Query: Inverse Kinematic Infrared Optical Finger Tracking

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Direct least squares fitting of ellipses <=SEP=> This work presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4 2 1 the new method incorporates the ellipticity constraint into the normalization factor. The new method combines several advantages: (i) It is ellipse-specific so that even bad data will always return an ellipse; (ii) It can be solved naturally by a generalized eigensystem and (iii) it is extremely robust, efficient and easy to implement. We compare the proposed method to other approaches and show its robustness on several examples in which other non-ellipse-specific approaches would fail or require computationally expensive iterative refinements. Source code for the algorithm is supplied and a demonstration is available on ! " ! $#% '& () "*) & +, ./10 0 32, . 4) "*) "*) % 5* 0

A survey of glove-based input <=SEP=> Clumsy intermediary devices constrain our interaction with computers and their applications. Glove-based input devices let us apply our manual dexterity to the task. We provide a basis for understanding the field by describing key hand-tracking technologies and applications using glove-based input. The bulk of development in glove-based input has taken place very recently, and not all of it is easily accessible in the literature. We present a cross-section of the field to date. Hand-tracking devices may use the following technologies: position tracking, optical tracking, marker systems, silhouette analysis, magnetic tracking or acoustic tracking. Actual glove technologies on the market include: Sayre glove, MIT LED glove, Digital Data Entry Glove, DataGlove, Dexterous HandMaster, Power Glove, CyberGlove and Space Glove. Various applications of glove technologies include projects into the pursuit of natural interfaces, systems for understanding signed languages, teleoperation and robotic control, computer-based puppetry, and musical performance.<<ETX>>

CHARADE: Remote Control of Objects Using Free-Hand Gestures <=SEP=> This paper presents an application that uses hand gesture input to control a computer while giving a presentation. In order to develop a prototype of this application, we have defined an interaction model, a notation for gestures, and a set of guidelines to design gestural command sets. This works aims to define interaction styles that work in computerized reality environments. In our application, gestures are used for interacting with the computer as well as for communicating with other people or operating other devices.

"Put-that-there": Voice and gesture at the graphics interface <=SEP=> Recent technological advances in connected-speech recognition and position sensing in space have encouraged the notion that voice and gesture inputs at the graphics interface can converge to provide a concerted, natural user modality.
 The work described herein involves the user commanding simple shapes about a large-screen graphics display surface. Because voice can be augmented with simultaneous pointing, the free usage of pronouns becomes possible, with a corresponding gain in naturalness and economy of expression. Conversely, gesture aided by voice gains precision in its power to reference.

Least-Squares Fitting of Circles and Ellipses ∗ <=SEP=> Fitting circles and ellipses to given points in the plane is a problem that arises in many application areas, e.g. computer graphics [1], coordinate metrology [2], petroleum engineering [11], statistics [7]. In the past, algorithms have been given which fit circles and ellipses in some least squares sense without minimizing the geometric distance to the given points [1], [6]. In this paper we present several algorithms which compute the ellipse for which the sum of the squares of the distances to the given points is minimal. These algorithms are compared with classical simple and iterative methods. Circles and ellipses may be represented algebraically i.e. by an equation of the form F (x) = 0. If a point is on the curve then its coordinates x are a zero of the function F . Alternatively, curves may be represented in parametric form, which is well suited for minimizing the sum of the squares of the distances. 1 Preliminaries and Introduction Ellipses, for which the sum of the squares of the distances to the given points is minimal will be referred to as “best fit” or “geometric fit”, and the algorithms will be called “geometric”. Determining the parameters of the algebraic equation F (x) = 0 in the least squares sense will be denoted by “algebraic fit” and the algorithms will be called “algebraic”. We will use the well known Gauss-Newton method to solve the nonlinear least squares problem (cf. [15]). Let u = (u1, . . . , un) T be a vector of unknowns and consider the nonlinear system of m equations f(u) = 0. If m > n, then we want to minimize

