Project for the analysis of technology transfer Quarterly report, 1 Apr. 1969 - 30 Jun. 1969

Patterns, statistical analyses, and case studies of transfer and utilization of NASA generated technolog

GigaFitter at CDF: Offline-Quality Track Fitting in a Nanosecond for Hadron Collider Triggers

Il sistema di selezione online degli eventi e` un aspetto fondamentale per la riuscita di un esperimento di Fisica delle Alte Energie ai collisionatori adronici. Infatti il numero di eventi interessanti per la fisica e` soppresso da un numero molti ordini di grandezza piu` elevato di eventi non interessanti; a questo si unisce il problema che il numero di eventi scrivibili su disco per l'analisi e` una piccola frazione di quelli prodotti. Dalla selezione online dipende la qualita` dei dati acquisiti, in termini di eventi di segnale interessante raccolti, ed e` necessario dotarsi di un sistema di selezione (trigger) sofisticato ed efficiente. La ricostruzione della traiettoria delle particelle cariche fornisce un'informazione dettagliata sull'evento che permette di costruire selezioni molto efficaci. E` pero` un problema estremamente difficile da risolvere nei tempi richiesti per il suo impiego online. Il processore SVT di CDF riesce a ricostruire per ogni evento le traiettorie di tutte le particelle cariche con pT > 2 GeV in poche decine di microsecondi e con qualita` paragonabile a quella offline. Le informazioni ricostruite da SVT vengono usate nella selezione di trigger di secondo livello. L'impiego di SVT a CDF e` alla base di molti dei successi di fisica dell'esperimento. Per rimanere sempre attuale e in grado di far fronte alla nuove caratteristiche dell'acceleratore, il quale incrementa le proprie prestazione nel corso del tempo, il sistema SVT si e` sottoposto ad una serie di upgrade nel corso della sua storia. In questa tesi e` descritto il processore GigaFitter, l'ultimo upgrade di SVT. Lo scopo del processore GigaFitter e` di sostituire il precedente sistema di calcolo di SVT, il Track Fitter++ (16 schede), con un piccolo (1 scheda), ma potente processore. Il GigaFitter rimuove alcune delle limitazioni poste dalle capacita` di calcolo del Track Fitter e permette di aumentare l'accettanza e l'efficienza del sistema SVT. Migliorare accettanza ed efficienza permette di migliorare la statistica del segnale interessante nel campione raccolto. Il GigaFitter e` basato sull'utilizzo di un potente FPGA con unita` di calcolo dedicate. Il sistema sviluppato e` in grado di eseguire 1.4 fit/ns. Il design dell'architettura e i dettagli di hardware e firmware sono descritti nella tesi. Sono riportati gli studi effettuati per la validazione delle performance dell'hardware, sia in termini di aumento d'efficienza che in termini di timing. E` inoltre mostrato uno studio su come sia possibile grazie al GigaFitter il recupero di traiettorie attualmente non ricostruite, portando l'efficienza di SVT sulla singola traccia dal 75% del vecchio sistema all'80%. Il GigaFitter e` parte di SVT da Febbraio 2010 e ha completamente sostituito il vecchio sistema di Track Fitter++. Le ottime prestazioni dimostrate dal processore GigaFitter e il suo impiego con successo in CDF sono inoltre un'importante validazione in prospettiva della costruzione del processore FTK per l'esperimento ATLAS ad LHC

Effective communication in interactive media through application of Gestalt principles

Not Include

スタンス分類における領域知識の活用に関する研究

Tohoku University乾健太郎課

Proximate influence on eusocial caste behaviour

Queens and workers of eusocial sweat bee species are morphologically and developmentally similar, which means that each female is capable of behaving as a queen or a worker. However, few females lay eggs and behave as queens, while the majority of females provision the queen’s offspring, rarely lay eggs, and behave as workers. This makes eusocial sweat bee species, such as Lasioglossum laevissimum, excellent models to study the underling environmental (social) and genetic factors that contribute to variation in caste behaviours. My research focused on describing some of the proximate mechanisms that influence caste behaviours in L. laevissimum females. The social environment of a sweat bee colony, specifically the behaviour of a queen, can have a dramatic impact on worker behaviour. Queens suppress worker reproduction by physically bullying their workers. In a nesting aggregation at Brock University, almost half of L. laevissimum nests became queenless, which provided me with a natural experiment to assess the direct influence by queens on worker behaviour. Dissection data showed that a small proportion (17%) of workers developed their ovaries in both queenright and queenless nests. This suggests that L. laevissimum queens exert an early, negative, and strong influence on worker egg-laying behaviour, which lasts after she is gone. Next I assessed the relationship between gene expression and L. laeivsismum caste behaviours. I predicited that queens would express a gene associated with egg-laying, vitellogenin, more than workers, and that workers would express genes associated with foraging, the foraging gene, more than queens. Lasioglossum laevissimum queens had higher vitellogenin expression levels than workers, and females with high ovarian development had high vitellogenin expression, regardless of caste. On the other hand, queens and workers had similar foraging expression levels. Gene expression comparisons between queens and workers highlight two important behavioural characteristics of sweat bee castes. First, in eusocial sweat bees, both queens and workers actively provision brood at some point during the breeding season, which is reflected in their similar foraging expression levels. Secondly, queens lay eggs while a small proportion of workers have queen-like ovarian development, reflected in vitellogenin expression differences between castes

