Miracle-dz/UniFAR-FTUs huggingface dataset:
Visualization of 3D Scientific Datasets Based on Interactive Clipping

We have exploited clipping in combination with multiresolution rendering to visualize large-scale threedimensional (3D) scientific datasets. Our interactive clipping approach involves the dynamic manipulation of a clip plane to expose any cross-section of a given volume data and the subsequent adjustment of the clipped surface to the best view position. The data are rendered as stacks of 2D textures at high-resolution (HR, the original resolution of input data/image) and low-resolution (LR, sampling at reduced-resolution). While clipping operations take place in LR to achieve an interactive frame rate, the best-view position also supports HR. Our approach thus enables a real-time exploration of any interior region of 3D volumetric data at a desired resolution. Its successful demonstration has been carried out for two scientific datasets, which are the simulated electronic density distributions in a crystal and confocal microscopy images of tissues in a plan stem.
The asymptotic dynamics of three-dimensional Einstein gravity with a negative cosmological constant

Liouville theory is shown to describe the asymptotic dynamics of three-dimensional Einstein gravity with a negative cosmological constant. This is because (i) Chern - Simons theory with a gauge group on a spacetime with a cylindrical boundary is equivalent to the non-chiral SL(2,R) WZW model; and (ii) the anti-de Sitter boundary conditions implement the constraints that reduce the WZW model to the Liouville theory.
The Bright Optical Companion to the Eclipsing Millisecond Pulsar in NGC 6397

We report the optical identification of the companion to the eclipsing millisecond pulsar PSR J1740-5340 in the globular cluster NGC 6397. A bright variable star with an anomalous red color and optical variability (~0.2 mag) that nicely correlates to the orbital period of the pulsar (~1.35 days) has been found close to the pulsar position. The peculiar shape of the optical light curves is unprecedented for a millisecond pulsar companion and is a clear signature of tidal distortions.
Definition response scoring with probabilistic ordinal regression

Word knowledge is often partial, rather than all-o r-n ne. In this paper, we describe a method for estimating partial word knowl edge on a trial-by-trial basis. Users generate a free-form synonym for a newly learned wo rd. We then apply a probabilistic regression model that combines features based on La tent Semantic Analysis (LSA) with features derived from a large-scale, multi-relation word graph model to estimate the similarity of the user response to the actual meani ng. This method allows us to predict multiple levels of accuracy, i.e., responses that p recisely capture a word's meaning versus those that are partially correct or incorrect. We t rain and evaluate our approach using a new gold-standard corpus of expert responses, and find consistently superior performance compared to a state-of-the-art multi-class logistic regression baseline. These findings are a promising step toward a new kind of adaptive tutori ng system that provides fine-grained, continuous feedback as learners acquire richer, mor e c mplete knowledge of words.
A stable and optimally convergent LaTIn-Cut Finite Element Method for multiple unilateral contact problems

In this paper, we propose a novel unfitted finite element method for the simulation of multiple body contact. The computational mesh is generated independently of the geometry of the interacting solids, which can be arbitrarily complex. The key novelty of the approach is the combination of elements of the CutFEM technology, namely the enrichment of the solution field via the definition of overlapping fictitious domains with a dedicated penalty-type regularisation of discrete operators, and the LaTIn hybrid-mixed formulation of complex interface conditions. Furthermore, the novel P1-P1 discretisation scheme that we propose for the unfitted LaTIn solver is shown to be stable, robust and optimally convergent with mesh refinement. Finally, the paper introduces a high-performance 3D level-set/CutFEM framework for the versatile and robust solution of contact problems involving multiple bodies of complex geometries, with more than two bodies interacting at a single point.
New channel hopping sequence for cognitive radio systems using p-ary m-sequence

In this paper, we propose the channel hopping sequence for cognitive radio system. The proposed sequence is generated using p-ary m-sequence. We also propose the maximum time to rendezvous (TTR) and expected TTR under the symmetric model and prove our suggestion in mathematical way. The suggested sequence shows better performance than that of Jump-Stay algorithm in the view of TTR and expected TTR under the symmetric model.
Implementing FPGA Design with the OpenCL Standard

Utilizing the Khronos Group’s OpenCLTM standard on an FPGA may offer significantly higher performance and at much lower power than is available today from hardware architectures such as CPUs, graphics processing units (GPUs), and digital signal processing (DSP) units. In addition, an FPGA-based heterogeneous system (CPU + FPGA) using the OpenCL standard has a significant time-to-market advantage compared to traditional FPGA development using lower level hardware description languages (HDLs) such as Verilog or VHDL.
Procurement Design with Corruption

