Miracle-dz/UniFAR-FTUs huggingface dataset:
Capturing Appearance

For computer graphics applications, capturing the appearance parameters of objects – reflectance, transmittance and small scale surface structures, is as important as capturing the overall shape. We briefly review recent approaches developed by the computer graphics community to solve this problem. Excellent results have been obtained by various researchers measuring spatially varying reflectance functions for some classes of objects. We will consider some challenges from two of the remaining problematic classes of objects. First we will describe our experience scanning and modeling the throne of Tutankhamen. The major difficulties in this case were that the base shape was a highly detailed non-convex geometry with complex topology, and the shape was covered by optically uncooperative gold and silver. Then we will discuss some observations from our ongoing project to scan and model historic buildings on the Yale campus. The major difficulties in this second case are quantity of data and the lack of control over acquisition conditions.
Jet Sideways Expansion Effect on Estimating the Gamma-Ray Burst Efficiency

The high efficiency of converting kinetic energy into gamma rays estimated with late-time afterglows in gamma-ray burst (GRB) phenomenon challenges the commonly accepted internal-shock model. However, the efficiency is still highly uncertain because it is sensitive to many effects. In this Letter we study the sideways expansion effect of jets on estimating the efficiency. We find that this effect is considerable, reducing the efficiency by a factor of ~0.5 for typical parameters, when the afterglow data ~10 hr after the GRB trigger are used to derive the kinetic energy. For a more dense circumburst medium, this effect is more significant. As samples, taking this effect into account, we specifically calculate the efficiency of two bursts whose parameters were well constrained. Almost the same results are derived. This suggests that the sideways expansion effect should be considered when the GRB efficiency is estimated with the late afterglow data.
Grouping strategy for solving hidden node problem in IEEE 802.15.4 LR-WPAN

The medium access control (MAC) protocol defined in IEEE 802.15. 4 standard is able to achieve low-power transmissions in low-rate and short-distance wireless personal area networks (WPANs). The modified CSMA/CA protocol used to minimize power consumption does not have the hidden-node protection mechanism, such as RST/CTS mechanism, for the sake of reducing the control overheads. Referring to previous research results, which proved that the probability of any two nodes in infrastructure network unheard each other is about 41%, the hidden-node problem (HNP) could result in inefficient data transmission in WPAN and quick power consumption. In this paper, we propose a simple and efficient grouping strategy to solve the IEEE 802.15. 4 HNP without needing extra control overheads in data transmissions. The proposed strategy groups nodes according to their hidden-node relationships and then separates the periodic transmission period into several non-overlapping sub-periods, one for each group. The WPAN coordinator is responsible for detecting the hidden-node situation and performing the grouping procedure if necessary. In this paper, we also prove that the maximal number of groups in a WPAN is five. Simulation results demonstrate that the proposed strategy is able to improve the standard transmission efficiency and to conserve energy by eliminating the unnecessary collisions.
Knowledge Exploration using Tables on the Web

The increasing popularity of mobile device usage has ushered in many features in modern search engines that help users with various information needs. One of those needs is Knowledge Exploration, where related documents are returned in response to a user query, either directly through right-hand side knowledge panels or indirectly through navigable sections underneath individual search results. Existing knowledge exploration features have relied on a combination of Knowledge Bases and query logs. In this paper, we propose Knowledge Carousels of two modalities, namely sideways and downwards, that facilitate exploration of IS-A and HAS-A relationships, respectively, with regard to an entity-seeking query, based on leveraging the large corpus of tables on the Web. This brings many technical challenges, including associating correct carousels with the search entity, selecting the best carousel from the candidates, and finding titles that best describe the carousel. We describe how we address these challenges and also experimentally demonstrate through user studies that our approach produces better result sets than baseline approaches.
Creating a safe, caring and inclusive school environment through a code of conduct that is educative, preventative and restorative in practice and response.

