Miracle-dz/UniFAR-FTUs huggingface dataset:
The Myth of the Corporate Economy: Great Britain's Cotton Textile Industry, 1900-1913

industry, for it has a unique place in the history of England's industhai revolution. Rostow gives it the ultimate accolade: "the original leading sector in the first take-off," and to Crafts and Harley, "the really big issue [in determining the rate of growth during the industrial revolution] is undoubtedly the weighting of cotton rather than the correct distribution of value added weights among the other sectors" [Rostow, 1990, p. 53; Crafts and Harley, 1992, p. 706]. Cotton overtook wool to become Britain's single most important source of income by 1810, and retained this position until the end of the nineteenth century. At its 1913 peak, the industry employed over half a million people and consumed over 2.1 billion pounds of raw cotton [Robson, 1957, pp. 331, 333; Deane and Cole, 1969, p. 163; Sandberg, 1981, p. 114; Mitchell and Deane, 1962, p. 186-8]. The industry's export performance was more remarkable still. It became the nation's biggest exporter during the Napoleonic Wars, a position it was to retain for 125 years; in 1830 it even exceeded all other exports combined [Deane and Cole, 1969, p. 31]. In 1880 over 80% of the world's cotton exports came from Britain, and mill owners boasted that they met the needs of the home market before breakfast and devoted the rest of the day to exports [Robson, 1957, p. 4; Aspin, 1981, p. 3]. At its peak in 1913, Britain exported over 7 billion yards of cloth, approximately equivalent to a shirt and pair of trousers for every man, woman, and child in the world [Sandberg, 1974, p. 4]. But 1918 saw the start of a decline that was to prove both long and unrelenting. By 1933 Japan had overtaken Britain to become the world's biggest exporter of cotton goods, although cotton was to remain Britain's biggest export until the outbreak of war [Aspin, 1981, p. 4]. In 1944 Keynes
Production and release of macrophage migration inhibitory factor from human adipocytes.

BACKGROUND AND AIM OF THE STUDY Macrophage migration inhibitory factor (MIF) has been identified as a critical mediator of inflammatory responses. Because of its potent migration inhibition activity, it regulates macrophage accumulation in tissues. We therefore analyzed whether human adipocytes produce MIF, in the search of candidate mediators of macrophage infiltration of obese adipose tissue. METHODS Human adipose tissue samples were obtained from various depots. The precursor cells were allowed to differentiate under defined adipogenic culture conditions. MIF expression was analyzed by RT-PCR, ELISA, and immunocytochemistry. RESULTS Human preadipocytes secreted MIF in a differentiation-dependent fashion with maximum concentrations at d 12, whereas MIF mRNA was detected in both undifferentiated and differentiated cells at relatively constant levels. Immunocytochemical analysis showed that MIF protein was present in preadipocytes and more pronounced in differentiated adipocytes. Freshly isolated mature adipocytes from sc, omental, and mammary depots released MIF at rates of up to 10,000 pg/ml.24 h. Most importantly, MIF production was positively correlated with donor body mass index. Secretion of MIF was not influenced by lipopolysaccharide, interferon-gamma, or IL-4. The rates of MIF release from sc and omental adipocytes were similar but approximately 10 times higher compared with mammary adipocytes. CONCLUSIONS Human preadipocytes and mature adipocytes from different depots spontaneously release substantial amounts of MIF. Expression levels were positively associated with donor body mass index. Hence, MIF may be an obesity-dependent mediator of macrophage infiltration of adipose tissue.
A Color Edge Detection Algorithm in RGB Color Space

Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A color image edge detection algorithm is proposed in this paper. Average maximum color difference value is used to predict the optimum threshold value for a color image and thinning technique is applied to extract proper edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.
Visual Navigation for Mobile Robots: a Survey

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Application of Generalised Regression Neural Networks in Lossless Data Compression

