Query: Spacetime expression cloning for blendshapes

Relevant passages:
A Morphable Model for the Synthesis of 3D Faces <=SEP=> In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an “unlikely” appearance. Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms. We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.

Face poser: Interactive modeling of 3D facial expressions using facial priors <=SEP=> This article presents an intuitive and easy-to-use system for interactively posing 3D facial expressions. The user can model and edit facial expressions by drawing freeform strokes, by specifying distances between facial points, by incrementally editing curves on the face, or by directly dragging facial points in 2D screen space. Designing such an interface for 3D facial modeling and editing is challenging because many unnatural facial expressions might be consistent with the user's input. We formulate the problem in a maximum a posteriori framework by combining the user's input with priors embedded in a large set of facial expression data. Maximizing the posteriori allows us to generate an optimal and natural facial expression that achieves the goal specified by the user. We evaluate the performance of our system by conducting a thorough comparison of our method with alternative facial modeling techniques. To demonstrate the usability of our system, we also perform a user study of our system and compare with state-of-the-art facial expression modeling software (Poser 7).

High-quality passive facial performance capture using anchor frames <=SEP=> We present a new technique for passive and markerless facial performance capture based on anchor frames. Our method starts with high resolution per-frame geometry acquisition using state-of-the-art stereo reconstruction, and proceeds to establish a single triangle mesh that is propagated through the entire performance. Leveraging the fact that facial performances often contain repetitive subsequences, we identify anchor frames as those which contain similar facial expressions to a manually chosen reference expression. Anchor frames are automatically computed over one or even multiple performances. We introduce a robust image-space tracking method that computes pixel matches directly from the reference frame to all anchor frames, and thereby to the remaining frames in the sequence via sequential matching. This allows us to propagate one reconstructed frame to an entire sequence in parallel, in contrast to previous sequential methods. Our anchored reconstruction approach also limits tracker drift and robustly handles occlusions and motion blur. The parallel tracking and mesh propagation offer low computation times. Our technique will even automatically match anchor frames across different sequences captured on different occasions, propagating a single mesh to all performances.

Good features to track <=SEP=> No feature-based vision system can work unless good features can be identi ed and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under a ne image transformations. We test performance with several simulations and experiments.

Hierarchical Model-Based Motion Estimation <=SEP=> This paper describes a hierarchical estimation framework for the computation of diverse representations of motion information. The key features of the resulting framework (or family of algorithms) a,re a global model that constrains the overall structure of the motion estimated, a local rnodel that is used in the estimation process, and a coa,rse-fine refinement strategy. Four specific motion models: affine flow, planar surface flow, rigid body motion, and general optical flow, are described along with their application to specific examPles.

Detailed Real-Time Urban 3D Reconstruction from Video <=SEP=> The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.

Establishment of the Diagnosis of Follicular Occlusion Tetrad <=SEP=> Background: Follicular occlusion tetrad (FOT) is a clinical syndrome consisting of suppurative hidradenitis, acne conglobata, dissecting cellulitis of the scalp, and pilonidal sinus. FOT is a rare case that is mostly found in severe conditions resulting in resistance to therapy. Diagnostic accuracy and therapy for all components of FOT is extremely important. Case: A 43-year old male presented with small, painful blisters and bumps filled with pus that had been ongoing for five years. We found pustules and erythematous nodules and multiple abscess with atrophic scars on the scalp, anterior and posterior trunks, abdomen, groin and gluteal regions on the facial region, we found multiple atrophic scars. The pus culture showed the presence of Escherichia coli, and fistulography examination revealed multiple cutaneous fistules and enterocutaneous sinus. The histopathological examination indicated rupture of hair follicles, follicular plugs, and infiltration of heavy-mixed infiltrate cells. Discussion: The FOT diagnosis was established through medical history recording, physical and histopathological examinations, and fistulography. Upon medical history recording and physical examination, we found pustules and nodules in several hairy areas. The fistulographic examination showed some fistules and sinus tracts, and the histopathological examination showed adnexal tissue damage caused by occlusion of hair follicles and inflammation due to accumulation of keratin and debris. All these findings led to the FOT diagnosis. ISSN 2476-2415

