Miracle-dz/UniFAR-FTUs huggingface dataset:
Citrullus ecirrhosus: Wild Source of Resistance Against Bemisia tabaci (Hemiptera: Aleyrodidae) for Cultivated Watermelon

Abstract Members of the highly polyphagous Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) species complex cause major crop damage by feeding and by transmitting plant viruses.The Middle East-Asia Minor 1 (MEAM1) of the B. tabaci complex is by far the most problematic whitefly affecting crops including cultivated watermelon (Citrullus lanatus; Cucurbitaceae: Cucurbitales). Watermelon cultivars share a narrow genetic base and are highly susceptible to whiteflies. We studied the potential of C. ecirrhosus, a perennial desert species that can be hybridized with C. lanatus, as a source of whitefly resistance for cultivated watermelon. The results of this study indicate that C. ecirrhosus offers resistance (although not total) against the MEAM1 B. tabaci based on, at least, antibiosis and antixenosis. Whitefly performance concerning developmental survival, body size attainment, and nonpreference were suppressed on C. ecirrhosus compared with the watermelon cultivar ‘Sugar Baby’. Also, our olfactometer results indicated that the adults were less attracted to leaf volatiles of C. ecirrhosus. Although there is a pungent odor associated with the leaves of C. ecirrhosus, the leaf volatiles had no toxic effect on adult whitefly survival as compared with cultivated watermelon. We also demonstrated that plants of C. ecirrhosus can be clonally propagated from vine cuttings of the parent plant. Using traditional breeding procedures, C. ecirrhosus was hybridized with C. lanatus and viable F1 and F2 seeds were produced. This is the first report of pest resistance in C. ecirrhosus. This wild species offers a source of resistance against whiteflies for the improvement of cultivated watermelon.
GWIPS-viz: development of a ribo-seq genome browser

We describe the development of GWIPS-viz (http://gwips.ucc.ie), an online genome browser for viewing ribosome profiling data. Ribosome profiling (ribo-seq) is a recently developed technique that provides genome-wide information on protein synthesis (GWIPS) in vivo. It is based on the deep sequencing of ribosome-protected messenger RNA (mRNA) fragments, which allows the ribosome density along all mRNA transcripts present in the cell to be quantified. Since its inception, ribo-seq has been carried out in a number of eukaryotic and prokaryotic organisms. Owing to the increasing interest in ribo-seq, there is a pertinent demand for a dedicated ribo-seq genome browser. GWIPS-viz is based on The University of California Santa Cruz (UCSC) Genome Browser. Ribo-seq tracks, coupled with mRNA-seq tracks, are currently available for several genomes: human, mouse, zebrafish, nematode, yeast, bacteria (Escherichia coli K12, Bacillus subtilis), human cytomegalovirus and bacteriophage lambda. Our objective is to continue incorporating published ribo-seq data sets so that the wider community can readily view ribosome profiling information from multiple studies without the need to carry out computational processing.
Compact Microstrip Wilkinson Power Dividers With Harmonic Suppression and Arbitrary Power Division Ratios

This paper describes a new Wilkinson power divider on a single-layer microstrip line that can reduce the occupied area, suppress the harmonic components, and/or provide the arbitrary power division ratios. It consists of two-section transmission lines, two inductors, and one isolation resistor. Four different designs have been conducted to investigate the capabilities of the structure. In addition, a compact divider along with harmonic suppression and a practical divider with a large power-dividing ratio has been constructed and measured. The simulation and measurement results are in good agreement with each other. This indicates that the structure can effectively be used as a power divider for miniaturized or arbitrary power division ratio applications.
New likelihood updating for the IMM approach application to outdoor vehicles localization

