophysical approaches to suggest that brain tissue stiffness is increased in Alexander disease. Our findings implicate altered mechanotransduction signaling as a key pathological cascade driving neuronal dysfunction and neurodegeneration in Alexander disease, and possibly also in other brain disorders characterized by gliosis., National Institute of Child Health and Human Development (U.S.) (P01HD076892), National Institute of Child Health and Human Development (U.S.) (U54HD090255), National Institute of Child Health and Human Development (U.S.) (U54HD090256), National Science Foundation (U.S.). Division of Materials Research (DMR-1310266), National Science Foundation (U.S.). Division of Materials Research (DMR-1420570), National Institutes of Health (U.S.) (P01HL120839-03), National Institutes of Health (U.S.) (P01GM096971)

Accurate and robust object state estimation enables successful object manipulation. Visual sensing is widely used to estimate object poses. However, in a cluttered scene or in a tight workspace, the robot's end-effector often occludes the object from the visual sensor. The robot then loses visual feedback and must fall back on open-loop execution. In this paper, we integrate both tactile and visual input using a framework for solving the SLAM problem, incremental smoothing and mapping (iSAM), to provide a fast and flexible solution. Visual sensing provides global pose information but is noisy in general, whereas contact sensing is local, but its measurements are more accurate relative to the end-effector. By combining them, we aim to exploit their advantages and overcome their limitations. We explore the technique in the context of a pusher-slider system. We adapt iSAM's measurement cost and motion cost to the pushing scenario, and use an instrumented setup to evaluate the estimation quality with different object shapes, on different surface materials, and under different contact modes., NSF (Award IIS-1427050)

Despite the ubiquity of carpentered items, the customization of carpentered items remains labor intensive. The generation of laymen editable templates for carpentry is difficult. Current design tools rely heavily on CNC fabrication, limiting applicability. We develop a template based system for carpentry and a robotic fabrication system using mobile robots and standard carpentry tools. Our end-to-end design and fabrication tool democratizes design and fabrication of carpentered items. Our method combines expert knowledge for template design, allows laymen users to customize and verify specific designs, and uses robotics system to fabricate parts. We validate our system using multiple designs to make customizable, verifiable templates and fabrication plans and show an end-to-end example that was designed, manufactured, and assembled using our tools. ©2018, NSF (grant no. CMMI-1644558)

Collision avoidance protocols such as COLREGS are written primarily for human operators resulting in a rule set that is open to some interpretation, difficult to quantify, and challenging to evaluate. Increasing use of autonomous control of vehicles emphasizes the need to more uniformly establish entry and exit criteria for collision avoidance rules, adopt a means to quantitatively evaluate performance, and establish a “road test” for autonomous marine vehicle collision avoidance. This paper presents a means to quantify and subsequently evaluate the otherwise subjective nature of COLREGS thus providing a path toward standardized evaluation and certification of protocol-constrained collision avoidance systems based on admiralty case law and on-water experience. Notional algorithms are presented for evaluation of COLREGS collision avoidance rules to include overtaking, head-on, crossing, give-way, and stand-on rules as well as applicable entry criteria. These rules complement and enable an autonomous collision avoidance road test as a first iteration of algorithm certification prior to vessels operating in human-present environments. Additional COLREGS rules are discussed for future development. Both real-time and post-mission protocol evaluation tools are introduced. While the motivation of these techniques applies to improvement of autonomous marine collision avoidance, the concepts for protocol evaluation and certification extend naturally to human-operated vessels. Evaluation of protocols governing other physical domains may also benefit from adapting these techniques to their cases. Keywords: COLREGS; Autonomous collision avoidance; Human–robot collaboration; Marine navigation

Path planning classically focuses on avoiding environmental contact. However, some assembly tasks permit contact through compliance, and such contact may allow for more efficient and reliable solutions under action uncertainty. But, optimal manipulation plans that leverage environmental contact are difficult to compute. Environmental contact produces complex kinematics that create difficulties for planning. This complexity is usually addressed by discretization over state and action space, but discretization quickly becomes computationally intractable. To overcome the challenge, we use the insight that only actions on configurations near the contact manifold are likely to involve complex kinematics, while segments of the plan through free space do not. Leveraging this structure can greatly reduce the number of states considered and scales much better with problem complexity. We develop an algorithm based on this idea and show that it performs comparably to full MDP solutions at a fraction of the computational cost.