ii Acknowledgments. iii Executive Summary . vii Glossary of Terms . x List of Figures . xix List of Appendices . xx Chapter 1: Introduction and Problem . 1 1 Organizational Context . 1 1.1 Leadership Problem of Practice . 5 1.2 Perspectives on the Problem of Practice . 9 1.3 Emerging Questions . 22 1.4 Leadership-Focused Vision for Change. 23 1.5 Organizational Change Readiness . 26 1.6 Communicating the Need for Change. 30 1.7 Conclusion . 33 Chapter 2: Planning and Development . 34 2 Introduction . 34 2.1 Framework for Leading the Change Process . 34 2.2 Critical Organizational Analysis . 39 2.2.1 Input. 40 2.2.2 Strategy. 41 2.2.3 Output. 41 2.3 Possible Solutions to Address this OIP . 46 2.3.1 Solution 1: Governance. 47 2.3.2 Solution 2: School Code of Conduct. 49 v 2.3.3 Solution 3: Systems Theory. 49 2.3.4 Solution 4: Distributed Leadership Practice. 50 2.3.5 Solution 5: Implementation of Professional Learning Communities. 52 2.4 Leadership Approaches to Change . 53 2.5 Conclusion . 56 Chapter 3: Implementation, Evaluation, and Communication. 57 3 Introduction . 57 3.1 The Change Implementation Plan . 58 3.1.1 The Awakening Stage. 62 3.1.2 The Mobilization Stage. 65 3.1.3 The Acceleration Stage. 68 3.1.4 The Institutionalization Stage. 70 3.2 The New School Organizational Chart. 72 3.3 Transition Management Plan and Evaluation. 76 3.3.1 Four District Components. 77 3.4 Building Momentum: Short-term Goals. 79 3.5 Building Momentum: Mid-term Goals. 80 3.6 Building Momentum: Long-term Goals. 81 3.7 Limitations. 81 3.8 Change Process Monitoring and Evaluation . 81 3.9 Leadership Ethics and Organizational Change . 83 3.9. 1 Rights. 84 3.9. 2 Responsibility. 84 3.9. 3 Respect. 85 3.10 Change Process Communications Plan . 86 3.10. 1 Prechange Approval Phase. 88
“Brown eyes are not the same as blue eyes” Educational narratives, identities and positioning in adult education in Denmark

In Denmark, the Adult Education Centres have a “sweeper” function for young adults who need to recommence education. This study explores two L2 (Danish as Second Language) students and how their educational narratives confirm or counter the master narratives of adult education. Students at the centres are commonly identified as adults with social and personal challenges, but their educational narratives and experiences are far more complex. As the edifying institutional narratives of the Adult Centres encompass both professionalism and care, the vernacular narratives characterise the centres as hang-outs for problematic adults without the ability to make a persistent effort in their own life. Students at the centres have to navigate and position themselves in relation to these conflicting stories by giving voice to educational struggles, social relations and agency in their own educational narratives.
A Trade Value Perspective on Ecommerce Research: An Integration of Transaction Value and Transaction Cost Theories

The impact of web-based electronic commerce on the process of disintermediation and re-intermediation has been extensively studied. Two major limitations of the existing work are the focus on a single economic measure i.e., transaction cost minimization and the examination of channel-structure decisions from only a single perspective the seller's. This paper introduces transactional value theory in the context of channel-structure research and integrates it with transaction cost theory to generate a trade value framework. The trade value framework considers channel-structure decisions from the perspectives of both buyers and sellers and is used to analyze the impact of web-based e-commerce on intermediated channel-structures. The proposed framework suggests that intermediaries function best in a channel-structure if they can reduce trade-inhibiting factors and improve trade-enhancing factors. Intermediaries may also prosper if they deliver extraordinary value on one side of the trade value framework to the point that inhibiting factors on the other end of the trade can be overlooked. Intermediaries maximize the value of the trade for both the buyers and the sellers by trading through an intermediated channel-structure as opposed to trading directly.
Interactive Visualization of Large Ontology Matching Results

