Query: Extending the CSG Tree - Warping, Blending and Boolean Operations in an Implicit Surface Modeling System

Relevant passages:
Free-form deformation of solid geometric models <=SEP=> A technique is presented for deforming solid geometric models in a free-form manner. The technique can be used with any solid modeling system, such as CSG or B-rep. It can deform surface primitives of any type or degree: planes, quadrics, parametric surface patches, or implicitly defined surfaces, for example. The deformation can be applied either globally or locally. Local deformations can be imposed with any desired degree of derivative continuity. It is also possible to deform a solid model in such a way that its volume is preserved.The scheme is based on trivariate Bernstein polynomials, and provides the designer with an intuitive appreciation for its effects.

Particle Systems - a Technique for Modeling a Class of Fuzzy Objects <=SEP=> This paper introduces particle systems--a method for modeling fuzzy objects such as fire, clouds, and water. Particle systems model an object as a cloud of primitive particles that define its volume. Over a period of time, particles are generated into the system, move and change form within the system, and die from the system. The resulting model is able to represent motion, changes of form, and dynamics that are not possible with classical surface-based representations. The particles can easily be motion blurred, and therefore do not exhibit temporal aliasing or strobing. Stochastic processes are used to generate and control the many particles within a particle system. The application of particle systems to the wall of fire element from the Genesis Demo sequence of the film Star Trek II: The Wrath of Khan [10] is presented.

Solid Modeling: A Historical Summary and Contemporary Assessment <=SEP=> A new generation of industrial geometry systems is emerging based on the technology of the 1970's. The generations of the eighties and nineties will require more research.

An image synthesizer <=SEP=> We introduce the concept of a Pixel Stream Editor. This forms the basis for an interactive synthesizer for designing highly realistic Computer Generated Imagery. The designer works in an interactive Very High Level programming environment which provides a very fast concept/implement/view iteration cycle.Naturalistic visual complexity is built up by composition of non-linear functions, as opposed to the more conventional texture mapping or growth model algorithms. Powerful primitives are included for creating controlled stochastic effects. We introduce the concept of "solid texture" to the field of CGI.We have used this system to create very convincing representations of clouds, fire, water, stars, marble, wood, rock, soap films and crystal. The algorithms created with this paradigm are generally extremely fast, highly realistic, and asynchronously parallelizable at the pixel level.

Design and Sensorless Control of a Novel Axial-Flux Permanent Magnet Machine for In-Wheel Applications <=SEP=> This paper proposes an axial-flux-modulation permanent magnet (PM) machine and its sensorless control strategy for in-wheel application in electrical vehicles. A Vernier structure is integrated with the axial-flux PM machine to include the magnetic gear effect and improve output torque. The sensorless control strategy of the proposed machine, including initial rotor position estimation and rotating position estimation, is proposed for flux-modulation motors in this paper. The initial rotor position estimation is based on the technique of rectangular pulse voltage injection and the rotating rotor position estimation is based on the sliding mode observer (SMO). The saturation effect on inductances, which is the theoretical basis of the rectangular pulse voltage injection, makes the stator parameter variation in different loads and affects the SMO estimation. To overcome this problem, a novel online parameter self-adjustment procedure for the SMO is introduced. The machine design and its sensorless control performance are verified by simulation and prototype experiments.

The Value Function Polytope in Reinforcement Learning <=SEP=> We establish geometric and topological properties of the space of value functions in finite stateaction Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To demonstrate this result, we exhibit several properties of the structural relationship between policies and value functions including the line theorem, which shows that the value functions of policies constrained on all but one state describe a line segment. Finally, we use this novel perspective to introduce visualizations to enhance the understanding of the dynamics of reinforcement learning algorithms.