This paper presents the problematic of outdoor vehicle localization under the IMM (interacting multiple model) approach. The IMM is now a well known modular approach, which is based on the discretization of the vehicle evolution space into simple maneuvers, represented each by a simple dynamic model such as constant velocity or constant turning etc. This allows the method to be optimized for highly dynamic vehicles. Unfortunately classical IMM shows some drawbacks concerning some real time multi sensors applications. In this work, we focus on outdoor vehicle localization with asynchronous sensors in order to report these drawbacks and then propose a new solution. Many tests carried out with simulated and real data confirm the interest for using such a solution in our applications.
Trajectories and predictors of risk for mental health problems throughout childhood

BACKGROUND Children fluctuate in their risk for mental health-related problems. While children have demonstrated consistent trajectories for various types of mental health problems, the existence of developmental trajectories of overall risk of mental health problems has not been explored. This study aimed to identify distinct trajectories of overall mental health risk among children from ages 4-12 years. A secondary objective was to identify predictors of the mental health risk trajectories. METHOD Data from the first five waves of the Longitudinal Study of Australian Children (LSAC) were used to assess mental health risk and potential predictors. The primary parent (n = 3717) completed questionnaires, time-use diaries, and face-to-face interviews over the five waves. Growth mixture modeling was used to identify distinct latent trajectories of mental health risk. Multinomial logistic regression was used to identify predictors of the trajectories. RESULTS Six distinct trajectories of mental health risk were identified: Low Difficulty (72.9%), Improvers (9.7%), Decliners (7.9%), Early Decliners/Late Improvers (4.7%), Early Improvers/Late Decliners (2.7%), and High Difficulty (2.2%). Child sex, sociability, parental warmth, sports participation, and household income were identified as significant predictors of mental health trajectories. CONCLUSIONS There are distinct trajectories of overall risk for mental health problems during childhood. Research should focus on the High Difficulties group and the Early Improvers/Late Decliners group to address the predictors and improve access to early mental health services.
{C}^0$ penalty methods for the fully nonlinear Monge-Ampère equation

In this paper, we develop and analyze C(0) penalty methods for the fully nonlinear Monge-Ampere equation det(D(2)u) = f in two dimensions. The key idea in designing our methods is to build discretizations such that the resulting discrete linearizations are symmetric, stable, and consistent with the continuous linearization. We are then able to show the well-posedness of the penalty method as well as quasi-optimal error estimates using the Banach fixed-point theorem as our main tool. Numerical experiments are presented which support the theoretical results.
to appear in Linguistic Inquiry Learning Long-Distance Phonotactics ∗

This paper shows that specific properties of long-distance phonotactic patterns derived from consonantal harmony patterns (Hansson 2001, Rose and Walker 2004) follow from a learner which only generalizes on the basis of the order of sounds, but not the the distance between them. The proposed learner is simple, efficient, provably correct, and does not require an a priori notion of tier or projection (contra Hayes and Wilson (2008)), nor rely on the additional structure provided by Optimality-theoretic (OT) grammars (Prince and Smolensky 1993, 2004) or grammars in the Principles and Parameters (P&P) framework (Chomsky 1981, Dresher and Kaye 1990, Gibson and Wexler 1994). Not only does the noncounting nature of non-local dependencies automatically follow from how the learner generalizes, it also explains the absence of blocking patterns from the typology. Finally, the learner lends support to the idea that long-distance phonotactic patterns are phenomenologically distinct from spreading patterns contra the hypothesis of Strict Locality (Gafos 1999, et seq). keywords: phonotactic learning, long-distance agreement, precedence, grammatical inference, formal language theory
When Does Disengagement Correlate with Performance in Spoken Dialog Computer Tutoring?