Temporal Pattern Attention for Multivariate Time Series Forecasting <=SEP=> Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate this task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews the information at each previous time step and selects relevant information to help generate the outputs; however, it fails to capture temporal patterns across multiple time steps. In this paper, we propose using a set of filters to extract time-invariant temporal patterns, similar to transforming time series data into its “frequency domain”. Then we propose a novel attention mechanism to select relevant time series, and use its frequency domain information for multivariate forecasting. We apply the proposed model on several real-world tasks and achieve state-of-the-art performance in all of these with a single exception. Our source code is available at https://github.com/gantheory/TPA-LSTM. * indicates equal contribution. This work was financially supported by the Ministry of Science and Technology of Taiwan. Shun-Yao Shih National Taiwan University E-mail: shunyaoshih@gmail.com Fan-Keng Sun National Taiwan University E-mail: b03901056@ntu.edu.tw Hung-yi Lee National Taiwan University E-mail: hungyilee@ntu.edu.tw ar X iv :1 80 9. 04 20 6v 2 [ cs .L G ] 2 7 N ov 2 01 8 2 Shun-Yao Shih* et al. Fig. 1 Historical prices of crude oil, gasoline, and lumber. Units are omitted and scales are normalized for simplicity.

A Case Study of Automated Face Recognition: The Boston Marathon Bombings Suspects <=SEP=> While face recognition can be a useful tool, helping authorities to narrow leads and confirm a criminal suspect's identity, the media tended to highlight the technology's limitations in the Boston Marathon bombings investigation. Consequently, we were motivated to reexamine the efficacy of unconstrained face recognition using this incident as a case study. We chose two automated face recognition systems known for their superior unconstrained matching performance based on tests conducted by the US National Institute of Standards and Technology: NEC's NeoFace v3.1 and Google-owned Pittsburgh Pattern Recognition v5.2.2. To conduct the experiment, we added six reference images of the Tsarnaevs taken from press releases and news articles following their identification to a background database of 1 million mug shots. Against this database, we searched for matches of the five face images of the brothers extracted by the FBI from surveillance, smartphone, or point-and-shoot camera footage before their identification.

Using link structure to infer opinions in social networks <=SEP=> The emergence of online social networks in the past few years has generated an enormous amount of information about potentially any subject. Valuable data containing users' opinions and thoughts are available on those repositories and several sentiment analysis techniques have been proposed that address the problem of understanding the opinion orientation of the users' postings. In this paper, we take a different perspective to the problem through a user centric approach, which uses a graph to model users (and their postings) and applies link mining techniques to infer opinions of users. Preliminary experiments on a Twitter corpus have shown promising results.

Political Facebook groups: Micro-activism and the digital front stage <=SEP=> The growth of social networking sites (SNS), like Facebook, has caused many to rethink how we understand political activism and citizen engagement. In 2010, 43% of Internet users reported using social networking sites " several times a day, " a sizeable increase from 2009 where only 34% reported using social networking at the same rate (Pew 2010). Research on digital activism has emphasized the use of websites, listservs, and other forums by formal organizations to raise awareness for their specific causes (McCaughey and Ayers 2003; Van de Donk 2004). Information communication technologies (ICT's) have been regarded as particularly effective tools in mobilizing disparate, resource-poor counter-spheres in collective off-line activity (Van de Donk 2004). In recent years, scholars have moved away from viewing information communication technologies (ICT's) as simply a tool for formal activist organizations. Breindl (2010) argues that on-line digital activism has largely been examined through a classic " social movement paradigm. " She calls for scholarship that looks at how the meaning and structure of activism has been transformed by ICT's. One key way it has been transformed is by allowing for small scale, many-to-many forms of politically oriented communication, what I conceptualize as a new term: microactivism. Examples include the formation of political Facebook groups, re-tweeting of articles of political interest and sharing politically relevant videos on YouTube. These acts reflect micro-level intentions and are not necessarily geared towards mobilization like more traditional forms of digital activism. This microactivism has helped bring about a radical reformulation of the political arena. One major change that emerges from the ease of many-to-many communication brought about by SMS is the reduced incentive to join social movements (Earl and Schussman 2003). These new applications encourage movement entrepreneurs (Garrett 2006) not affiliated with formal social movements but interested in fomenting social change. The ease with which individuals can create content and connect with one another to share content is viewed by others as a harbinger of a more democratic and egalitarian society (Benkler 2006, Jenkins 2007, Shirky 2010). Salter (2005) argues that sites like SNS' provide new radical public spheres that provide additional spaces for voice cultivation and political citizenship formation. More importantly, in his view information technologies help shift political discourse to more informal venues that are less subject to elite control (Dahlberg 2004). Social critics like Morozov (2009) suggest, however, that microactivism might do more harm than good. Morozov (2009) refers …