This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel extensions to explicitly manage the complexities that arise when targeting these systems. The framework is designed for the areas of image processing, stencils, linear algebra and deep learning. Tiramisu has two main features: it relies on a flexible representation based on the polyhedral model and it has a rich scheduling language allowing fine-grained control of optimizations. Tiramisu uses a four-level intermediate representation that allows full separation between the algorithms, loop transformations, data layouts, and communication. This separation simplifies targeting multiple hardware architectures with the same algorithm. We evaluate Tiramisu by writing a set of image processing, deep learning, and linear algebra benchmarks and compare them with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu matches or outperforms existing compilers and libraries on different hardware architectures, including multicore CPUs, GPUs, and distributed machines., arXiv admin note: substantial text overlap with arXiv:1803.00419

The biomedical field offers many learning tasks that share unique challenges: large amounts of unpaired data, and a high cost to generate labels. In this work, we develop a method to address these issues with semi-supervised learning in regression tasks (e.g., translation from source to target). Our model uses adversarial signals to learn from unpaired datapoints, and imposes a cycle-loss reconstruction error penalty to regularize mappings in either direction against one another. We first evaluate our method on synthetic experiments, demonstrating two primary advantages of the system: 1) distribution matching via the adversarial loss and 2) regularization towards invertible mappings via the cycle loss. We then show a regularization effect and improved performance when paired data is supplemented by additional unpaired data on two real biomedical regression tasks: estimating the physiological effect of medical treatments, and extrapolating gene expression (transcriptomics) signals. Our proposed technique is a promising initial step towards more robust use of adversarial signals in semi-supervised regression, and could be useful for other tasks (e.g., causal inference or modality translation) in the biomedical field.

We analyze the computational complexity of the many types of pencil-and-paper-style puzzles featured in the 2016 puzzle video game The Witness. In all puzzles, the goal is to draw a simple path in a rectangular grid graph from a start vertex to a destination vertex. The different puzzle types place different constraints on the path: preventing some edges from being visited (broken edges); forcing some edges or vertices to be visited (hexagons); forcing some cells to have certain numbers of incident path edges (triangles); or forcing the regions formed by the path to be partially monochromatic (squares), have exactly two special cells (stars), or be singly covered by given shapes (polyominoes) and/or negatively counting shapes (antipolyominoes). We show that any one of these clue types (except the first) is enough to make path finding NP-complete ("witnesses exist but are hard to find"), even for rectangular boards. Furthermore, we show that a final clue type (antibody), which necessarily "cancels" the effect of another clue in the same region, makes path finding $\Sigma_2$-complete ("witnesses do not exist"), even with a single antibody (combined with many anti/polyominoes), and the problem gets no harder with many antibodies. On the positive side, we give a polynomial-time algorithm for monomino clues, by reducing to hexagon clues on the boundary of the puzzle, even in the presence of broken edges, and solving "subset Hamiltonian path" for terminals on the boundary of an embedded planar graph in polynomial time., Comment: 72 pages, 59 figures. Revised proof of Lemma 3.5. A short version of this paper appeared at the 9th International Conference on Fun with Algorithms (FUN 2018)

© 2018 Copyright is held by the owner/author(s). Recent research has shown how to change the color of existing objects using photochromic materials. These materials can switch their appearance from transparent to colored when exposed to light of a certain wavelength. The color remains active even when the object is removed from the light source. The process is fully reversible allowing users to recolor the object as many times as they want. So far, these systems have been limited to single color changes, i.e. changes from transparent to colored. In this paper, we present ColorMod, a method to extend this approach to multi-color changes (e.g., red-to-yellow). We accomplish this using a multi-color pattern with one color per voxel across the surface of the object. When recoloring the object, our system locally activates only those voxels that have the desired color and turns all other voxels off. We describe ColorMod's hardware/software system and its user interface. The user interface comes with a conversion tool for 3D printing and a painting tool for matching physical voxels with the desired appearance. We also provide our own material formula for a 3D-printable photochromic ink., Japan Society for the Promotion of Science. Grant-in-Aid for Scientific Research (grant number 16J00049)

Recent research has shown how to change the color of existing objects using photochromic materials. These materials can switch their appearance from transparent to colored when exposed to light of a certain wavelength. The color remains active even when the object is removed from the light source. The process is fully reversible allowing users to recolor the object as many times as they want. So far, these systems were limited to single color changes (i.e. transparent to colored). We present ColorMod, a method to accomplish multi-color changes (e.g., red-to-yellow). We achieve this using a multi-color pattern with one color per voxel across the surface of the object. When recoloring the object, our system locally activates only those voxels that have the desired color and turns all other voxels off. We describe Color Mod ’s hardware/software system and its user interface. The user interface comes with a conversion tool for 3D printing and a painting tool for matching physical voxels with the desired appearance. We also provide our own material formula for a 3D-printable photochromic ink., Japan Society for the Promotion of Science. Grant-in-Aid for Scientific Research (grant number 16J00049)