Visualizing Translation Variation: Shakespeare's Othello <=SEP=> Recognized as great works of world literature, Shakespeare’s poems and plays have been translated into dozens of languages for over 300 years. Also, there are many re-translations into the same language, for example, there are more than 60 translations of Othello into German. Every translation is a different interpretation of the play. These large quantities of translations reflect changing culture and express individual thought by the authors. They demonstrate wide connections between different world regions today, and reveal a retrospective view of their cultural, intercultural, and linguistic histories. Researchers from Arts and Humanities at Swansea University are collecting a large number of translations of William Shakespeare’s Othello. In this paper, we have developed an interactive visualization system to present, analyze and explore the variations among these different translations. Our system is composed of two parts: the structure-aware Treemap for document selection and meta data analysis, and Focus + Context parallel coordinates for in-depth document comparison and exploration. In particular, we want to learn more about which content varies highly with each translation, and which content remains stable. We also want to form hypotheses as to the implications behind these variations. Our visualization is evaluated by the domain experts from Arts and Humanities.

Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding <=SEP=> Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of individual ICD-10 codes, and it explicitly uses the sequential order of codes for matching. Empirical evaluation on a state-wide cancer data collection shows that our proposed method achieves significantly higher matching performance compared with state-of-the-art methods ignoring the sequential order. Our method better identifies similar patients in a number of clinical outcomes including readmission and mortality outlook. Although this paper focuses on ICD-10 diagnosis code sequences, our method can be adapted to work with other codified sequence data.

Long-Term Visual Localization Using Semantically Segmented Images <=SEP=> Robust cross-seasonal localization is one of the major challenges in long-term visual navigation of autonomous vehicles. In this paper, we exploit recent advances in semantic segmentation of images, i.e., where each pixel is assigned a label related to the type of object it represents, to attack the problem of long-term visual localization. We show that semantically labeled 3D point maps of the environment, together with semantically segmented images, can be efficiently used for vehicle localization without the need for detailed feature descriptors (SIFT, SURF, etc.), Thus, instead of depending on hand-crafted feature descriptors, we rely on the training of an image segmenter. The resulting map takes up much less storage space compared to a traditional descriptor based map. A particle filter based semantic localization solution is compared to one based on SIFT-features, and even with large seasonal variations over the year we perform on par with the larger and more descriptive SIFT-features, and are able to localize with an error below 1 m most of the time.

A Concept Analysis of Systems Thinking. <=SEP=> PURPOSE
This concept analysis, written by the National Quality and Safety Education for Nurses (QSEN) RN-BSN Task Force, defines systems thinking in relation to healthcare delivery.


METHODS
A review of the literature was conducted using five databases with the keywords "systems thinking" as well as "nursing education," "nursing curriculum," "online," "capstone," "practicum," "RN-BSN/RN to BSN," "healthcare organizations," "hospitals," and "clinical agencies." Only articles that focused on systems thinking in health care were used. The authors identified defining attributes, antecedents, consequences, and empirical referents of systems thinking.


FINDINGS
Systems thinking was defined as a process applied to individuals, teams, and organizations to impact cause and effect where solutions to complex problems are accomplished through collaborative effort according to personal ability with respect to improving components and the greater whole. Four primary attributes characterized systems thinking: dynamic system, holistic perspective, pattern identification, and transformation.


CONCLUSION
Using the platform provided in this concept analysis, interprofessional practice has the ability to embrace planned efforts to improve critically needed quality and safety initiatives across patients' lifespans and all healthcare settings.

Cloud RAN for Mobile Networks—A Technology Overview <=SEP=> Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can address a number of challenges the operators face while trying to support growing end-user's needs. The main idea behind C-RAN is to pool the Baseband Units (BBUs) from multiple base stations into centralized BBU Pool for statistical multiplexing gain, while shifting the burden to the high-speed wireline transmission of In-phase and Quadrature (IQ) data. C-RAN enables energy efficient network operation and possible cost savings on baseband resources. Furthermore, it improves network capacity by performing load balancing and cooperative processing of signals originating from several base stations. This paper surveys the state-of-the-art literature on C-RAN. It can serve as a starting point for anyone willing to understand C-RAN architecture and advance the research on C-RAN.

