Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishment of Hairy Root Cultures of Rhaponticum carthamoides (Willd.) Iljin for the Production of Biomass and Caffeic Acid Derivatives

The aim of the study was to obtain transformed roots of Rhaponticum carthamoides and evaluate their phytochemical profile. Hairy roots were induced from leaf explants by the transformation of Agrobacterium rhizogenes strains A4 and ATCC 15834. The best response (43%) was achieved by infection with A4 strain. The effects of different liquid media (WPM, B5, SH) with full and half-strength concentrations of macro- and micronutrients on biomass accumulation of the best grown hairy root line (RC3) at two different lighting conditions (light or dark) were investigated. The highest biomass (93 g L−1 of the fresh weight after 35 days) was obtained in WPM medium under periodic light. UPLC-PDA-ESI-MS3 and HPLC-PDA analyses of 80% aqueous methanol extracts from the obtained hairy roots revealed the presence of eleven caffeoylquinic acids and their derivatives and five flavonoid glycosides. The production of caffeoylquinic acids and their derivatives was elevated in hairy roots grown in the light. Only light-grown hairy roots demonstrated the capability for the biosynthesis of such flavonoid glycosides as quercetagetin, quercetin, luteolin, and patuletin hexosides. Chlorogenic acid, 3,5-di-O-caffeoylquinic acid and a tentatively identified tricaffeoylquinic acid derivative were detected as the major compounds present in the transformed roots.
A Rainbow k-Matching in the Complete Graph with r Colors

An $r$-edge-coloring of a graph is an assignment of $r$ colors to the edges of the graph. An exactly $r$-edge-coloring of a graph is an $r$-edge-coloring of the graph that uses all $r$ colors. A matching of an edge-colored graph is called rainbow matching , if no two edges have the same color in the matching. In this paper, we prove that an exactly $r$-edge-colored complete graph of order $n$ has a rainbow matching of size $k(\ge 2)$ if $r \ge max\{{2k-3\choose 2}+2, {k-2\choose 2}+(k-2)(n-k+2)+2 \}$, $k \ge 2$, and $n \ge 2k+1$. The bound on $r$ is best possible.
Nodule Parenchyma–Specific Expression of the Sesbania rostrata Early Nodulin Gene SrEnod2 Is Mediated by Its 3′ Untranslated Region

The early nodulin Enod2 gene encodes a putative hydroxyproline-rich cell wall protein and is expressed exclusively in the nodule parenchyma cell layer. The latter finding suggests that the Enod2 protein may contribute to the special morphological features of the nodule parenchyma and to the creation of an oxygen diffusion barrier. The Enod2 gene of the stem-nodulating legume Sesbania rostrata (SrEnod2) is induced specifically in roots by the plant hormone cytokinin, and this induction occurs at a post-transcriptional level. Here, we characterize the cis determinant(s) in the SrEnod2 locus responsible for nodule parenchyma–specific expression and show that the 3 ′ untranslated region (UTR) of the SrEnod2 gene is both required and sufficient for directing chimeric reporter gene expression in the nodule parenchyma of transgenic Lotus corniculatus plants. Moreover, we show that the SrEnod2 3′ UTR does not act as a tissue-specific enhancer element. By conducting a detailed deletion analysis of the 5′ and 3′ SrEnod2 regions, we delimited the minimal promoter of the SrEnod2 gene, and it appears that the 5′ flanking sequences are not essential for nodule parenchyma–specific expression. This finding is in contrast with the report that the 5′ upstream region of the soybean Enod2 gene directs nodule parenchyma–specific expression, indicating that different mechanisms may be involved in regulating the expression of these two genes. We definitively demonstrate that the cis element(s) for tissue-specific expression is located within the 3′ UTR of a plant nuclear gene.
Case study. Narcissus: visualising information

