Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality Improvement of Sintered Ore in Relation to Blast Furnace Operation

High productivity operation of a blast furnace requires enhancing reducibility and improving reduction behavior at high temperatures of sintered ore. Analysis results of sintered ores with extensive ranges of chemical composition showed that reduction behavior of sinter depended on their chemistry and pore structure as a result of changes in the amount of liquid phase, viscosity and smelting-reduction rate of formed slag containing 'FeO'. Sinter with High Fe content, proper basicity range (1.5-2.0), low Al 2 O 3 content and fine pores below 15μm in size had superior reduction behavior. Finally, sinter with low SiO 2 , low MgO and low Al 2 O 3 has been developed and manufactured in all works in Nippon Steel, resulting in improvement of blast furnace operation.
Optimierung Und Kontrolle Projektbereich Diskrete Optimierung on the Tsp with a Relaxed General Distribution Matrix on the Traveling Salesman Problem with a Relaxed Monge Matrix

We show that the traveling salesman problem with a symmetric relaxed Monge matrix as distance matrix is pyramidally solvable and can thus be solved by dynamic programming. Furthermore, we present a polynomial time algorithm that decides whether there exists a renumbering of the cities such that the resulting distance matrix becomes a relaxed Monge matrix.
Reinforcement learning without rewards

Machine learning can be broadly defined as the study and design of algorithms that improve with experience. Reinforcement learning is a variety of machine learning that makes minimal assumptions about the information available for learning, and, in a sense, defines the problem of learning in the broadest possible terms. Reinforcement learning algorithms are usually applied to "interactive" problems, such as learning to drive a car, operate a robotic arm, or play a game. In reinforcement learning, an autonomous agent must learn how to behave in an unknown, uncertain, and possibly hostile environment, using only the sensory feedback that it receives from the environment. As the agent moves from one state of the environment to another, it receives only a reward signal—there is no human "in the loop" to tell the algorithm exactly what to do. The goal in reinforcement learning is to learn an optimal behavior that maximizes the total reward that the agent collects. Despite its generality, the reinforcement learning framework does make one strong assumption: that the reward signal can always be directly and unambiguously observed. In other words, the feedback a reinforcement learning algorithm receives is assumed to be a part of the environment in which the agent is operating, and is included in the agent's experience of that environment. However, in practice, rewards are usually manually-specified by the practitioner applying the learning algorithm, and specifying a reward function that elicits the desired behavior from the agent can be a subtle and frustrating design problem. Our main focus in this thesis is the design and analysis of reinforcement learning algorithms which do not require complete knowledge of the rewards. The contributions of this thesis can be divided into three main parts: (1) In Chapters 2 and 3, we review the theory of two-player zero-sum games, and present a novel analysis of existing no-regret algorithms for solving these games. Our results show that no-regret algorithms can be used to compute strategies in games that satisfy a much stronger definition of optimality than is commonly used. (2) In Chapters 4 and 5, we present new algorithms for apprenticeship learning, a generalization of reinforcement learning where the true rewards are unknown. The algorithms described in Chapter 5 will leverage the game-theoretic results from Chapters 2 and 3. (3) In Chapter 6, we show how partial knowledge of the rewards can be used to accelerate imitation learning, an alternative to reinforcement learning where the goal is to imitate another agent in the environment. In summary, we design and analyse several new algorithms for reinforcement learning that do not require access to a fully observable or fully accurate reward signal, and by doing so, add considerable flexibility to the traditional reinforcement learning framework.
Creation of Interpretable Summary Measures in Displaying Results from Mixed-effects Logit Models