Democracy and Education Spending in Africa <=SEP=> While it is widely believed that electoral competition influences public spending decisions, there has been relatively little effort to examine how recent democratization in the developing world has resulted in changes in basic service provision. There have been even fewer attempts to investigate whether democracy matters for public spending in the poorest developing countries, where “weak institutions” may mean that the formal adoption of electoral competition has little effect on policy. In this article I confront these questions directly, asking whether the shift to multiparty competition in African countries has resulted in increased spending on primary education. I develop an argument, illustrated with a game-theoretic model, which suggests that the need to obtain an electoral majority may have prompted African governments to spend more on education and to prioritize primary schools over universities within the education budget. I test three propositions from the model using panel data on electoral competition and education spending in African countries. I find clear evidence that democratically elected African governments have spent more on primary education, while spending on universities appears unaffected by democratization.

Bitter to Better - How to Make Bitcoin a Better Currency <=SEP=> Bitcoin is a distributed digital currency which has attracted a substantial number of users. We perform an in-depth investigation to understand what made Bitcoin so successful, while decades of research on cryptographic e-cash has not lead to a large-scale deployment. We ask also how Bitcoin could become a good candidate for a long-lived stable currency. In doing so, we identify several issues and attacks of Bitcoin, and propose suitable techniques to address them.

Unscented Kalman Filtering on Riemannian Manifolds <=SEP=> In recent years there has been a growing interest in problems, where either the observed data or hidden state variables are confined to a known Riemannian manifold. In sequential data analysis this interest has also been growing, but rather crude algorithms have been applied: either Monte Carlo filters or brute-force discretisations. These approaches scale poorly and clearly show a missing gap: no generic analogues to Kalman filters are currently available in non-Euclidean domains. In this paper, we remedy this issue by first generalising the unscented transform and then the unscented Kalman filter to Riemannian manifolds. As the Kalman filter can be viewed as an optimisation algorithm akin to the Gauss-Newton method, our algorithm also provides a general-purpose optimisation framework on manifolds. We illustrate the suggested method on synthetic data to study robustness and convergence, on a region tracking problem using covariance features, an articulated tracking problem, a mean value optimisation and a pose optimisation problem.

Active Learning Algorithms for Multi-label Data <=SEP=> The iterative supervised learning setting, in which learning algorithms can actively query an oracle for labels, e.g. a human annotator that understands the nature of the problem, is called active learning. As the learner is allowed to interactively choose the data from which it learns, it is expected that the learner would perform better with less training. The active learning approach is appropriate to machine learning applications where training labels are costly to obtain but unlabeled data is abundant. Although active learning has been widely considered for single-label learning, this is not the case for multi-label learning, in which objects can have more than one class label and a multi-label learner is trained to assign multiple labels simultaneously to an object. There are different scenarios to query the annotator. This work focuses on the scenario in which the evaluation of unlabeled data is taken into account to select the object to be labeled. In this scenario, several multilabel active learning algorithms were identified in the literature. These algorithms were implemented in a common framework and experimentally evaluated in two multi-label datasets which have different properties. The influence of the properties of the datasets in the results obtained by the multi-label active learning algoritm is highlighted.

Automated Image Annotation based on YOLOv3 <=SEP=> A typical pedestrian protection system requires sophisticated hardware and robust detection algorithms. To solve these problems the existing systems use hybrid sensors where mono and stereo vision merged with active sensors. One of the most assuring pedestrian detection sensors is far infrared range camera. The classical pedestrian detection approach based on Histogram of oriented gradients is not robust enough to be applied in devices which consumers can trust. An application of deep neural network-based approach is able to perform with significantly higher accuracy. However, the deep learning approach requires a high number of labeled data examples. The investigation presented in this paper aimed the acceleration of pedestrian labeling in far-infrared image sequences. In order to accelerate pedestrian labeling in far-infrared camera videos, we have integrated the YOLOv3 object detector into labeling software. The verification of the pre-labeled results was around eleven times faster than manual labeling of every single frame.

