An Empirical Study of Fault Localization in Python Programs

Despite its massive popularity as a programming language, especially in novel domains like data science programs, there is comparatively little research about fault localization that targets Python. Even though it is plausible that several findings about programming languages like C/C++ and Java -- the most common choices for fault localization research -- carry over to other languages, whether the dynamic nature of Python and how the language is used in practice affect the capabilities of classic fault localization approaches remain open questions to investigate. This paper is the first large-scale empirical study of fault localization on real-world Python programs and faults. Using Zou et al.'s recent large-scale empirical study of fault localization in Java as the basis of our study, we investigated the effectiveness (i.e., localization accuracy), efficiency (i.e., runtime performance), and other features (e.g., different entity granularities) of seven well-known fault-localization techniques in four families (spectrum-based, mutation-based, predicate switching, and stack-trace based) on 135 faults from 13 open-source Python projects from the BugsInPy curated collection. The results replicate for Python several results known about Java, and shed light on whether Python's peculiarities affect the capabilities of fault localization. The replication package that accompanies this paper includes detailed data about our experiments, as well as the tool FauxPy that we implemented to conduct the study.Comment: Related work update

Deep reinforcement learning for drone delivery

This work was funded by the Ministry of Science, Innovation and Universities of Spain under Grant No. TRA2016-77012-R.Drones are expected to be used extensively for delivery tasks in the future. In the absence of obstacles, satellite based navigation from departure to the geo-located destination is a simple task. When obstacles are known to be in the path, pilots must build a flight plan to avoid them. However, when they are unknown, there are too many or they are in places that are not fixed positions, then to build a safe flight plan becomes very challenging. Moreover, in a weak satellite signal environment, such as indoors, under trees canopy or in urban canyons, the current drone navigation systems may fail. Artificial intelligence, a research area with increasing activity, can be used to overcome such challenges. Initially focused on robots and now mostly applied to ground vehicles, artificial intelligence begins to be used also to train drones. Reinforcement learning is the branch of artificial intelligence able to train machines. The application of reinforcement learning to drones will provide them with more intelligence, eventually converting drones in fully-autonomous machines. In this work, reinforcement learning is studied for drone delivery. As sensors, the drone only has a stereo-vision front camera, from which depth information is obtained. The drone is trained to fly to a destination in a neighborhood environment that has plenty of obstacles such as trees, cables, cars and houses. The flying area is also delimited by a geo-fence; this is a virtual (non-visible) fence that prevents the drone from entering or leaving a defined area. The drone has to avoid visible obstacles and has to reach a goal. Results show that, in comparison with the previous results, the new algorithms have better results, not only with a better reward, but also with a reduction of its variance. The second contribution is the checkpoints. They consist of saving a trained model every time a better reward is achieved. Results show how checkpoints improve the test results.Peer ReviewedPostprint (published version

Turn-by-wire: Computationally mediated physical fabrication

Advances in digital fabrication have simultaneously created new capabilities while reinforcing outdated workflows that constrain how, and by whom, these fabrication tools are used. In this paper, we investigate how a new class of hybrid-controlled machines can collaborate with novice and expert users alike to yield a more lucid making experience. We demonstrate these ideas through our system, Turn-by-Wire. By combining the capabilities of a traditional lathe with haptic input controllers that modulate both position and force, we detail a series of novel interaction metaphors that invite a more fluid making process spanning digital, model-centric, computer control, and embodied, adaptive, human control. We evaluate our system through a user study and discuss how these concepts generalize to other fabrication tools

Habitat Selection of a Coastal Shark Species Estimated from an Autonomous Underwater Vehicle

Quantifying habitat selection in marine organisms is challenging because it is difficult to obtain species location information with multiple corresponding habitat measurements. In the ocean, habitat conditions vary on many spatiotemporal scales, which have important consequences for habitat selection. While macroscale biotic and abiotic features influence seasonal movements (spatial scales of 100-1000 km), selectivity of conditions on mesoscales (1-100 km) reflects an animal’s response to the local environment. In this study, we examined habitat selectivity by pairing acoustic telemetry with environmental habitat parameters measured by an autonomous underwater vehicle (AUV), and demonstrate that migrating sand tiger sharks Carcharias taurus along the East Coast of the USA did not randomly use the coastal environment. Of the variables examined, we found evidence to suggest that sand tigers were selecting their habitat based on distance to shore, salinity, and colored dissolved organic matter (CDOM). Notably, temperature was not predictive of habitat use in our study. We posit that during their coastal migration, sand tigers select for specific mesoscale coastal habitats that may inform navigation or feeding behaviors. To our knowledge, this is the first empirical measure of mesoscale habitat selection by a coastal marine organism using an AUV. The applications of this method extend beyond the habitat selectivity of sand tigers, and will prove useful for future studies combining in situ observations of marine habitats and animal observations

