Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods of Search for Solving Polynomial Equations

The problem of determining a zero of a given polynomial with guaranteed error bounds, using an amount of work that can be estimated a priori, is attacked here by means of a class of algorithms based on the idea of systematic search. Lehmer''s "machine method" for solving polynomial equations is a special case. The use of the Schur-Cohn algorithm in Lehmer''s method is replaced by a more general proximity test which reacts positively if applied at a point close to a zero of a polynomial. Various such tests are described, and the work involved in their use is estimated. The optimality and non-optimality of certain methods, both on a deterministic and on a probabilistic basis, are established.
Exploration of Simulation Experiments by Discovery

We exemplify in this paper, how a discovery system is applied to the analysis of simulation experiments in practical political planning, and show what kind of new knowledge can be discovered in an application area that differs from others by the high amount of knowledge that the analyst holds already about the process that generates the data. Subgoals like "low classification accuracy", "high homogeneity", "disjoint rules", etc. are introduced into Explora, to select between different statistical tests for each pattern and several search algorithms, allowing the user to adapt the discovery process to the special requirements of the application. The combination of discovery with simulation is endowed with the main characteristics of both Knowledge Discovery in Databases (KDD) and Automated Scientific Discovery (ASD), i.e. discovery in large databases and experimentation. Analysing a real system with simulation models allows to freely set the experimental conditions. In distinction to the usual KDD assumption of fixed given data, when combining discovery with simulation, additional data can be generated by running new simulations according to the needs of the discovery component. First, we identify four tasks relevant for exploring simulation experiments which can be supported by discovery methods. Then we describe the application of socio-economic modeling for political planning. Third, we demonstrate for a simple law (financial support of families with children), how the current state of Explora is used for this application. Finally, we discuss some new approaches of Explora to deal with subgoals and outline further work.
Ada-boosting Extreme learning machines for handwritten digit and digit strings recognition

Automatic handwriting recognition of digit strings, is of academic and commercial interest. Current algorithms are already quite good at learning to recognize handwritten digits, which enables to use them for sorting letters and reading personal checks. Neural networks are a powerful technology for classification of visual inputs arising from documents, and have been extensively used in many fields due to their ability to approximate complex nonlinear mappings directly from the input sample. Different variations of Multi-Layer Neural Networks (MLNN), using back propagation training algorithm, yield a very high recognition rates on handwritten digits benchmarks, but lacks the aspect of speed in training time. Learning time is an important factor while designing any computational intelligent algorithm for classifications, especially when online improving by adapting new samples is needed. Extreme Learning Machine (ELM) has been proposed as an alternative ANN, which significantly reduce the amount of time needed to train a MLNN and has been widely used for many applications. The ELM analytical process of learning reduces the time of learning comparing to back propagation by avoiding the process of iterative learning. In this paper, we present a process which boosts few Extreme learning machines using Ada-boosting in order to improve the recognition rates iteratively. A pre-processing step is used to improve the ability of the ELM, and special weighting process to improve the boosting process. To evaluate the presented approach, we have used the (HDRC 2013) data-set which have bee used at the 2014 competition on handwritten digit string recognition organized in conjunction with ICFHR2014 of Western Arabic digit string recognition with varying length. Very high accurate results in terms of very low error rates while keeping efficient time of online training were achieved by the presented approach, which enables on demand time/precision tradeoff.
The neurobiology of food intake in an obesogenic environment

The objective of this non-systematic review of the literature is to highlight some of the neural systems and pathways that are affected by the various intake-promoting aspects of the modern food environment and explore potential modes of interaction between core systems such as hypothalamus and brainstem primarily receptive to internal signals of fuel availability and forebrain areas such as the cortex, amygdala and meso-corticolimbic dopamine system, primarily processing external signals. The modern lifestyle with its drastic changes in the way we eat and move puts pressure on the homoeostatic system responsible for the regulation of body weight, which has led to an increase in overweight and obesity. The power of food cues targeting susceptible emotions and cognitive brain functions, particularly of children and adolescents, is increasingly exploited by modern neuromarketing tools. Increased intake of energy-dense foods high in fat and sugar is not only adding more energy, but may also corrupt neural functions of brain systems involved in nutrient sensing as well as in hedonic, motivational and cognitive processing. It is concluded that only long-term prospective studies in human subjects and animal models with the capacity to demonstrate sustained over-eating and development of obesity are necessary to identify the critical environmental factors as well as the underlying neural systems involved. Insights from these studies and from modern neuromarketing research should be increasingly used to promote consumption of healthy foods.
Novel SER standards: Backgrounds and methodologies