Neural networks are a popular technology that exploits massive parallelism and distributed storage and processing for speed and error tolerance. Most neural networks tend to rely on linear, step or sigmoidal activation functions for decision making. The generalised regression neural network (GRNN) is a radial basis network (RBN) which uses the Gaussian activation function in its processing element (PE). This paper proposes the use of the GRNN for lossless data compression. It is applied in the first stage of the lossless twostage predictor-encoder scheme. Three different approaches using the GRNN are proposed. Batch training with different block sizes is applied to each approach. Two popular encoders, namely arithmetic coding and Huffman coding, are used in the second stage. The performance of the proposed singleand two-stage schemes are evaluated in terms of the compression ratios achieved for telemetry data test files of different sizes and distributions. It is shown that the compression performance of the GRNN schemes is better than existing implementations using the finite impulse response (FIR) and adaptive normalised least mean squares (NLMS) filters, as well as an implementation using a recurrent neural network. Key-Words: Lossless data compression, neural network, two-stage, predictor, encoder, radial basis.
GPU+COW parallel computing system to accelerate the FDTD method

Since Finite-Difference Time-Domain (FDTD) method was proposed by Yee in 1966, its hardware acceleration problem arouses much concern. In this paper, how to use GPU+COW parallel computing system to accelerate the FDTD method was introduced briefly. The acceleration scheme and the simulation results were presented, and then the feasibility and efficiency of the proposed scheme were proved.
Energy-efficient Motion Estimation using Error-Tolerance

Presented is an energy-efficient motion estimation architecture using error-tolerance. The technique employs overscaling of the supply voltage (voltage overscaling (VOS)) to reduce power at the expense of timing errors, which are then corrected using algorithmic noise-tolerance (ANT) techniques. Referred to as input subsampled replica ANT (ISR-ANT), the proposed technique incorporates an input subsampled replica of the main sum of absolute difference (MSAD) block for obtaining the motion vectors in the presence of errors induced by VOS. Simulations show that the proposed technique can save up to 60% power over an optimal error-free present day system in a 130nm CMOS technology. Power savings increase to 79% in a 45nm predictive process technology
Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor under Thermal Overstress Conditions

Electrolytic capacitors are used in several applications ranging from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuators. Past experiences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress conditions during operations. This makes them good candidates for prognostics and health management. Model-based prognostics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of health and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation models for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.
Linguistically Aware Semantic Network for Automated Information Tracking

The paper presents the result of an ongoing research into extending the WiSENet semantic network by adding an extra tier of data, which could be applied in a number of text processing tasks. It was envisaged that this new tier of data could provide a more detailed description of adjectives and adverbs contained in the semantic network. This detailed description was centrally concerned with the construction of sentiment vectors that convey data on whether a given term of interest is positive, negative or neutral. To this end, an experiment was carried out which made it possible to build such sentiment vectors. In the building phase a corpus of over 12000 documents containing opinions on hotels, books and movies along with corresponding satisfaction scores was used. In order to check if sentiment vectors are a useful instrument for performing text processing tasks, a test case was proposed to help prepare the satisfaction level prognosis for the tested documents. In the course of the research it became apparent that not all sentiment vectors were eligible for use in the prognosis. In order to uncover plausible reasons for the ineligibility a linguistic analysis was carried out. Finally, an experiment designed to evaluate the usefulness of sentiment vectors and the level of accuracy of the prognosis based on them was performed. It had a success rate of 61.33%, which is highly satisfactory in comparison to other studies.
Early home learning environment predicts children’s 5th grade academic skills

ABSTRACT We examined whether the early learning environment predicts children’s 5th grade skills in 2,204 families from ethnically diverse, low-income backgrounds; tested the mediating roles of children’s pre-kindergarten school-related skills and later learning environment; and asked whether lagged associations generalize across White, Black, Hispanic English-speaking, and Hispanic Spanish-speaking samples. Children’s early learning environment comprised measures of literacy activities, the quality of mothers’ engagements with children, and learning materials assessed at 14 months, 2 and 3 years, and at pre-kindergarten; learning environments were again assessed in 5th grade. At pre-kindergarten and in 5th grade, children were assessed on pre-academic and academic skills respectively. Early learning environments predicted children’s 5th grade academic skills, and children’s pre-kindergarten skills and 5th grade learning environment mediated longitudinal associations. The early learning environment supports the emergence of pre-academic skills that are stable into early adolescence, and pathways generalize across ethnic/racial groups.
Recovery of Stimuli Encoded with Hodgkin-Huxley Neurons