In this paper, we explore neural network models that learn to associate segments of spoken audio captions with the semantically relevant portions of natural images that they refer to. We demonstrate that these audio-visual associative localizations emerge from network-internal representations learned as a by-product of training to perform an image-audio retrieval task. Our models operate directly on the image pixels and speech waveform, and do not rely on any conventional supervision in the form of labels, segmentations, or alignments between the modalities during training. We perform analysis using the Places 205 and ADE20k datasets demonstrating that our models implicitly learn semantically-coupled object and word detectors. Keywords: vision and language; sound; speech; convolutional networks; multimodal learning; unsupervised learning

We present a detailed study of the off-state leakage current in scaled self-aligned InGaAs FinFETs. In long-channel devices, band-to-band tunneling at the drain-end of the channel is shown to be the root cause of excessive off-state current. This conclusion emerges from its characteristic electric field and temperature behavior and the absence of gate length and fin width dependencies. In short-channel devices, off-state current is significantly larger and it increases as the gate length shortens or the fin widens. We attribute this behavior to current multiplication through the gain of a floating-base parasitic bipolar transistor that is present inside the MOSFET. We extract the bipolar current gain which in short-channel devices is found to increase as the gate length shortens and decrease as the fin width narrows. In long channel devices, the current gain drops exponentially due to base recombination. This has allowed us to extract the diffusion length of electrons in the body of the transistor., DTRA (Grant HDTRA1-14-1-0057), NSF (Grant 0959514)

Despite the emergence of many soft-bodied robotic systems, model-based feedback control has remained an open challenge. This is largely due to the intrinsic difficulties in designing controllers for systems with infinite dimensions. In this paper we propose an alternative formulation of the soft robot dynamics which connects the robot’s behavior with the one of a rigid bodied robot with elasticity in the joints. The matching between the two system is exact under the common hypothesis of Piecewise Constant Curvature. Based on this connection we introduce two control architectures, with the aim of achieving accurate curvature control and Cartesian regulation of the robot’s impedance, respectively. The curvature controller accounts for the natural softness of the system, while the Cartesian controller adapts the impedance of the end effector for interactions with an unstructured environment. This work proposes the first closed loop dynamic controller for a continuous soft robot. The controllers are validated and evaluated on a physical soft robot capable of planar manipulation., National Science Foundation (U.S.) (Grant NSF 1117178), National Science Foundation (U.S.) (Grant NSF IIS1226883), National Science Foundation (U.S.) (Grant NSF CCF1138967), Horizon 2020 Framework Programme (European Commission) (Grant 645599)

In this paper, we explore a new class of electric motor-driven compliant actuators based on handed shearing auxetic cylinders. This technique combines the benefits of compliant bodies from soft robotic actuators with the simplicity of direct coupling to electric motors. We demonstrate the effectiveness of this technique by creating linear actuators, a four degree-of-freedom robotic platform, and a soft robotic gripper. We compare the soft robotic gripper against a state of the art pneumatic soft gripper, finding similar grasping performance in a significantly smaller and more energy-efficient package., Boeing Company, National Science Foundation (U.S.) (grant numbers NSF IIS- 1226883), National Science Foundation (U.S.) (grant numbers NSF CCF-1138967)

© 2018 IEEE. Many database columns contain string or numerical data that conforms to a pattern, such as phone numbers, dates, addresses, product identifiers, and employee ids. These patterns are useful in a number of data processing applications, including understanding what a specific field represents when field names are ambiguous, identifying outlier values, and finding similar fields across data sets.One way to express such patterns would be to learn regular expressions for each field in the database. Unfortunately, existing techniques on regular expression learning are slow, taking hundreds of seconds for columns of just a few thousand values. In contrast, we develop XSYSTEM, an efficient method to learn patterns over database columns in significantly less time.We show that these patterns can not only be built quickly, but are expressive enough to capture a number of key applications, including detecting outliers, measuring column similarity, and assigning semantic labels to columns (based on a library of regular expressions). We evaluate these applications with datasets that range from chemical databases (based on a collaboration with a pharmaceutical company), our university data warehouse, and open data from MassData.gov.