The See-Through System: A VANET-enabled assistant for overtaking maneuvers <=SEP=> The use of wireless technology based on Vehicular Ad hoc Networks (VANETs) for information exchange can influence the drivers' behavior towards improving driving performance and reducing road accidents. This information can even be more relevant if it is presented as a video stream. In this paper we propose a system that relies on VANET and video-streaming technology: the See-Through System (STS). The system enhances driver's visibility and supports the driver's overtaking decision in challenging situations, such as overtaking a vision-obstructing vehicle. The use of the See-Through System provides the driver with an additional tool for determining if traffic conditions permit starting an overtaking maneuver thus reducing the risk of overtaking. We tested the See-Through System on an experimental vehicle on the road as well as in the context of a driving simulator for real world environment. Results are promising, since the use of the 802.11p standard wireless communication protocol allows a vehicle-to-vehicle connection without significant delay and the totality of the participants regarded the information provided by the STS as useful.

The Collateral Damage of Internet Censorship by DNS Injection <=SEP=> Some ISPs and governments (most notably the Great Firewall of China) use DNS injection to block access to “unwanted” websites. The censorship tools inspect DNS queries near the ISP’s boundary routers for sensitive domain keywords and injecting forged DNS responses, blocking the users from accessing censored sites, such as twitter.com and facebook. com. Unfortunately this causes large scale collateral damage, affecting communication beyond the censored networks when outside DNS traffic traverses censored links. In this paper, we analyze the causes of the collateral damages comprehensively and measure the Internet to identify the injecting activities and their effect. We find 39 ASes in China injecting forged replies even for transit DNS traffic, and 26% of 43,000 measured open resolvers outside China, distributed in 109 countries, may suffer some collateral damage. Different from previous work, we find that most collateral damage arises from resolvers querying TLD name servers who’s transit passes through China rather than effects due to root servers (F, I, J) located in China.

SQL Database Primitives for Decision Tree Classifiers <=SEP=> Scalable data mining in large databases is one of today's challenges to database technologies. Thus, substantial effort is dedicated to a tight coupling of database and data mining systems leading to database primitives supporting data mining tasks. In order to support a wide range of tasks and to be of general usage these primitives should be rather building blocks than implementations of specific algorithms. In this paper, we describe primitives for building and applying decision tree classifiers. Based on the analysis of available algorithms and previous work in this area we have identified operations which are useful for a number of classification algorithms. We discuss the implementation of these primitives on top of a commercial DBMS and present experimental results demonstrating the performance benefit.

The Dreaming Variational Autoencoder for Reinforcement Learning Environments <=SEP=> Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in shortand long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.

The Double Dance of Agency : a socio-theoretic account of how machines and humans interact <=SEP=> The nature of the relationship between information technology (IT) and organizations has been a long-standing debate in the Information Systems literature. Does IT shape organizations, or do people in organisations control how IT is used? To formulate the question a little differently: does agency (the capacity to make a difference) lie predominantly with machines (computer systems) or humans (organisational actors)? Many proposals for a middle way between the extremes of technological and social determinism have been put advanced; in recent years researchers oriented towards social theories have focused on structuration theory and (lately) actor network theory. These two theories, however, adopt different and incompatible views of agency. Thus, structuration theory sees agency as exclusively a property of humans, whereas the principle of general symmetry in actor network theory implies that machines may also be agents. Drawing on critiques of both structuration theory and actor network theory, this paper develops a theoretical account of the interaction between human and machine agency: the double dance of agency. The account seeks to contribute to theorisation of the relationship between technology and organisation by recognizing both the different character of human and machine agency, and the emergent properties of their interplay.

Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence. <=SEP=> Both physiological and behavioral studies have suggested that stimulus-driven neural activity in the sensory pathways can be modulated in amplitude during selective attention. Recordings of event-related brain potentials indicate that such sensory gain control or amplification processes play an important role in visual-spatial attention. Combined event-related brain potential and neuroimaging experiments provide strong evidence that attentional gain control operates at an early stage of visual processing in extrastriate cortical areas. These data support early selection theories of attention and provide a basis for distinguishing between separate mechanisms of attentional suppression (of unattended inputs) and attentional facilitation (of attended inputs).

MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices <=SEP=> We present a class of extremely efficient CNN models, MobileFaceNets, which use less than 1 million parameters and are specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. We first make a simple analysis on the weakness of common mobile networks for face verification. The weakness has been well overcome by our specifically designed MobileFaceNets. Under the same experimental conditions, our MobileFaceNets achieve significantly superior accuracy as well as more than 2 times actual speedup over MobileNetV2. After trained by ArcFace loss on the refined MS-Celeb-1M, our single MobileFaceNet of 4.0MB size achieves 99.55% accuracy on LFW and 92.59% TAR@FAR1e-6 on MegaFace, which is even comparable to state-of-the-art big CNN models of hundreds MB size. The fastest one of MobileFaceNets has an actual inference time of 18 milliseconds on a mobile phone. For face verification, MobileFaceNets achieve significantly improved efficiency over previous state-of-the-art mobile CNNs.

Forensic Image Inspection Assisted by Deep Learning <=SEP=> Investigations on the charge of possessing child pornography usually require manual forensic image inspection in order to collect evidence. When storage devices are confiscated, law enforcement authorities are hence often faced with massive image datasets which have to be screened within a limited time frame. As the ability to concentrate and time are highly limited factors of a human investigator, we believe that intelligent algorithms can effectively assist the inspection process by rearranging images based on their content. Thus, more relevant images can be discovered within a shorter time frame, which is of special importance in time-critical investigations of triage character.
 While currently employed techniques are based on black- and whitelisting of known images, we propose to use deep learning algorithms trained for the detection of pornographic imagery, as they are able to identify new content. In our approach, we evaluated three state-of-the-art neural networks for the detection of pornographic images and employed them to rearrange simulated datasets of 1 million images containing a small fraction of pornographic content. The rearrangement of images according to their content allows a much earlier detection of relevant images during the actual manual inspection of the dataset, especially when the percentage of relevant images is low. With our approach, the first relevant image could be discovered between positions 8 and 9 in the rearranged list on average. Without using our approach of image rearrangement, the first relevant image was discovered at position 1,463 on average.

Getting users to pay attention to anti-phishing education: evaluation of retention and transfer <=SEP=> Educational materials designed to teach users not to fall for phishing attacks are widely available but are often ignored by users. In this paper, we extend an embedded training methodology using learning science principles in which phishing education is made part of a primary task for users. The goal is to motivate users to pay attention to the training materials. In embedded training, users are sent simulated phishing attacks and trained after they fall for the attacks. Prior studies tested users immediately after training and demonstrated that embedded training improved users' ability to identify phishing emails and websites. In the present study, we tested users to determine how well they retained knowledge gained through embedded training and how well they transferred this knowledge to identify other types of phishing emails. We also compared the effectiveness of the same training materials delivered via embedded training and delivered as regular email messages. In our experiments, we found that: (a) users learn more effectively when the training materials are presented after users fall for the attack (embedded) than when the same training materials are sent by email (non-embedded); (b) users retain and transfer more knowledge after embedded training than after non-embedded training; and (c) users with higher Cognitive Reflection Test (CRT) scores are more likely than users with lower CRT scores to click on the links in the phishing emails from companies with which they have no account.

EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras <=SEP=> Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort with marker suits, and their recording volume is severely restricted and often constrained to indoor scenes with controlled backgrounds. Alternative suit-based systems use several inertial measurement units or an exoskeleton to capture motion with an inside-in setup, i.e. without external sensors. This makes capture independent of a confined volume, but requires substantial, often constraining, and hard to set up body instrumentation. Therefore, we propose a new method for real-time, marker-less, and egocentric motion capture: estimating the full-body skeleton pose from a lightweight stereo pair of fisheye cameras attached to a helmet or virtual reality headset - an optical inside-in method, so to speak. This allows full-body motion capture in general indoor and outdoor scenes, including crowded scenes with many people nearby, which enables reconstruction in larger-scale activities. Our approach combines the strength of a new generative pose estimation framework for fisheye views with a ConvNet-based body-part detector trained on a large new dataset. It is particularly useful in virtual reality to freely roam and interact, while seeing the fully motion-captured virtual body.

Deep Cosine Metric Learning for Person Re-identification <=SEP=> Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to direct metric learning objectives such as siamese networks that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large-scale pedestrian re-identification datasets where competitive results are achieved overall. In particular, we achieve better generalization on the test set compared to a network trained with triplet loss.

Grail: context-aware fixing of concurrency bugs <=SEP=> Writing efficient synchronization for multithreaded programs is notoriously hard. The resulting code often contains subtle concurrency bugs. Even worse, many bug fixes introduce new bugs. A classic example, seen widely in practice, is deadlocks resulting from fixing of an atomicity violation. These complexities have motivated the development of automated fixing techniques. Current techniques generate fixes that are typically conservative, giving up on available parallelism. Moreover, some of the techniques cannot guarantee the correctness of a fix, and may introduce deadlocks similarly to manual fix, whereas techniques that ensure correctness do so at the expense of even greater performance loss. We present Grail, a novel fixing algorithm that departs from previous techniques by simultaneously providing both correctness and optimality guarantees. Grail synthesizes bug-free yet optimal lock-based synchronization. To achieve this, Grail builds an analysis model of the buggy code that is both contextual, distinguishing different aliasing contexts to ensure efficiency, and global, accounting for the entire synchronization behavior of the involved threads to ensure correctness. Evaluation of Grail on 12 bugs from popular codebases confirms its practical advantages, especially compared with existing techniques: Grail patches are, in general, >=40% more efficient than the patches produced by other techniques, and incur only 2% overhead.

The State of Public Infrastructure-as-a-Service Cloud Security <=SEP=> The public Infrastructure-as-a-Service (IaaS) cloud industry has reached a critical mass in the past few years, with many cloud service providers fielding competing services. Despite the competition, we find some of the security mechanisms offered by the services to be similar, indicating that the cloud industry has established a number of “best-practices,” while other security mechanisms vary widely, indicating that there is also still room for innovation and experimentation. We investigate these differences and possible underlying reasons for it. We also contrast the security mechanisms offered by public IaaS cloud offerings and with security mechanisms proposed by academia over the same period. Finally, we speculate on how industry and academia might work together to solve the pressing security problems in public IaaS clouds going forward.

Multiple Trench Split-gate SOI LDMOS Integrated With Schottky Rectifier <=SEP=> In this paper, a multiple trench split-gate silicon-on-insulator (SOI) lateral double-diffused MOSFET with a Schottky rectifier (MTS-SG-LDMOS) is proposed and its characteristics are studied using 2-D simulations. The new structure features double oxide trenches, a floating polysilicon split-gate, and a Schottky rectifier. Each oxide trench includes a vertical field plate which enhances the depletion of the drift region and modulates the bulk electric field. As the simulation results, when compared to the conventional SOI LDMOS (C-LDMOS), the breakdown voltage in the MTS-SG-LDMOS increases from 297 to 350 V, the specific on-state resistance (<inline-formula> <tex-math notation="LaTeX">${R} _{ \mathrm{\scriptscriptstyle ON},\mathsf {sp}}$ </tex-math></inline-formula>) decreases from 142.9 to 48.2 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot \mathsf {cm}^{{\mathsf {2}}}$ </tex-math></inline-formula>, and the gate–drain charge (<inline-formula> <tex-math notation="LaTeX">${Q} _{{\mathsf {gd}}}$ </tex-math></inline-formula>) decreases from 19 to 9 pC. Moreover, the reverse recovery time of the proposed structure shows a 73.6% reduction as compared to the C-LDMOS device.