The authors of this article developed new approaches to present analytic results from mixed-effects binary logit models in longitudinal data analysis. We first described basic specifications of mixed-effects logit models, the derivation of the fixed and the random effects, and nonlinear predictions of the response probability and the corresponding standard errors. Particular attention was paid to the interpretability of the conventional odds ratio in the longitudinal setting. The authors contended that without information on averaging of the random effects for two population subgroups of interest, the regression coefficient of an explanatory variable and its antilog in mixed-effects binary logit models are not interpretable. We recommended the computation of the conditional effect and the conditional odds ratio to aid in displaying a covariate's effect on the longitudinal binary response. An empirical illustration was provided to demonstrate how to create interpretable summary measures for aiding in the interpretation of the results from mixed-effects logit models when analyzing binary longitudinal data.
New Complete Mitochondrial DNA Sequence of the Lancelet Branchiostoma lanceolatum (Cephalochordata) and the Identity of this Species' Sequences

Abstract Three mitochondrial (mt) genes were sequenced for two Atlantic lancelet species, Branchiostoma lanceolatum and B. floridae, to examine a serious discrepancy among previously published results of molecular studies: substantial sequence difference in a nuclear gene vs. virtual identity in the mt genome sequence. The results revealed that three mt genes of B. lanceolatum, collected from Helgoland in the North Sea and Naples in the Mediterranean, were quite diverged from those of B. floridae, collected from Tampa Bay, Florida. Therefore, the previously recognized identity in the mt genome between the two species is attributable to misidentification of materials used. To correct this misleading information, the complete mtDNA sequence of B. lanceolatum was determined for an individual from Helgoland.
Gated-Vdd: a circuit technique to reduce leakage in deep-submicron cache memories

Deep-submicron CMOS designs have resulted in large leakage energy dissipation in microprocessors. While SRAM cells in on-chip cache memories always contribute to this leakage, there is a large variability in active cell usage both <italic>within</italic> and <italic>across</italic> appliðcations. This paper explores an integrated architectural and circuit-level approach to reducing leakage energy dissipation in instrucðtion caches. We propose, <italic>gated-V<subscrpt>dd</subscrpt>,</italic> a circuit-level technique to gate the supply voltage and reduce leakage in unused SRAM cells. Our results indicate that gated-V<subscrpt>dd</subscrpt> together with a novel resizable cache architecture reduces energy-delay by 62% with minimal impact on performance.
Multiple receptors involved in human rhinovirus attachment to live cells

Minor group human rhinoviruses (HRVs) attach to members of the low-density lipoprotein receptor family and are internalized via receptor-mediated endocytosis. The attachment of HRV2 to the cell surface, the first step in infection, was characterized at the single-molecule level by atomic force spectroscopy. Sequential binding of multiple receptors was evident from recordings of characteristic quantized force spectra, which suggests that multiple receptors bound to the virus in a timely manner. Unbinding forces required to detach the virus from the cell membrane increased within a time frame of several hundred milliseconds. The number of receptors involved in virus binding was determined, and estimates for on-rate, off-rate, and equilibrium binding constant of the interaction between HRV2 and plasma membrane-anchored receptors were obtained.
Strings between branes

D-brane configurations containing fundamental strings are constructed as classical solutions of Yang-Mills theory. The fundamental strings in these systems stretch between D-branes. In the case of D1-branes, this construction gives smooth (classical) resolutions of string junctions and string networks. Using a non-abelian Yang-Mills analysis of the string current, the string charge density is computed and is shown to have support in the region between the D-brane world-volumes. The 't Hooft-Polyakov monopole is analyzed using similar methods, and is shown to contain D-strings whose flux has support off the D-brane world-volume defined by the Higgs scalar field, when this field is interpreted in terms of a transverse dimension. The constructions presented here are used to give a qualitative picture of tachyon condensation in the Yang-Mills limit, where fundamental strings and lower-dimensional D-branes arise in a volume of space-time where brane-antibrane annihilation has occurred.
Effect of Salinity, pH, Light Intensity on Growth and Lipid Production of Microalgae for Bioenergy Application