Standard methods to quantify SER susceptibility in memory devices have been established during 2000–2008. JESD89A issued in 2006 covers a wide variety of test methods for terrestrial neutrons and alpha particles. Spallation and (quasi-) monoenergetic neutron tests are among the best options for the SER tests. The methods, however, are being recognized as getting more inaccurate as device scaling proceeds. SER in logic devices is also getting more serious so that standard testing methods have to be established for logic devices. The new standards may include strategies for mitigation of SERs as well. The backgrounds and methodologies to promote new SER standards for device, chip, board layers are discussed in the present paper.
Optical and Near-Infrared Observations of the Highly Reddened, Rapidly Expanding Type Ia Supernova SN 2006X in M100

We present extensive optical (UBVRI), near-infrared (JK) light curves and optical spectroscopy of the Type Ia supernova SN 2006X in the nearby galaxy NGC 4321 (M100). Our observations suggest that either SN 2006X has an intrinsically peculiar color evolution or it is highly reddened [E(B − V)host = 1.42 ± 0.04 mag ] with RV = 1.48 ± 0.06, much lower than the canonical value of 3.1 for the average Galactic dust. SN 2006X also has one of the highest expansion velocities ever published for an SN Ia. Compared with the other SNe Ia we analyzed, SN 2006X has a broader light curve in the U band, a more prominent bump/shoulder feature in the V and R bands, a more pronounced secondary maximum in the I and NIR bands, and a remarkably smaller late-time decline rate in the B band. The B − V color evolution shows an obvious deviation from the Lira-Phillips relation at 1-3 months after maximum brightness. At early times, optical spectra of SN 2006X displayed strong, high-velocity features of both intermediate-mass elements (Si, Ca, and S) and iron peak elements, while at late times they showed a relatively blue continuum, consistent with the blue U − B and B − V colors at similar epochs. A light echo and/or the interaction of the SN ejecta and its circumstellar material may provide a plausible explanation for its late-time photometric and spectroscopic behavior. Using the Cepheid distance of M100, we derive a Hubble constant of 72.8 ± 8.2 km s−1 Mpc−1 (statistical) from the normalized dereddened luminosity of SN 2006X. We briefly discuss whether abnormal dust is a universal signature for all SNe Ia and whether the most rapidly expanding objects form a subclass with distinct photometric and spectroscopic properties.
Preferences and Learning in Multi-agent Negotiation

In online, dynamic environments, the service requested by consumers may not be readily served by the producers. This requires the consumers and producers to negotiate on the content of the service. To automate this process, agents play a key role in e-commerce. As far as the agents' negotiation strategies are concerned, understanding and reasoning on their users' preferences are important to generate the right offers on behalf of their users. Besides taking other partici-pant's needs into account is important to be able to negotiate effectively. However, preferences of participants are almost always private. The best that can happen is that participants may learn each other's preferences through interactions over time. As agents learn each other's preferences, they can provide better-targeted offers and thus enable faster negotiation. My research direction involves representing and reasoning on preferences, and learning preferences though interaction in automated negotiation.
Determinants of adoption and spatial diversity of wheat varieties on household farms in Turkey

The Heckman two-stage estimation procedure was used to investigate factors influencing the adoption of modern and/or landrace wheat varieties and spatial diversity of wheat varieties in Turkey. In the first stage, the multinomial logit choice model (MNLM) was used to determine factors influencing farmers’ adoption of modern varieties (MVs) and/or landrace varieties (LVs) of wheat. Conditional on the choice of a given wheat variety or combination of MVs and LVs, a Tobit regression model was used to assess the determinants of on-farm spatial diversity of wheat varieties in the second stage. Our empirical approach allows for the analysis of partial adoption decision of wheat varieties and controls for self-selection problem in analyzing the determinants of spatial diversity of wheat varieties. The empirical model was conceptualized based on random utility model(RUM). The analysis was based on cross-sectional survey data collected on 486 sample households in six provinces of Turkey. Results showed that household size, the number of owned cattle, the number of buildings on farm, farm size, farm land fragmentation, the percentage of irrigable farm plots and regional variations are the important factors in determining the farmers’ first-stage choice of wheat variety types. The selfselection problem was significant only in one of the three cases for the landrace wheat varieties. In the second stage, the farm size and land fragmentation were found to be the key variables influencing the level of on-farm spatial diversity of wheat varieties. The results showed that considerable spatial wheat genetic diversity was maintained on-farm at the household level, mainly through the simultaneous adoption of modern and traditional wheat varieties. Growing a combination of modern and landrace wheat varieties was observed to yield significantly higher level of spatial diversity of wheat genetic resources as compared to growing modern varieties alone or landrace varieties alone. This result suggests that the modern and landrace wheat varieties can coexist and could still support more on-farm spatial diversity of wheat genetic resources. This finding has significant implications for future extension, research and policy efforts for on-farm conservation and utilization of wheat genetic resources in Turkey. There is a need for the government and private sector research and extension efforts to support farmers’ use of both modern and landrace varieties, for example, in terms of seed supply, provision of extension and credit services and marketing support instead of just giving undue priority to popularization and adoption of modern varieties alone.
Hard and Transparent Films Formed by Nanocellulose–TiO2 Nanoparticle Hybrids