This paper investigates the design of optimal procurement mechanisms in the presence of corruption. After the sponsor and the contractor sign the contract, the latter may bribe the inspector to misrepresent quality. Thus, the mechanism affects whether bribery occurs. I show how to include bribery as an additional constraint in the optimal-control problem that the sponsor solves, and characterize the optimal contract. I discuss both the case of fixed bribes and bribes that depend on the size of the quality misrepresentation, and also uncertainty about the size of the bribe. In all cases, the optimal contract curtails quality not only for low e¢ ciency contractors but also for the most efficient contractors. Implementation is also discussed.
Enhancing learner-learner interaction using video communications in higher education: Implications from theorising about a new model

As visual connectivity improves, an opportunity to enrich and rethink the place of learning design in online and distance education is presenting itself. The opportunity is derived from the increasing scope for personalised synchronous interaction which has been missing in computer mediated communications (CMC) and previous generations of distance education. This paper presents a model which distinguishes between planned learner-content interaction and learner-learner interaction and suggests that a blend of planned and non-planned learner-learner interaction is worthwhile. It concentrates on technology enhanced learning using video communications which provide opportunities for more authentic online collaborative learning, formally and informally. This conceptualisation is underpinned by beliefs which promote constructivist and transformative learning in the hope of stimulating debate as universities focus more towards quality learning. The intent is to explore the implications of learning in higher education becoming enhanced in online environments as learner-learner dialogue occurs ‘face-to-face’, thus, extending the potential of online learner-content environments.
THE METHOD (AND MADNESS) OF EVALUATING ONLINE DISCUSSIONS

In addressing how to evaluate online discussions, this paper will describe several concepts, tools, or frameworks that have been used in evaluations and discuss differences in approach based on instructor purpose, be it research, assessment, or learning. Then several common problems are described, including use of content analysis, identification of latent content, inadequate training of coders, lack of reliability, and choosing the correct unit of analysis. Two examples are provided of coding decisions made on portions of student discussions; these examples use two different frameworks to elucidate the process and its difficulties. Conclusions focus on the importance of following standard good research or assessment practice and preparing for a time-consuming and often frustrating coding process.
Forward and inverse models in motor control and cognitive control

It is now commonly accepted that the motor system makes use of so-called forward and inverse models in order to control the musculoskeletal system during rapid, skilled, motor behaviour. Inverse models are held to allow the system to determine the motor commands necessary to achieve a desired state, while forward models are held to allow the system to predict the expected sensory feedback of a motor command, allowing rapid error detection when actual and predicted feedback do not match. It has recently been suggested that these ideas from control theory might also be applied to the control of cognitive processes, allowing (for example) the cognitive system to anticipate processing conflict and pre-emptively minimise it by adjusting processing strategies or the allocation of processing resources. 12 This paper reviews theories of cognitive control that are broadly consistent with the use of complementary forward and inverse models. It is argued that there is indeed a role for such models in cognitive control – particularly in relation to a putative monitoring function – but that the models involved are likely to be somewhat impoverished.
How Frontoparietal Brain Regions Mediate Imitative and Complementary Actions: An fMRI Study

The ‘mirror neuron system’ (MNS), located within inferior parietal lobe (IPL) and inferior frontal gyrus (IFG), creates an internal motor representation of the actions we see and has been implicated in imitation. Recently, the MNS has been implicated in non-identical responses: when the actions we must execute do not match those that we observe. However, in such conflicting situations non action-specific cognitive control networks also located in frontoparietal regions may be involved. In the present functional magnetic resonance imaging (fMRI) study participants made both similar and dissimilar actions within two action contexts: imitative and complementary. We aimed to determine whether activity within IPL/IFG depends on (i) responding under an imitative versus complementary context (ii) responding with similar versus dissimilar responses, and (iii) observing hand actions versus symbolic arrow cue stimuli. Activity within rIPL/rIFG regions was largest during observation of hand actions compared with arrow cues. Specifically, rIPL/rIFG were recruited only during the imitative context, when participants responded with similar actions. When responding to symbolic arrow cues, rIPL/rIFG activity increased during dissimilar responses, reflecting increased demands placed on general cognitive control mechanisms. These results suggest a specific role of rIPL/rIFG during imitation of hand actions, and also a general role of frontoparietal areas in mediating dissimilar responses to both hand actions and symbolic stimuli. We discuss our findings in relation to recent work that has examined the role of frontoparietal brain structures in joint-actions and inter-actor cooperation. We conclude that the specific brain regions identified here to show increased activation during action observation conditions are likely to form part of a mechanism specifically involved in matching observed actions directly with internal motor plans. Conversely, observation of arrow cues recruited part of a wider cognitive control network involved in the rapid remapping of stimulus-response associations.
Feynman parametrization and Mellin summation at finite temperature