The crisis of energy producing molecules (fuels) is expected to increase in future, which is currently produced from crude mineral oil. Biodiesel is most reliable, non-toxic, biocompatible liquid fuel that can replace the existing unsustainable sources of energy. Among all the known sources, microalgae display high potential for the production of biodiesel owing to their numerous benefits like higher biomass productivities than plants, no agricultural land requirement, cultivation in waste water and accumulation of 20-50% triacylglycerols. Microalgae biomass and lipid content plays an important role in commercial production of biodiesel. The present work was carried out to develop an axenic culture of a potential microalga Chlorella sp. for high biomass and enhanced lipid accumulation. The important growth parameters like pH, light colour, light intensity and photoperiod were studied for better production of Chlorella biomass. The effect of salinity on cell growth was also studied and compared with normal Fogg’s medium grown cells. The main biomolecules like carbohydrate, protein, lipid and chlorophyll content were also estimated with the help of standard biochemical methods in salt supplemented and without salt Fogg’s medium. The cellular lipid content was increased by growing the cells under different salt concentrations. The micro algal strain showed highest growth of 0.822 g L-1 and 1.021g L-1 in Fogg’s medium and under 0.2 M NaCl supplemented medium respectively. However, the maximum lipid production of 0.1842 g L-1 was estimated by growing the cells in Fogg’s medium including 0.5 M NaCl with slight compromise in cell growth (0.858 g L-1). The lipid content of Chlorella sp. was found to be 26.84% as compared to 14% obtained under normal culture condition. Thus, growing Chlorella sp. under salt supplemented medium and optimizing light requirement will produce high biomass and oil for biodiesel production.
Principal component analysis of nucleosome DNA conformational data

The deformation mechanism of double helical DNA can be elucidated by the correlations between various conformational properties of its constituent dinucleotides. In this paper, we study the structural features of nucleosomes by performing principle component analysis on structural parameters within three categories: sugar-phosphate backbone, base pairs and base steps. The aim of this study is to find out the coupling modes of these conformational properties which are probably responsible for the special conformational settings of dinucleotides in nucleosomes. For comparison, a number of B-form oligomers are selected and subjected to PCA. Analysis of similarity and difference between nucleosomes and oligomers is implemented. The result shows that nucleosomes have a series of unique coupling patterns and reveals the multidimension-coordinated deformation mechanisms.
On non-parametric models for detecting outages in the mobile network

The wireless/cellular communications network is composed of a complex set of interconnected computation units that form the mobile core network. The mobile core network is engineered to be fault tolerant and redundant; small errors that manifest themselves in the network are usually resolved automatically. However, some errors remain latent, and if discovered early enough can provide warnings to the network operator about a pending service outage. For mobile network operators, it is of high interest to detect these minor anomalies near real-time. In this work we use performance data from a 4G-LTE network carrier to train two parameter-free models. A first model relies on isolation forests, and the second is histogram based. The trained models represent the data characteristics for normal periods; new data is matched against the trained models to classify the new time period as being normal or abnormal. We show that the proposed methods can gauge the mobile network state with more subtlety than standard success/failure thresholds used in real-world networks today.
Nanocarriers for Diagnosis and Targeting of Breast Cancer

Breast cancer nanotherapeutics is consistently progressing and being used to remove the various limitations of conventional method available for the diagnosis and treatment of breast cancer. Nanoparticles provide an interdisciplinary area for research in imaging, diagnosis, and targeting of breast cancer. With advanced physicochemical properties and better bioavailability, they show prolonged blood circulation with efficient tumor targeting. Passive targeting mechanisms by using leaky vasculature, tumor microenvironment, or direct local application and active targeting approaches using receptor antibody, amplification in the ability of nanoparticles to target specific tumor can be achieved. Nanoparticles are able to reduce cytotoxic effect of the active anticancer drugs by increasing cancer cell targeting in comparison to conventional formulations. Various nanoparticles-based formulations are in the preclinical and clinical stages of development; among them, polymeric drug micelles, liposomes, dendrimer, carbon nanotubes, and nanorods are the most common. In this review, we have discussed the role of nanoparticles with respect to oncology, by particularly focusing on the breast cancer and various nanodelivery systems used for targeting action.
Differential Degradation of Nonylphenol Isomers by Sphingomonas xenophaga Bayram