Microstrip antenna phased array with electromagnetic bandgap substrate <=SEP=> Uniplanar compact electromagnetic bandgap (UC-EBG) substrate has been proven to be an effective measure to reduce surface wave excitation in printed antenna geometries. This paper investigates the performance of a microstrip antenna phased array embedded in an UC-EBG substrate. The results show a reduction in mutual coupling between elements and provide a possible solution to the "blind spots" problem in phased array applications with printed elements. A novel and efficient UC-EBG array configuration is proposed. A probe fed patch antenna phased array of 7/spl times/5 elements on a high dielectric constant substrate was designed, built and tested. Simulation and measurement results show improvement in the active return loss and active pattern of the array center element. The tradeoffs used to obtain optimum performance are discussed.

Automatic Solution of Jigsaw Puzzles <=SEP=> We present a method for automatically solving apictorial jigsaw puzzles that is based on an extension of the method of differential invariant signatures. Our algorithms are designed to solve challenging puzzles, without having to impose any restrictive assumptions on the shape of the puzzle, the shapes of the individual pieces, or their intrinsic arrangement. As a demonstration, the method was successfully used to solve two commercially available puzzles. Finally we perform some preliminary investigations into scalability of the algorithm for even larger puzzles.

Neural Graph Machines: Learning Neural Networks Using Graphs <=SEP=> Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training framework with a graph-regularised objective, namely Neural Graph Machines, that can combine the power of neural networks and label propagation. This work generalises previous literature on graphaugmented training of neural networks, enabling it to be applied to multiple neural architectures (Feed-forward NNs, CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the neural networks to harness both labeled and unlabeled data by: (a) allowing the network to train using labeled data as in the supervised setting, (b) biasing the network to learn similar hidden representations for neighboring nodes on a graph, in the same vein as label propagation. Such architectures with the proposed objective can be trained efficiently using stochastic gradient descent and scaled to large graphs, with a runtime that is linear in the number of edges. The proposed joint training approach convincingly outperforms many existing methods on a wide range of tasks (multi-label classification on social graphs, news categorization, document classification and semantic intent classification), with multiple forms of graph inputs (including graphs with and without node-level features) and using different types of neural networks. Work done during an internship at Google.

A parameterized cell design for high-Q, variable width and spacing spiral inductors <=SEP=> The on-chip planar spiral inductors having variable width (W) and spacing (S) across their turns are known to exhibit higher quality factors (Q). In this paper, we present an efficient parameterized cell (pcell) design in cadence using SKILL scripts for automatic layout generation of these complex, high-Q, variable W&S spiral inductors comprising of single ended and symmetric structures with rectangular, hexagonal, octagonal and circular spirals. Electromagnetic simulations are performed on the inductor layouts generated using the developed pcells. The constant W&S and variable W&S spiral inductor structures are fabricated in a 0.18 μm silicon on insulator process. Measurements show ~25% improvement in the quality factor of variable W%S spiral inductors compared to their constant W&S counterparts and also validates the proper operation of the developed inductor parameterized cells. The presented variable W&S inductor pcell significantly reduces the layout design time of RF circuit designers and also helps in the design automation of these complex inductor structures to boost their own performance and the RF circuits as well.

A Novel IoT Authorization Architecture on Hyperledger Fabric With Optimal Consensus Using Genetic Algorithm <=SEP=> This paper proposes a distributed authorization architecture for Internet of Things adopting a hyperledger fabric framework. This paper also applies Genetic Algorithm to enhance the consensus component, traditionally used for Kafka, in order to determine the best configuration over different parameters, such as input transactions and their success rates. The experimental results confirm our superior performance in that the transaction transfer rate of our optimization, called GA Kafka, outperforms the traditional Kafka by 69.43%, and 89.97% for transaction transfer success rate.

Perspectives on usability guidelines for smartphone applications: An empirical investigation and systematic literature review <=SEP=> Context: Several usability guidelines have been proposed to improve the usability of smartphone apps. These guidelines can be classified into three disjoint sets: platform specific guidelines, genre specific guidelines, and generic guidelines. However, smartphone applications are usually developed for multiple platforms and targeted for a variety of users. Hence the usefulness of existing guidelines is severally limited. Objective: This study aims to develop a comprehensive list of usability guidelines suitable for multiple platforms and genres of smartphone applications.