The formation of hybrids of nanofibrillated cellulose and titania nanoparticles in aqueous media has been studied. Their transparency and mechanical behavior have been assessed by spectrophotometry and nanoindentation. The results show that limiting the titania nanoparticle concentration below 16 vol% yields homogeneous hybrids with a very high Young’s modulus and hardness, of up to 44 GPa and 3.4 GPa, respectively, and an optical transmittance above 80%. Electron microscopy shows that higher nanoparticle contents result in agglomeration and an inhomogeneous hybrid nanostructure with a concomitant reduction of hardness and optical transmittance. Infrared spectroscopy suggests that the nanostructure of the hybrids is controlled by electrostatic adsorption of the titania nanoparticles on the negatively charged nanocellulose surfaces.
Calibration of Machine Learning Models

The evaluation of machine learning models is a crucial step before their application because it is essential to assess how well a model will behave for every single case. In many real applications, not only is it important to know the “total” or the “average” error of the model, it is also important to know how this error is distributed and how well confidence or probability estimations are made. Many cur - rent machine learning techniques are good in overall results but have a bad distribution assessment of the error. For these cases, calibration techniques have been developed as postprocessing techniques in order to improve the probability estimation or the error distribution of an existing model. This chapter presents the most common calibration techniques and calibration measures. Both classification and regression are covered, and a taxonomy of calibration techniques is established. Special attention is given to probabilistic classifier calibration.
Mining Web Technical Discussions to Identify Malware Capabilities

The exponential growth of unique malware binary artifacts has led researchers to explore automated techniques for characterizing unknown malware binaries' capabilities. Thus far, automatic malware analysis systems have relied on labeled training data and analyst defined rules to identify malware samples' software features and functional categories. Such approaches require substantial expert analyst effort to maintain, as malware authors change programming languages, APIs, malicious tactics, and operating system targets. In this paper we present preliminary results demonstrating the viability of a new research direction for malware capability identification that addresses these issues, the concept of mining web technical documentation to automatically identify malware capabilities. This approach does not require expert generation of rules or training labels and automatically stays up to date with the latest software engineering trends. We make two contributions aimed at demonstrating the value of this research direction: first, with a corpus of 6 million web technical postings from the programming question and answer website StackOverflow.com, we show that symbols found in a corpus of malicious executable files, such as registry keys, file names, and API call names, also occur frequently in the StackOverflow data, suggesting that applying natural language processing to the StackOverflow posts (and other technical documents) may help us automatically generate characterizations of technical symbols, and, thereby, capabilities, found in malware. Our second contribution is to show that by analyzing function call symbol co-occurrence within StackOverflow posts, as well as the semantic tags associated with these posts, we can create function relationship graphs over the symbols which show promise in helping to identifying malware software capabilities. We argue that these early findings demonstrate the promise of a web technical document based approach to automating malware capability identification.
An active guarding circuit for giga-hertz substrate noise suppression

This paper presents an active guarding circuit employing noise decoupling and inversion feedback for substrate noise suppression in high frequency up to GHz. Proposed inversion feedback circuit can efficiently suppress substrate noise up to GHz range by introducing a zero within an amplitude controller. The noise decoupling circuit not only provides a decoupling path, but also senses the noise level for the amplitude controller to perform noise cancellation. By applying the proposed active guarding circuit, the noise suppression of passive guard ring can be improved more than 14dB in the frequency range from DC to 1 GHz and 7.2 dB up to 5G Hz. For 1 dB degradation of noise suppression performance, the input noise level can be up to 90 mV at 1 MHz and 86 mV at 1 GHz, respectively.
Homophilic adhesion between Ig superfamily carcinoembryonic antigen molecules involves double reciprocal bonds