We add to the widely used AgreementMaker system the capability to visualize the results of matching large ontologies with a user interface that supports navigation and search of the ontologies. The interface also supports user intervention when using a feedback loop strategy where users validate candidate mappings that have been computed automatically by matching algorithms. The interface further displays properties of the concepts to facilitate the decision process.
Heart Rate–Corrected QT Interval Is an Independent Predictor of All-Cause and Cardiovascular Mortality in Individuals With Type 2 Diabetes: The Diabetes Heart Study

OBJECTIVE Heart rate–corrected QT (QTc) interval is associated with mortality in the general population, but this association is less clear in individuals with type 2 diabetes. We assessed the association of QTc interval with all-cause and cardiovascular disease (CVD) mortality in the Diabetes Heart Study. RESEARCH DESIGN AND METHODS We studied 1,020 participants with type 2 diabetes (83% European Americans; 55% women; mean age 61.4 years) who were free of atrial fibrillation, major ventricular conduction defects, and antiarrhythmic therapy at baseline. QT duration was automatically calculated from a standard 12-lead electrocardiogram (ECG). Following American Heart Association/American College of Cardiology Foundation recommendations, a linear scale was used to correct the QT for heart rate. Using Cox regression, risk was estimated per 1-SD increase in QTc interval as well as prolonged QTc interval (>450 ms) vs. normal QTc interval for mortality. RESULTS At baseline, the mean (SD) QTc duration was 414.9 ms (18.1), and 3.0% of participants had prolonged QTc. After a median follow-up time of 8.5 years (maximum follow-up time 13.9 years), 204 participants were deceased. In adjusted multivariate models, a 1-SD increase in QTc interval was associated with an 18% higher risk for all-cause mortality (hazard ratio 1.18 [95% CI 1.03–1.36]) and 29% increased risk for CVD mortality (1.29 [1.05–1.59]). Similar results were obtained when QTc interval was used as a categorical variable (prolonged vs. normal) (all-cause mortality 1.73 [0.95–3.15]; CVD mortality 2.86 [1.35–6.08]). CONCLUSIONS Heart rate QTc interval is an independent predictor of all-cause and CVD mortality in this population with type 2 diabetes, suggesting that additional prognostic information may be available from this simple ECG measure.
Learning Sparse Kernel Classifiers for Multi-Instance Classification

We propose a direct approach to learning sparse kernel classifiers for multi-instance (MI) classification to improve efficiency while maintaining predictive accuracy. The proposed method builds on a convex formulation for MI classification by considering the average score of individual instances for bag-level prediction. In contrast, existing formulations used the maximum score of individual instances in each bag, which leads to nonconvex optimization problems. Based on the convex MI framework, we formulate a sparse kernel learning algorithm by imposing additional constraints on the objective function to enforce the maximum number of expansions allowed in the prediction function. The formulated sparse learning problem for the MI classification is convex with respect to the classifier weights. Therefore, we can employ an effective optimization strategy to solve the optimization problem that involves the joint learning of both the classifier and the expansion vectors. In addition, the proposed formulation can explicitly control the complexity of the prediction model while still maintaining competitive predictive performance. Experimental results on benchmark data sets demonstrate that our proposed approach is effective in building very sparse kernel classifiers while achieving comparable performance to the state-of-the-art MI classifiers.
EMPIRICAL EVALUATION OF AUTOMATICALLY EXTRACTED ROAD AXES