Crafting Adversarial Examples For Speech Paralinguistics Applications <=SEP=> Computational paralinguistic analysis is increasingly being used in a wide range of applications, including securitysensitive applications such as speaker verification, deceptive speech detection, and medical diagnostics. While state-ofthe-art machine learning techniques, such as deep neural networks, can provide robust and accurate speech analysis, they are susceptible to adversarial attacks. In this work, we propose a novel end-to-end scheme to generate adversarial examples by perturbing directly the raw waveform of an audio recording rather than specific acoustic features. Our experiments show that the proposed adversarial perturbation can lead to a significant performance drop of state-of-the-art deep neural networks, while only minimally impairing the audio quality.

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models <=SEP=> The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach for training GP-LVMs is based on maximum likelihood, where the latent projection variables are maximized over rather than integrated out. In this paper we present a Bayesian method for training GP-LVMs by introducing a non-standard variational inference framework that allows to approximately integrate out the latent variables and subsequently train a GP-LVM by maximizing an analytic lower bound on the exact marginal likelihood. We apply this method for learning a GP-LVM from iid observations and for learning non-linear dynamical systems where the observations are temporally correlated. We show that a benefit of the variational Bayesian procedure is its robustness to overfitting and its ability to automatically select the dimensionality of the nonlinear latent space. The resulting framework is generic, flexible and easy to extend for other purposes, such as Gaussian process regression with uncertain inputs and semi-supervised Gaussian processes. We demonstrate our method on synthetic data and standard machine learning benchmarks, as well as challenging real world datasets, including high resolution video data.

Wideband Patch Antenna for HPM Applications <=SEP=> This paper presents the design, fabrication, and characterization of a compact wideband antenna for high-power microwave applications. The antennas proposed are array of high-power wideband patches with high compactness and less than λ/10 thick. The concept developed can be fed by high-voltage signals (up to 60 kV) in repetitive operation. Two designs are produced at central frequencies of 350 MHz and 1 GHz. Their relative bandwidth is larger than 40% at 350 MHz and 25% at 1 GHz for S11 <; - 10 dB, respectively. The arrays studied produce a gain of more than 14 dB.

Aligning multiple genomic sequences with the threaded blockset aligner. <=SEP=> We define a "threaded blockset," which is a novel generalization of the classic notion of a multiple alignment. A new computer program called TBA (for "threaded blockset aligner") builds a threaded blockset under the assumption that all matching segments occur in the same order and orientation in the given sequences; inversions and duplications are not addressed. TBA is designed to be appropriate for aligning many, but by no means all, megabase-sized regions of multiple mammalian genomes. The output of TBA can be projected onto any genome chosen as a reference, thus guaranteeing that different projections present consistent predictions of which genomic positions are orthologous. This capability is illustrated using a new visualization tool to view TBA-generated alignments of vertebrate Hox clusters from both the mammalian and fish perspectives. Experimental evaluation of alignment quality, using a program that simulates evolutionary change in genomic sequences, indicates that TBA is more accurate than earlier programs. To perform the dynamic-programming alignment step, TBA runs a stand-alone program called MULTIZ, which can be used to align highly rearranged or incompletely sequenced genomes. We describe our use of MULTIZ to produce the whole-genome multiple alignments at the Santa Cruz Genome Browser.

