Miracle-dz/UniFAR-FTUs huggingface dataset:
MANAGING DAIRY PROFIT RISK USING WEATHER DERIVATIVES

Weather conditions are a primary source of dairy production risk. Hot and humid weather induces heat stress, which reduces lactation. Heat abatement, such as ventilation, directly affects the temperature and humidity. Abatement can increase expected profit, but cannot eliminate the lost revenue caused by heat stress. Weather derivatives can reduce weather-induced profit risk and act as a substitute for abatement at the margin. We test the risk management value of weather derivatives in a utility-maximization framework. The result is that weather derivatives can expand the efficient portfolio frontier. Simultaneously using the weather derivatives and abatement equipment is more favorable than using either alone.
A Multiresolution Model for Soft Objects Supporting Interactive Cuts and Lacerations

Performing a really interactive and physically‐based simulation of complex soft objects is still an open problem in computer animation/simulation. Given the application domain of virtual surgery training, a complete model should be quite realistic, interactive and should enable the user to modify the topology of the objects. Recent papers propose the adoption of multiresolution techniques to optimize time performance by representing at high resolution only the object parts considered more important or critical. The speed up obtainable at simulation time are counterbalanced by the need of a preprocessing phase strongly dependent on the topology of the object, with the drawback that performing dynamic topology modification becomes a prohibitive issue. In this paper we present an approach that couples multiresolution and topological modifications, based on the adoption of a particle systems approach to the physical simulation. Our approach is based on a tetrahedral decomposition of the space, chosen both for its suitability to support a particle system and for the ready availability of many techniques recently proposed for the simplification and multiresolution management of 3D simplicial decompositions. The multiresolution simulation system is designed to ensure the required speedup and to support dynamic changes of the topology, e.g. due to cuts or lacerations of the represented tissue.
Generation of magnetic field in the pre-recombination era

We study the possibility of generating magnetic fields during the evolution of electron, proton and photon plasma in the pre-recombination era. We show that a small magnetic field can be generated in the second order of perturbation theory for scalar modes with adiabatic initial conditions. The amplitude of the field is 10 -30 G at the present epoch for scales from subkiloparsec to ≥ 100 Mpc.
Concurrent Probabilistic Temporal Planning : Initial Results

Probabilistic planning problems are often modeled as Markov decision problems (MDPs), which assume that a single action is executed per decision epoch and that actions take unit time. However, in the real world it is common to execute several actions in parallel, and the durations of these actions may differ. This paper presents our ongoing work on incorporating concurrent, durative actions in probabilistic planning. In particular we describeConcurrent MDPs , MDPs which allow multiple instantaneous actions to be executed simultaneously, and present two algorithms which perform orders of magnitude faster than naive approaches. We then add explicit action durations into our model and encode them as concurrent MDPs in an augmented state space. We present a novel heuristic and prove it admissible. Initial experimental results demonstrate the promise of our approach, showing speedup due to both the heuristic and our sampled RTDP algorithm.
Music Mood Classification via Deep Belief Network

In this paper we present a study on music mood classification by using only lyrics information. Specially considering the Chinese songs, the Chinese word-segmentation has caused intolerable errors and inadequate use of lyrics information. Our work proposes to use bag-of-character features instead of bag-of-word features to avoid the word segmentation error, which makes the classification more inaccurate. Further more, the use of DBN which trains useful features automatically can make more use of lyrics information. With the experiments on 1200 songs collected, we demonstrate that our high level features trained by DBN and joint bag-of-character perform much better than the traditional features in music mood classification.
Unfilled pauses in Japanese sentences read aloud by non-native learners

Perception experiments suggest that natives judge non-native unfilled pauses as indiscriminate and indecisive. Multiple regression analyses of unfilled pauses indicate a connection between syntactic structure and pause location and duration. Native speakers uniformly pause at large syntactic breaks with marked duration, whereas non-natives' unfilled pauses are spread over various locations, possibly reflecting limited syntactic planning. Our method might be used to synthesize appropriate unfilled pauses in text-to-speech systems, and to train pausing behavior in automated pronunciation learning systems for non-native learners. Index Terms: unfilled pauses, second language learning, syntactic structure, perception experiments, multiple regression analyses
Serotyping of Cryptococcus neoformans by dot enzyme assay

A method is described for the serotyping of Cryptococcus neoformans based on direct analysis of culture supernatants for the major type-specific capsular antigen, glucuronoxylomannan. Factor sera prepared by absorption of polyclonal rabbit antisera (Iatron Laboratories, Inc., Tokyo, Japan) or selected anti-C. neoformans monoclonal antibodies were used in a dot enzyme assay to detect the presence of antigen.
Flexible Security and Search Capability for a Relational Database with Externally Linked Multimedia Data Files

