Miracle-dz/UniFAR-FTUs huggingface dataset:
Meetings, Manners and Civilization: The Development of Modern Meeting Behaviour

Part 1 Introduction: meeting behaviour and civilization the development of the concept of meeting. Part 2 Meetings in a military-agrarian society: the militarization and demilitarization of meetings the courtization of meetings. Part 3 Meetings during the transition from an agrarian to an industrial society: the emergence of a Protestant meeting order the formation of the first meeting estate in Europe. Part 4 Meeting in an industrializing and industrialized societies: development and spread of parliamentary manners the professionalization of meeting manners epilogue - changing meeting behaviour as an aspect of civilizing processes.
Algorithms Based on Divide and Conquer for Topic-Based Publish/Subscribe Overlay Design

Overlay design for topic-based publish/subscribe (pub/sub) systems is of primary importance because the overlay forms the basis for the system and directly impacts its performance. This paper focuses on the MinAvg-TCO problem: Use the minimum number of edges to construct a topic-connected overlay (TCO) such that all nodes that are interested in the same topic are organized in a directly connected dissemination suboverlay. Existing algorithms for MinAvg-TCO suffer from three key drawbacks: 1) prohibitively high runtime cost; 2) reliance on global knowledge and centralized operation; and 3) nonincremental operation by reconstructing the TCO from scratch. From a practical point of view, these are all severe limitations. To address these concerns, we develop algorithms that dynamically join multiple TCOs. Inspired by the divide-and-conquer character of this idea, we derive a number of algorithms for the original MinAvg-TCO problem that accommodate a variety of practical pub/sub workloads. Both theoretical analysis and experimental evaluations demonstrate that our divide-and-conquer algorithms seek a balance between time efficiency and the number of edges required: Our algorithms cost a fraction (up to 1.67%) of the runtime cost of their greedy alternatives, which come at the expense of an empirically insignificant increase in the average node degree. Furthermore, in order to reduce the probability of poor partitioning at the divide phase, we develop a bulk-lightweight partitioning scheme on top of random partitioning. This more refined partitioning imposes a marginally higher runtime cost, but leads to improvements in the output TCOs, including average node degrees and topic diameters.
Ensemble-based factor analysis of survey data on computer science courses

Analysis of course survey data are foundationally important because it has a close relation with students' skills training and talent development. However, there are multiple categories of classification information that characterized different students from different perspectives, such as genders, classes, types or regions. Compared to the correlation between survey data with individual kind of classification, consensus between those with multiple categories is more informative. However, traditional methods can not handle this problem with multiple kinds of classification categories. In view of the problem above, in this work, we proposed to conduct a new factor analysis method in the consensus fashion. Specifically, we computed a consensus structure of different categories of classifications and the similarity matrices for each factor, respectively. Motivated by the famous clustering measure RAND, we proposed a generalized method called Rmm which is used to compute the consistency of two different similarity matrices, with the option to adopt weights. Followed the similar spirit of Rmm, we also computed pairwise consistency between different factors without category information. Experimental results show that: (i) Our Rmm method is capable to combine different categories of classification information in an effective manner;(ii) our Rmm method preserve promising linear property for the combination of classifications; and (iii) Our Rmm method can be used in the case of fuzzy similarity, which is more applicable in different scenarios. With the proposed method, this work attempted to figure out the veiled laws and covered from read data, with the hope to provide suggestions for course arrangement and personnel cultivating program in universities, and to shed light on personal development of university students who major on computer science.
Horizontal Versus Vertical Tax Competition: An Empirical Test

In a federation, where different levels of government tax the same base, one can observe two types of externalities: a horizontal externality, working among jurisdictions of the same hierarchical level and leading to tax rates that are suboptimally low; and a vertical externality, working between different hierarchical levels of government and leading to suboptimally high tax rates. By parameterising a model of taxation in a federation that incorporates those two forces (Keen and Kotsogiannis, AER, 2002), we develop an empirically implementable discriminating hypothesis to distinguish horizontal from vertical tax-setting externalities. In essence, the test predicts that increasing jurisdictional fragmentation of a federation lowers the average tax rate of the lower-level jurisdiction if horizontal externalities dominate, but raises their average tax rate if vertical externalities dominate. This test is applied to a new and original data set on Swiss municipal and cantonal taxes, exploiting Switzerland's uniquely decentralised three-tier hierarchical structure of tax-raising powers. The main attraction of this data set is that it allows comparisons across federations (i.e. cantons) as well as within federations (over time), while existing studies of vertical tax externalities had to rely on pure time-series data. Using panel data models with instrumental variables to account for the potential endogeneity of the tax rates applied by higher-level jurisdictions, we estimate the theory-based discriminatory relationship between municipal tax rates and the jurisdictional fragmentation of cantons. This is done separately for corporate and personal tax rates applying to different representative types of tax payers. We find evidence of both vertical and horizontal externalities, depending on the type of tax and tax payer
Cost-effectiveness assessment of CO2 reducing measures in shipping