We investigate whether an overall student disengagement label and six different labels of disengagement type are predictive of learning in a spoken dialog computer tutoring corpus. Our results show first that although students' percentage of overall disengaged turns negatively correlates with the amount they learn, the individual types of disengagement correlate differently with learning: some negatively correlate with learning, while others don't correlate with learning at all. Second, we show that these relationships change somewhat depending on student prerequisite knowledge level. Third, we show that using multiple disengagement types to predict learning improves predictive power. Overall, our results suggest that although adapting to disengagement should improve learning, maximizing learning requires different system interventions depending on disengagement type.
CDO TERM STRUCTURE MODELLING WITH LÉVY PROCESSES AND THE RELATION TO MARKET MODELS

This paper considers the modelling of collateralized debt obligations (CDOs). We propose a top-down model via forward rates generalizing Filipovic, Overbeck and Schmidt (2009) to the case where the forward rates are driven by a finite dimensional Levy process. The contribution of this work is twofold: we provide conditions for absence of arbitrage in this generalized framework. Furthermore, we study the relation to market models by embedding them in the forward rate framework in spirit of Brace, Gatarek and Musiela (1997).
Sensorized and Actuated Instruments for Minimally Invasive Robotic Surgery

Minimally invasive surgery (MIS) challenges the surgeon's skills due to his separation from the operation area which can be reached with long instruments only. To overcome the drawbacks of conventional MIS robotic surgery systems with integrated haptic feedback play an important role. This paper describes the development of actuated and sensorized instruments for minimally invasive robotic surgery which are a necessary prerequisite for haptic feedback.
Synthesis and Fabrication of Graphene and Graphene Oxide: A Review

The field of nanotechnology has advanced following the discovery of a two-dimensional material of sp2 hybridized carbon atoms, graphene in 2004 by Geim and Novoselov. Graphene has received so much attention due to its exceptional electronic, thermal, mechanical, and optical properties in addition to its large surface area and single-atom thickness. This has led to the discovery of several techniques to obtain graphene such as chemical exfoliation, chemical vapor deposition (CVD), chemical synthesis etc. However, these techniques are majorly challenged with developing graphene with fewer defects and in large scale; thus, there is an increasing need to produce graphene in large quantities with high quality. Several studies have been carried out to find routes to producing high-quality graphene. This paper focuses majorly on the synthesis and fabrication methods of producing graphene and its derivative, graphene oxide. Characterization techniques to identify graphene such as optical microscopy, scanning electron microscopy (SEM), Raman spectroscopy, scanning probe microscopy (SPM) used to determine number of layers, quality, atomic structures, and defects in graphene is also briefly discussed. This article also covers a short description of graphene applications in transparent electrodes, composites and energy storage devices.
Differences between students according to physical activity and their motivation, basic psychological needs and responsibility

The objective of this research was to analyse the differences in physical activity, basic psychological needs, motivation and responsibility in students. A sample of 109 participants was chosen (M = 16.29, SD = .95), who were given a questionnaire to assess their satisfaction of basic psychological needs (PNSE), motivation (BREQ-2) and responsibility (PSRQ), together with a physical activity measurement instrument. The participants were grouped for analysis according to the percentile of physical activity in "low active", "some active" or "very active". The results indicated no differences according to gender in physical activity. On the one hand the very active group had higher values of physical activity with respect to the rest of the groups, and these values were higher at weekends compared to the rest of the week, whereas, low and some active groups obtained higher values during the week. In addition, the very active group presented higher values in intrinsic motivation, autonomy, competence and lower in amotivation. We concluded the need to promote physical activity especially at weekends, encouraging the satisfaction of basic psychological needs to obtain higher levels of motivation.
Decentralization, Communication, and the Origins of Fluctuations

We consider a class of convex, competitive, neoclassical economies in which agents are rational; the equilibrium is unique; there is no room for randomization devices; and there are no shocks to preferences, technologies, endowments, or other fundamentals. In short, we rule out every known source of macroeconomic volatility. And yet, we show that these economies can be ridden with large and persistent fluctuations in equilibrium allocations and prices. These fluctuations emerge because decentralized trading impedes communication and, in so doing, opens the door to self-fulfilling beliefs despite the uniqueness of the equilibrium. In line with Keynesian thinking, these fluctuations may be attributed to "coordination failures" and "animal spirits". They may also take the form of "fads", or waves of optimism and pessimism that spread in the population like contagious diseases. Yet, these ostensibly pathological phenomena emerge at the heart of the neoclassical paradigm and require neither a deviation from rationality, nor multiple equilibria, nor even a divergence between private and social motives.
RXTE Investigation into Extended Hard X-Ray Emission from Elliptical Galaxies