Recently, a prototype relational database system has been developed that has the capability of storing and querying analog and video information for interaction with data analysis applications [8]. The system provides for numerous querying categories via indexed meta-data using pre-filled, drop-own combo boxes. A shortcoming of the design is the growing number of categories that could in time become unmanageable. One purpose of this study was to explore a more flexible design that would reduce the number of categories for storing and querying the database. In this design, the user enters a compound, logical query statement with the desired keywords in addition to using the existing combo boxes. This proved to be a more efficient and flexible strategy for accommodating expanding search categories. Another purpose of this study was to add security features in accessing database information to restrict sharing of information about subject populations. This has become an important requirement in accessing subject information in research studies. To this end we have developed an easily reusable security hierarchy in which individual users and groups of users can be granted rights to objects of the system e.g., database tables or user interface tabs. The permissions granted to a user are dynamically updated by group permissions as the user joins or leaves these groups. The initial implementation of security features has proven to be efficiently handled by the interface. Thus, a prototype for a flexible and secure database querying capability has been implemented that can be scaled to meet the expanding needs of researchers.
Binary and Multiple Star Formation in Magnetic Clouds: Bar Growth and Fragmentation

In the standard scenario of isolated low-mass star formation, strongly magnetized molecular clouds are envisioned to condense gradually into dense cores, driven by ambipolar diffusion. Once the cores become magnetically supercritical, they collapse to form stars. Previous studies based on this scenario are limited to axisymmetric calculations leading to single supercritical core formation. The assumption of axisymmetry has precluded a detailed investigation of cloud fragmentation, generally thought to be a necessary step in the formation of binary and multiple stars. In a series of papers, we studied the nonaxisymmetric evolution of initially magnetically subcritical clouds, using a two-dimensional magnetohydrodynamic code based on the physically motivated thin-disk approximation. We found that such clouds become unstable to nonaxisymmetric perturbations after the supercritical cores are formed, because of ambipolar diffusion. In this paper, we focus on the evolution of clouds perturbed by an m = 2 mode of a modest fractional amplitude of 5%, with an eye on binary and multiple star formation. We show that for a wide range of initial cloud parameters, the m = 2 mode grows nonlinearly into a bar during the isothermal collapse after the supercritical core formation. The instability is driven by the domination of the magnetically diluted gravity over the combined thermal and magnetic pressure gradient in the supercritical cores. Such gravity-dominated cores can break up into fragments during or after the isothermal phase of cloud evolution. The outcome of fragmentation depends on the initial cloud conditions, such as the magnetic field strength, rotation rate, amount of cloud mass (relative to thermal Jeans mass), and mass distribution. It is classified into three different types: (1) "separate-core formation," in which the bar (m = 2) mode breaks up into two separate cores during the isothermal collapse, with a core separation of order 104 AU, (2) "bar fragmentation," in which the m = 2 mode evolves into a needle-like, opaque "first bar" (at a density n ≳ 1012 cm-3), which breaks up into multiple fragments with initial masses of order 10-2 M☉ and separations of order 102-103 AU, and (3) "disk fragmentation," in which the bar growth remains slow during the isothermal collapse and the central region evolves into a rapidly rotating, opaque "first disk," which breaks up into several self-gravitating blobs with separations less than the disk size (≲102 AU). These three types of fragmentation loosely correspond to the empirical classification of embedded binary and multiple systems of Looney, Mundy, & Welch, based on millimeter dust continuum observations. The well-studied starless core L1544 appears to belong to the bar fragmentation type. We expect it to produce a highly elongated, opaque bar at the center in the future, which should break up into fragments of initial masses in the substellar regime.
Teachers, human rights and diversity : educating citizens in multicultural societies

How should we educate citizens in multicultural societies? This question is receiving increasing attention across the world. In this volume authors from England, Northern Ireland, the Republic of Ireland and the United States report on recent research in this field and consider the implications for teachers, teacher educators and student teachers. Case studies illustrate how young citizens can learn to apply the principles of human rights and equality in resolving complex and controversial issues. The contributors include Hilary Claire, Colm O'Cuanachain, Carole Hahn, Anne Hudson, Ulrike Neins and Jackie Reilly, Jill Rutter, Chris Wilkins.
A Conjugate Based on Anti-HER2 Diaffibody and Auristatin E Targets HER2-Positive Cancer Cells