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.National Science Foundation (IRI-87-16960, IRI-87-6960); Air Force Office of Scientific Research (90-0175); Defense Advanced Research Projects Agency (90-0083

Production and analysis of synthetic Cascade variants

CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR assoziiert) ist ein adaptives Immunsystem in Archaeen und Bakterien, das fremdes genetisches Material mit Hilfe von Ribonukleoprotein-Komplexen erkennt und zerstört. Diese Komplexe bestehen aus einer CRISPR RNA (crRNA) und Cas Proteinen. CRISPR-Cas Systeme sind in zwei Hauptklassen und mehrere Typen unterteilt, abhängig von den beteiligten Cas Proteinen. In Typ I Systemen sucht ein Komplex namens Cascade (CRISPR associated complex for antiviral defence) nach eingedrungener viraler DNA während einer Folgeinfektion und bindet die zu der eingebauten crRNA komplementäre Sequenz. Anschließend wird die Nuklease/Helikase Cas3 rekrutiert, welche die virale DNA degradiert (Interferenz). Das Typ I System wird in mehrere Subtypen unterteilt, die Unterschiede im Aufbau von Cascade vorweisen. Im Fokus dieser Arbeit steht eine minimale Cascade-Variante aus Shewanella putrefaciens CN-32. Im Vergleich zur gut untersuchten Typ I-E Cascade aus Escherichia coli fehlen in diesem Komplex zwei Untereinheiten, die gewöhnlicher Weise für die Zielerkennung benötigt werden. Dennoch ist der Komplex aktiv. Rekombinante I-Fv Cascade wurde bereits aus E. coli aufgereinigt und es war möglich, den Komplex zu modifizieren, indem das Rückgrat entweder verlängert oder verkürzt wurde. Dadurch wurden synthetische Varianten mit veränderter Protein-Stöchiometrie erzeugt. In der vorliegenden Arbeit wurde I-Fv Cascade weiter mit in vitro Methoden untersucht. So wurde die Bindung von Ziel-DNA beobachtet und die 3D Struktur zeigt, dass strukturelle Veränderungen im Komplex die fehlenden Untereinheiten ersetzen, möglicherweise um viralen Anti-CRISPR Proteinen zu entgehen. Die Nuklease/Helikase dieses Systems, Cas2/3fv, ist eine Fusion des Cas3 Proteins mit dem Interferenz-unabhängigen Protein Cas2. Ein unabhängiges Cas3fv ohne Cas2 Untereinheit wurde aufgereinigt und in vitro Assays zeigten, dass dieses Protein sowohl freie ssDNA als auch Cascadegebundene Substrate degradiert. Das komplette Cas2/3fv Protein bildet einen Komplex mit dem Protein Cas1 und zeigt eine reduzierte Aktivität gegenüber freier ssDNA, möglicherweise als Regulationsmechanismus zur Vermeidung von unspezifischer Aktivität. Weiterhin wurde ein Prozess namens „RNA wrapping“ etabliert. Synthetische Cascade-Komplexe wurden erzeugt, in denen die grundlegende RNA-Bindung des charakteristischen Cas7fv RückgratProteins auf eine ausgewählte RNA gelenkt wird. Diese spezifische Komplexbildung kann in vivo durch eine Repeat-Sequenz der crRNA stromaufwärts der Zielsequenz und durch Bindung des Cas5fv Proteins initiiert werden. Die erzeugten Komplexe beinhalten die ersten 100 nt der markierten RNA, die anschließend isoliert werden kann. Innerhalb der Komplexe ist die RNA stabilisiert und geschützt vor Degradation durch RNasen. Komplexbildung kann außerdem genutzt werden, um ReportergenTranskripte stillzulegen. Zusätzlich wurden erste Hinweise geliefert, dass das Rückgrat der synthetischen Komplexe durch Fusion mit weiteren Reporterproteinen modifiziert werden kann.CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR associated) is an adaptive immune system of Archaea and Bacteria. It is able to target and destroy foreign genetic material with ribonucleoprotein complexes consisting of CRISPR RNAs (crRNAs) and certain Cas proteins. CRISPR-Cas systems are classified in two major classes and multiple types, according to the involved Cas proteins. In type I systems, a ribonucleoprotein complex called Cascade (CRISPR associated complex for antiviral defence) scans for invading viral DNA during a recurring infection and binds the sequence complementary to the incorporated crRNA. After target recognition, the nuclease/helicase Cas3 is recruited and subsequently destroys the viral DNA in a step termed interfere nce. Multiple subtypes of type I exist that show differences in the Cascade composition. This work focuses on a minimal Cascade variant found in Shewanella putrefaciens CN-32. In comparison to the well-studied type I-E Cascade from Escherichia coli, this complex is missing two proteins usually required for target recognition, yet it is still able to provide immunity. Recombinant I-Fv Cascade was previously purified from E. coli and it was possible to modulate the complex by extending or shortening the backbone, resulting in synthetic variants with altered protein stoichiometry. In the present study, I-Fv Cascade was further analyzed by in vitro methods. Target binding was observed and the 3D structure revealed structural variations that replace the missing subunits, potentially to evade viral anti-CRISPR proteins. The nuclease/helicase of this system, Cas2/3fv, is a fusion of the Cas3 protein with the interference-unrelated protein Cas2. A standalone Cas3fv was purified without the Cas2 domain and in vitro cleavage assays showed that Cas3fv degrades both free ssDNA as well as Cascade-bound substrates. The complete Cas2/3fv protein forms a complex with the protein Cas1 and was shown to reduce cleave of free ssDNA, potentially as a regulatory mechanism against unspecific cleavage. Furthermore, we established a process termed “RNA wrapping”. Synthetic Cascade assemblies can be created by directing the general RNA-binding ability of the characteristic Cas7fv backbone protein on an RNA of choice such as reporter gene transcripts. Specific complex formation can be initiated in vivo by including a repeat sequence from the crRNA upstream a given target sequence and binding of the Cas5fv protein. The created complexes contain the initial 100 nt of the tagged RNA which can be isolated afterwards. While incorporated in complexes, RNA is stabilized and protected from degradation by RNases. Complex formation can be used to silence reporter gene transcripts. Furthermore, we provided initial indications that the backbone of synthetic complexes can be modified by addition of reporter proteins