We present results of the first RXTE investigation of hard X-ray emission from normal elliptical galaxies. Joint spectral analysis of RXTE Proportional Counter Array and ASCA Gas Imaging Spectrometer data for NGC 4472 and NGC 4649 reveals the presence of ~5 × 10-12 ergs cm-2 s-1 of 2-10 keV emission emanating from a region outside of the optical galaxy and of a spectral hardness intermediate between the galactic hot interstellar gas and X-ray binary components. A large fraction of this emission originates in the Virgo intracluster medium (ICM), and we present constraints on the ICM temperature and metallicity in the vicinity of these two galaxies. However, the diffuse flux is significantly greater than expected based on the soft X-ray intensity measured with ROSAT, allowing for the possible presence of an additional diffuse nonthermal component. We also derive new constraints on the integrated X-ray binary emission from these two galaxies and on the presence of a nuclear flat-spectrum point source.
Unsupervised Text Recap Extraction for TV Series

Sequences found at the beginning of TV shows help the audience absorb the essence of previous episodes, and grab their attention with upcoming plots. In this paper, we pro-pose a novel task, text recap extraction. Compared with conventional summarization, text recap extraction captures the duality of summarization and plot contingency between adjacent episodes. We present a new dataset, TVRecap , for text recap extraction on TV shows. We propose an unsupervised model that identiﬁes text recaps based on plot descriptions. We introduce two contingency factors, concept coverage and sparse reconstruction , that encourage recaps to prompt the up-coming story development. We also propose a multi-view extension of our model which can incorporate dialogues and synopses. We conduct extensive experiments on TVRecap , and conclude that our model outperforms summarization approaches.
Stream Clipper: Scalable Submodular Maximization on Stream

We propose a streaming submodular maximization algorithm "stream clipper" that performs as well as the offline greedy algorithm on document/video summarization in practice. It adds elements from a stream either to a solution set $S$ or to an extra buffer $B$ based on two adaptive thresholds, and improves $S$ by a final greedy step that starts from $S$ adding elements from $B$. During this process, swapping elements out of $S$ can occur if doing so yields improvements. The thresholds adapt based on if current memory utilization exceeds a budget, e.g., it increases the lower threshold, and removes from the buffer $B$ elements below the new lower threshold. We show that, while our approximation factor in the worst case is $1/2$ (like in previous work, and corresponding to the tight bound), we show that there are data-dependent conditions where our bound falls within the range $[1/2, 1-1/e]$. In news and video summarization experiments, the algorithm consistently outperforms other streaming methods, and, while using significantly less computation and memory, performs similarly to the offline greedy algorithm.
Learning Social Preferences in Games Content areas : Cognitive modeling , negotiation , game playing Tracking

This paper presents a machine-learning approach to modeling human behavior in one-shot games. It provides a framework for representing and reasoning about social preferences which affect people’s play. The model predicts how a human player is likely to react to different actions of a computer player, and these predictions are used to determine the best possible strategy for the computer player. Data collection and evaluation of the model was performed on a negotiation game in which humans played against each other, and against computer models playing various strategies. When playing with people, our model performs better than Nash equilibrium and Nash bargaining computer players, as well as humans. It can also generalize to players and situations it had not seen before.
Image Resolution Enhancement using Multi-wavelet Transforms with Interpolation Technique.