ABSTRACT Sphingomonas xenophaga Bayram, isolated from the activated sludge of a municipal wastewater treatment plant, was able to utilize 4-(1-ethyl-1,4-dimethylpentyl)phenol, one of the main isomers of technical nonylphenol mixtures, as a sole carbon and energy source. The isolate degraded 1 mg of 4-(1-ethyl-1,4-dimethylpentyl)phenol/ml in minimal medium within 1 week. Growth experiments with five nonylphenol isomers showed that the three isomers with quaternary benzylic carbon atoms [(1,1,2,4-tetramethylpentyl)phenol, 4-(1-ethyl-1,4-dimethylpentyl)phenol, and 4-(1,1-dimethylheptyl)phenol] served as growth substrates, whereas the isomers containing one or two hydrogen atoms in the benzylic position [4-(1-methyloctyl)phenol and 4-n-nonylphenol] did not. However, when the isomers were incubated as a mixture, all were degraded to a certain degree. Differential degradation was clearly evident, as isomers with more highly branched alkyl side chains were degraded much faster than the others. Furthermore, the C9 alcohols 2,3,5-trimethylhexan-2-ol, 3,6-dimethylheptan-3-ol, and 2-methyloctan-2-ol, derived from the three nonylphenol isomers with quaternary benzylic carbon atoms, were detected in the culture fluid by gas chromatography-mass spectrometry, but no analogous metabolites could be found originating from 4-(1-methyloctyl)phenol and 4-n-nonylphenol. We propose that 4-(1-methyloctyl)phenol and 4-n-nonylphenol were cometabolically transformed in the growth experiments with the mixture but that, unlike the other isomers, they did not participate in the reactions leading to the detachment of the alkyl moiety. This hypothesis was corroborated by the observed accumulation in the culture fluid of an as yet unidentified metabolite derived from 4-(1-methyloctyl)phenol.
A Hull and White Formula for a General Stochastic Volatility Jump-Diffusion Model with Applications to the Study of the Short-Time Behavior of the Implied Volatility

In this paper, generalizing results in Alos, Leon and Vives (2007b), we see that the dependence of jumps in the volatility under a jump-diffusion stochastic volatility model, has no effect on the short-time behaviour of the at-the-money implied volatility skew, although the corresponding Hull and White formula depends on the jumps. Towards this end, we use Malliavin calculus techniques for Levy processes based on Lokka (2004), Petrou (2006), and Sole, Utzet and Vives (2007).
A selective algorithm for the reduction of irregular noise in speech communication

The properties of noise signals can be the main problem associated with noise cancellation systems. In order to overcome this problem, high complexity algorithms have to be used in order to reduce the noise embedded in the useful signals such as speech. This method can be impractical, especially in real-time applications where the computational power is a crucial issue. Adaptive filters give applicable solutions, but most literature proposed a single, yet complex algorithm to removing the noise. This paper proposes an alternative approach to eliminate background noise in corrupted speech signals. The method is achieved by letting the system assigns an appropriate algorithm according to the characteristics of the noise. The criterion used here is based on the calculation of eigenvalue spread in the autocorrelation matrix of the input signal. In addition, an algorithm derived from set-membership filtering is also used among the selected algorithms. This approach showed its potential capability in eliminating different types of environmental noise from corrupted speech signals. The technique presented here exhibited fast convergence speed and improvement in signal-to-noise ratio compared with other single adaptive algorithms.
Why should cognitive neuroscientists study the brain's resting state?