We propose a new approach that utilizes semantic information to register 2D monocular video frames to the world using 3D geo-referenced data, for augmented reality driving applications. The geo-registration process uses our predicted vehicle pose to generate a rendered depth map for each frame, allowing 3D graphics to be convincingly blended with the real world view. We also estimate absolute depth values for dynamic objects, up to 120 meters, based on the rendered depth map and update the rendered depth map to reflect scene changes over time. This process also creates opportunistic global heading measurements, which are fused with other sensors, to improve estimates of the 6 degrees-of-freedom global pose of the vehicle over state-of-the-art outdoor augmented reality systems [5, 18, 19]. We evaluate the navigation accuracy and depth map quality of our system on a driving vehicle within various large-scale environments for producing realistic augmentations.

Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. Totally there are 25k images of the complex everyday scenes containing a variety of objects in their natural spatial context. On average there are 19.5 instances and 10.5 object classes per image. Based on ADE20K, we construct benchmarks for scene parsing and instance segmentation. We provide baseline performances on both of the benchmarks and re-implement state-of-the-art models for open source. We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance. We show that the networks trained on ADE20K are able to segment a wide variety of scenes and objects., NSF (grant 1524817)

Background RNA-binding proteins (RBPs) play vital roles in many processes in the cell. Different RBPs bind RNA with different sequence and structure specificities. While sequence specificities for a large set of 205 RBPs have been reported through the RNAcompete compendium, structure specificities are known for only a small fraction. The main limitation lies in the design of the RNAcompete technology, which tests RBP binding against unstructured RNA probes, making it difficult to infer structural preferences from these data. We recently developed RCK, an algorithm to infer sequence and structural binding models from RNAcompete data. The set of binding models enables, for the first time, a large-scale assessment of RNA structure in the RBPome. Results We re-validate and uncover the role of RNA structure in the RPBome through novel analysis of the largest-scale dataset to date. First, we show that RNA structure exists in presumably unstructured RNA probes and that its variability is correlated with RNA-binding. Second, we examine the structural binding preferences of RBPs and discover an overall preference to bind RNA loops. Third, we significantly improve protein-binding prediction using RNA structure, both in vitro and in vivo. Lastly, we demonstrate that RNA structural binding preferences can be inferred for new proteins from solely their amino acid content. Conclusions By counter-intuitively demonstrating through our analysis that we can predict both the RNA structure of and RBP binding to these putatively unstructured RNAs, we transform a compendium of RNA-binding proteins into a valuable resource for structure-based binding models. We uncover the important role RNA structure plays in protein-RNA interaction for hundreds of RNA-binding proteins. Electronic supplementary material The online version of this article (10.1186/s12864-018-4540-1) contains supplementary material, which is available to authorized users.

© 2018 by authors.All right reserved. We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.

Over the last decades quaternions have become a crucial and very successful tool for attitude representation in robotics and aerospace. However, there is a major problem that is continuously causing trouble in practice when it comes to exchanging formulas or implementations: there are two quaternion multiplications commonly in use, Hamilton&rsquo, s multiplication and its flipped version, which is often associated with NASA&rsquo, s Jet Propulsion Laboratory. This paper explains the underlying problem for the popular passive world-to-body usage of rotation quaternions, and promotes an alternative solution compatible with Hamilton&rsquo, s multiplication. Furthermore, it argues for discontinuing the flipped multiplication. Additionally, it provides recipes for efficiently detecting relevant conventions and migrating formulas or algorithms between them.

Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta (2016) framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a `good' hierarchical clustering is one that minimizes some cost function. He showed that this cost function has certain desirable properties, such as in order to achieve optimal cost disconnected components must be separated first and that in `structureless' graphs, i.e., cliques, all clusterings achieve the same cost. We take an axiomatic approach to defining `good' objective functions for both similarity and dissimilarity-based hierarchical clustering. We characterize a set of admissible objective functions (that includes the one introduced by Dasgupta) that have the property that when the input admits a `natural' ground-truth hierarchical clustering, the ground-truth clustering has an optimal value. Equipped with a suitable objective function, we analyze the performance of practical algorithms, as well as develop better and faster algorithms for hierarchical clustering. For similarity-based hierarchical clustering, Dasgupta (2016) showed that a simple recursive sparsest-cut based approach achieves an O(log^3/2 n)-approximation on worst-case inputs. We give a more refined analysis of the algorithm and show that it in fact achieves an O(√log n)-approximation. This improves upon the LP-based O(log n)-approximation of Roy and Pokutta (2016). For dissimilarity-based hierarchical clustering, we show that the classic average-linkage algorithm gives a factor 2 approximation, and provide a simple and better algorithm that gives a factor 3/2 approximation. This aims at explaining the success of this heuristics in practice. Finally, we consider `beyond-worst-case' scenario through a generalisation of the stochastic block model for hierarchical clustering. We show that Dasgupta's cost function also has desirable properties for these inputs and we provide a simple algorithm that for graphs generated according to this model yields a 1 + o(1) factor approximation.