Antibody-drug conjugates (ADCs) have recently emerged as efficient and selective cancer treatment therapeutics. Currently, alternative forms of drug carriers that can replace monoclonal antibodies are under intensive investigation. Here, a cytotoxic conjugate of an anti-HER2 (Human Epidermal Growth Factor Receptor 2) diaffibody with monomethyl-auristatin E (MMAE) is proposed as a potential anticancer therapeutic. The anti-HER2 diaffibody was based on the ZHER2:4 affibody amino acid sequence. The anti-HER2 diaffibody has been expressed as a His-tagged protein in E. coli and purified by Ni-nitrilotriacetyl (Ni-NTA) agarose chromatography. The molecule was properly folded, and the high affinity and specificity of its interaction with HER2 was confirmed by surface plasmon resonance (SPR) and flow cytometry, respectively. The (ZHER2:4)2DCS-MMAE conjugate was obtained by coupling the maleimide group linked with MMAE to cysteines, which were introduced in a drug conjugation sequence (DCS). Cytotoxicity of the conjugate was evaluated using the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide MTT assay and the xCELLigence Real-Time Cell Analyzer. Our experiments demonstrated that the conjugate delivered auristatin E specifically to HER2-positive tumor cells, which finally led to their death. These results indicate that the cytotoxic diaffibody conjugate is a highly potent molecule for the treatment of various types of cancer overexpressing HER2 receptors.
The Application of Microarray Technology to Neuropathology: Cutting Edge Tool With Clinical Diagnostics Potential or Too Much Information?

Microarray technology is a tremendously powerful method for simultaneously monitoring the expression of thousands of species of nucleic acids, usually cellular mRNA, producing a high-resolution representation of the genes encoded or expressed in a cell. As such, microarray technology has great potential for impacting research and clinical approaches to treatment. However, this complex technology has been challenging to apply as a result of difficulties discerning biologic variation from technologic issues, therefore slowing the application of the technology to human diagnostics. Nevertheless, significant advances in microarray technology, improvements that avoid potential pitfalls, and a wider spectrum of application are making this technology easier to apply. Indeed, microarray technology has provided valuable insights into mechanisms involving gene regulation and expression in Alzheimer disease, and it remains a powerful tool to identify biomarkers for disease diagnosis. Ultimately, the most robust markers will enable the application of more specific treatments particular to disease stages or subcategories. Currently, no widely applicable molecular test is available to identify those at risk for developing Alzheimer disease or those who have early markers of pathology but show discernible cognitive impairment. The progression of this technology will lead to earlier detection of the disease through enhanced understanding of disease onset and progression.
Spam detection of Twitter traffic: A framework based on random forests and non-uniform feature sampling

Law Enforcement Agencies cover a crucial role in the analysis of open data and need effective techniques to filter troublesome information. In a real scenario, Law Enforcement Agencies analyze Social Networks, i.e. Twitter, monitoring events and profiling accounts. Unfortunately, between the huge amount of internet users, there are people that use microblogs for harassing other people or spreading malicious contents. Users' classification and spammers' identification is a useful technique for relieve Twitter traffic from uninformative content. This work proposes a framework that exploits a non-uniform feature sampling inside a gray box Machine Learning System, using a variant of the Random Forests Algorithm to identify spammers inside Twitter traffic. Experiments are made on a popular Twitter dataset and on a new dataset of Twitter users. The new provided Twitter dataset is made up of users labeled as spammers or legitimate users, described by 54 features. Experimental results demonstrate the effectiveness of enriched feature sampling method.
High Dimensional Data Clustering Using Hybridized Teaching-Learning-Based Optimization

TLBO is a newly introduced population based optimization algorithm inspired by passing on knowledge within a classroom environment. The most prominent feature of TLBO is that it does not require the algorithm parameters to be tuned. T his paper proposes a Hybridized Teaching-Learning-Based Optimization (HTLBO) Technique in order to abstract/gain useful information from high dimensional data. The new HTLBO algorithm is evaluated on some high dimensional datasets and compared to the performance of Hybridized K-means Clustering Algorithm and Genetic Algorithm.
ADOVA: Anomaly Detection in Online and Virtual spAces

Online and virtual spaces comprise a myriad of ad-hoc networks and online communities. Such communities are composed of smart devices, agents, systems and people who seek to interact in one way or another. We argue that the task of detecting anomalies in such settings is non-trivial. The complexity is further compounded since there is no clear cut definition/specification of what normal behaviour is, and how far out an outlier should be before it is detected as an anomaly. This is often the case with online and virtual spaces as there is little or no regulation of the interactions between the various players in online communities. Hence, detecting anomalous behaviour in such settings poses a huge challenge. In this paper, we investigate how evolutionary clustering could be exploited to support decision makers, designers and data scientists in the autonomous detection of anomalies in online and virtual spaces. We present preliminary ideas in tackling this issue using a freeform online social media community (Twitter) and explore how emerging patterns and trends could help identify clusters of players (or normal behaviour) and, conversely, anomalies.
emporal Dimension Evaluation by Fuzzy TOPSIS Method