Images with high resolution give better results for image processing applications. Image resolution enhancement is the process of manipulating an image so that resultant image is more suitable for specific application such as medical, agricultural, satellite image processing. This paper is based on image resolution enhancement by combination of SWT & DWT . In DWT main loss is on high frequency components. The edge loss in high frequency components of DWT is minimized by adding it with high frequency components of SWT which is transition invariant transform. The interpolated high frequency sub-bands of DWT are added with high frequency sub bands of SWT. Then the added high frequency sub-bands (of SWT & DWT) as well as the input image are interpolated by factor of α/2to enlarge the input image by factor of α. Afterwards all these images have been combined using IDWT to generate a super resolved image. Algorithm considering Haar function is developed for DWT & inverse IDWT instead of using db9/7. This obtained image is compared with original high resolution image. This technique has been tested on standard bench mark images. The quantitative (Peak signal to noise ratio) and visual results are superior over conventional image resolution enhancement techniques.
Boundary fermions and the plane wave

We construct branes in the plane wave background under the inclusion of fermionic boundary fields. The resulting deformed boundary conditions in the bosonic and fermionic sectors give rise to new integrable and supersymmetric branes of type (n,n). The extremal case of the spacetime filling (4,4)-brane is shown to be maximally spacetime supersymmetric.
A Tandem BLSTM-DBN Architecture for Keyword Spotting with Enhanced Context Modeling

We propose a novel architecture for keyword spotting which is composed of a Dynamic Bayesian Network (DBN) and a bidirectional Long Short-Term Memory (BLSTM) recurrent neural net. The DBN is based on a phoneme recognizer and uses a hidden garbage variable as well as the concept of switching parents to discriminate between keywords and arbitrary speech. Contextual information is incorporated by a BLSTM network, providing a discrete phoneme prediction feature for the DBN. Together with continuous acoustic features, the discrete BLSTM output is processed by the DBN which detects keywords in speech sequences. Due to the flexible design of our Tandem BLSTM-DBN recognizer, new keywords can be added to the vocabulary without having to re-train the model. Further, our concept does not require the training of an explicit garbage model. Experiments on the TIMIT corpus show that incorporating a BLSTM network into the DBN architecture can increase the true positive rate by up to 10% at equal false positive rates.
Semantics-based software techniques for maintainable multimodal input processing in real-time interactive systems

Maintainability, i.e. reusability, modifiability, and modularity, is a critical non-functional quality requirement, especially for software frameworks. Its fulfilment is already challenging for low-interactive application areas. It is additionally complicated by complex system designs of Real-time Interactive Systems (RISs), required for Augmented, Mixed, and Virtual Reality, as well as computer games. If such systems incorporate AI methods, as required for the implementation of multimodal interfaces or smart environments, it is even further exacerbated. Existing approaches strive to establish software technical solutions to support the close temporal and semantic coupling required for multimodal processing and at the same time preserve a general decoupling principle between involved software modules. We present two key solutions that target the semantic coupling issue: (1) a semantics-based access scheme to principal elements of the application state and (2) the specification of effects by means of semantic function descriptions for multimodal processing. Both concepts are modeled in an OWL ontology. The applicability of our concepts is showcased by a prototypical implementation and explained by an interaction example that is applied for two application areas.
Dark matter halo occupation: environment and clustering

We use a large dark matter simulation of a Λ cold dark matter (ΛCDM) model to investigate the clustering and environmental dependence of the number of substructures in a halo. Focusing on redshift z = 1, we find that the halo occupation distribution is sensitive at the tens of per cent level to the surrounding density and to a lesser extent to asymmetry of the surrounding density distribution. We compute the autocorrelation function of haloes as a function of occupation, building on the finding of Wechsler et al. and Gao & White that haloes (at fixed mass) with more substructure are more clustered. We compute the relative bias as a function of occupation number at fixed mass, finding a strong relationship. At fixed mass, haloes in the top 5 per cent of occupation can have an autocorrelation function ∼1.5–2 times higher than the mean. We also compute the bias as a function of halo mass, for fixed halo occupation. We find that for group- and cluster-sized haloes, when the number of subhaloes is held fixed, there is a strong anticorrelation between bias and halo mass. Such a relationship represents an additional challenge to the halo model.