Three scenarios fro valuable planetary science missions on Mars: next generation of CubeSats to support space exploration

Planetary science originally tended to make use of “flagship” missions characterized by big satellites and expensive resources. In the near future this traditional satellite paradigm could dramatically change with the introduction of very small satellites. This shift towards smaller, less expensive devices mirrors the paradigm shift that happened in the computer industry with the miniaturization of electronics, as focus has moved from massive machines to personal computer up to smart phones. The ultimate expression of spacecraft miniaturization is today represented by CubeSats, but while over a hundred CubeSats have been launched into Earth orbit, space-based research beyond LEO struggles to find practical application. CubeSat small size poses hard challenges for independent planetary exploration, nevertheless they remain highly attractive due to the reduced development time and cost coming from platform modularity and standardization, availability of COTS parts, reduced launch cost. Constellations of CubeSats, collaborative networks, fractionated or federated systems are becoming popular concepts as they can offer spatially distributed measurements and the opportunity to be used as disposable sensors with a flexibility not achievable by single-satellite platforms. We have worked towards advancing the state of the art in CubeSat missions design and implementation by defining the range of science capabilities for CubeSats beyond LEO, and by enhancing the top technological challenges to support science objectives (e.g. propulsion, communications, radiation environment protection). Planet Mars was chosen as target destination to the purpose of this work, by selecting a set of scientific objectives for CubeSats to serve astrobiology goals and future human exploration. Missions to accomplish orbital and atmospheric measurement, in situ analyses related to biosignatures detection and environmental characterization have been explored. The opportunity to rely on already existing space assets in the proximity of Mars, or on a “mothership” for data relay or orbit insertion, has been considered in this context. A tradespace exploration led to the definition of three classes of mission architectures, respectively based on surface penetrators, atmosphere scouts and orbiting fleet. Each architecture has been assessed in the perspective of science return against a set of leading indicators that draw out cost, utility, complexity, technology readiness among others. For each class a mission concept has been created, providing a basis to elicit the definition of top-level requirements and to assess the value of science return in the context of complex mission scenarios

Economic Costs of Historic Overfishing on Recreational Fisheries: South Atlantic & Gulf of Mexico Regions