It is becoming increasingly important that support is provided for users who are dealing with complex information spaces. The need is driven by the growing number of domains where there is a requirement for users to understand, navigate and manipulate large sets of computer based data; by the increasing size and complexity of this information and by the pressures to use this information efficiently. The paradigmatic example is the World Wide Web, but other domains include software systems, information systems and concurrent engineering. One approach to providing this support is to provide sophisticated visualisation tools which lead the users to form an intuitive understanding of the structure and behaviour of their domain and which provide mechanisms which allow them to manipulate objects within their system. The paper describes such a tool and a number of visualisation techniques that it implements.
State‐level Tobacco Control Programs and the Demand for Cigarettes: a Retrospective Analysis Using Panel Data

State‐level tobacco control programs in the U.S. are often funded by cigarette taxes, which are themselves an important policy tool. This study uses econometric analysis of panel data to show that the long‐term impact of these taxes on cigarette sales is significantly greater than the impact arising through the effect on prices alone, and that this distinct impact increased during the 1989‐2005 time period. The tax effects on demand could be caused by the funding of anti‐smoking programs, signaling or alternative sources of supply. The increasing size of the tax effect gives an indication that tobacco control programs are becoming more effective at achieving their goals over time as they gain critical mass.
Semantic gap in CBIR: automatic objects spatial relationships semantic extraction and representation

The explosive growth of image data leads to the need of research and development of Image retrieval. Image retrieval researches are moving from keyword, to low level features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and time consuming while low level features cannot always describe high level concepts in the users’ mind. This paper is proposed a novel technique for objects spatial relationships semantics extraction and representation among objects exists in images. All objects are identified based on low level features extraction integrated with proposed line detection techniques. Objects are represented using a Minimum Bound Region (MBR) with a reference coordinate. The reference coordinate is used to compute the spatial relation among objects. There are 8 spatial relationship concepts are determined: “Front”, “Back”, “Right”, “Left”, “Right-Front”, “Left-Front”, “Right-Back”, “Left-Back” concept. The user query in text form is automatically translated to semantic meaning and representation. Besides, the image similarity of objects spatial relationships semantic has been proposed.
Aperture " lters

Except in the case of very low-bit signals (images), unconstrained design of mean-square-error optimal digital window-based "lters from sample signals is hampered by the inability to obtain su$cient data to make acceptably precise estimates of the large number of conditional expectations required for the "lter. Even under typical nonlinear constraints such as increasingness or iterative decomposition, estimation remains intractable. This paper mitigates the estimation dilemma by windowing in the range, as well as in the domain. At each point, the signal is viewed through an aperture, which is the product between a domain window and a gray-range window that is chosen according to the signal values in the domain window. Signal values above and below the range window are projected into the top and bottom of the aperture, respectively. This projection compresses the probability mass of the observed signal into a smaller set of variables in such a way as not to alter the mass of observations within the aperture (which carry the most mass) and minimally alter the mass of those outside the aperture. Experiments show that, as opposed to commonly employed increasing nonlinear "lters, aperture "lters can outperform linear "lters for deblurring, especially in the restoration of edges. This paper addresses several issues concerning aperture "lters: positioning of the aperture, the e! ect of range constraint on probability mass, the size of the aperture relative to estimation precision and the amount of training data, estimation of conditional probabilities, and representation by decision trees. A sampling is provided of the many experiments carried out to study the e! ects of aperture "lters on corruption by additive noise and blurring. ( 2000 Elsevier Science B.V. All rights reserved.
Locality Based Protocol for MultiWriter Replication systems

One of the challenging problems in building replication systems is how to design the appropriate replication protocol set that can achieve the target level of availability, performance, and consistency while minimizing the replication cost. But various existing replication protocols could not provide the scalability and performance desired by replicated applications when the workload characteristics change dynamically. We propose a locality based replication protocol that combines the benefits of existing protocols. By taking advantage of the read/write locality, this protocol scales well under workload with all read-to-write ratios.
A Syntactic Pattern Recognition Method for the Automatic Location of Potential Enhancement Regions in Running Speech

