Miracle-dz/UniFAR-FTUs huggingface dataset:
IGRAINE-an Implication GRaph-bAsed engINE for fast implication, justification, and propagation

Implication, justification, and propagation are three important Boolean problems that have to be solved during many tasks in electronic design automation (EDA) for digital circuits. As they constitute the key components of automatic test pattern generation (ATPG) most algorithms that tackle these problems originate in ATPG research. Due to their fundamental nature these ATPG-based methods have successfully been adopted by logic synthesis and formal verification where they have helped advance the fields of netlist optimization and Boolean equivalence checking. Despite their high importance and wide applicability, the data structures and algorithms suggested so far have proven to be suboptimal and inflexible in several respects. Therefore, we propose IGRAINE, a fast and flexible engine for performing implication, justification, and propagation in combinational circuits that is specifically optimized with respect to these tasks. Due to its modular design, IGRAINE is easily included into new applications that require ATPG-based methods. Our approach is based on a new implication graph (IG) model which forms the core of IGRAINE. Contrary to other IG models, the proposed IG represents all information on the implemented logic function as well as the topology of a combinational circuit in a single graph model. In order to demonstrate the performance of the presented IG-based algorithms for implication, justification, and propagation, we provide experimental results for stuck-at and path delay fault ATPG as well as Boolean equivalence checking. They show that TIP outperforms the state-of-the-art in SAT-based and structure-based ATPG. A comparison with tools for Boolean equivalence checking demonstrates the high effectiveness of our approach.
Local versus landscape‐scale effects of savanna trees on grasses

1. Savanna ecosystems – defined by the coexistence of trees and grasses – cover more than one‐fifth the world’s land surface and harbour most of the world’s rangelands, livestock and large mammal diversity. Savanna trees can have a variety of effects on grasses, with consequences for the wild and domestic herbivores that depend on them.
Solution-processed oxide thin film transistors on shape memory polymer enabled by photochemical self-patterning

Solution-processed metal oxide electronics on flexible substrates can enable applications from military to health care. Due to limited thermal budgets and mismatched coefficients of thermal expansion between oxides and substrates, achieving good performance in solution-processed oxide films remains a challenge. Additionally, the use of traditional photolithographic processes is incompatible with low-cost, high-throughput roll-to-roll processing. Here, we demonstrate solution-deposited oxide thin film transistors (TFTs) on a shape memory polymer substrate, which offers unique control of final device shape and modulus. The key enabling step is the exposure of the precursor film to UV-ozone through a shadow mask to perform patterning and photochemical conversion simultaneously. These TFTs exhibit mobility up to 160 cm^2/(V s), subthreshold swing as low as 110 mV/dec, and threshold voltage between −2 and 0 V, while maintaining compatibility with a flexible form factor at processing temperatures below 250 °C.
ChunkCast: An Anycast Service for Large Content Distribution

Fast and efficient large content distribution is a challenge in the Internet due to its high traffic volume. In this paper, we propose ChunkCast, an anycast service that optimizes large content distribution. We present a distributed locality-aware directory that supports an efficient query for large content. Our system improves the median downloading time by at least 32% compared to previous approaches and emulates multicast trees without any explicit coordination of peers.
MiLAN: Middleware Linking Applications and Networks

Current trends in computing include increases in both distribution and wireless connectivity, leading to highly dynamic, complex environments on top of which applications must be built. The task of designing and ensuring the correctness of applications in these environments is similarly becoming more complex. The unified goal of much of the research in distributed wireless systems is to provide higher level abstractions of complex low-level concepts to application programmers, easing the design and implementation of applications. This is also the goal of the proposed Milan middleware platform, but Milan''s unique feature is its ability to continuously control the network functionality with respect to the application''s changing demands. pp Applications targeted by Milan are characterized by their ability to adapt to changing sets of available components, and their need to further constrain the active components for application-performance reasons. Physical resources (e.g., transmission distance, bandwidth) and minimum application performance limit the input to certain subsets of available components. It is the job of Milan to identify these feasible sets and determine which set optimizes the tradeoff between application performance and network cost (e.g., energy dissipation). Milan must then configure the network so that components in the selected feasible set are linked to the application. A key feature of Milan is the separation of the policy for managing the network, which is defined by the application, from the mechanisms for implementing the policy, which is effected within Milan. This report describes the initial design of Milan as well as our plans for future research.
Key-point detection with multi-layer center-surround inhibition

We present a biologically inspired algorithm for key-point detection based on multi-layer and nonlinear center-surround inhibition. A Bag-of-Visual-Words framework is used to evaluate the performance of the detector on the Oxford III-T Pet Dataset for pet recognition. The results demonstrate an increased performance of our algorithm compared to the SIFT key-point detector. We further improve the recognition rate by separately training codebooks for the ON- and OFF-type key points. The results show that our key-point detection algorithms outperform the SIFT detector by having a lower recognition-error rate over a whole range of different key-point densities. Randomly selected key-points are also outperformed.
Design quality and robustness with neural networks