International shipping is a significant contributor to global greenhouse gas (GHG) emissions, and is under mounting pressure to contribute to overall GHG emission reductions. There is an ongoing debate regarding how much the sector could be expected to reduce emissions and how the reduction could be achieved. This paper details a methodology for assessing the cost-effectiveness of technical and operational measures for reducing CO2 emissions from shipping, through the development of an evaluation parameter called the Cost of Averting a Tonne of CO2-eq Heating, CATCH, and decision criterion, against which the evaluation parameter should be evaluated. The methodology is in line with the Intergovernmental Panel on Climate Change (IPCC) and with regulatory work on safety and environmental protection issues at the International Maritime Organization (IMO). The results of this study suggest that CATCH <50 $/tonne of CO2-eq should be used as a decision criterion for investment in emission reduction measures for shipping. In total, 13 specific measures for reducing CO2 emissions have been analysed for two selected case ships to illustrate the methodology. Results from this work shows that several measures are cost effective according to the proposed criterion. The results suggest that cost effective reductions for the fleet may well be in the order of 30% for technical measures, and above 50% when including speed reductions. The results of this study show that the cost effectiveness approach for the regulation of shipping emissions is viable and should be pursued in the ongoing regulatory process.
Bijections for maps with boundaries: Krikun's formula for triangulations, and a quadrangulation analogue

We present bijections for planar maps with boundaries. In particular, we obtain bijections for triangulations and quadrangulations of the sphere with boundaries of prescribed lengths. For triangulations we recover the beautiful factorized formula obtained by Krikun using a (technically involved) generating function approach. The analogous formula for quadrangulations is new. Our method is to show that these maps with boundaries can be endowed with certain "canonical" orientations, making them amenable to the master bijection approach we developed in previous articles. As an application of our formulas, we note that they provide an exact solution of the dimer model on rooted triangulations and quadrangulations.
Building a Taxonomy of Attributes for Fine-Grained Scene Understanding

This paper presents the first effort to discover and exploit a diverse taxonomy of scene attributes. Starting with the fine-grained SUN database, we perform crowd-sourced human studies to find over 100 attributes that discriminate between scene categories. We construct an attributelabeled dataset on top of the SUN database [7]. This “SUN Attribute database” spans more than 700 categories and 14,000 images and has potential for use in high-level scene understanding, attribute-based hierarchy construction, and fine-grained scene recognition.
Multi-criteria optimization of steel alloys for crankshafts production

The paper presents application of multi-criteria optimization to steel alloy composition determination aimed at obtaining improved properties material for crankshafts production. Neural network model of steel mechanical characteristics in dependence on amounts of its alloying elements were used for simulation purpose. Then several Pareto optimal solutions were found based on obtained by simulation numerous compositions in the investigated region of interest.
Bayesian Unsupervised Labeling of Web Document Clusters

Information technologies have recently led to a surge of electronic documents in the form of emails, webpages, blogs, news articles, etc. To help users decide which documents may be interesting to read, it is common practice to organize documents by categories/topics. A wide range of supervised and unsupervised learning techniques already exist for automated text classification and text clustering. However, supervised learning requires a training set of documents already labeled with topics/categories, which is not always readily available. In contrast, unsupervised learning techniques do not require labeled documents, but assigning a suitable category to each resulting cluster remains a difficult problem. The state of the art consists of extracting keywords based on word frequency (or related heuristics). In this thesis, we improve the extraction of keywords for unsupervised labeling of document clusters by designing a Bayesian approach based on topic modeling. More precisely, we describe an approach that uses a large side corpus to infer a language model that implicitly encodes the semantic relatedness of different words. This language model is then used to build a generative model of the cluster in such a way that the probability of generating each word depends on its frequency in the cluster as well as the frequency of its semantically related words. The words with the highest probability of generation are then extracted to label the cluster. In this approach, the side corpus can be thought as a source of domain knowledge or context. However, there are two potential problems: processing a large side corpus can be time consuming and if the content of this corpus is not similar enough to the cluster, the resulting language model may be biased. We deal with those issues by designing a Bayesian transfer learning framework that allows us to process the side corpus just once offline and to weigh its importance based on the degree of similarity with the cluster.
The Algorithm of Recognizing and Comprehending Vague Tasks Based on Fuzzy Mathematical Theory

