Miracle-dz/UniFAR-FTUs huggingface dataset:
Visible and infrared image fusion based on Curvelet transform

Infrared detection is significant in many fields, and sometimes infrared images are used to cover the shortcomings of visible images. Then image fusion between visible and infrared image is important in practical use. In this paper, we introduce Curvelet transform and its implementation to decompose images to different resolutions, and standard fusion rules are built to integrate information from both images. This algorithm is carried out on a set of standard image set, and is compared to a traditional method. It proves that this fusion algorithm based on Curvelet transform is effective enough to accomplish visible and infrared image fusion. Image preprocessing is mentioned but not discussed in detail.
Effects of Visual Deprivation on Gait Dynamic Stability

Vision can improve bipedal upright stability during standing and affect spatiotemporal parameters during walking. However, little is known about the effects of visual deprivation on gait dynamic stability. We have tested 28 subjects during walking under two different visual conditions, full vision (FV) and no vision (NV), measuring their upper body accelerations. Lower accelerations were found in NV for the reduced walking speed. However, the normalized accelerations were higher in the NV than in the FV condition, both in anteroposterior (1.05 ± 0.21 versus 0.88 ± 0.16, P = 0.001) and laterolateral (0.99 ± 0.26 versus 0.78 ± 0.19, P < 0.001) directions. Vision also affected the gait anteroposterior harmony (P = 0.026) and, interacting with the environment, also the latero-lateral one (P = 0.017). Directly (as main factor of the ANOVA) or indirectly (by means of significant interactions with other factors), vision affected all the measured parameters. In conclusion, participants showed an environment-dependent reduction of upper body stability and harmony when deprived by visual feedback.
Exploiting Semantic Relatedness Measures for Multi-label Classifier Evaluation

In the multi-label classication setting, documents can be labelled with a number of concepts (instead of just one). Evaluating the performance of classiers in this scenario is often as simple as measuring the percentage of correctly assigned concepts. Classiers that do not retrieve a single concept existing in the ground truth annotation are all considered equally poor. However, some classiers might perform better than others, in particular those, that assign concepts which are semantically similar to the ground truth annotation. Thus, exploiting the semantic relatedness between the classier-assigned and the ground truth concepts leads to a more rened evaluation. A number of well-known algorithms compute the semantic relatedness between concepts with the aid of general-world knowledge bases such as WordNet 1 . When the concepts are domain specic, however, such approaches cannot be employed out-of-the-box. Here, we present a study, inspired by a real-world problem, where we rst investigate the performance of well-known semantic relatedness measures on a domain-dependent thesaurus. We then employ the best performing measure to evaluate multilabel classiers. We show that (i) measures which perform well on WordNet do not reach a comparable performance on our thesaurus and that (ii) an evaluation based on semantic relatedness yields results which are more in line with human ratings than the traditional F-measure.
Research on the Coverage of Heterogeneous Nodes in Wireless Sensor Networks

Research on coverage of wireless sensor networks are mostly aiming at isomorphic nodes, however, which gives little attention on heterogeneous nodes. There haven’t been effective solutions up to now. Adopting Probability Coverage Model is more efficient relatively, which can obtain more accurate data. By means of computing the coverage probability of each node, we infer the coverage probability of the covered region. Thus, we can estimate redundant nodes, which gives to the credible basis for hierarchical scheduling. Eventually we ensure the rationality of node-scheduling. To take a step further, it availably prolongs the lifetime of the whole network.
Partial bus-invert coding for power optimization of system level bus

We present a partial bus-invert coding scheme for power optimization of system level bus. In the proposed scheme, we select a sub-group of bus lines involved in bus encoding to avoid unnecessary inversion of bus lines not in the subgroup thereby reducing the total number of bus transitions. We propose a heuristic algorithm that selects the sub-group of bus lines for bus encoding. Experiments on benchmark examples indicate that the partial bus-invert coding reduces the total bus transitions by 62.6% on the average, compared to that of the unencoded patterns.
Involvement of Maize Dof Zinc Finger Proteins in Tissue-Specific and Light-Regulated Gene Expression

Dof is a novel family of plant proteins that share a unique and highly conserved DNA binding domain with one C2–C2 zinc finger motif. Although multiple Dof proteins associated with diverse gene promoters have recently been identified in a variety of plants, their physiological functions and regulation remain elusive. In maize, Dof1 (MNB1a) is constitutively expressed in leaves, stems, and roots, whereas the closely related Dof2 is expressed mainly in stems and roots. Here, by using a maize leaf protoplast transient assay, we show that Dof1 is a transcriptional activator, whereas Dof2 can act as a transcriptional repressor. Thus, differential expression of Dof1 and Dof2 may permit leaf-specific gene expression. Interestingly, in vivo analyses showed that although DNA binding activity of Dof1 is regulated by light-dependent development, its transactivation activity and nuclear localization are not. Moreover, in vivo transcription and in vitro electrophoretic mobility shift assays revealed that Dof1 can interact specifically with the maize C4 phosphoenolpyruvate carboxylase gene promoter and enhance its promoter activity, which displays a light-regulated expression pattern matching Dof1 activity. We propose that the evolutionarily conserved Dof proteins can function as transcriptional activators or repressors of tissue-specific and light-regulated gene expression in plants.
Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia

Fibromyalgia is a medical condition characterized by widespread muscle pain and tenderness and is often accompanied by fatigue and alteration in sleep, mood, and memory. Poor sleep quality and fatigue, as prominent characteristics of fibromyalgia, have a direct impact on patient behavior and quality of life. As such, the detection of extreme cases of sleep quality and fatigue level is a prerequisite for any intervention that can improve sleep quality and reduce fatigue level for people with fibromyalgia and enhance their daytime functionality. In this study, we propose a new supervised machine learning method called Learning Using Concave and Convex Kernels (LUCCK). This method employs similarity functions whose convexity or concavity can be configured so as to determine a model for each feature separately, and then uses this information to reweight the importance of each feature proportionally during classification. The data used for this study was collected from patients with fibromyalgia and consisted of blood volume pulse (BVP), 3-axis accelerometer, temperature, and electrodermal activity (EDA), recorded by an Empatica E4 wristband over the courses of several days, as well as a self-reported survey. Experiments on this dataset demonstrate that the proposed machine learning method outperforms conventional machine learning approaches in detecting extreme cases of poor sleep and fatigue in people with fibromyalgia.
Fingerprint Extraction of Executables Based on the Potential Field Graph of Callgraph

This paper describes a novel method for extracting fingerprints of executables. A new data visual approach based on data field is imported. The 3-tuple (in-degree, out-degree, function call relationship) extracted from call graph are used for constructing data field and the potential field graph. This potential field graph is fingerprinted by the method of shape invariant moments which is mature and statistic approach to analyze the shape of image. Experimental results show that the shape invariant moments of the potential field graph can be used to identify different executable programs as a fingerprint.
Evidence that High‐ and Low‐Affinity DL‐α‐Amino‐3‐Hydroxy‐5‐Methylisoxazole‐4‐Propionic Acid (AMPA) Binding Sites Reflect Membrane‐Dependent States of a Single Receptor

Abstract: Binding of DL‐α‐[3H]amino‐3‐hydroxy‐5‐methylisoxazole‐4‐propionic acid ([3H]AMPA) to lysed rat brain membranes in the presence of potassium thiocyanate resulted in curvilinear Scatchard plots that could be resolved by regression analysis into a large low‐affinity component and a small high‐affinity component. Solubilization with Triton X‐100 resulted in solubilized and nonsolubilized fractions that were considerably enriched in the high‐affinity component and correspondingly reduced in the low‐affinity component. It thus appears that solubilization converts low‐affinity AMPA receptors into high‐affinity receptors. Also, synaptic plasma membranes were found to be greatly enriched in the low‐affinity form and deficient in the high‐affinity form of the AMPA receptor. These experiments provide evidence for the hypothesis that the high‐ and low‐affinity components of AMPA binding are inter‐convertible states of the same receptor rather than separate binding sites and that the conversion of these receptors from their native high‐affinity state to the low‐affinity state occurs on insertion of the receptors into synapses.
Recursive Global Games

The present paper contributes to the literature on dynamic games with strategic complementarities, in two interrelated ways. First, we identify a class of dynamic complete information games in which intertemporal complementarities and multiple equilibria can be fruitfully analyzed. Second, we extend the analysis to an incomplete information framework, where results from the literature on global games can be applied to select a unique Markov perfect equilibrium in monotone strategies.
Induction of Cellular Genes Is Mediated by the Bel1 Transactivator in Foamy Virus-Infected Human Cells

ABSTRACT To gain insight into human foamy virus (HFV; also called spumaretrovirus)-induced alterations of cellular genes, the expression profiles of defined genes in HFV-infected primary human cells were analyzed by cDNA array assays. Several distinct cellular genes activated by HFV infection were identified; the identities of the cellular genes were confirmed by RNA blot analyses. Compared with mock-infected controls, the concentrations of cellular Kip2, Egr-1, COUP-TF1, insulin-like growth factor II (IGF-II), and EphB3 mRNAs were significantly increased in HFV-infected cells and showed a gene-specific and time-dependent induction. Immunoblot analyses with antibodies against some of the cellular gene products revealed increased levels of the corresponding proteins. To investigate mechanisms of HFV-induced alterations in cellular gene expression, the capacity of known HFV genes to increase expression of defined cellular genes was analyzed by transient expression experiments. Plasmids that encode the HFV Bel1 transcriptional transactivator were necessary and sufficient to strongly increase expression of p57Kip2, IGF-II, and EphB3 genes in 293T cells. Potential mechanisms and consequences of activation of cellular genes during HFV infection and Bel1 transactivation of the Kip2 gene are discussed.
Neurotrapping: Cellular Screens to Identify the Neural Substrates of Behavior in Drosophila