Classification of Model Transformation Approaches <=SEP=> While the current OMG standards such as the Meta Object Facility (MOF) [MOF] and the UML [UML] provide a well-established foundation for defining PIMs and PSMs, no such wellestablished foundation exists for transforming PIMs into PSMs [GLR+02]. In 2002, in its effort to change this situation, the OMG initiated a standardization process by issuing a Request for Proposal (RFP) on Query / Views / Transformations (QVT) [QVT]. This process will eventually lead to an OMG standard for defining model transformations, which will be of interest not only for PIM-to-PSM transformations, but also for defining views on models and synchronization between models. Driven by practical needs and the OMG’s request, a large number of approaches to model transformation have recently been proposed.

Slant: A Blackboard System to Generate Plot, Figuration, and Narrative Discourse Aspects of Stories <=SEP=> We introduce Slant, a system that integrates more than a decade of research into computational creativity, and specifically story generation, by connecting subsystems that deal with plot, figuration, and the narrative discourse using a blackboard. The process of integrating these systems highlights differences in the representation of story and has led to a better understanding of how story can be usefully abstracted. The plot generator MEXICA and a component of Curveship are used with little modification in Slant, while the figuration subsystem Fig-S and the template generator GRIOT-Gen, inspired by GRIOT, are also components. The development of the new subsystem Verso, which deals with genre, shows how different genres can be computationally modeled and applied to in-development stories to generate results that are surprising in terms of their connections and valuable in terms of their relationship to cultural questions. Example stories are discussed, as is the potential of the system to allow for broader collaboration, the empirical testing of how subsystems interrelate, and possible contributions in literary and artistic contexts.

Probabilistic Horn Abduction and Bayesian Networks <=SEP=> This paper presents a simple framework for Horn clause abduc tion with probabilities associated with hypotheses The framework incorporates assumptions about the rule base and independence as sumptions amongst hypotheses It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework The main contribution is in nding a relationship between logical and probabilistic notions of evidential reasoning This provides a useful representation language in its own right providing a compromise between heuristic and epistemic ad equacy It also shows how Bayesian networks can be extended be yond a propositional language This paper also shows how a language with only unconditionally independent hypotheses can represent any probabilistic knowledge and argues that it is better to invent new hy potheses to explain dependence rather than having to worry about dependence in the language Scholar Canadian Institute for Advanced Research Probabilistic Horn abduction and Bayesian networks

Locking the Elbow: Improved Antibody Fab Fragments as Chaperones for Structure Determination. <=SEP=> Antibody Fab fragments have been exploited with significant success to facilitate the structure determination of challenging macromolecules as crystallization chaperones and as molecular fiducial marks for single particle cryo-electron microscopy approaches. However, the inherent flexibility of the "elbow" regions, which link the constant and variable domains of the Fab, can introduce disorder and thus diminish their effectiveness. We have developed a phage display engineering strategy to generate synthetic Fab variants that significantly reduces elbow flexibility, while maintaining their high affinity and stability. This strategy was validated using previously recalcitrant Fab-antigen complexes where introduction of an engineered elbow region enhanced crystallization and diffraction resolution. Furthermore, incorporation of the mutations appears to be generally portable to other synthetic antibodies and may serve as a universal strategy to enhance the success rates of Fabs as structure determination chaperones.