A method for the automatic location of phonetically important events in continuous speech signals is presented. The events chosen are plosive bursts, fricatives, nasals and vocalic onsets and offsets. These events have been chosen to match the requirements of a system that enhances these regions to make the signal more robust to channel degradation. The technique uses a syntactic pattern recognition approach distinguished from automatic speech recognition or from knowledge-based approaches. Performance on read speech from many talkers was approximately 20% misses and 20% false alarms. This performance seems broadly similar to other published techniques, although direct comparisons on equivalent tasks have yet to be undertaken.
Voronoi diagrams of music

The Voronoi diagram is a mathematical tool applied across many scientific disciplines, but somehow rarely in the field of music. The diagram is described in this essay along with some of its mathematical properties which have found many uses since the 17th century. Recent applications in the fields of medicine and data visualisation, and a solitary but inspiring paper in the field of music are then explored. From there I explore the practicalities behind mapping musical structures to the diagram, and finally introduce some ideas and experiments towards introducing possibly novel applications of the Voronoi diagram to music.
Facial Expression Recognition using Analytical Hierarchy Process

- Face Expression Recognition and Analysis is an actively researched topic since early nineties due to its significant contribution in Human-Computer Interaction which has paved the way for Affect-Sensitive Computing, also called Human Centered Computing. There have been several advances in terms of face detection and tracking, feature extraction methods and the techniques adopted for expression classification. But most face expression analysis systems utilize low-level visual features to recognize face expressions, while the user perception of facial expression recognition often varies with each individual. Low level visual features suffer a high degree of variability due to a number of factors and are unstable due to the variation of imaging conditions. So it is very important to introduce the semantic knowledge into the automatic recognition process in order to improve the recognition rate. In this paper, a semantic-based facial expression recognition model is proposed that incorporate both, the low-level feature and the human semantics using a multi-criteria decision making model, called Analytical Hierarchy Process (AHP). Experimental results show that the recognition rate is improved with this approach .
Statistical Reconstruction and Analysis of Autoregressive Signals in Impulsive Noise Statistical Reconstruction and Analysis of Autoregressive Signals in Impulsive Noise

Modelling and reconstruction methods are presented for noise reduction of autocorrelated signals in non-Gaussian, impulsive noise environments. A Bayesian probabilistic framework is adopted and Markov chain Monte Carlo methods are developed for detection and correction of impulses. Individual noise sources are modelled as Gaussian with unknown scale (variance), allowing for robustness tòheavy-tailed' impulse distributions, while the underlying signal is modelled as autoregressive (AR). Results are presented for both arti-cial and real data from voice and music recordings and comparisons are made with existing techniques. The new techniques are found to give improved detection and elimination of impulses in adverse noise conditions at the expense of some extra computational complexity.
Epidemiological Modelling of Peer-to-Peer Viruses and Pollution

The popularity of peer-to-peer (P2P) networks makes them an attractive target to the creators of viruses and other malicious code. Recently a number of viruses designed specifically to spread via P2P networks have emerged. Pollution has also become increasingly prevalent as copyright holders inject multiple decoy versions in order to impede item distribution. In this paper we derive deterministic epidemiological models for the propagation of a P2P virus through a P2P network and the dissemination of pollution. We report on discrete simulations that provide some verification that the models remain sufficiently accurate despite variations in individual peer conduct to provide insight into the behaviour of the system. The paper examines the steady-state behaviour and illustrates how the models may be used to estimate in a computationally efficient manner how effective object reputation schemes will be in mitigating the impact of viruses and preventing the spread of pollution.
Gender, Personality Traits and the Ellsberg Paradox