The availability of new tools for manipulating neuronal activity, coupled with the development of increasingly sophisticated techniques for targeting these tools to subsets of cells in living, behaving animals, is permitting neuroscientists to tease apart brain circuits by a method akin to classical mutagenesis. Just as mutagenesis can be used to introduce changes into an organism's DNA to identify the genes required for a given biological process, changes in activity can be introduced into the nervous system to identify the cells required for a given behavior. If the changes are introduced randomly, the cells can be identified without any prior knowledge of their properties. This strategy, which we refer to here as “neurotrapping,” has been implemented most effectively in Drosophila, where transgenes capable of either suppressing or stimulating neuronal activity can be reproducibly targeted to arbitrary subsets of neurons using so-called “enhancer-trap” techniques. By screening large numbers of enhancer-trap lines, experimenters have been able to identify groups of neurons which, when suppressed (or, in some cases, activated), alter a specific behavior. Parsing these groups of neurons to identify the minimal subset required for generating a behavior has proved difficult, but emerging tools that permit refined transgene targeting are increasing the resolution of the screening techniques. Some of the most recent neurotrapping screens have identified physiological substrates of behavior at the single neuron level.
Counter Attack on Byzantine Generals: Parameterized Model Checking of Fault-tolerant Distributed Algorithms

We introduce an automated parameterized verification method for fault-tolerant distributed algorithms (FTDA). FTDAs are parameterized by both the number of processes and the assumed maximum number of Byzantine faulty processes. At the center of our technique is a parametric interval abstraction (PIA) where the interval boundaries are arithmetic expressions over parameters. Using PIA for both data abstraction and a new form of counter abstraction, we reduce the parameterized problem to finite-state model checking. We demonstrate the practical feasibility of our method by verifying several variants of the well-known distributed algorithm by Srikanth and Toueg. Our semi-decision procedures are complemented and motivated by an undecidability proof for FTDA verification which holds even in the absence of interprocess communication. To the best of our knowledge, this is the first paper to achieve parameterized automated verification of Byzantine FTDA.
Low-power colourless reflective components for energy-efficient optical networks

Exponentially-increasing demands on the current telecommunication infrastructure are driving the development of next-generation ultra-high-bandwidth network architectures with sufficiently low energy consumptions. Within the scope of the EU FP7 C3PO project, we are developing novel, energy-efficient, colourless photonic technologies for metro applications. The colourless transmitters will leverage reflective photonic integrated circuits, specifically reflective electroabsorption modulator-based phase and amplitude modulators, in conjunction with multi-frequency lasers and low-loss piezoelectric beam-steering optical matrix switches, in order to achieve wavelength reconfigurability without the requirement for tuneable lasers. A specific target is a dynamically reconfigurable metro node which supports duobinary modulation for high dispersion tolerance and efficient spectral usage, to enable 100 Gb/s Ethernet dense wavelength-division-multiplexed transport networks. We report on recent progress towards these metro transport networks, providing the latest system test results obtained using novel hybrid photonic integrated devices.
Optimality Properties and Driver Input Parameterization for Trail-braking Cornering

In this work we present an analysis of rally-racing driving techniques using numerical optimization. We provide empirical guidelines for executing a Trail- Braking (TB) maneuver, one of the common cornering techniques used in rally-racing. These guidelines are verified via experimental data collected from TB maneuvers performed by an expert rally driver. We show that a TB maneuver can be generated as a special case of the minimum-time cornering problem subject to specific boundary conditions. We propose simple parameterizations of the driver's steering, throttle and braking commands that lead to an alternative formulation of the optimization problem with a reduced search space. We show that the proposed parametrization of the driver's commands can reproduce TB maneuvers along a variety of corner geometries, requiring only a small number of parameters.
Natural Language Processing for computer-supported instruction

Computer-supported instruction includes many applications whose goal is instruction and education: from animated agents that execute instructions to intelligent tutoring systems, from systems that produce instructional manuals to systems that facilitate student collaboration. In this paper, I present some of the research I have undertaken in recent years to build such systems and their Natural Language interfaces. My research includes linguistic investigation, computational modeling and system evaluation. All the work presented is supported by rigorous corpus analysis.
A transport model of graded sands in the oscillatory sheet-flow regime.