In the reverse correlation method [1] to neural encoding, the neural system is treated as a black box and both the input stimuli and output spike trains are assumed to be observable. Based on the correlation between the input and the output, the black box is identified as the kernel of a Wiener filter. When investigating problems arising in olfactory coding, an explicit characterization of odor stimuli is, however, not available. In addition, sensory systems are exposed to unknown stimuli in real-time. Under these conditions, investigations of sensory encoding of natural stimuli assume that only the spike train is observable and the neural system can be described as a non-linear dynamical system. The Hodgkin-Huxley neuron is the model of choice.
Gestion Autonomique de la Répartition de Charge pour le Contrôle de la Congestion dans les Réseaux Sans Fil

Dans cet article, nous proposons un systeme de gestion autonomique des reseaux 802.11 a l'aide d'un algorithme de repartition de charge baptise ALBA. L'idee fondamentale et originale d'ALBA est sa capacite a reorganiser localement le reseau et repartir dynamiquement et au mieux sur une zone les stations sur les points d'acces, afin d'assurer l'efficacite du service et la satisfaction des clients. L'autonomicite intervient dans le systeme afin d'enrichir la connaissance du reseau et de fournir a chaque point d'acces une vue situee de son voisinage et du trafic des clients. L'evaluation de la solution revele des gains importants de performance en terme de capacite d'accueil en stations du reseau.
Research on Ontology-Based Multi-source Engineering Information Retrieval in Integrated Environment of Enterprise

With the extensive use of computer aided systems in product design, manufacture and analysis, a mass of documents are produced. These documents provide engineering information in integrated environment of enterprise for engineers, but maintained by different systems in different forms. Current information retrieval approaches usually lack semantic supports for different kinds of documents, which leads to insufficiency of content representation and misunderstanding of query intention. To effectively search engineering information in various documents, and consider semantic information in engineering domain, an ontology-based information retrieval framework is presented. As the center of framework, ontologies are established to support document analysis and query processing through information representation in semantic level, and complete the mapping between user queries and document resources. The framework provides a unified platform for multi-source engineering information retrieval(EIR) from various documents in integrated environment.
Under Consideration for Publication in J. Functional Programming Stack-based Access Control and Secure Information Flow

Access control mechanisms are often used with the intent of enforcing confidentiality and integrity policies, but few rigorous connections have been made between information flow and runtime access control. The Java virtual machine and the . NET runtime system provide a dynamic access control mechanism in which permissions are granted to program units and a runtime mechanism checks permissions of code in the calling chain. We investigate a design pattern by which this mechanism can be used to achieve confidentiality and integrity goals: a single interface serves callers of more than one security level and dynamic access control prevents release of high information to low callers. Programs fitting this pattern would be rejected by previous flow analyses. We give a static analysis that admits them, using permission-dependent security types. The analysis is given for a class-based object-oriented language with features including inheritance, dynamic binding , dynamically allocated mutable objects, type casts and recursive types. The analysis is shown to ensure a noninterference property formalizing confidentiality and integrity.
Intuitive Topic Discovery by Incorporating Word-Pair's Connection Into LDA

We demonstrate a generative model that incorporates word-pair connection into the smoothed LDA model to intuitively discover people's wish related activities. The widely used model, LDA topic model, generally generates clusters in the form of separate words. However, this form is not intuitive enough to express people's activities. Therefore, we consider the word-pairs led by verbs can better describe users' intentions and activities, and we prefer to present this collocation under topics as the clustering results. We mathematically present the relatedness between verbs and non-verb words through association rule, and build the physical connection of word-pairs and possible topics. By incorporating the connection lattice into the smoothed LDA, the word-pair LDA model is created. In the experiments, Twitter posts about "new year's resolutions" were chosen as the data source. The results show that the proposed model performs well on perplexity, and presents excellent intuitive character.
Babelchord: a social tower of DHT-based overlay networks

Chord is a protocol to distribute and retrieve information at large scale. It builds a large but rigid overlay network without taking into account the social nature and the underlying topology of large platforms, made of the interconnection of many independent smaller networks. Thus, new approaches are required to build overlay networks. In this paper, we propose Babelchord, a more flexible and social overlay interconnecting different Chord networks, which are floors of a social tower. Peers can belong to several floors, allowing this interconnection. By connecting smaller structured overlay networks in an unstructured way, it provides a costeffective alternative to hierarchical structured P2P systems requiring costly merging. Routing of lookup messages is performed as in Chord within one floor, but a peer belonging to several floors forwards the request to the different floors it belongs to. These co-located peers act as a sort of neural synapse. Results from simulations show that Babelchord scales up logarithmically with the number of Babelchord peers. Moreover a small number of synapses is enough to ensure a high exhaustiveness level.
The Role of Gender, Violence, and Sexism