Soft Man system is a virtual robot system living in cyberspace. In this paper, we design the fuzzy general knowledge tree model to describe the task derived from "Soft Man" system and we also present an algorithm to solve the model. The method we presented can expand the recognizing and processing capability of "Soft Man" system to some extent. After the comparative experiment, it's valuable that we expand the recognizing and processing capability of Soft Man system, though it may increase the cost and the error rate of system.
WiFi indoor location determination via ANFIS with PCA methods

This paper proposes the WiFi indoor location determination method based on adaptive neuro-fuzzy inference system (ANFIS) with principal component analysis (PCA). It reduces the WiFi signal vectors dimensions and saves the storage cost and simplifies the fuzzy rules generated by subtractive clustering method for ANFIS training. In the off-line phase, the received signal strength (RSS) or signal to noise ratio (SNR) from multiple access points (APs) is recorded for the establishment of radio map. And in the on-line phase, two steps should be considered for the position determination. The first step is space transformation to principal component space with lower dimensions compared to original space for the signal vectors. And the second step is the estimation of real two or three dimensional coordinates of mobile terminal (MT). Feasibility and effectiveness of ANFIS system based on FCA method are verified according to the analysis of the iterative number of training and experimental comparison with K-nearest neighbor (KNN), probability, artificial neural network (ANN) and ANFIS indoor location system without FCA.
Population Screening for Colorectal Cancer Means Getting FIT: The Past, Present, and Future of Colorectal Cancer Screening Using the Fecal Immunochemical Test for Hemoglobin (FIT)

Fecal immunochemical tests for hemoglobin (FIT) are changing the manner in which colorectal cancer (CRC) is screened. Although these tests are being performed worldwide, why is this test different from its predecessors? What evidence supports its adoption? How can this evidence best be used? This review addresses these questions and provides an understanding of FIT theory and practices to expedite international efforts to implement the use of FIT in CRC screening.
A Prediction Approach for Multichannel EEG Signals Modeling Using Local Wavelet SVM

Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal.
Mechanism-Based Screen for G1/S Checkpoint Activators Identifies a Selective Activator of EIF2AK3/PERK Signalling

Human cancers often contain genetic alterations that disable G1/S checkpoint control and loss of this checkpoint is thought to critically contribute to cancer generation by permitting inappropriate proliferation and distorting fate-driven cell cycle exit. The identification of cell permeable small molecules that activate the G1/S checkpoint may therefore represent a broadly applicable and clinically effective strategy for the treatment of cancer. Here we describe the identification of several novel small molecules that trigger G1/S checkpoint activation and characterise the mechanism of action for one, CCT020312, in detail. Transcriptional profiling by cDNA microarray combined with reverse genetics revealed phosphorylation of the eukaryotic initiation factor 2-alpha (EIF2A) through the eukaryotic translation initiation factor 2-alpha kinase 3 (EIF2AK3/PERK) as the mechanism of action of this compound. While EIF2AK3/PERK activation classically follows endoplasmic reticulum (ER) stress signalling that sets off a range of different cellular responses, CCT020312 does not trigger these other cellular responses but instead selectively elicits EIF2AK3/PERK signalling. Phosphorylation of EIF2A by EIF2A kinases is a known means to block protein translation and hence restriction point transit in G1, but further supports apoptosis in specific contexts. Significantly, EIF2AK3/PERK signalling has previously been linked to the resistance of cancer cells to multiple anticancer chemotherapeutic agents, including drugs that target the ubiquitin/proteasome pathway and taxanes. Consistent with such findings CCT020312 sensitizes cancer cells with defective taxane-induced EIF2A phosphorylation to paclitaxel treatment. Our work therefore identifies CCT020312 as a novel small molecule chemical tool for the selective activation of EIF2A-mediated translation control with utility for proof-of-concept applications in EIF2A-centered therapeutic approaches, and as a chemical starting point for pathway selective agent development. We demonstrate that consistent with its mode of action CCT020312 is capable of delivering potent, and EIF2AK3 selective, proliferation control and can act as a sensitizer to chemotherapy-associated stresses as elicited by taxanes.
Reconfigurable Architecture for Minimizing the Network Delays in the Multi-core Systems

