Fabrication of large area diffraction grating using LIGA process

X-ray imaging is a very important technology in the fields of medical, biological, inspection, material science, etc. However, it is not enough to get the clear X-ray imaging with low absorbance. We have produced a diffraction gratings for obtaining high resolution X-ray phase imaging, such as X-ray Talbot interferometer. In this X-ray Talbot interferometer, diffraction gratings were required to have a fine, high accuracy, high aspect ratio structure. Then, we succeeded to fabricate a high aspect ratio diffraction grating with a pitch of 8 μm and small area using a deep X-ray lithography technique. We discuss that the diffraction gratings having a narrow pitch and an large effective area to obtain imaging size of practical use in medical application. If the pitch of diffraction gratings were narrow, it is expected high resolution imaging for X-ray Talbot interferometer. We succeeded and fabricated the diffraction grating with pitch of 5.3 μm, Au height of 28 μm and an effective area of 60 × 60 mm².     

Second-Order Abstract Categorial Grammars as Hyperedge Replacement Grammars

Second-order abstract categorial grammars (de Groote in Association for computational linguistics, 39th annual meeting and 10th conference of the European chapter, proceedings of the conference, pp. 148–155, 2001) and hyperedge replacement grammars (Bauderon and Courcelle in Math Syst Theory 20:83–127, 1987; Habel and Kreowski in STACS 87: 4th Annual symposium on theoretical aspects of computer science. Lecture notes in computer science, vol 247, Springer, Berlin, pp 207–219, 1987) are two natural ways of generalizing “context-free” grammar formalisms for string and tree languages. It is known that the string generating power of both formalisms is equivalent to (non-erasing) multiple context-free grammars (Seki et al. in Theor Comput Sci 88:191–229, 1991) or linear context-free rewriting systems (Weir in Characterizing mildly context-sensitive grammar formalisms, University of Pennsylvania, 1988). In this paper, we give a simple, direct proof of the fact that second-order ACGs are simulated by hyperedge replacement grammars, which implies that the string and tree generating power of the former is included in that of the latter. The normal form for tree-generating hyperedge replacement grammars given by Engelfriet and Maneth (Graph transformation. Lecture notes in computer science, vol 1764. Springer, Berlin, pp 15–29, 2000) can then be used to show that the tree generating power of second-order ACGs is exactly the same as that of hyperedge replacement grammars.     

Estimation of a nonvisible field-of-view mobile target incorporating optical and acoustic sensors

This paper presents a nonvisible field-of-view (NFOV) target estimation approach that incorporates optical and acoustic sensors. An optical sensor can accurately localize a target in its field-of-view whereas the acoustic sensor could estimate the target location over a much larger space, but only with limited accuracy. A recursive Bayesian estimation framework where observations of the optical and acoustic sensors are probabilistically treated and fused is proposed in this paper. A technique to construct the observation likelihood when two microphones are used as the acoustic sensor is also described. The proposed technique derives and stores the interaural level difference of observations from the two microphones for different target positions in advance and constructs the likelihood through correlation. A parametric study of the proposed acoustic sensing technique in a controlled test environment, and experiments with an NFOV target in an actual indoor environment are presented to demonstrate the capability of the proposed technique.     

A theoretical framework for designing human-centered automotive automation systems

Increasingly sophisticated and robust automotive automation systems are being developed to be applied in all aspects of driving. Benefits, such as improving safety, task performance, and workload have been reported. However, several critical accidents involving automation assistance have also been reported. Although automation systems may work appropriately, human factors such as drivers errors, overtrust in and overreliance on automation due to lack of understanding of automation functionalities and limitations as well as distrust caused by automation surprises may trigger inappropriate human–automation interactions that lead to negative consequences. Several important methodologies and efforts for improving human–automation interactions follow the concept of human-centered automation, which claims that the human must have the final authority over the system, have been called. Given that the human-centered automation has been proposed as a more cooperative automation approach to reduce the likelihood of human–machine misunderstanding. This study argues that, especially in critical situations, the way control is handed over between agents can improve human–automation interactions even when the system has the final decision-making authority. As ways of improving human–automation interactions, the study proposes adaptive sharing of control that allows dynamic control distribution between human and system within the same level of automation while the human retains the final authority, and adaptive trading of control in which the control and authority shift between human and system dynamically while changing levels of automation. Authority and control transitions strategies are discussed, compared and clarified in terms of levels and types of automation. Finally, design aspects for determining how and when the control and authority can be shifted between human and automation are proposed with recommendations for future designs.

     

Doing Business with Theory: Communities of Practice in Knowledge Management

We explore how the notion of communities of practice (CoPs) was translated and popularized from its original inception by Lave and Wenger in 1991. We argue that the Institute for Research on Learning (IRL), a spin-off of Xerox PARC, proved instrumental in enrolling CoPs into the knowledge management (KM) discipline. IRL objectified, packaged, and made a business out of CoPs. CoPs in KM are now a formalized process coupled with technological artifacts to build groups of people who effectively share knowledge across boundaries. Drawing from participant observations, archival documents, and interviews with KM practitioners in the aerospace industry as well as key players of IRL, our research seeks to unveil the invisible history that the popularization of a theory can often obscure. We argue that CoPs provide a case study for understanding how abstract concepts in science are strategically and subconsciously reified, or made objects of inquiry, and appropriated by actors. This reification of a “soft” science blurs the line between theory and technology.     