This study investigates the structure of Struckman-Johnson and StruckmanJohnson’s Male Rape Myth Scale, examines gender differences in rape myth acceptance, and explores the underlying ideologies that facilitate male rape myth acceptance. A three-factor model, with rape myths regarding Trauma, Blame, and Denial as separate subscales, is the best fitting solution. However, the results indicate that additional scale development and validity tests are necessary. In exploratory analyses, men are more accepting of male rape myths than are women. Benevolent sexism toward men and acceptance of interpersonal violence are strong predictors of male rape myth acceptance for both men and women. Thus, the attitudes that facilitate rape myth acceptance against men appear to be similar to those that facilitate rape myth acceptance against women. Suggestions for future scale development are outlined and theoretical implications of the findings are discussed.
Effectiveness of sensor-augmented insulin-pump therapy in type 1 diabetes.

BACKGROUND Recently developed technologies for the treatment of type 1 diabetes mellitus include a variety of pumps and pumps with glucose sensors. METHODS In this 1-year, multicenter, randomized, controlled trial, we compared the efficacy of sensor-augmented pump therapy (pump therapy) with that of a regimen of multiple daily insulin injections (injection therapy) in 485 patients (329 adults and 156 children) with inadequately controlled type 1 diabetes. Patients received recombinant insulin analogues and were supervised by expert clinical teams. The primary end point was the change from the baseline glycated hemoglobin level. RESULTS At 1 year, the baseline mean glycated hemoglobin level (8.3% in the two study groups) had decreased to 7.5% in the pump-therapy group, as compared with 8.1% in the injection-therapy group (P<0.001). The proportion of patients who reached the glycated hemoglobin target (<7%) was greater in the pump-therapy group than in the injection-therapy group. The rate of severe hypoglycemia in the pump-therapy group (13.31 cases per 100 person-years) did not differ significantly from that in the injection-therapy group (13.48 per 100 person-years, P=0.58). There was no significant weight gain in either group. CONCLUSIONS In both adults and children with inadequately controlled type 1 diabetes, sensor-augmented pump therapy resulted in significant improvement in glycated hemoglobin levels, as compared with injection therapy. A significantly greater proportion of both adults and children in the pump-therapy group than in the injection-therapy group reached the target glycated hemoglobin level. (Funded by Medtronic and others; ClinicalTrials.gov number, NCT00417989.)
Characterizing perception of ecological risk.

Relatively little attention has been paid to the role of human perception and judgment in ecological risk management. This paper attempts to characterize perceived ecological risk, using the psychometric paradigm developed in the domain of human health risk perception. The research began by eliciting a set of scale characteristics and risk items (e.g., technologies, actions, events, beliefs) from focus group participants. Participants in the main study were 68 university students who completed a survey instrument that elicited ratings for each of 65 items on 30 characteristic scales and one scale regarding general risk to natural environments. The results are presented in terms of mean responses over individuals for each scale and item combination. Factor analyses show that five factors characterize the judgment data. These have been termed: impact on species, human benefits, impact on humans, avoidability, and knowledge of impacts. The factor results correspond with initial expectations and provide a plausible characterization of judgments regarding ecological risk. Some comparisons of mean responses for selected individual items are also presented.
XStruct: Efficient Schema Extraction from Multiple and Large XML Documents

XML is the de facto standard format for data exchange on the Web. While it is fairly simple to generate XML data, it is a complex task to design a schema and then guarantee that the generated data is valid according to that schema. As a consequence much XML data does not have a schema or is not accompanied by its schema. In order to gain the benefits of having a schema - efficient querying and storage of XML data, semantic verification, data integration, etc.- this schema must be extracted. In this paper we present an automatic technique, XStruct, for XML Schema extraction. Based on ideas of [5], XStruct extracts a schema for XML data by applying several heuristics to deduce regular expressions that are 1-unambiguous and describe each element’s contents correctly but generalized to a reasonable degree. Our approach features several advantages over known techniques: XStruct scales to very large documents (beyond 1GB) both in time and memory consumption; it is able to extract a general, complete, correct, minimal, and understandable schema for multiple documents; it detects datatypes and attributes. Experiments confirm these features and properties.