Kindred (exhibition catalog)

Kindred presents a selection of art and design work from artists who identify as being of African descent. The assembled graduate, undergraduate, and alumni students are among the 83 students on the RISD campus who share this African ancestry. The artists featured in the show demonstrate a cross section of ethnic affiliations which include Black, African- American, African American-European, Haitian, Trinidadian-Puerto Rican, Jamaican-Japanese, Caribbean-Filipino, Guinean-American, Nigerian-American, and Swedish-Ugandan. Students hail from the following departments: Apparel Design, Film/Animation/Video, Furniture Design, Glass, Graphic Design, Illustration, Industrial Design, Painting, Photography, and Printmaking. The students work in and outside their formal disciplines to highlight the complexities of race, identity politics, gender stereotypes, homosexuality, religious freedom, Afro-futurism and the connections between nature, physical objects and the human condition

Uncovering Features in Behaviorally Similar Programs

The detection of similar code can support many so ware engineering tasks such as program understanding and program classification. Many excellent approaches have been proposed to detect programs having similar syntactic features. However, these approaches are unable to identify programs dynamically or statistically close to each other, which we call behaviorally similar programs. We believe the detection of behaviorally similar programs can enhance or even automate the tasks relevant to program classification. In this thesis, we will discuss our current approaches to identify programs having similar behavioral features in multiple perspectives. We first discuss how to detect programs having similar functionality. While the definition of a program’s functionality is undecidable, we use inputs and outputs (I/Os) of programs as the proxy of their functionality. We then use I/Os of programs as a behavioral feature to detect which programs are functionally similar: two programs are functionally similar if they share similar inputs and outputs. This approach has been studied and developed in the C language to detect functionally equivalent programs having equivalent I/Os. Nevertheless, some natural problems in Object Oriented languages, such as input generation and comparisons between application-specific data types, hinder the development of this approach. We propose a new technique, in-vivo detection, which uses existing and meaningful inputs to drive applications systematically and then applies a novel similarity model considering both inputs and outputs of programs, to detect functionally similar programs. We develop the tool, HitoshiIO, based on our in-vivo detection. In the subjects that we study, HitoshiIO correctly detect 68.4% of functionally similar programs, where its false positive rate is only 16.6%. In addition to functional I/Os of programs, we attempt to discover programs having similar execution behavior. Again, the execution behavior of a program can be undecidable, so we use instructions executed at run-time as a behavioral feature of a program. We create DyCLINK, which observes program executions and encodes them in dynamic instruction graphs. A vertex in a dynamic instruction graph is an instruction and an edge is a type of dependency between two instructions. The problem to detect which programs have similar executions can then be reduced to a problem of solving inexact graph isomorphism. We propose a link analysis based algorithm, LinkSub, which vectorizes each dynamic instruction graph by the importance of every instruction, to solve this graph isomorphism problem efficiently. In a K Nearest Neighbor (KNN) based program classification experiment, DyCLINK achieves 90 + % precision. Because HitoshiIO and DyCLINK both rely on dynamic analysis to expose program behavior, they have better capability to locate and search for behaviorally similar programs than traditional static analysis tools. However, they suffer from some common problems of dynamic analysis, such as input generation and run-time overhead. These problems may make our approaches challenging to scale. Thus, we create the system, Macneto, which integrates static analysis with machine topic modeling and deep learning to approximate program behaviors from their binaries without truly executing programs. In our deobfuscation experiments considering two commercial obfuscators that alter lexical information and syntax in programs, Macneto achieves 90 + % precision, where the groundtruth is that the behavior of a program before and after obfuscation should be the same. In this thesis, we offer a more extensive view of similar programs than the traditional definitions. While the traditional definitions of similar programs mostly use static features, such as syntax and lexical information, we propose to leverage the power of dynamic analysis and machine learning models to trace/collect behavioral features of pro- grams. These behavioral features of programs can then apply to detect behaviorally similar programs. We believe the techniques we invented in this thesis to detect behaviorally similar programs can improve the development of software engineering and security applications, such as code search and deobfuscation