We show that the Mellin summation technique (MST) is a well-defined and useful tool to compute loop integrals at finite temperature in the imaginary-time formulation of thermal field theory, especially when interested in the infrared limit of such integrals. The method makes use of the Feynman parametrization which has been claimed to have problems when the analytical continuation from discrete to arbitrary complex values of the Matsubara frequency is performed. We show that without the use of the MST, such problems are not intrinsic to the Feynman parametrization but instead, they arise as a result of (a) not implementing the periodicity brought about by the possible values taken by the discrete Matsubara frequencies before the analytical continuation is made and (b) to the changing of the original domain of the Feynman parameter integration, which seemingly simplifies the expression but in practice introduces a spurious end point singularity. Using the MST, there are no problems related to the implementation of the periodicity but instead, care has to be taken when the sum of denominators of the original amplitude vanishes. We apply the method to the computation of loop integrals appearing when the effects of external weak magnetic fields on the propagationmore » of scalar particles is considered. « less
Reuse-oriented reverse engineering of functional components from x86 binaries

Locating, extracting, and reusing the implementation of a feature within an existing binary program is challenging. This paper proposes a novel algorithm to identify modular functions corresponding to such features and to provide usable interfaces for the extracted functions. We provide a way to represent a desired feature with two executions that both execute the feature but with different inputs. Instead of reverse engineering the interface of a function, we wrap the existing interface and provide a simpler and more intuitive interface for the function through concretization and redirection. Experiments show that our technique can be applied to extract varied features from several real world applications including a malicious application.
Pna Probability, Networks and Algorithms Probability, Networks and Algorithms a Note on the Benefits of Buffering a Note on the Benefits of Buffering

CWI's research has a theme-oriented structure and is grouped into four clusters. Listed below are the names of the clusters and in parentheses their acronyms. ABSTRACT Gaussian traffic models are capable of representing a broad variety of correlation structures, ranging from short-range dependent (e.g. Ornstein-Uhlenbeck type) to long-range dependent (e.g. fractional Brownian motion, with Hurst parameter H exceeding 1/2). This note focuses on queues fed by a large number (n) of Gaussian sources, emptied at constant service rate nc. In particular, we consider the probability of exceeding buffer level nb, as a function of b. This probability decaying (asymptotically) exponentially in n, the essential information is contained in the exponential decay rate I(b). The main result of this note describes the duality relation between the shape of I(.) and the correlation structure. More specifically, it is shown that the curve I(\cdot) is convex at some buffer size b if and only if there are negative correlations on the time scale at which the overflow takes place. Abstract Gaussian traffic models are capable of representing a broad variety of correlation structures, ranging from short-range dependent (e.g. Ornstein-Uhlenbeck type) to long-range dependent (e.g. fractional Brow-nian motion, with Hurst parameter H exceeding 1 2). This note focuses on queues fed by a large number (n) of Gaussian sources, emptied at constant service rate nc. In particular, we consider the probability of exceeding buffer level nb, as a function of b. This probability decaying (asymptotically) exponentially in n, the essential information is contained in the exponential decay rate I(b). The main result of this note describes the duality relation between the shape of I(·) and the correlation structure. More specifically, it is shown that the curve I(·) is convex at some buffer size b if and only if there are negative correlations on the time scale at which the overflow takes place.
Novelty Enhancements in Memory Are Dependent on Lateral Prefrontal Cortex

Physiological evidence indicates that several brain regions, including the medial temporal lobes and prefrontal cortex (PFC), are involved in processing events that are novel or distinctive in their immediate context. However, behavioral studies that investigate whether these regions are critical for producing stimulus novelty advantages in memory are limited. For example, evidence from an animal lesion study indicated that the PFC is involved in stimulus novelty effects, but this has not been examined in humans. In the current study, we used a von Restorff novelty paradigm to test a large cohort of lateral PFC patients (n = 16). We found that patients with lateral PFC damage were impaired in recollection- and familiarity-based recognition, and they did not exhibit a normal memory advantage for novel compared with non-novel items. These results provide neuropsychological evidence supporting a key role for the lateral PFC in producing stimulus novelty advantages in memory.
Removing spikes caused by quantization noise from high‐resolution histograms

A novel method is presented for the removal of spikes, caused by the division of two series of integers, from high‐resolution histograms. When two series of integers are divided the results take the form of a nonuniform distribution. Such a division is often used in medical imaging, due to the storage of most images as integers. An example of this is the division of the saturated and unsaturated signal intensities to obtain a magnetization transfer ratio. Histograms produced using these methods often contain spikes relating to the nonuniform distribution mentioned above. These spikes can have serious implications for certain histogram characteristics. Most commonly, peak height and location can be seriously distorted by these spikes, which have predictable locations. These spikes can be removed by the addition of uniformly distributed noise to the integer signal intensities before division. Magn Reson Med 50:649–653, 2003. © 2003 Wiley‐Liss, Inc.
On brain-inspired hybrid topologies for nano-architectures - a Rent’s rule approach -