Data sets that are gathered for industrial applications are frequently noisy and present challenges that necessitate use of different methodologies.We present a successful example of generating concise and accurate neural-network models for multiple quality characteristics in injection molding. These models map process measurements to product quality. They are used for product and process design, addressing material and processing issues, and defining the operating window and its robustness. The models are developed based on data from designed and other experiments. Linear regression, decision tree induction, nonlinear regression, as well as "stepwise neural networks" were used for feature selection and model comparison. The final model consists of a neural network with three inputs, one hidden layer and five outputs, modeling five critical to quality variables (CTQ's) simultaneously with high accuracy. The neural network was visualized for validation and insight.
A Feedforward Control Approach to the Local Navigation Problem for Autonomous Vehicles

This report describes the state space model which forms the core technology of an integrated autonomous navigation system incorporating perception, control, and position estimation called RANGER. The high speed local navigation problem is formulated as an optimal control problem in state space. This report concentrates on the trajectory tracking, obstacle avoidance and sensor stabilization, aspects of the system. These algorithms form the basis of RANGER’s Controller object.
Loss of Lrp2 in zebrafish disrupts pronephric tubular clearance but not forebrain development

Low‐density lipoprotein receptor‐related protein 2 (LRP2) is a multifunctional cell surface receptor conserved from nematodes to humans. In mammals, it acts as regulator of sonic hedgehog and bone morphogenetic protein pathways in patterning of the embryonic forebrain and as a clearance receptor in the adult kidney. Little is known about activities of this LRP in other phyla. Here, we extend the functional elucidation of LRP2 to zebrafish as a model organism of receptor (dys)function. We demonstrate that expression of Lrp2 in embryonic and larval fish recapitulates the patterns seen in mammalian brain and kidney. Furthermore, we studied the consequence of receptor deficiencies in lrp2 and in lrp2b, a homologue unique to fish, using ENU mutagenesis or morpholino knockdown. While receptor‐deficient zebrafish suffer from overt renal resorption deficiency, their brain development proceeds normally, suggesting evolutionary conservation of receptor functions in pronephric duct clearance but not in patterning of the teleost forebrain. Developmental Dynamics 240:1567–1577, 2011. © 2011 Wiley‐Liss, Inc.
Impaired nerve regeneration in reeler mice after peripheral nerve injury

Reelin, an extracellular matrix protein, plays an important role in the regulation of neuronal migration and cortical lamination in the developing brain. Little is known, however, about the role of this protein in axonal regeneration. We have previously shown that Reelin is secreted by Schwann cells in the peripheral nerve compartment during postnatal development and that it is up‐regulated following nerve injury in adult mice. In this work, we generated mice deficient in Reelin (reeler) that express yellow fluorescent protein (YFP) in a subset of neurons and examined the axonal regeneration following nerve crush. We found that axonal regeneration was significantly altered compared with wild‐type mice. By contrast, retrograde tracing with Fluorogold dye after sciatic nerve crush was unaffected in these mutants, being comparable with normal axonal transport observed in wild‐type. These results indicate that the absence of Reelin impairs axonal regeneration following injury and support a role for this protein in the process of peripheral nerve regeneration.
An Exploration of Features for Recognizing Word Emotion

Emotion words have been well used as the most obvious choice as feature in the task of textual emotion recognition and automatic emotion lexicon construction. In this work, we explore features for recognizing word emotion. Based on Ren-CECps (an annotated emotion corpus) and MaxEnt (Maximum entropy) model, several contextual features and their combination have been experimented. Then PLSA (probabilistic latent semantic analysis) is used to get semantic feature by clustering words and sentences. The experimental results demonstrate the effectiveness of using semantic feature for word emotion recognition. After that, "word emotion components" is proposed to describe the combined basic emotions in a word. A significant performance improvement over contextual and semantic features was observed after adding word emotion components as feature.
Optical, structural and Photoconductivity properties of organic photovoltaic thin films based on polymer/fullerene

Morphological properties of two p-type semiconductors, used separately with fullerene: MEH-PPV and the commonly used P3HT, are investigated in order to study their influence on optical and Photoconductivity properties of organic thin films based on MEH-PPV/P3HT, separately, with and without PCBM. The aim is to better understand the charge carriers mechanisms inside the polymers and the effect of PCBM when added in blend. To measure morphologic and optical properties of the prepared thin films, different characterizations are performed. A comparative study of both polymers is discussed using X ray diffraction, absorption, photolumenescence and Raman spectra.
Optimization of Weighted Curvature for Image Segmentation