Cognitive neuroscience investigates how cognitive function is produced by the brain. Seen from a reverse angle, cognitive neuroscience studies how brain activity is modulated by the execution of cognitive tasks. In the former case, cognitive function is characterized in terms of neural properties associated with the execution of given cognitive tasks, while in the latter it can be thought of as a probe exposing information on brain dynamics. Brain activity displays dynamics independently of whether a particular task is carried out or not. The question is then: should cognitive neuroscience get interested in the properties of resting brain activity? And, if so, how and to what extent can studying resting brain activity help characterizing the neural correlates of cognitive processes?
Identification of Influential Scientists vs. Mass Producers by the Perfectionism Index

The concept of h-index has been proposed to easily assess a researcher's performance with a single number. However, by using only this number, we lose significant information about the distribution of citations per article in an author's publication list. In this article, we study an author's citation curve and we define two new areas related to this curve. We call these "penalty areas", since the greater they are, the more an author's performance is penalized. We exploit these areas to establish new indices, namely PI and XPI, aiming at categorizing researchers in two distinct categories: "influentials" and "mass producers"; the former category produces articles which are (almost all) with high impact, and the latter category produces a lot of articles with moderate or no impact at all. Using data from Microsoft Academic Service, we evaluate the merits mainly of PI as a useful tool for scientometric studies. We establish its effectiveness into separating the scientists into influentials and mass producers; we demonstrate its robustness against self-citations, and its uncorrelation to traditional indices. Finally, we apply PI to rank prominent scientists in the areas of databases, networks and multimedia, exhibiting the strength of the index in fulfilling its design goal.
Medical students' experiences in medical emergency procedures upon qualification

Summary. The experiences of eighty‐five final‐year medical students in twenty practical procedures identified as relevant to medical emergencies were registered. Notable differences between exposures to some of these procedures within and outside teaching situations were found. Instruction must be seen as inadequate prior to first performance in real‐life situations. Experiences in cardiopulmonary resuscitation were acceptable, however, while exposures to procedures with special equipment, e.g. lumbar puncture or bladder cathterization, were variable.
Aligning WordNet with Additional Lexical Resources

This paper explores the relationship between WordNet and other conventional linguistically-based lexical resources. We introduce an algorithm for aligning word senses from different resources, and use it in our exper~nent to sketch the role played by WordNet, as far as sense discrimination is concerned, when put in the context of other lexical databases. The results show how and where the resources systematically differ from one another with respect to the degree of polysemy, and suggest how we can (i) overcome the inadequacy of individual resources to achieve an overall balanced degree of sense discrimination, and (ii) use a combination of semantic classification schemes to enrich lexical information for NLP.
Improving Performance Through Channel Diversity in the Presence of Bursty Losses

As more applications migrate to IP networks, ensuring a consistent level of service is increasingly important. One option is for the network to offer service guarantees. Another is to leverage the path diversity that the Internet intrinsically offers. Our focus is on understanding if and when one can indeed take advantage of multiple disjoint paths to improve performance. We consider an environment where loss patterns are bursty and where coding is used to provide robustness against packet losses. We assume that only long-term loss statistics are known about each path, and we seek to identify the best strategy for sending packets over the available paths. Our contributions are two-fold. First we demonstrate that even with minimal knowledge of channel characteristics and using simple transmission policies, path diversity can help significantly improve performance. Second, we derive an efficient method for identifying optimal policies, and more importantly characterize when having access to multiple paths can be of benefit.
Extending corpus-based identification of light verb constructions using a supervised learning framework

Light verb constructions (LVCs), such as “make a call” in English, can be said to be complex predicates in which the verb plays only a functional role. LVCs pose challenges for natural language understanding, as their semantics differ from usual predicate structures. We extend the existing corpus-based measures for identifying LVCs between verb-object pairs in English, by proposing using new features that use mutual information and assess other syntactic properties. Our work also incorporates both existing and new LVC features into a machine learning approach. We experimentally show that us-ing the proposed framework incorporating all features outperforms previous work by 17%. As machine learning techniques model the trends found in training data, we believe the proposed LVC detection framework and statistical features is easily extendable to other languages.