Noc architecture performs better comparing to bus based when the number of processors is small. On the other hand bus based performs better than noc when number of the processors is large. This leads to new architecture which is hybrid bus based architecture where each node is packet switched in a mesh network of noc architecture that contains bus based system with small number of processors. Few results showed that this hybrid architecture performs optimally better than either purely noc based or purely bus based architecture. Hybrid architecture contains a processor connected to the bus, the bus in turn connected to the router. Each processor contains a private Level 1 (L1) cache. When hybrid architecture is preferable, the optimal number of processors on each bus subsystem varies based on the application. Hence the proposed architecture allows scalable bus-based multiprocessor subsystems on each node in the NoC. This system provides a multi-bus execution environment where each processor is connected to a bus and the bus-based subsystems communicate via routers connected in a mesh-style configuration. The system can be reconfigured to vary the number of bus subsystems and the number of processors on each subsystem. This architecture provides reliability and adaptability and reduces the network delays. Implementing and presenting the details of architecture and experimental results using ns2 indicating the advantages of this architecture.
A fast integer-based batch full-homomorphic encryption scheme over finite field

In view of the problems that the plaintext space is too small in the existing schemes. In this paper, a new improved scheme is presented by improving the DGHV scheme. The plaintext space of the improved scheme is extended from finite prime field F2 in the original scheme to finite prime field Fp. Combine and apply the method of encryption in the batch encryption scheme was proposed in 2013, and the plaintext space is further extended to finite fields Fq. The new improved scheme encrypts the message by applying the modular mathematical operation and the Chinese remainder theorem, and the security of the scheme is based on the the difficulty of approximate greatest common divisor problem and the spare subset sum problem. The improved scheme we got has the advantages of encrypt fast, and the size of ciphertext is small. So compared with the original scheme, it is better for practical application.
Host cell remodeling by pathogens: the exomembrane system in Plasmodium-infected erythrocytes

Malaria is caused by infection of erythrocytes by parasites of the genus Plasmodium. To survive inside erythrocytes, these parasites induce sweeping changes within the host cell, one of the most dramatic of which is the formation of multiple membranous compartments, collectively referred to as the exomembrane system. As an uninfected mammalian erythrocyte is devoid of internal membranes, the parasite must be the force and the source behind the formation of these compartments. Even though the first evidence of the presence these of internal compartments was obtained over a century ago, their functions remain mostly unclear, and in some cases completely unknown, and the mechanisms underlying their formation are still mysterious. In this review, we provide an overview of the different parts of the exomembrane system, describing the parasitophorous vacuole, the tubovesicular network, Maurer's clefts, the caveola-vesicle complex, J dots and other mobile compartments, and the small vesicles that have been observed in Plasmodium-infected cells. Finally, we combine the data into a simplified view of the exomembrane system and its relation to the alterations of the host erythrocyte.
Predictions and estimations of colorectal cancer mortality, prevalence and incidence in Aragon, Spain, for the period 1998-2022.

OBJECTIVE estimate colorectal cancer incidence and prevalence in, based on mortality and survival data from the period 1998-2007, and provide projections of incidence, prevalence and mortality until the year 2022. METHODS general and colorectal cancer mortality rates were obtained from the National Statistics Institute and survival data was obtained from the EUROCARE study. Estimations were carried out through the program MIAMOD. The joinpoint program was used to quantify the annual change expected in the projections. RESULTS in men, an increase in prevalence is expected, from 237.2 (Crude Rate - CR = 303.5) to 237.7 (CR = 412.7) per 100.000 inhabitants/year in 2022. Incidence rates would increase from 48.2 (CR = 61.6) in 2007 to 55.2 (CR = 83.1), and mortality would increase from 22.7 (CR = 29.4) to 26.0 (CR = 39.6) when comparing 2007 and women, a reduction in prevalence is expected from 181.5 (CR = 268.3) to 167.9 (CR = 286.2) cases per 100,000 inhabitants/year. Incidence would change from 25.0 (CR = 38.0) in 2007 to 22.7 (CR = 39.2), and for mortality there is also an expected decrease, from 11.3 (CR =18.0) to 10.3 (CR = 18.5). CONCLUSION the projections indicate that colorectal cancer in follows an increasing trend in incidence, mortality and prevalencein men, in opposition to corresponding decreasing trends in women.These projections must be considered in order to plan more effective prevention and treatment measures.