Minimization of boundary curvature is a classic regularization technique for image segmentation in the presence of noisy image data. Techniques for minimizing curvature have historically been derived from descent methods which could be trapped in a local minimum and therefore required a good initialization. Recently, combinatorial optimization techniques have been applied to the optimization of curvature which provide a solution that achieves nearly a global optimum. However, when applied to image segmentation these methods required a meaningful data term. Unfortunately, for many images, particularly medical images, it is difficult to find a meaningful data term. Therefore, we propose to remove the data term completely and instead weight the curvature locally, while still achieving a global optimum.
Design of a compact balun with three octant-wavelength coupled lines

A novel microwave balun with three coupled lines is provided in this paper. With broadside coupling and edge coupling among three coupled lines, the proposed balun is compact and a wide operating frequency range is obtained. At a central frequency of 2.45 GHz, this balun is fabricated on the FR4 printed circuit board and with overall size of 1.5 mm × 1.2 mm. The prototypical balun is simulated with the full-wave electromagnetic simulator IE3D. A good match between simulated and measured results validates the proposed balun.
UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields

We describe our entry to SemEval 2016 Task 10: Detecting Minimal Semantic Units and their Meanings. Our approach uses a discriminative ﬁrst-order sequence model similar to Schneider and Smith (2015). The chief nov-elty in our approach is a factorization of the labels into multiword expression and supersense labels, and restricting ﬁrst-order dependencies within these two parts. Our submitted models achieved ﬁrst place in the closed competition (CRF) and second place in the open competition (2-CRF).
A recursive Newton-Euler algorithm for robots with elastic joints and its application to control

We consider the problem of computing the inverse dynamics of a serial robot manipulator with N elastic joints in a recursive numerical way. The solution algorithm is a generalized version of the standard Newton-Euler approach, running still with linear complexity O(N) but requiring to set up recursions that involve higher order derivatives of motion and force variables. Mimicking the case of rigid robots, we use this algorithm and a numerical factorization of the link inertia matrix (which needs to be inverted in the elastic joint case) for implementing on-line a feedback linearization control law for trajectory tracking purposes. The complete method has a complexity that grows as O(N3). The developed tools are generic, easy to use, and do not require symbolic Lagrangian modeling and customization, thus being of particular interest when the number N of elastic joints becomes large.
Acquired prosopagnosia with spared within-class object recognition but impaired recognition of degraded basic-level objects

We present a new case of acquired prosopagnosia resulting from extensive lesions predominantly in the right occipitotemporal cortex. Functional brain imaging revealed atypical activation of all core face areas in the right hemisphere, with reduced signal difference between faces and objects compared to controls. In contrast, Herschel's lateral occipital complex showed normal activation to objects. Behaviourally, Herschel is severely impaired with the recognition of familiar faces, discrimination between unfamiliar identities, and the perception of facial expression and gender. Notably, his visual recognition deficits are largely restricted to faces, suggesting that the damaged mechanisms are face-specific. He showed normal recognition memory for a wide variety of object classes in several paradigms, normal ability to discriminate between highly similar items within a novel object category, and intact ability to name basic objects (except four-legged animals). Furthermore, Herschel displayed a normal face composite effect and typical global advantage and global interference effects in the Navon task, suggesting spared integration of both face and nonface information. Nevertheless, he failed visual closure tests requiring recognition of basic objects from degraded images. This abnormality in basic object recognition is at odds with his spared within-class recognition and presents a challenge to hierarchical models of object perception.
LPC-based object tracking in image processing

The tracking method based on local position color information (LPC) is a subregion model matching approach. A new color representation method is proposed firstly to eliminate illumination efficiently. And then the target region is divided into similar color subregion, in each subregion the histogram is calculated in certain color range, furthermore, the target can be represented by the position-weighted histogram. At last, the minimum cross entropy method is used as the similarity function between the target model and the target candidates. The target tracking algorithm has better effect in the experimental result.
Integration of model reference adaptive control (MRAC) with PX4 firmware for quadcopters

Standard control laws have unique parameters that yield a certain response according to which model they are implemented on. Changes in the model will result in changes in the response, yielding the need for new ideal parameters. Adaptive controllers have the advantage of reducing the number of control parameters to be chosen by the pilot. Reducing this number of parameters can be beneficial, as obtaining the ideal parameters can become a time consuming process. Model reference adaptive control (MRAC) is the control approach that is considered in this paper. This approach has previously been implemented on a quadcopter by Achtelik [Adaptive Control oof a Quadcopter in the Presence of large/complete Parameter Uncertainties, 1, (2011)]. It is desired to implement the adaptive controller on the Pixhawk flight controller. The designed controller should integrate well with the existing PX4 firmware to allow users to still be able to use the flight controller as before. In order to integrate the adaptive controller with the PX4 firmware some modifications to the approach followed by Achtelik [1] is required. This paper focuses on the implementation of MRAC in PX4 firmware. This requires the use of rotation matrices as opposed to Euler angles. The mixer is also extracted from the adaptive law. From simulations it can be seen that rotation matrices show a significant improvement in reference tracking when it comes to simultaneous pitch, roll and yaw inputs. An autonomous mission was flown with the newly designed adaptive controller and also with the original PX4 controller. This showed successful integration of the adaptive controller with the existing firmware. An improvement in reference tracking for the adaptive controller, as opposed to the PX4 controller, was also found.
Towards a trans-disciplinary methodology for a game-based intervention development process