A novel quarter‐wave retardation film for improving viewing angle properties in time‐sequential stereoscopic 3D‐LCDs

We have successfully developed a quarter‐wave retardation film (QWF) for wide viewing angle 3D liquid crystal displays (3D‐LCDs) that provides high luminance, low crosstalk, low color change, and low head‐tilt‐angle dependency. It was found that the out‐of‐plane retardation (Rth) of the QWF in the LCD needs to be close to 0 nm in order to improve the 3D display properties at an off‐axis position and that the in‐plane retardation (Re) needs to be adjusted from 120 to 130 nm to achieve low color change with head tilting. We adopted a coating process for making our QWF because of its potential for retardation control. 3D‐LCDs with this QWF whose Rth was nearly zero had high performance and allowed off‐axis other than on‐axis.     

A Goal-Dependent Abstraction for Legal Reasoning by Analogy

This paper presents a new algorithm to find an appropriate similarityunder which we apply legal rules analogically. Since there may exist a lotof similarities between the premises of rule and a case in inquiry, we haveto select an appropriate similarity that is relevant to both thelegal rule and a top goal of our legal reasoning. For this purpose, a newcriterion to distinguish the appropriate similarities from the others isproposed and tested. The criterion is based on Goal-DependentAbstraction (GDA) to select a similarity such that an abstraction basedon the similarity never loses the necessary information to prove the ground (purpose of legislation) of the legal rule. In order to cope withour huge space of similarities, our GDA algorithm uses some constraintsto prune useless similarities.     

Making infrastructure visible for nomadic work

In this study we revisit Weiser’s (1991) [5] vision of seamlessly integrated computing. We focus on nomadic workers who rely extensively on mobile technologies to conduct work. Using Star and Ruhleder’s (1996) [10] properties of infrastructure, we discuss how nonroutine users of infrastructure face particular challenges. We conducted an ethnographic investigation of nomadic workers in a large distributed organization. We examined strategies that they use to assemble their mobile office, to seek resources, and to synchronize with others across time zones. We discuss how their strategies are challenged by their lack of local knowledge of infrastructure. Our results suggest that until the field of pervasive computing can attain Weiser’s vision, the very users of ubiquitous computing need infrastructure to be visible. We discuss the implications for a design to support nomadic work.     

An application of the 2-nitrobenzenesulfenyl method to proteomic profiling of human colorectal carcinoma: A novel approach for biomarker discovery

In the development of novel biomarkers, the proteomic approach is advantageous because using it the cancer-associated proteins can be directly identified. We previously developed a 2-nitrobenzenesulfenyl (NBS) method to improve quantitative proteome analysis. Here, we applied this method to proteomic profiling of colorectal carcinoma (CRC) to identify novel proteins with altered expression in CRC. Each pair of tumor and normal tissue specimens from 12 CRC patients was analyzed, and approximately 5000 NBS-labeled paired peaks were quantified. Peaks with altered signal intensities (>1.5-fold) and occurring frequently in the samples (>70%) were selected, and 128 proteins were identified by MS/MS analyses as differentially expressed proteins in CRC tissues. Many proteins were newly revealed to be CRC related; 30 were reported in earlier studies of CRC. Six proteins that were up-regulated in CRC (ZYX, RAN, RCN1, AHCY, LGALS1, and VIM) were further characterized and validated by Western blot and immunohistochemistry. All six were found to be CRC-localized, either in cancer cells or in stroma cells near the cancer cells. These results indicate that the proteins identified in this study are novel candidates for CRC markers, and that the NBS method is useful in proteome mining to discover novel biomarkers.     

Domain Adaptation for Structured Regression

Discriminative regression models have proved effective for many vision applications (here we focus on 3D full-body and head pose estimation from image and depth data). However, dataset bias is common and is able to significantly degrade the performance of a trained model on target test sets. As we show, covariate shift, a form of unsupervised domain adaptation (USDA), can be used to address certain biases in this setting, but is unable to deal with more severe structural biases in the data. We propose an effective and efficient semi-supervised domain adaptation (SSDA) approach for addressing such more severe biases in the data. Proposed SSDA is a generalization of USDA, that is able to effectively leverage labeled data in the target domain when available. Our method amounts to projecting input features into a higher dimensional space (by construction well suited for domain adaptation) and estimating weights for the training samples based on the ratio of test and train marginals in that space. The resulting augmented weighted samples can then be used to learn a model of choice, alleviating the problems of bias in the data; as an example, we introduce SSDA twin Gaussian process regression (SSDA-TGP) model. With this model we also address the issue of data sharing, where we are able to leverage samples from certain activities (e.g., walking, jogging) to improve predictive performance on very different activities (e.g., boxing). In addition, we analyze the relationship between domain similarity and effectiveness of proposed USDA versus SSDA methods. Moreover, we propose a computationally efficient alternative to TGP (Bo and Sminchisescu 2010), and it’s variants, called the direct TGP. We show that our model outperforms a number of baselines, on two public datasets: HumanEva and ETH Face Pose Range Image Dataset. We can also achieve 8–15 times speedup in computation time, over the traditional formulation of TGP, using the proposed direct formulation, with little to no loss in performance.