For a social network analysis of computer networks: a sociological perspective on collaborative work and virtual community <=SEP=> When computer networks link people as well as machines, they become social networks. Social network analysis provides a useful approach to moving beyond the concept of “group” in studying virtual communities and computer supported cooperative work and telework. Such computer supported social networks (CSSNS) sustain strong, intermediate and weak ties that provide information and social support in both specialized and broadly-based relationships. They foster informal workplace communities that are usually partial and narrowly-focused, although some do become encompassing and broadly-based. CSSNS connect workers within and between organizations who often are physically dispersed. The nature of the medium both constrains and facilitates management control. Although many relationships function offline as well as online, CSSNS have developed their own norms and structures. They have strong societal implications, fostering situations that combine global connectivity, the fragmentation of solidarities and the de-emphasis of local organization, and the importance of home bases.

World Knowledge as Indirect Supervision for Document Clustering <=SEP=> One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then, the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this article, we provide an example of using world knowledge for domain-dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then, we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.
 A preliminary version of this work appeared in the proceedings of KDD 2015 [Wang et al. 2015a]. This journal version has made several major improvements. First, we have proposed a new and general learning framework for machine learning with world knowledge as indirect supervision, where document clustering is a special case in the original paper. Second, in order to make our unsupervised semantic parsing method more understandable, we add several real cases from the original sentences to the resulting logic forms with all the necessary information. Third, we add details of the three semantic filtering methods and conduct deep analysis of the three semantic filters, by using case studies to show why the conceptualization-based semantic filter can produce more accurate indirect supervision. Finally, in addition to the experiment on 20 newsgroup data and Freebase, we have extended the experiments on clustering results by using all the combinations of text (20 newsgroup, MCAT, CCAT, ECAT) and world knowledge sources (Freebase, YAGO2).

Exploratory Innovation, Exploitative Innovation, and Performance: Effects of Organizational Antecedents and Environmental Moderators <=SEP=> AND

Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach <=SEP=> Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain. However, in many real-world settings one seeks to adapt to multiple, but somewhat similar, target domains. Applying pairwise adaptation approaches to this setting may be suboptimal, as they fail to leverage shared information among multiple domains. In this work we propose an information theoretic approach for domain adaptation in the novel context of multiple target domains with unlabeled instances and one source domain with labeled instances. Our model aims to find a shared latent space common to all domains, while simultaneously accounting for the remaining private, domainspecific factors. Disentanglement of shared and private information is accomplished using a unified information-theoretic approach, which also serves to establish a stronger link between the latent representations and the observed data. The resulting model, accompanied by an efficient optimization algorithm, allows simultaneous adaptation from a single source to multiple target domains. We test our approach on three challenging publicly-available datasets, showing that it outperforms several popular domain adaptation methods.

An application of neural net chips: handwritten digit recognition <=SEP=> A general-purpose, fully interconnected neural-net chip was used to perform computationally intensive tasks for handwritten digit recognition. The chip has nearly 3000 programmable connections, which can be set for template matching. The templates can be reprogrammed as needed during the recognition sequence. The recognition process proceeds in four major steps. First, the image is captured using a TV camera and a digital framegrab. This image is converted, using a digital computer, to either black or white pixels and scaled to fill a 16*16-pixel frame. Next, using the neural-net chip, the image is skeletonized, i.e. the image is thinned to a backbone one pixel wide. Then, the chip is programmed, and a feature map is created by template-matching stored primitive patterns on the chip with regions on the skeletonized image. Finally, recognition, based on the feature map, is achieved using any one of a variety of statistical and heuristic techniques on a digital computer. Best scores range between 90% and 99% correct classification, depending on the quality of the original handwritten digits.<<ETX>>