Both carcinoembryonic antigen (CEA) and neural cell adhesion molecule (NCAM) belong to the immunoglobulin supergene family and have been demonstrated to function as homotypic Ca(++)-independent intercellular adhesion molecules. CEA and NCAM cannot associate heterotypically indicating that they have different binding specificities. To define the domains of CEA involved in homotypic interaction, hybrid cDNAs consisting of various domains from CEA and NCAM were constructed and were transfected into a CHO-derived cell line; stable transfectant clones showing cell surface expression of CEA/NCAM chimeric-proteins were assessed for their adhesive properties by homotypic and heterotypic aggregation assays. The results indicate that all five of the Ig(C)-like domains of NCAM are required for intercellular adhesion while the COOH-terminal domain containing the fibronectin-like repeats is dispensable. The results also show that adhesion mediated by CEA involves binding between the Ig(V)-like amino-terminal domain and one of the Ig(C)-like internal repeat domains: thus while transfectants expressing constructs containing either the N domain or the internal domains alone were incapable of homotypic adhesion, they formed heterotypic aggregates when mixed. Furthermore, peptides consisting of both the N domain and the third internal repeat domain of CEA blocked CEA-mediated cell aggregation, thus providing direct evidence for the involvement of the two domains in adhesion. We therefore propose a novel model for interactions between immunoglobulin supergene family members in which especially strong binding is effected by double reciprocal interactions between the V-like domains and C-like domains of antiparallel CEA molecules on apposing cell surfaces.
Estresse e coping em vítimas de acidente de trânsito

Os acidentes de trânsito (AT) e as hospitalizações consequentes destes são situações potencialmente estressantes. Situações estressantes exigem das vítimas a mobilização de estratégias de enfrentamento (Coping). Este estudo verificou a relação entre estresse e coping em vítimas de AT hospitalizadas. A amostra foi composta por 10 sujeitos hospitalizados. Foram aplicados o Inventário de Estratégias de Coping de Folkman e Lazarus e o Inventário de Sintomas de Stress para Adultos de Lipp. A maioria dos participantes (80%) apresentava índice de estresse relevante, se dividindo entre 50% na fase de resistência, 10% na fase de quase exaustão e 20% na fase de exaustão. No grupo de pacientes que apresentaram estresse, o coping do tipo fuga/esquiva apresentou correlação positiva significativa com nível de estresse (rho=0,835). Sugere-se que as equipes de saúde atuem junto aos pacientes incentivando o enfrentamento mais funcional da situação.
Framework for analysis of opportunistic schedulers: average sum rate vs. average fairness

Channel aware and opportunistic scheduling algorithms exploit the channel knowledge and fading to increase the average throughput. The maximum throughput scheduler (MTS) transmits only to the best user at a time. It is unfair to users at the cell edge. Obviously, there is a tradeoff between average throughput and fairness in the system. In this work, we study four representative schedulers, namely the MTS, the proportional fair scheduler (PFS), the (relative) opportunistic round robin scheduler (ORS), and the round robin scheduler (RRS). We show that the average sum rate performance and the average worst-case delay depend strongly on the user distribution within the cell. MTS gains from asymmetrically distributed users whereas the other three schedulers suffer. On the other hand, the average fairness of MTS and PFS decreases with asymmetrical user distribution. The key contribution of this paper is to put these tradeoffs and observations on a solid theoretical basis. The scaling laws of the average sum rate with the number of users as well as of the average worst-case delay are derived. Both the PFS and the ORS provide a reasonable performance in terms of throughput and fairness. However, PFS outperforms ORS for symmetrical user distributions, whereas ORS outperforms PFS for asymmetrical user distributions.
Integrated system for sensing and traverse of cliff faces

A long duration robotic presence on lunar and planetary surfaces will allow the acquisition of scientifically interesting information from a diverse set of surface and sub-surface sites. The wide range of terrain types including plains, cliffs, sand dunes, and lava tubes will require the development of robotic systems that can adapt to possibly rapidly changing terrain. These systems include single as well as teams of robots. In this paper, we describe the development of an integrated suite of autonomous, adaptive hardware/software control methods called SMART (System for Mobility and Access to Rough Terrain) that enables mobile robots to explore potentially important science sites currently beyond the reach of conventional rover designs. SMART uses the behavior coordination mechanisms of CAMPOUT, a previously developed system for multi-agent control. For the specific application area of cliffside exploration, SMART consists of a distributed sensing system for cooperative map-making called MITSAF (Model-based Information Theoretic Sensing and Fusion), mobility system for rappelling down a cliff and moving to a designated way-point, and science sample acquisition from the cliff face. We also report the results of some experimental studies on highly sloped cliff faces.