This paper will start by comparing brainpsilas connectivity (based on different analyses of neurological data) versus well-known network topologies (originally used in massively parallel super-computers), in view of the latest interpretation of Rentpsilas rule. These will reveal that the brain is in very good agreement with Rentpsilas rule average growth rate. With respect to classical network topologies, the crossbar (only for quite small sizes) and the cube connected cycles (for a wider range) look like promising contenders (for the brain), while in fact any network topology falls short of properly mimicking brainpsilas connectivity. That is why, we will go on exploring hybrid (hierarchical) combination of two network topologies, allowing us to identify those (hybrid network topologies) which could closely emulate brainpsilas connectivity (as well as the particular ranges where this is happening).
Enhancement of Capsid Gene Expression: Preparing the Human Papillomavirus Type 16 Major Structural Gene L1 for DNA Vaccination Purposes

ABSTRACT Expression of the structural proteins L1 and L2 of the human papillomaviruses (HPV) is tightly regulated. As a consequence, attempts to express these prime-candidate genes for prophylactic vaccination against papillomavirus-associated diseases in mammalian cells by means of simple DNA transfections result in insufficient production of the viral antigens. Similarly, in vivo DNA vaccination using HPV L1 or L2 expression constructs produces only weak immune responses. In this study we demonstrate that transient expression of the HPV type 16 L1 and L2 proteins can be highly improved by changing the RNA coding sequence, resulting in the accumulation of significant amounts of virus-like particles in the nuclei of transfected cells. Data presented indicate that, in the case of L1, adaptation for codon usage accounts for the vast majority of the improvement in protein expression, whereas translation-independent posttranscriptional events contribute only to a minor degree. Finally, the adapted L1 genes demonstrate strongly increased immunogenicity in vivo compared to that of unmodified L1 genes.
Analogical Problem Solving in Children With Verbal and Nonverbal Learning Disabilities

Analogical reasoning—perceiving similarities in different situations and the transfer of such information—facilitates learning and understanding. However, children with learning disabilities (LD) typically demonstrate deficits in such information processing strategies. In this study, we investigated the analogical problem-solving differences between children with verbal learning disabilities (VLD), nonverbal learning disabilities (NLD), or non-LD. Results indicated better recall of component stories by children without disabilities but no significant differences between the NLD and VLD participants. However, the success rate for target problem solving was much lower for the NLD group than for the VLD and non-LD groups. The poor performance of the NLD children may be attributed to some of their characteristic weaknesses, critical for analogical problem solving. Yet the VLD group was significantly weaker in recall than the non-LD group, but this did not hamper their analogical problem-solving abilities. These findings confirm that analogical thinking requires more than memory.
Learning to Talk Cisco IOS Inferring the IOS Command Language from Router Conﬁguration Data

Networks and the functionality that they deliver are growing increasingly complex, making network management a steadily growing challenge. A key component of network management is conﬁguration management . Router conﬁgurations capture and reﬂect all levels of network operation, and it is highly challenging to manage the detailed conﬁgurations of the potentially huge number of routers that run a network. While tools exist to help automate network conﬁguration management, none of the tools of which we are aware directly address the problem of feature evolution and expansion . We present the design and implementation of a learning system for the Cisco IOS router conﬁguration language and an adaptive parser built on top of the learning system, which constitute the ﬁrst step to building a conﬁguration management system capable of adapting to new and evolving network features. Based on valid con-ﬁgurations, the learning system infers the IOS language, from which we can generate a parser for IOS . Our tools allow for incremental learning and relieve a network analyst of the burden of writing an IOS parser and maintaining it as the IOS language changes. We have built a prototype system and validated its accuracy and efﬁciency by running it on the conﬁguration ﬁles of Tier-1 ISP networks. Our results show that from only 81 conﬁguration ﬁles, we can learn enough IOS to successfully parse all of the 819 IOS conﬁgurations in under 10 minutes.
Forecasting Chinese Foreign Exchange with Monetary Fundamentals using Artificial Neural Networks

We employ artificial neural networks (ANNs) and unconditional Vector Autoregressive model (VAR) to perform one-month-ahead out-of-sample predictions of both official and market Yuan/USD exchange rates using monetary fundamentals from 1992:M3 to 2010:M10. The optimal ANNs are attained systematically based on random validation sets. We empirically demonstrated that the generalized regression neural network is superior to the multilayer feedforward network in Chinese foreign exchange forecasting. ANNs generally outperformed in market rate forecasts in which suggest that market rates are supported by monetary fundamentals. On the contrary, official rates do not explained well by monetary fundamentals.