The application of game-based learning adds play into educational and instructional contexts. Even though there is a lack of standard methodologies or formulaic frameworks to better inform game-based intervention development, there exist scientific and empirical studies that can serve as benchmarks for establishing scientific validity in terms of the efficacy of using games to achieve serious outcomes. The development of these games does not normally follow a specific set of guidelines, which limits replication. There is a need to reflect on such a multidisciplinary process and infuse knowledge from relevant disciplines towards developing a unity of considerations and approaches beyond the disciplinary perspectives. An infused and trans-disciplinary methodological framework could serve as a guideline to inform the development process of a game-based approach. With these perspectives, this paper aims to provide an example of how relevant theories and frameworks can be adopted collectively in order to inform a development process. Based on a digital game intervention aiming to support the delivery of Relationships and Sex Education ( RSE) in the UK, this paper reflects on trans-disciplinary considerations, informed by frameworks such as the Four-Dimensional Framework (4 DF), the Intervention Mapping ( IM) approach, Mechanics Dynamics Aesthetics model ( MDA) and Learning Mechanics-Game Mechanics ( LM-GM) mapping. IM, when infused with the game design considerations of 4 DF, provides a more procedural perspective to game-based intervention development, collectively reflecting a participatory development approach. This subsequently provides the basis upon which other theoretical and methodological frameworks such as the MDA and the LM-GM models can be embedded in order to marry the pedagogical aspects with the entertainment attributes of gameplay. These components, when integrated, may formulate a trans-disciplinary model that can be adopted and adapted by other researchers, designers and developers. [ABSTRACT FROM AUTHOR]
The Reliability of Pulse Contour-Derived Cardiac Output During Hemorrhage and After Vasopressor Administration

BACKGROUND: Reliable measurement of cardiac output (CO) is important in the critically ill. Pulse contour-derived CO (PCCO) has been evaluated during stable hemodynamics, but is sensitive to changes in vascular tone and has not been validated under conditions of changing hemodynamics. Furthermore, PCCO requires calibration for the individual vascular impedance by transpulmonary thermodilution CO (TPCO), and the required frequency of recalibration to maintain accurate measurements, especially during changing conditions, has not been confirmed. We compared PCCO measurements of CO with TPCO and continuous and bolus pulmonary artery CO (CCO and BCO, respectively) during conditions of uncontrolled hemorrhage and resuscitation with norepinephrine. METHODS: Thirteen pigs were anesthetized and instrumented for determination of CO by BCO and CCO, respectively, as well as bolus TPCO and PCCO. Uncontrolled hemorrhage was accomplished by liver incision. When mean arterial blood pressure was <25 mm Hg, or heart rate declined progressively to <20% of its peak value, vasopressor therapy was started. TPCO and BCO were performed after induction of anesthesia and 15 min after start of therapy, and PCCO and CCO were obtained repeatedly. CO measurements were compared using Bland-Altman analysis. RESULTS: Mean arterial blood pressure, CO and systemic vascular resistance decreased after hemorrhage (P < 0.001 and <0.01, respectively). Bias and limits of agreement between CCO and PCCO (0.54 L/min; 1.46 L/min) increased after hemorrhage (−3.49; 6.12) and further deteriorated after norepinephrine administration (−8.01; 9.9). After recalibration, bias and limits of agreement returned to −0.51 and 1.28. CONCLUSIONS: PCCO needs frequent recalibration during hemorrhage and after vasopressor administration.
Demonstration of a deflection routing 2*2 photonic switch for computer interconnects

The first reported demonstration of a 2*2 self-routing photonic switch is presented. Output port contention is handled using a deflection routing protocol implemented by a pipelined electrooptic processor. Routing and data bits are encoded in separated multiwavelength channels occupying only a portion of a full packet period. This encoding technique accommodates timing uncertainty, reduces the effective electronic processing rate at the switching node, and maintains a high-link throughput. In the authors' single-node demonstration. The packet header contains two address bits plus one priority bit for contention resolution, and one packet payload is represented by a single bit. Control information is encoded at 830 nm with 3.6 nm channel spacing and data at 1300 nm. The switch, implemented with longer wavelength transmission bands, is an efficient building block for large-scale, wide-area, high-capacity networks. <<ETX>>