Double Pole Four Throw switch design with CMOS inverter <=SEP=> To avoid the uses of multiple RF chain associated with the multiple antennas, RF switch is most essential component. Multiple antennas systems are used to replace traditional single antennas circuitry in the radio transceiver system in order to improve the transmission capability and reliability. The desired switching system must have low cost and simple structure, yet still can capture all the advantage of Multiple Inputs Multiple Output (MIMO) systems. In this paper, we have proposed a Double Pole Four Throw (DP4T) RF switch using Complementary Metal Oxide Semiconductor (CMOS) technology with inverter property. This switch is low in cost, and capable of selecting data streams to or from the two antennas for transmitting or receiving processes, respectively.

Understanding Instructions on Large Scale for Human-Robot Interaction <=SEP=> Correctly interpreting human instructions is the first step to human-robot interaction. Previous approaches to semantically parsing the instructions relied on large numbers of training examples with annotation to widely cover all words in a domain. Annotating large enough instructions with semantic forms needs exhaustive engineering efforts. Hence, we propose propagating the semantic lexicon to learn a semantic parser from limited annotations, whereas the parser still has the ability of interpreting instructions on a large scale. We assume that the semantically-close words have the same semantic form based on the fact that human usually uses different words to refer to a same object or task. Our approach softly maps the unobserved words/phrases to the semantic forms learned from the annotated copurs through a metric for knowledge-based lexical similarity. Experiments on the collected instructions showed that the semantic parser learned with lexicon propagation outperformed the baseline. Our approach provides an opportunity for the robots to understand the human instructions on a large scale.

Trellis: Privilege Separation for Multi-user Applications Made Easy <=SEP=> Operating systems provide a wide variety of resource isolation and access control mechanisms, ranging from traditional user-based security models to fine-grained permission systems as found in modern mobile operating systems. However, comparatively little assistance is available for defining and enforcing access control policies within multiuser applications. These applications, often found in enterprise environments, allow multiple users to operate at different privilege levels in terms of exercising application functionality and accessing data. Developers of such applications bear a heavy burden in ensuring that security policies over code and data in this setting are properly expressed and enforced. We present Trellis, an approach for expressing hierarchical access control policies in applications and enforcing these policies during execution. The approach enhances the development toolchain to allow programmers to partially annotate code and data with simple privilege level tags, and uses a static analysis to infer suitable tags for the entire application. At runtime, policies are extracted from the resulting binaries and are enforced by a modified operating system kernel. Our evaluation demonstrates that this approach effectively supports the development of secure multi-user applications with modest runtime performance overhead.

Markov Chains for Exploring Posterior Distributions <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

White matter lesion extension to automatic brain tissue segmentation on MRI <=SEP=> A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.

T-bar reconstruction of constricted ears and a new classification. <=SEP=> For the correction of constricted ears, many techniques are described in the literature, the majority based on Tanzer's classification of 1975. The improvements in ear reconstruction published by Brent, Nagata, Firmin and Park make better outcomes possible. It is therefore that a new classification for constricted ears is proposed, together with an alternative technique for correction of group IIA and IIB deformities, using a T-strut of costal cartilage to reconstruct the underdeveloped or missing superior crus of the antihelix.