The biology of interleukin-6. <=SEP=> B CELLS ARE the only eukaryotic cells that are able to produce antibody molecules. Their growth and differentiation into antibody producing cells require the presence of T cells and macrophages; the function of these cells was found to be replaced by soluble factors. 3 In the early i980s it was shown that at least two different kinds of factors were required in the regulation of B cell response, one for growth of activated B cells, B-cell growth factor (BCGF), and the other for antibody induction in B cells, B-cell differentiation factor (BCDF). 6 Since then, a variety of factors regulating the B cell response have been reported in the human and murine systems. Finally, in 1986 the cDNAs for three B-cell stimulatory factors have been cloned; interleukin-4 (IL-4) (BCGF1 /BSF1 ) for the early activation of resting B cells,7’8 IL-5 (BCGFII) for the growth ofactivated B cells,9 and IL-6 (BCDF/BSF2) for the final differentiation of B cells into antibody producing cells’#{176} (Fig 1). Human IL-6 (BSF2) was originally identified as a factor in the culture supernatants of mitogen or antigen-stimulated peripheral mononuclear cells, which induced immunoglobulin production in Epstein Barr virus (EBV) transformed B-cell lines or in Staphylococcus aureus Cowan 1 (SAC) stimulated normal B cells.t1 This molecule was found to be separable from other factors, such as IL-2 and BCGFs, and the establishment of human T cell hybridoma generating BSF2 activity confirmed that this is a distinct molecule from other cytokines.6 BSF2 was purified to homogeneity from the culture supernatant of a human T-cell leukemia virus type 1 (HTLV1 ) transformed T-cell line and its partial N-terminal amino acid sequence was determined.t2 Based on these findings, the cDNA encoding human BSF2 was cloned. #{176} Approximately at the same time, the molecular cloning and the nucleotide sequences of the molecules termed interferon / 2 (IFN 32)’3 and 26 Kd protein were reportedt4 and the results revealed that BSF2, IFN 32, and 26 Kd protein were identical. In I 980, an inducible mRNA species of about 1 3S encoding for a novel human fibroblast-type interferon (IFN), named 1FNf32 was reported.’5 The isolated cDNA clone for such an induced mRNA was trancribed in vitro into a protein of 26 Kd. One group detected an antiviral activity that was neutralized with anti-IFNf 2 and thus called this molecule IFNj32)5 On the other hand, another group could not detect any antiviral activity in this protein’6 and its interferon activity was controversial until recombinant molecules became available. In 1987 recombinant IL-6 (r IL-6) was shown to have no IFN activity and to have antigenically and functionally no relations with IFNfl.’7 Growth factors for plasmacytomas/myelomas have been reported by several investigators.’8’20 In 1 986, N-terminal amino acid sequence of a human cytokine that showed hybridoma/plasmacytoma growth factor activity was determined and the result again showed that the factor was identical with BSF2/IFN/32/26 Kd protein.2’ Subsequently, the cDNA cloning of murine hybridoma/plasmacytoma growth factor was completed and the sequence indicated that it was the murine homologue of IL-6/BSF2.2223 Therefore, all the results demonstrated that IL-6 has the growth activity in plasmacytoma/myeloma cells. The other major activty of IL-6 is induction of acute phase proteins in hepatocytes. The studies with IL-6 and anti-IL-6 antibody carried out by Gauldie et a124 and Andus et a125 clearly demonstrated that IL-6 functioned as a hepatocyte stimulating factor (HSF) and induced the production of major acute phase proteins. As described, the molecular cloning of the cDNA of IL-6 indicated that the function of IL-6 is not restricted to B lineage cells but shows a wide variety of biological activities on various tissues and cells. Table 1 summarizes the activities exerted by molecules identified to be identical to IL-6.

FontMatcher: Font Image Paring for Harmonious Digital Graphic Design <=SEP=> One of the important aspects in graphic design is choosing the font of the caption that matches aesthetically the associated image. To obtain a good match, users would exhaustively examine a long font list requiring them a substantial effort. This paper presents FontMatcher, which supports users to design digital graphic works harmoniously pairing fonts with an image. The system provides three features, recommendation, explaination and feedback. If a warm feeling image is given as input, the system recommends warm feeling fonts, and then explains what is the distinguishing features of the recommendation, e.g. a cursive shape. Users can also provide feedback to find fonts which correspond to their intention. Our evaluation results show that the recommended fonts scored better than selected fonts by novices and provides competing results with the ones chosen by experienced graphic designers. The system also provides explanations that help increasing the reliability of the recommended results.