Internal self-diagnosis and external evaluation of the obt ained results are of major importance for the relevance of any automatic system for practical appl ications. Obviously, this statement is also true for automatic image analysis in photogrammetry an d remote sensing. However, so far only relatively little work has been carried out in this area . This is mostly due to the moderate results achieved. Only recently automatic systems reached a state in which a systematic evaluation of the results seems to be meaningful. This paper deals with the external evaluation of automatic r oad extraction algorithms by comparison to manually plotted linear road axes used as referen ce data. The comparison is performed in two steps: (1) Matching of the extracted primitives to the ref rence network; (2) Calculation of quality measures. Each step depends on the other: the less to l rant is matching, the less exhaustive the extraction is considered to be, but the more accurate it l ooks. Therefore, matching is an important part of the evaluation process. The quality measures pr oposed for the automatically extracted road data comprise completeness, correctness, quality, re dundancy, planimetric RMS differences, and gap statistics. They aim at evaluating exhaustivity as w ell as assessing geometrical accuracy. The evaluation methodology is presented and discussed in de tail. Results of a comparative evaluation of three different automatic road extraction approac hes are presented. They show the overall status of the road extractors, as well as the individual stre ngths and weaknesses of each individual approach. Thus, the applicability of the evaluation method is proven.
Beam and RB allocation in LTE uplink with opportunistic beamforming

Opportunistic beamforming is investigated in the context of multicell LTE uplink resource allocation. A scheduling algorithm based on beam and resource block (RB) allocation with opportunistic beamforming is presented. Beams are formed in a pseudorandom fashion thus allowing users to opportunistically benefit from the antenna gain and interference rejection capabilities. Results show that enhanced performance is obtained with opportunistic beamforming.
Correction of Verbs in English Corpus by using the Concept of AI

The present paper aims at automated correction of verb from huge English corpus and addition of verbs at run time through single sentence. The approach deals with salient issues in the applications that use the artificial intelligence with respect to three key properties. The basic differences between the approaches and the computational aspects have been discussed. In support of this discussion, and approaches we evaluate Natural Language Processing (NLP) systems and these are addressed with the help of hierarchical inheritance.
Stimulus-Dependent Frequency Modulation of Information Transmission in Neural Systems

Neural oscillations are universal phenomena and can be observed at different levels of neural systems, from single neuron to macroscopic brain. The frequency of those oscillations are related to the brain functions. However, little is know about how the oscillating frequency of neural system affects neural information transmission in them. In this paper, we investigated how the signal processing in single neuron is modulated by subthreshold membrane potential oscillation generated by upstream rhythmic neural activities. We found that the high frequency oscillations facilitate the transferring of strong signals, whereas slow oscillations the weak signals. Though the capacity of information convey for weak signal is low in single neuron, it is greatly enhanced when weak signals are transferred by multiple pathways with different oscillation phases. We provided a simple phase plane analysis to explain the mechanism for this stimulus-dependent frequency modulation in the leakage integrate-and-fire neuron model. Those results provided a basic understanding of how the brain could modulate its information processing simply through oscillating frequency.
A New Formation Channel for Double Neutron Stars Without Recycling: Implications for Gravitational Wave Detection

We report on a new evolutionary path leading to the formation of close double neutron stars (NSs), with the unique characteristic that none of the two NSs ever had the chance to be recycled by accretion. The existence of this channel stems from the evolution of helium-rich stars (cores of massive NS progenitors), which has been neglected in most previous studies of double compact object formation. We find that these nonrecycled NS-NS binaries are formed from bare carbon-oxygen cores in tight orbits, with formation rates comparable to or maybe even higher than those of recycled NS-NS binaries. On the other hand, their detection probability as binary pulsars is greatly reduced (by ~103) relative to recycled pulsars because of their short lifetimes. We conclude that, in the context of gravitational wave detection of NS-NS in-spiral events, this new type of binaries calls for an increase of the rate estimates derived from the observed NS-NS systems with recycled pulsars, typically by factors of 1.5-3 or even higher.
Caching with an Optical Network