This paper evaluates and ranks the temporal dimensions, known as fourth dimension of urban design, of a number of places in a city by TOPSIS method. TOPSIS method is technique for order preference by similarity to ideal solution. TOPSIS is one of the renowned methods for classical multi-criteria decision-making (MCDM) problems that defines the positive ideal solution and negative ideal solution to maximize the benefit criteria and minimize the cost criteria. The best solution is a point that has the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. Because of the vagueness of the input data, triangular fuzzy numbers are applied. In addition, Euclidian distance and a new positive and negative ideal solution are used in this paper. This technique is implemented in Marand, Iran to evaluate fifteen important places based on eight criteria of temporal dimensions. Closeness coefficient values verify the ranking order of fifteen important places, which is a vital decision for the urban managers.
Water use of a multigenotype poplar short‐rotation coppice from tree to stand scale

Short‐rotation coppice (SRC) has great potential for supplying biomass‐based heat and energy, but little is known about SRC's ecological footprint, particularly its impact on the water cycle. To this end, we quantified the water use of a commercial scale poplar (Populus) SRC plantation in East Flanders (Belgium) at tree and stand level, focusing primarily on the transpiration component. First, we used the AquaCrop model and eddy covariance flux data to analyse the different components of the stand‐level water balance for one entire growing season. Transpiration represented 59% of evapotranspiration (ET) at stand scale over the whole year. Measured ET and modelled ET were lower as compared to the ET of reference grassland, suggesting that the SRC only used a limited amount of water. Secondly, we compared leaf area scaled and sapwood area scaled sap flow (Fs) measurements on individual plants vs. stand scale eddy covariance flux data during a 39‐day intensive field campaign in late summer 2011. Daily stem diameter variation (∆D) was monitored simultaneously with Fs to understand water use strategies for three poplar genotypes. Canopy transpiration based on sapwood area or leaf area scaling was 43.5 and 50.3 mm, respectively, and accounted for 74%, respectively, 86%, of total ecosystem ET measured during the intensive field campaign. Besides differences in growth, the significant intergenotypic differences in daily ∆D (due to stem shrinkage and swelling) suggested different water use strategies among the three genotypes which were confirmed by the sap flow measurements. Future studies on the prediction of SRC water use, or efforts to enhance the biomass yield of SRC genotypes, should consider intergenotypic differences in transpiration water losses at tree level as well as the SRC water balance at stand level.
Predicting the quality of web services based on two layer model

Recently, Web service has become an important issue in the research community. Especially, predicting the Quality of Service (QoS) for users has been a hot topic which needs researching and applicating. In the other hand, with the rapid growth of the number of service providers and users, it results a large number of datasets. It significantly effects on the QoS as management and supervision to describe the functional and non-functional characteristics of Web service. In this context, predicting QoS on big data dataset is an urgent issue that needs to be solved. In this paper, we present a new model to handle this issue based on a Restricted Boltzmann Machines (RBM), it is called Two Layer Model (TLM). We have used this model to deal with the big data datasets and the model used in efficient learning and inference procedures to predict the missing QoS value of web service. Our experiments have been performed based on two data sets in the WS-DREAM dataset and the experimental results have proved that the proposed model was effective.
Analysis of Protein-RNA Interactions at Atomic Level

Many studies of protein-RNA complexes focus on protein instead of RNA or both, and often ignore water-mediated protein-RNA complexes despite their abundant existence. The primary focus of our study is to find interaction propensities not only for proteins but also for RNAs. This paper presents the analysis of 952 hydrogen bonds in the most representative set of protein-RNA complexes, including both direct hydrogen bonds and water-mediated hydrogen bonds.
Impedance Compensation of SUBAR for Back-Drivable Force-Mode Actuation

The Sogang University biomedical assistive robot (SUBAR), which is an advanced version of the exoskeleton for patients and the old by Songang (EXPOS) is a wearable robot developed to assist physically impaired people. It provides a person with assistive forces controlled by human intentions. If a standard geared DC motor is applied, however, the control efforts will be used mainly to overcome the resistive forces caused by the friction, the damping, and the inertia in actuators. In this paper, such undesired properties are rejected by applying a flexible transmission. With the proposed method, it is intended that an actuator exhibits zero impedance without friction while generating the desired torques precisely. Since the actuation system of SUBAR has a large model variation due to human-robot interaction, a control algorithm for the flexible transmission is designed based on a robust control method. In this paper, the mechanical design of SUBAR, including the flexible transmission and its associated control algorithm, are presented. They are also verified by experiments.