This paper analyzes gender differences in ambiguity and whether they can be explained by differences in personality traits. In an experiment with 347 high school students, we investigate choices under ambiguity and link ambiguity to cognitive and personality traits. Participants value ambiguity less than risk. Women are more risk averse, while men are more ambiguity averse than women. Conditioning on risk aversion, the gender gap in ambiguity vanishes. Results for a representative sample of experimenters differ from results using a subset of volunteers. Psychometric measures are more strongly related to risk than to ambiguity. Conditioning on these measures explains roughly 20% of the difference in ambiguity aversion between men and women. 1 This paper has benefited from comments by Steffen Altmann, Liam Delaney, Thomas Dohmen, Angela Lee Duckworth, Philipp Eisenhauer, Armin Falk, Helga Fehr-Duda, Lars Peter Hansen, Michael Hurd, Erin Krupka, Peter Neary, Friedhelm Pfeiffer, Arthur van Soest, Harald Uhlig, Terry Vaughn and participants of the 2008 EEA conference in Milan and the Conference on Understanding Economic Decision-making in Jackson Hole. We thank Stella Maris High School for support in the organization of the experiment. The research was financed partly by Golsteyn’s Meteor research grant M.08. 5217. Golsteyn thanks IZA where he stayed during this research for its hospitality. 2 Additional information about the experiment is available in Borghans et al. (2009). 3 Corresponding author: b.golsteyn@algec.unimaas. nl.
A new VLSI model of neural microcircuits including spike time dependent plasticity

This work presents a new VLSI model for biological neural systems, a unified research tool for neuro- as well as computer science. It allows construction of neural microcircuits close to the biological specimen while maintaining a speed several orders faster than real time. The synapse model includes an implementation of spike time dependent plasticity (STDP). Therefore, the VLSI system allows the investigation of key aspects of plasticity without a speed penalty. Additionally, this system is a research tool for new concepts of information processing like liquid or any-time computing. The analog, continuous-time operation of the neuron is implemented in a contemporary deep-submicron process technology. Thereby it realizes a powerful computing system that is not based on the Turing paradigm.
CATRA- congestion aware trapezoid-based routing algorithm for on-chip networks

Congestion occurs frequently in Networks-on-Chip when the packets demands exceed the capacity of network resources. Congestion-aware routing algorithms can greatly improve the network performance by balancing the traffic load in adaptive routing. Commonly, these algorithms either rely on purely local congestion information or take into account the congestion conditions of several nodes even though their statuses might be out-dated for the source node, because of dynamically changing congestion conditions. In this paper, we propose a method to utilize both local and non-local network information to determine the optimal path to forward a packet. The non-local information is gathered from the nodes that not only are more likely to be chosen as intermediate nodes in the routing path but also provide up-to-date information to a given node. Moreover, to collect and deliver the non-local information, a distributed propagation system is presented.
Neural net pruning-why and how

A continuing question in neural net research is the size of network needed to solve a particular problem. If training is started with too small a network for the problem no learning can occur. The researcher must then go through a slow process of deciding that no learning is taking place, increasing the size of the network and training again. If a network that is larger than required is used, then processing is slowed, particularly on a conventional von Neumann computer. An approach to this problem is discussed that is based on learning with a net which is larger than the minimum size network required to solve the problem and then pruning the solution network. The result is a small, efficient network that performs as well or better than the original which does not give a complete answer to the question, since the size of the initial network is still largely based on guesswork but it gives a very useful partial answer and sheds some light on the workings of a neural network in the process. <<ETX>>
Block Nonnegative Matrix Factorization for Single Channel Source Separation

Nonnegative Matrix Factorization (NMF) [1, 2] has been widely used in audio research, e.g. automatic music transcription [3], musical source separation [4], and speech enhancement [5]. The key strategy for applying NMF to audio-related tasks is to find a lower rank representation of the Short Time Fourier Transformed (STFT) input signal and use the basis vectors as dictionaries. For example, in the single channel source separation, we assume that the dictionaries learned from different training sets of target sources are distinct so that their activations for reconstructing a mixture signal will have discriminant patterns per a source.
Automatic and Reliable Electrical Characterization of MOSFETs