Enterprise Architecture Characteristics in Context Enterprise Governance Base On COBIT 5 Framework <=SEP=> The existence of the enterprise architecture is an attempt of managing and planning over the evolution of information systems in the sphere of an enterprise with model-based. In developing the enterprise architecture, there are several tools definition of components in the system. This tool is known as enterprises architecture (EA) framework. In this paper, we present a method to build a model of enterprise architecture in accordance with the needs of the Organization by Understanding the characteristics of the EA framework such as Zachman, TOGAF, and FEAF. They are selected as the EA framework will be used to determine the characteristics of an EA because the framework is most widely used in corporate or Government. In COBIT 5 framework, there is a process associated with enterprise architecture it is APO03 Manage Enterprise Architecture. At this stage of the research, we describe the link between the characteristics of the EA with one process in COBIT 5 framework. The results contribute to give a recommendation how to design EA for organization based on the characteristic of EA in Context Enterprise Governance using COBIT 5 Framework.

Penetration testing in a box <=SEP=> Network and application vulnerability assessments have a tendency to be difficult and costly; however, failing to have an assessment done and fixing security loopholes may result in a security breach by malicious attackers. A security breach can cost an organization time and money remediating the damage, such as lost confidential business information, which far exceeds the cost of a security assessment. Our solution for this problem is a semi-automated system that can help a penetration tester, security professional, or systems administrator, scan and report on the vulnerabilities of a network and services running on the network. It is also able to perform some simulated attacks. This system relies on a miniaturized computer to host the necessary components to conduct a security assessment. This system has been done in an open source manner to allow others to contribute and benefit from a community effort in the name of security.

X-Band GaN SPDT MMIC with over 25 Watt Linear Power Handling <=SEP=> Single pole double throw (SPDT) switches are becoming more and more key components in phased-array radar transmit/receive modules. An SPDT switch must be able to handle the output power of a high power amplifier and must provide enough isolation to protect the low noise amplifier in the receive chain when the T/R module is transmitting. Therefore gallium nitride technology seems to become a key technology for high power SPDT switch design. The technology shows good performance on microwave frequencies and is able to handle high power. An X-band SPDT switch, with a linear power handling of over 25 W, has been designed, measured and evaluated. The circuit is designed in the coplanar waveguide AlGaN/GaN technology established at QinetiQ.

The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. <=SEP=> Procrastination is a prevalent and pernicious form of self-regulatory failure that is not entirely understood. Hence, the relevant conceptual, theoretical, and empirical work is reviewed, drawing upon correlational, experimental, and qualitative findings. A meta-analysis of procrastination's possible causes and effects, based on 691 correlations, reveals that neuroticism, rebelliousness, and sensation seeking show only a weak connection. Strong and consistent predictors of procrastination were task aversiveness, task delay, self-efficacy, and impulsiveness, as well as conscientiousness and its facets of self-control, distractibility, organization, and achievement motivation. These effects prove consistent with temporal motivation theory, an integrative hybrid of expectancy theory and hyperbolic discounting. Continued research into procrastination should not be delayed, especially because its prevalence appears to be growing.

Ultrasound measurements of axillary recess capsule thickness in unilateral frozen shoulder: study of correlation with MRI measurements <=SEP=> The aims of this study were to compare the ultrasound thickness of the affected axillary recess capsule (ARC) with that of the unaffected ARC in patients with frozen shoulder (FS), to analyze whether the ultrasound measurements of the ARC thickness correlate with those obtained using MRI, and to assess whether the ultrasound thickness of the ARC correlates with the patterns of range of motion limitation. Forty-four patients with clinically diagnosed unilateral FS and MRI evaluation performed ultrasound measurement of ARC. The ultrasound measurement of the ARC thickness was performed with the patients in a supine position with their shoulder abducted by 40°. The ARC thickness was also measured by MRI on oblique coronal images by another physician blinded to the ultrasound measurements. With both ultrasound and MRI, ARC thickness was determined at the widest portion of the capsule. The ultrasound thickness of ARC was significantly higher in the affected shoulder (4.4 ± 1.1 mm) than in the unaffected one (2.2 ± 0.5 mm) (p < 0.001). The ultrasound thickness of the ARC in the affected shoulder correlated with that measured by MRI (8.9 ± 1.9 mm) (p < 0.001, r = 0.83). The ARC thickness, whether measured by ultrasound or MRI, was not significantly related to the limitation of movement in specific directions. Ultrasound can demonstrate the difference in ARC thickness between affected and unaffected shoulders in patients with unilateral FS. The ARC thickness measured by ultrasound correlates with that measured by MRI.