Modern fabrication techniques, such as additive manufacturing, can be used to create materials with complex custom internal structures. These engineered materials exhibit a much broader range of bulk properties than their base materials and are typically referred to as metamaterials or microstructures. Although metamaterials with extraordinary properties have many applications, designing them is very difficult and is generally done by hand. We propose a computational approach to discover families of microstructures with extremal macroscale properties automatically. Using efficient simulation and sampling techniques, we compute the space of mechanical properties covered by physically realizable microstructures. Our system then clusters microstructures with common topologies into families. Parame-terized templates are eventually extracted from families to generate new microstructure designs. We demonstrate these capabilities on the computational design of mechanical metamaterials and present five auxetic microstructure families with extremal elastic material properties. Our study opens the way for the completely automated discovery of extremal microstructures across multiple domains of physics, including applications reliant on thermal, electrical, and magnetic properties., United States. Defense Advanced Research Projects Agency. Simplifying Complexity in Scientific Discovery (N66001-15-C-4030)

Operating a high degree of freedom mobile manipulator, such as a humanoid, in a field scenario requires constant situational awareness, capable perception modules, and effective mechanisms for interactive motion planning and control. A well-designed operator interface presents the operator with enough context to quickly carry out a mission and the flexibility to handle unforeseen operating scenarios robustly. By contrast, an unintuitive user interface can increase the risk of catastrophic operator error by overwhelming the user with unnecessary information. With these principles in mind, we present the philosophy and design decisions behind Director—the open-source user interface developed by Team MIT to pilot the Atlas robot in the DARPA Robotics Challenge (DRC). At the heart of Director is an integrated task execution system that specifies sequences of actions needed to achieve a substantive task, such as drilling a wall or climbing a staircase. These task sequences, developed a priori, make online queries to automated perception and planning algorithms with outputs that can be reviewed by the operator and executed by our whole-body controller. Our use of Director at the DRC resulted in efficient high-level task operation while being fully competitive with approaches focusing on teleoperation by highly trained operators. We discuss the primary interface elements that comprise Director, and we provide an analysis of its successful use at the DRC., United States. Defense Advanced Research Projects Agency. (Air Force Research Laboratory (award FA8750-12-1-0321)), United States. Office of Naval Research (Award N00014-12-1-0071)

When an infection spreads in a community, an individual's probability of becoming infected depends on both her susceptibility and exposure to the contagion through contact with others. While one often has knowledge regarding an individual's susceptibility, in many cases, whether or not an individual's contacts are contagious is unknown. We study the problem of predicting if an individual will adopt a contagion in the presence of multiple modes of infection (exposure/susceptibility) and latent neighbor influence. We present a generative probabilistic model and a variational inference method to learn the parameters of our model. Through a series of experiments on synthetic data, we measure the ability of the proposed model to identify latent spreaders, and predict the risk of infection. Applied to a real dataset of 20,000 hospital patients, we demonstrate the utility of our model in predicting the onset of a healthcare associated infection using patient room-sharing and nurse-sharing networks. Our model outperforms existing benchmarks and provides actionable insights for the design and implementation of targeted interventions to curb the spread of infection., NSF (Award IIS-1553146), NIAID of the NIH (Grant U01AI124255), NIH (Award P50-0267666-0002)

Enclaves have emerged as a particularly compelling primitive to implement trusted execution environments: strongly isolated sensitive user-mode processes in a largely untrusted software environment. While the threat models employed by various enclave systems differ, the high-level guarantees they offer are essentially the same: attestation of an enclave's initial state, as well as a guarantee of enclave integrity and privacy in the presence of an adversary. This work describes Sanctorum, a small trusted code base (TCB), consisting of a generic enclave-capable system, which is sufficient to implement secure enclaves akin to the primitive offered by Intel's SGX. While enclaves may be implemented via unconditionally trusted hardware and microcode, as it is the case in SGX, we employ a smaller TCB principally consisting of authenticated, privileged software, which may be replaced or patched as needed. Sanctorum implements a formally verified specification for generic enclaves on an in-order multiprocessor system meeting baseline security requirements, e.g., the MIT Sanctum processor and the Keystone enclave framework. Sanctorum requires trustworthy hardware including a random number generator, a private cryptographic key pair derived via a secure bootstrapping protocol, and a robust isolation primitive to safeguard sensitive information. Sanctorum's threat model is informed by the threat model of the isolation primitive, and is suitable for adding enclaves to a variety of processor systems., Comment: 6 pages