Low-Voltage Wilson Current Mirrors in CMOS <=SEP=> In this paper, we describe three simple low-voltage CMOS analogs of the Wilson current mirror that function well at all current levels, ranging from weak inversion to strong inversion. Each of these current mirrors can operate on a low power-supply voltage of a diode drop plus two saturation voltages and features a wide output-voltage swing with a cascode-type incremental output impedance. Two of the circuits requires an input voltage of a diode drop plus a saturation voltage while the third one features a low input voltage of a saturation voltage. We present experimental results from versions of these three current mirrors that were fabricated in a 0.5-mum CMOS process through MOSIS, comparing them with CMOS implementations of the conventional Wilson and super-Wilson current mirrors.

Trend Recalling Algorithm for Automated Online Trading in Stock Market <=SEP=> Unlike financial forecasting, a type of mechanical trading technique called Trend Following (TF) doesn’t predict any market movement; instead it identifies a trend at early time of the day, and trades automatically afterwards by a pre-defined strategy regardless of the moving market directions during run time. Trend following trading has a long and successful history among speculators. The traditional TF trading method is by human judgment in setting the rules (aka the strategy). Subsequently the TF strategy is executed in pure objective operational manner. Finding the correct strategy at the beginning is crucial in TF. This usually involves human intervention in first identifying a trend, and configuring when to place an order and close it out, when certain conditions are met. In this paper, we presented a new type of TF, namely Trend Recalling algorithm that operates in a totally automated manner. It works by partially matching the current trend with one of the proven successful patterns from the past. Our experiments based on real stock market data show that this algorithm has an edge over the other trend following methods in profitability. The new algorithm is also compared to time-series forecasting type of stock trading, and it can even outperform the best forecasting type in a

A comprehensive study on security attacks on SSL/TLS protocol <=SEP=> Secure Socket Layer (SSL) protocol was introduced in 1994 and was later renamed as transport layer security (TLS) protocol for securing transport layer. SSL/TLS protocol is used for securing communication on the network by ensuring data confidentiality, data integrity and authenticity between the communicating party. Authentication of the communicating party and securing transfer of data is done through certificates, key exchange and cipher suites. Security issues were found during evolutionary development of SSL/TLS protocol. The paper gives a detailed chronological order of attacks of past 22 years on SSL/TLS protocol.

Illuminant Aware Gamut-Based Color Transfer <=SEP=> This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white-balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same ‘white axis’ and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant-aware strategy gives a better result than directly working with the original source and target image’s luminance channel as done by many previous methods. Afterwards, our method performs a full gamut-based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant-aware and gamut-based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.

Locate the Hate: Detecting Tweets against Blacks <=SEP=> Although the social medium Twitter grants users freedom of speech, its instantaneous nature and retweeting features also amplify hate speech. Because Twitter has a sizeable black constituency, racist tweets against blacks are especially detrimental in the Twitter community, though this effect may not be obvious against a backdrop of half a billion tweets a day. We apply a supervised machine learning approach, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels “racist” and “nonracist.” The classifier has a 76% average accuracy on individual tweets, suggesting that with further improvements, our work can contribute data on the sources of anti-black hate speech.