In this paper we propose a simple extension to the optical net work of a scalable multiprocessor that optimizes page swapouts significantly. More specifically, we propose to extend the ne twork with an optical ring that not only transfers swapped-out pag es between the local memories and the disks of the multiprocessor , but also acts as a system-wide write cache for these pages. This e xtended optical network has several performance benefits: it provides a staging area where swapped-out pages can reside until the d isk is free; it increases the possibility of combining several wri tes to disk; and it acts as a victim cache for pages that are swapped out and subsequently accessed by the same or a different processor. In rder to evaluate the extent to which these benefits affect perf ormance, we use detailed execution-driven simulations of several ou t-of-core parallel applications running on an 8-node scalable multip rocessor. Our results demonstrate that our optical ring provides consistent performance improvements, coming mostly from fast er page swap-outs and victim caching. Based on these results and on o ur parameter space study, our main conclusion is that our optic al r ng is highly efficient under several architectural assumption s a d for most out-of-core parallel applications.
Distributed Region Growing Algorithm for Medical Image Segmentation

of pathologists; it is especially true for image processing software products. High-resolution digital images have taken over the role of traditional tissue slides on a glass plate. In addition to the direct effects of this advancement (sharing images, remote access, etc.), a new option appeared: the possibility of using image processing software for automatic (or semi-automatic) diagnostics. One of the most important tasks in this procedure is the segmentation of the tissue images; we have to identify the main components (in the case of colon tissue samples, these are the cell nuclei, glands and surface epithelium). There are several traditional image segmentation methods for this purpose, but none of them provides both acceptable accuracy and runtime. This paper presents a distributed region growing method implemented on CPUs and GPGPUs.
Witnessing Moral Violations Increases Conformity in Consumption

Consumers frequently encounter moral violations (e.g., financial scandal, cheating, and corruption) in their daily lives. Yet little is known about how exposure to moral violations may affect consumer choice. By synthesizing insights from research on social order and conformity, we suggest that mere exposure to others’ immoral behaviors heightens perceived threat to social order, which increases consumers’ endorsement of conformist attitudes and hence their preferences for majority-endorsed choices in subsequently unrelated consumption situations. Five studies conducted across different experimental contexts and different product categories provided convergent evidence showing that exposure to moral violations increases consumers’ subsequent conformity in consumption. Moreover, the effect disappears (a) when the moral violator has already been punished by third parties (study 4) and (b) when the majority-endorsed option is viewed as being complicit with the moral violation (study 5). This research not only demonstrates a novel downstream consequence of witnessing moral violations on consumer choice but also advances our understanding of how conformity can buffer the negative psychological consequences of moral violations and how moral considerations can serve as an important basis for consumer choice.
Suboptimal Multiple Model Filter for Mobile Robot Localization

The problem of mobile robot localization by using sensor information appeals to different communities since the need for accurate position has become crucial for many robot subtasks. The Kalman filter (KF) has been acknowledged as an appropriate tool for a suitable dynamic combination of the different measurements using the state and measurement models. However, when there are discrete uncertainties about the models, without additional restrictions, the performance of KF degrades drastically as the predicted estimate tends to be updated by a wrong measurement. This paper focuses on a suboptimal multiple model filter to deal with the associated measurement/model. Experimental and simulated results are achieved, which prove the feasibility of the proposal. The experimental setup consists of a structured environment constituted of elementary features such as walls and corners, while a possibly unmodeled obstacle may be encountered. The robot is equipped with odometry and a set of ultrasonic sensors.
Design and Implementation of a Lexical Data Base

This paper is concerned with the specifications and the implementation of a particular concept of word-based lexicon to be used for large natural language processing systems such as machine translation systems, and compares it with the morpheme-based conception of the lexicon traditionally assumed in computational linguistics.It will be argued that, although less concise, a relational word-based lexicon is superior to a morpheme-based lexicon from a theoretical, computational and also practical viewpoint.
The Use of Genetic Algorithms in UV Disinfection of Drinking Water

In order to have drinking water, some countries have to use chlorine. It is use cause is effective and it's cheap. An alternative to this process is the UV disinfection of drinking water. Most of the devices in the market use UV bulbs or mercury lamps. The UV LED, which is cheaper and smaller, allows creating new smaller devices. The main contribution of this paper is the use of Genetic Algorithms to help design a drinking water device with UV LEDs.