The 1DV two-layer model of oscillatory sheet flow of Malarkey et al. (2003) which was developed for a single grain size has been modified to include grain fractions. The model includes a suspension layer above a sheet-flow layer consisting of prescribed profiles of concentration and velocity. Since this model keeps track of the total sediment in the water column through the wave cycle it is ideally suited for representing graded sediments. The modified model has been compared with the graded sediment data of Hassan et al. (1999). The results show that the model can reproduce the statistical characteristics of the sediment in suspension and produces fractional transport rates that are consistent with the experiments. However, it also demonstrates that a sediment weighting function limited by the amount of sediment in suspension is not sufficient to give rise to the apparent effects of hiding and exposure seen in the data.
Emotional Labor in the Moderation of Online Communities

Emotional labor is an essential component of knowledge work. As knowledge work increasingly moves online, we need to understand emotional labor performed online. Extant emotion research cautions against blindly applying insights about emotional labor from a face-to-face context to an online context. Yet, we know little about emotional labor performed online. We raise the following questions about emotional labor by community moderators: What constitutes “appropriate emotional displays” in online communities? How do community members’ emotional displays interact with use of technology features to influence moderators’ displays? Our findings suggest that the high-/low-energy dimension of emotion is more salient to the emotional labor performed by community moderators than is the conventionally-studied positive versus negative affect dimension. Findings further reveal the tendency for community members’ use of technology features in stylizing their posts to induce mindfulness in moderators’ emotional displays in response to those posts.
Seismic waveform inversion in the frequency domain; Part 1, Theory and verification in a physical scale model

Seismic waveforms contain much information that is ignored under standard processing schemes; seismic waveform inversion seeks to use the full information content of the recorded wavefield. In this paper I present, apply, and evaluate a frequency‐space domain approach to waveform inversion. The method is a local descent algorithm that proceeds from a starting model to refine the model in order to reduce the waveform misfit between observed and model data. The model data are computed using a full‐wave equation, viscoacoustic, frequency‐domain, finite‐difference method. Ray asymptotics are avoided, and higher‐order effects such as diffractions and multiple scattering are accounted for automatically. The theory of frequency‐domain waveform/wavefield inversion can be expressed compactly using a matrix formalism that uses finite‐difference/finite‐element frequency‐domain modeling equations. Expressions for fast, local descent inversion using back‐propagation techniques then follow naturally. Implementation of .
An allosteric model of the inositol trisphosphate receptor with nonequilibrium binding

The inositol trisphosphate receptor (IPR) is a crucial ion channel that regulates the Ca2+ influx from the endoplasmic reticulum (ER) to the cytoplasm. A thorough study of the IPR channel contributes to a better understanding of calcium oscillations and waves. It has long been observed that the IPR channel is a typical biological system which performs adaptation. However, recent advances on the physical essence of adaptation show that adaptation systems with a negative feedback mechanism, such as the IPR channel, must break detailed balance and always operate out of equilibrium with energy dissipation. Almost all previous IPR models are equilibrium models assuming detailed balance and thus violate the dissipative nature of adaptation. In this article, we constructed a nonequilibrium allosteric model of single IPR channels based on the patch-clamp experimental data obtained from the IPR in the outer membranes of isolated nuclei of the Xenopus oocyte. It turns out that our model reproduces the patch-clamp experimental data reasonably well and produces both the correct steady-state and dynamic properties of the channel. Particularly, our model successfully describes the complicated bimodal [Ca2+] dependence of the mean open duration at high [IP3], a steady-state behavior which fails to be correctly described in previous IPR models. Finally, we used the patch-clamp experimental data to validate that the IPR channel indeed breaks detailed balance and thus is a nonequilibrium system which consumes energy.
Efficient Face Segmentation for Recognition in Group Photographs

Group photograph has a critical issues like size and partial images. The most difficult challenge is to detect faces in clumsier group photos. Many algorithms are available to identify the individual images from image data bases of good quality images with proper image information density. There is a strong requirement to identify faces from group photographs. Our work presents an interactive algorithm segment out and recognize a person's face from a group photograph. The method involves a fast, reliable, and effective algorithm that will recognize the faces present in the group photo. Also analysis has done to determine the effect of photo resolution on the face recognition result. The proposed system is designed to recognise the faces from the color group image. Face recognition system has two modules: face detection and face recognition. In a group photo the faceregions are detected. Then the detected face is recognized by comparing with the database face image. Forface segmentation, HSV and YCbCr components will be identified from the given RGB components. From this the skin elements will be identified. Then using image segmentation, the skin and non-skin areas will be separated into two segments. Then the skin elements will be marked. After face detection, the faces should be recognized. The GLCM features of the face image are extracted. The same process will be applied onthe database face images. The features of the detected face will be compared with the features of the existing database faces and recognized. Proposed system extracts and recognize individuals from the group photograph containing multiple people. Also with this the algorithm is tested by taking various resolution of the same input photo for the face detection and face recognition. Also with this the individual photos which are stored in the database also resized to determine the proper size of the photos so face recognition will be correct.