Morpheo: Traceable Machine Learning on Hidden data <=SEP=> Morpheo is a transparent and secure machine learning platform collecting and analysing large datasets. It aims at building state-of-the art prediction models in various fields where data are sensitive. Indeed, it offers strong privacy of data and algorithm, by preventing anyone to read the data, apart from the owner and the chosen algorithms. Computations in Morpheo are orchestrated by a blockchain infrastructure, thus offering total traceability of operations. Morpheo aims at building an attractive economic ecosystem around data prediction by channelling cryptomoney from prediction requests to useful data and algorithms providers. Morpheo is designed to handle multiple data sources in a transfer learning approach in order to mutualize knowledge acquired from large datasets for applications with smaller but similar datasets. Document purpose: This is a white-paper: an introduction to the technology powering Morpheo. It is evolving with the project to reflect the current vision and development status of Morpheo. It introduces Morpheo as a data-agnostic platform, although Morpheo is originally developed for a medical application, namely sleep data analysis. For technical details about Morpheo, see the online documentation and source code.

A Method for Refocusing Photos using Depth from Defocus <=SEP=> This paper will describe a method for refocusing photos taken with a regular camera. By capturing a series of images from the same viewpoint with varying focal depth, a depth metric for the scene can be calculated and used to artificially blur the photo in a realistic way to emulate a new focal depth.

ROUTERLESS NETWORKS-ON-CHIP <=SEP=> Traditional bus-based interconnects are simple and easy to implement, but the scalability is greatly limited. While router-based networks-on-chip (NoCs) offer superior scalability, they also incur significant power and area overhead due to complex router structures. In this paper, we explore a new class of on-chip networks, referred to as Routerless NoCs, where routers are completely eliminated. We propose a novel design that utilizes on-chip wiring resources smartly to achieve comparable hop count and scalability as router-based NoCs. Several effective techniques are also proposed that significantly reduce the resource requirement to avoid new network abnormalities in routerless NoC designs. Evaluation results show that, compared with a conventional mesh, the proposed routerless NoC achieves 9.5X reduction in power, 7.2X reduction in area, 2.5X reduction in zero-load packet latency, and 1.7X increase in throughput. Compared with a state-of-the-art low-cost NoC design, the proposed approach achieves 7.7X reduction in power, 3.3X reduction in area, 1.3X reduction in zero-load packet latency, and 1.6X increase

Transferable Semi-Supervised Semantic Segmentation <=SEP=> The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations. However, even the largest public datasets only provide samples with pixel-level annotations for rather limited semantic categories. Such data scarcity critically limits scalability and applicability of semantic segmentation models in real applications. In this paper, we propose a novel transferable semi-supervised semantic segmentation model that can transfer the learned segmentation knowledge from a few strong categories with pixel-level annotations to unseen weak categories with only image-level annotations, significantly broadening the applicable territory of deep segmentation models. In particular, the proposed model consists of two complementary and learnable components: a Label transfer Network (L-Net) and a Prediction transfer Network (PNet). The L-Net learns to transfer the segmentation knowledge from strong categories to the images in the weak categories and produces coarse pixel-level semantic maps, by effectively exploiting the similar appearance shared across categories. Meanwhile, the P-Net tailors the transferred knowledge through a carefully designed adversarial learning strategy and produces refined segmentation results with better details. Integrating the L-Net and P-Net achieves 96.5% and 89.4% performance of the fully-supervised baseline using 50% and 0% categories with pixel-level annotations respectively on PASCAL VOC 2012. With such a novel transfer mechanism, our proposed model is easily generalizable to a variety of new categories, only requiring image-level annotations, and offers appealing scalability in real applications.