Ocean fish are a vital renewable resource for human populations, providing food, employment, and recreation. Many fish stocks worldwide, however, are in a state of serious decline due to overfishing, environmental degradation, climate change, and other stressors. Fishing effort worldwide has remained relatively constant with only slight increases recorded, while the global production of marine fisheries has decreased (Food and Agriculture Organization of the United Nations, 2010). The South Atlantic and Gulf of Mexico regions of the United States have witnessed significant declines in fish stocks that are important to recreational and commercial fisheries. As of 2011, nine fish populations across the two regions were officially classified as "overfished." An additional 12 populations were classified as "subject to overfishing." Biological overfishing occurs when harvest rates from fishing exceed the growth rates of fish stocks. The resulting declines in fish populations can impact the economy at large. This study examines an important component of the costs of overfishing in the South Atlantic and Gulf of Mexico regions -- recreational catch losses from historic overfishing and their associated economic impacts. Our analysis covers nine federally managed overfished stocks in these two regions over the period 2005–2009, the most recent years for which the necessary data were available prior to the 2010 Deepwater Horizon oil spill in the Gulf. Those stocks are black sea bass, red grouper, red porgy, red snapper, and snowy grouper in the South Atlantic; and gag, gray triggerfish, greater amberjack, and red snapper in the Gulf of Mexico. Recreational fishing has long been an important economic activity in these regions. The money spent by recreational fishermen on charter fishing excursions, tackle, bait, fuel, and other expenses supports employment and economic activity across those regions. Our analysis assumes that recreational fisheries could have contributed more to regional economic activity had the stocks been capable of producing greater yields over the study period of 2005–2009. We estimate the size of the recreational catch loss for each species for each year and the economic activity that could have resulted had that catch been available. To arrive at our estimates of recreational catch loss, we compared average annual recreational harvests and effort for each stock for each year over the study period to potential estimated harvests and effort had the stocks been producing at optimum yield. We sourced our measures of optimum yield and maximum sustainable yield for each individual stock from regional stock assessments and fishery management plans. We valued the resulting catch loss by using data on trip expenditures by recreational fishermen in the South Atlantic and Gulf of Mexico. Effort and expenditure data were sourced from the Marine Recreational Fisheries Statistics Survey and includes trips that caught, targeted, or caught and/or targeted the stocks in our analysis.1 Economic multipliers were used to estimate the total direct, indirect, and induced economic activity that could have been generated by those recreational fishing expenditures. Our estimates of catch loss and associated economic impacts are not additive across stocks since trips and their respective expenditures may be associated with multiple stocks. Our analysis finds that recreational fisheries in the South Atlantic and Gulf of Mexico could have contributed millions of dollars more in additional recreational expenditures and associated economic activity had the fish species been producing at optimum yield over the study period. The greatest direct losses were associated with South Atlantic black sea bass and South Atlantic red snapper. Recreational fishermen in the South Atlantic spent $41.7 million on average annually to realize 44$52.8 million greater each year over the five-year study period had the stock been producing at optimum yield. An additional $52.8 million in recreational expenditures each year could have generated an additional$138 million in economic output and $40.3 million in income, and supported 896 jobs annually for the region. In the case of South Atlantic red snapper, fishermen spent$9.2 million on average annually over the study period to catch 37% of the recreational catch that could have been available under optimum yield. We estimate that recreational expenditures on South Atlantic red snapper could have been $15.9 million greater each year and could have contributed an additional$41.6 million in economic output and $12.2 million in income, and supported 270 jobs for the region annually between 2005–2009. In the Gulf region, the greatest losses were associated with red snapper, where recreational fishermen spent$22.4 million to realize 64% of the optimal catch that could have been available. We estimate that recreational expenditures on Gulf red snapper could have been $12.7 million greater each year had the stock been producing at optimum yield. An additional$12.7 million in recreational expenditures each year could have generated an additional $33.2 million in economic output and$9.7 million in income, and supported 215 jobs annually for the region. Our findings support the conclusion that overfished stocks can lead to significant economic losses for regional economies through forgone recreational fishing expenditures. This is only one component of the cost of overfishing. Our analysis does not estimate the value of catch losses in commercial fisheries or the broader impacts on ecosystems and biodiversity. The total value of catch losses resulting from historic overfishing would be greater still if other impacts had been considered. Despite these limitations, this study provides strong economic evidence in support of maintaining healthy ocean fish populations and continuing efforts to rebuild stocks currently subject to overfishing or classified as overfished