A low-cost switching system based on PIC 18F4550 microcontroller, which enables the successive measurement of both electrical characteristics (midgap-subthreshold technique, MGT) and charge-pumping currents (charge-pumping technique, CPT) of metal-oxide-semiconductor field-effect transistor (MOSFET) has been developed. The system can be used for switching between MGT and CPT instead of the expensive commercial switching systems. It has been used for characterization (MGT and CPT measurements) of RADFETs with the oxide thicknesses of 0.4 μm and 1 μm.
Deleterious effects of light exposure on immunity and sexual coloration in birds

Summary 1. Sunlight ultraviolet (UV) exposure can be highly damaging to biological tissues, being known to cause cellular damage and immunosuppression in humans and rodents, and depletion of carotenoid-based sexual plumage coloration in birds (i.e. photobleaching). However, it remains unknown whether sunlight may cause photobleaching in living tissues that comprise sexual signals such as bare parts in birds. It seems possible that any carotenoids depleted from bare parts by sunlight could be replenished, but if so, this could impact the availability of carotenoids for other functions such as immunity and antioxidant defence. Such trade-offs seem particularly likely in individuals that have a low dietary intake of carotenoids, or small amounts of carotenoids in body storage. 2. We investigated the effects of exposure to simulated sunlight UV (UV+) on bill coloration, blood plasma and liver (i.e. body storage) carotenoids and pro-inflammatory immune responses in male zebra finches (Taeniopygia guttata) in vivo, compared with individuals maintained in an identical environment but with the UV wavelengths removed by a filter (UV−). We also investigated whether any deleterious effects of UV exposure were mitigated by dietary carotenoid supplementation (UV+ Car+). 3. Exposure to simulated sunlight UV was associated with low levels of carotenoids in liver, and elevated levels of carotenoids in blood plasma, suggesting the mobilisation of stored carotenoids. Simulated sunlight UV also caused impaired pro-inflammatory immune responses, whereas this was not seen in carotenoid-supplemented birds. We found no effects of simulated sunlight UV or carotenoid supplementation on oxidative damage in blood plasma or bill. 4. Bill ‘carotenoid chroma’ diminished, and bill ‘UV chroma’ correspondingly increased, in all groups during the experiment (i.e. even in UV+ Car+ birds, and in UV− birds). This likely arose because the illuminance in the experimental cages was far higher than in standard laboratory housing conditions and suggests that carotenoid pigmentation is highly susceptible to photobleaching even under exposure to human-visible wavelengths. 5. These results highlight the potential for sunlight exposure to invoke a range of deleterious consequences for birds, including photobleaching and depression of immunity via pathways mediated by carotenoid allocation trade-offs. The expression of carotenoid-based sexual signals may therefore reveal an individual’s history of sunlight exposure and its deleterious effects on immunity.
Automatic User Interaction Detection and Scheduling with RSIO

Response time is one of the most important factors for the overall usability of a computer system. We present RSIO, a processor scheduling framework for improving the response time of latency-sensitive applications by monitoring accesses to I/O channels and inferring when user interactions occur. RSIO provides a general mechanism for all user interactions, including direct interactions via local HCI devices such as mouse and keyboard, indirect interactions through middleware, and remote interactions through networks. It automatically and dynamically identifies processes involved in a user interaction and boosts their priorities at the time the interaction occurs to improve system response time. RSIO detects processes that directly handle a user interaction as well as those indirectly involved in processing the interaction, automatically accounting for dependencies and boosting their priorities accordingly. RSIO works with existing schedulers, processes that may mix interactive and batch activities, and requires no application modifications to identify periods of latency-sensitive application activity. We have implemented RSIO in Linux and measured its effectiveness on microbenchmarks and real applications. Our results show that RSIO is easy to use and can provide substantial improvements in system performance for latency-sensitive applications.