Evolution of the mid range theory of comfort for outcomes research. <=SEP=> constructs. VOLUME 49  NUMBER 2 NURSING OUTLOOK 88 Evolution of the Mid Range Theory of Comfort for Outcomes Research Kolcaba Figure 2. MR theory of comfort. Reprinted with permission from Aspen Publishers, Inc. Copyright © 1992 Aspen Publishers, Inc. Adv Nurs Sci 1992;15:1. The work of psychologist Murray in 1938 met these criteria. Because his theory was about human needs, it was applicable to patients who experience multiple stimuli in stressful health care situations. This was the deductive stage of theory development: beginning with an abstract, general theoretic construction and substructing downward to more specific levels that included concepts for nursing practice. Each nursing concept then could be operationalized relative to a specific research setting. Murray's intent was to synthesize a grand theory for psychology from existing lesser psychologic theories of his time. His concepts are found in Figure 2, lines 1, 2, and 3. Because comfort was perceived by patients, it was logically substructed under Murray's concept of "perception." "Obstructing forces" were substructed for nursing as health care needs; "facilitating forces" were nursing interventions, and "interacting forces" were intervening variables (line 4). This was the first and altruistic part of the theory, which stated that nurses identified unmet comfort needs of their patients, designed interventions to address those needs, and sought to enhance their patients' comfort, the immediate desired outcome. The second and practical part of the theory addressed the question? "Why comfort?" For nursing, unitary trend was substructed to health thema, which was further substructed to HSBs. HSB was Schlotfeldt's concept and represented the broad category of subsequent desired outcomes. She stated that HSBs could be internal, external, or a peaceful death. Some examples of HSBs are decreased length of stay, improved functional status, better response (or effort) to therapy, faster healing, or increased patient satisfaction. PATIENT COMFORT IN OUTCOMES RESEARCH: THE RETRODUCTIVE STAGE Retroduction is a form of reasoning that originates ideas. It is useful for the selection of phenomena that can be developed further and tested. This type of reasoning is applied in fields in which there are few available theories. Such is the case with outcomes research that, to date, is centered on collecting large databases for selected outcomes and relating those outcomes to types of nursing, medical, or institutional protocols. Adding a nursing theoretic framework to outcomes research would enhance this area of nursing investigation because theory-based practice enables nurses to design interventions that are congruent with desired outcomes, thus increasing the likelihood of finding significant results. Significant results on desired outcomes would provide data to respective institutions about nursing's "productivity" and the importance of nursing in the present competitive market. Murray's 20th century framework could not account for 21st century emphasis on institutional outcomes. However, with the use of retroduction, the concept of institutional integrity was added to the MR theory of comfort (Figure 3). Institutional integrity is conceptualized as the quality or state of health care corporations being complete, whole, sound, upright, honest, and sincere. The term has normative and descriptive components. Adding the term to the theory of comfort extends the theory to a consideration of the relationships between HSBs and institutional integrity. NURSING OUTLOOK MARCH/APRIL 2001 89 Figure 3. Comfort theory adapted for outcomes research. Box 1. Propositions in Theory of Comfort 1. Nurses identify patients' comfort needs that have not been met by existing support systems. 2. Nurses design interventions to address those needs. 3. Intervening variables are taken into account in designing interventions and mutually agreeing on reasonable immediate (enhanced comfort) and/or subsequent (HSBs) outcomes. 4. If enhanced comfort is achieved, patients are strengthened to engage in health-seeking behaviors. 5. When patients engage in health-seeking behaviors as a result of being strengthened by comforting actions, nurses and patients are more satisfied with their health care. 6. When patiencs are satisfied with their health care in a specific institution, that institution retains its integrity; institutional integrity has a normative and descriptive component. Box 2. Assumptions underpinning the Theory of Comfort 1. Human beings have holistic responses to complex stimuli. 2. Comfort is a desirable holistic outcome that is germane to the discipline of nursing. 3. Human beings strive to meet, or to have met, their basic comfort needs; it is an active endeavor. 4. Institutional integrity has a normative and descriptive component that is based on a patient-oriented value system. The theory now predicts that when patients engage fully in essential (comfort care). The concepts are (1) health care needs HSBs, such as their rehabilitation program or medical that include physical, psychospiritual, social, and environregimen, institutional integrity is enhanced also. Institutional mental needs that arise for patients in stressful health care situintegrity can be operationalized as patient satisfaction, ations; (2) nursing interventions, an umbrella term for successful discharges, cost-benefit ratios, or other outcomes commitment by nurses and institutions to promote comfort that are essential to institutional integrity. All of these concepts care (intentional care by nurses directed to meeting comfort are indicators of the integrity of the institution. The definineeds of patients); (3) intervening variables that will affect tions of underlined concepts (Figure 2) will be given; proposioutcomes (for example, institutions that are committed to tions that link the concepts are in Box 1, and assumptions that achieving improved outcomes through comfort care must underpin this theory are in Box 2. provide adequate staffing of registered nurses to meet comfort This theory describes traditional nursing practice as needs associated with existing patient acuity on any given unit); humanistic, needs-related, and holistic. Further, it relates prac(4) patient comfort, defined as the immediate state of being tice to institutional outcomes that make those nursing actions strengthened by having needs met in 4 contexts of human that promote soundness of health care institutions visible and experience (physical, psychospiritual, social, and environ90 VOLUME 49  NUMBER 2 NURSING OUTLOOK Evolution of the Mid Range Theory of Comfort for Outcomes Research Kolcaba Evolution of the Mid Range Theory of Comfort for Outcomes Research Kolcaba Box 3. Evaluation of the Teory of Comfort 1. Concepts are specific for nursing. 1 Comfort has been called the essence of nursing. 2. Concepts and propositions are readily operationalized. The theory has been tested in many settings. The outcome of comfort is opera-tionaiized easily using the taxonomic structure of comfort as a guide for item generation. 11 3. The theory can be applied to many situations. Through The Comfort Line, students and researchers are working with the author to adapt the theory to micro-level situations. 4. Propositions range from causal to associative. Propositions generated from the theory are shown in Box 1 and have the desired range. 5. Assumptions fit the theory. The theory is holistic and needs based and theoretically empowers patients to engage in health-seeking behaviors. Institutional integrity is an important theoretic link to outcomes research. 6. The theory is relevant for potential users of the theory. Nursing students learn this theory easily and apply it to practice and research. 7. The theory is oriented to an outcome chat is important to patients. Qualitative research indicates chat patients want, and often need, their nurses to assist them in meeting their comfort needs. 8. The theory entails a nursing-sensitive outcome. A traditional goal of nursing has been to attend to patient comfort. Patients expect this from nurses and give them credit when comfort is delivered. Through deliberate actions of nurses, patients receive what they need and want from their nurses. This theory explicates how and why to do so. mental) that can be operationalized by the general comfort questionnaire; (5) HSBs, defined as patient actions of which they may or may not be aware and which may or may not be observed that are predictors or indicators of improved health (categorized as internal [eg, healing, immune function], as external [eg, functional status, perception of health], or as a peaceful death; HSBs are more accurate indicator of nurse productivity than the number of patients cared for; and (6) institutional integrity (previously defined). The definition of comfort has grown from its early definition9 to one that incorporates the strengthening component of comfort, the immediate desired outcome of nursing care. It is this strengthening component that facilitates patients' increased engagement in HSBs, the subsequent outcome. These subsequent HSBs, as indicators of nurse productivity, are of great interest to health care administrators because they facilitate decreased lengths of stay, successful discharges, and improved public relations when patients and families are happy with their health care. The 3 parts of the theory can be tested separately, or all concepts can be tested in one study. Path analysis can indicate which variables have direct or indirect influences on desired outcomes. Now, by linking HSBs to institutional integrity in an explicit way, outcomes research is theoretically based in nursing. CRITERIA FOR ADEQUACY OF MR THEORIES The expanded theory of comfort meets the following criteria for MR theory: (1) its concepts and propositions are specific to nursing, (2) it is readily operationalized, (3) it can be applied to many situati

General 3D modelling of novel objects from a single view <=SEP=> In this paper we present a method for building models for grasping from a single 3D snapshot of a scene composed of objects of daily use in human living environments. We employ fast shape estimation, probabilistic model fitting and verification methods capable of dealing with different kinds of symmetries, and combine these with a triangular mesh of the parts that have no other representation to model previously unseen objects of arbitrary shape. Our approach is enhanced by the information given by the geometric clues about different parts of objects which serve as prior information for the selection of the appropriate reconstruction method. While we designed our system for grasping based on single view 3D data, its generality allows us to also use the combination of multiple views. We present two application scenarios that require complete geometric models: grasp planning and locating objects in camera images.