Miracle-dz/UniFAR-FTUs huggingface dataset:
Instant Efficient Pollution Abatement Under Non-Linear Taxation and Asymmetric Information: The Differential Tax Revisited

This paper analyzes incentives for polluting firms to exchange abatement cost information under the non-linear pollution tax scheme (‘differential tax’) introduced by Kim and Chang [J. Regul. Econom. 5, 1993, 193-197]. It shows that polluting firms have - under mild conditions - an incentive to join a coalition whose members mutually truthfully exchange information as well as commit themselves with respect to their abatement decisions. As a result, the differential tax triggers instantly - i.e. no abatement adaptation is needed – efficient abatement levels without the regulator knowing marginal abatement costs. Consequently, this paper shows that differential taxation results in lower social costs than traditional non-linear taxation which triggers efficient emissions only after a period of non-efficient abatement.
SOFA: Statistic Based Collaborative Filtering Algorithm

The classic user-based collaborative filtering algorithm has some shortcomes in its similarity calculation. In this paper, we propose a statistic based collaborative filtering algorithm (SOFA). The contributions are three-fold: 1) a threshold is used to filter those inaccurate similarities between users who have less intersection, 2) users' statistics, such as mean, and variance, are used for similarity measurements, 3) two similarities are aggregated for more accurate prediction. The experiments are conducted on MovieLens data set, and the results show that the proposed method performs better than traditional ones in several popular metrics, i.e. MAE, Coverage, Precision, Recall, and F-measure etc.
Relevant dimensions for classification and visualization

In this paper we introduce a dimensionality reduction method based on object class, which can be of interest both to define an appropriate distance measure or to visualize objects in a multidimensional space. The method we present is derived from data analysis techniques, such as Principal Components Analysis (PCA). In this paper we propose to consider only pairs of objects that belong to different classes, i.e. to maximise inter-classes distance. Moreover, we introduce a weight parameter that limits the influence of distant objects and favours the influence of close ones, in order to focus on local spatial organization, and thus raise the quality of nearest neighbour classification. Various tests results are presented, which show that a small set of characteristic dimensions can be sufficient to achieve a very satisfying good classification rate.
Virtual High School:Learning Communities for American Indian Students

A case study was conducted to learn about the experiences of American Indian students attending a virtual high school who successfully completed coursework. Appreciative inquiry was used to gain student and coordinator perspectives regarding the factors contributing to successful course completion. Data from interviews, document analysis, and observations were analyzed using a critical and culturally responsive lens. Gang influences, pregnancy and parenting, social isolation, and disengagement with traditional forms of schooling led students to leave high school and later enroll in a virtual school to pursue the goal of a high school diploma. Enrollees were drawn to the virtual school for a variety of reasons, including the 24/7 availability of coursework, the opportunity to complete lessons online, and another chance to graduate. Course completion and graduation rates improved over time, demonstrating the need for sustained investment and programmatic adjustment based on student needs. Additional support beyond the coursework served as factors in student success. This included career education and cultural programming. A financial analysis showed the level of investment needed to offer an alternative program and also compared the costs associated with the virtual school to alternative educational program costs required to serve high need students. A comprehensive program to promote academic engagement should include a variety of student incentives to learn, provider flexibility, engaging coursework, enrichment programs, and program-wide integration of Tribal language and culture. Virtual delivery alone does not guarantee student success and district and state budgetary constraints put program sustainability at risk due to the high cost of serving at risk students. Recommendations included ways to counter financial challenges associated with initial low completion rates as well as a description of the added programming needed to support one of the most at risk populations with regard to educational attainment in the United States.
Appraisal of membrane processes for technology selection in centralized wastewater reuse scenarios

The problem of fresh water scarcity is becoming a global issue, due to rapid urbanisation, population growth, water pollution, climate change and compounded effects. The incorporation of water reuse in upcoming urban water systems will not only satisfy a large part of water demand but also create a situation where wastewater treatment will be essential due to accountability towards the end users of water reuse. There are various technical and non-technical challenges towards the conceptualisation and materialisation of water reuse for a sustainable urban water system. The selection of appropriate technologies for a specific urban settlement is one of the key challenges. A plethora of existing technologies, such as membrane assisted technologies, for wastewater treatment and reuse may pose difficulty in decision making. Decision makers, such as water authorities and industries, often come across a lot of information and facts for a variety of technologies and reuse concepts. A systematic analysis of available technologies and options is necessary in order to increase the chances of success in wastewater reuse projects. Multi-criteria analysis (MCA) is a decision making method for systematic appraisal of wastewater reuse technologies. In this research the MCA has been carried out for the ranking of candidate technologies, after incorporating all the factors in terms of numerical values. Eight membrane assisted technologies have been short-listed due to their suitability under considered water reuse scenarios. Subsequently, most relevant technology selection criteria have been assessed. The MCA based ranking of candidate technologies provides a relative indication of the suitability of various membrane assisted water reuse technologies. It has been concluded that a systematic and rigorous analysis of candidate technologies can help decision makers to compare various available technological options and select the best available option for a sustainable water reuse concept.
Integrative Network Analysis Unveils Convergent Molecular Pathways in Parkinson's Disease and Diabetes

Background Shared dysregulated pathways may contribute to Parkinson's disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson's disease and type 2 diabetes are linked at the molecular level. Methods and Findings Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson's disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson's disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP), previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Prognostic Biomarker Study (PROBE), revealed that expression of APP is significantly upregulated in Parkinson's disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson's disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients. Conclusions These results provide the first evidence that Parkinson's disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first time that increased expression of APP in blood may modulate the neurodegenerative phenotype in type 2 diabetes patients.
Increased Microerythrocyte Count in Homozygous α+-Thalassaemia Contributes to Protection against Severe Malarial Anaemia

Background The heritable haemoglobinopathy α+-thalassaemia is caused by the reduced synthesis of α-globin chains that form part of normal adult haemoglobin (Hb). Individuals homozygous for α+-thalassaemia have microcytosis and an increased erythrocyte count. α+-Thalassaemia homozygosity confers considerable protection against severe malaria, including severe malarial anaemia (SMA) (Hb concentration < 50 g/l), but does not influence parasite count. We tested the hypothesis that the erythrocyte indices associated with α+-thalassaemia homozygosity provide a haematological benefit during acute malaria. Methods and Findings Data from children living on the north coast of Papua New Guinea who had participated in a case-control study of the protection afforded by α+-thalassaemia against severe malaria were reanalysed to assess the genotype-specific reduction in erythrocyte count and Hb levels associated with acute malarial disease. We observed a reduction in median erythrocyte count of ∼1.5 × 1012/l in all children with acute falciparum malaria relative to values in community children (p < 0.001). We developed a simple mathematical model of the linear relationship between Hb concentration and erythrocyte count. This model predicted that children homozygous for α+-thalassaemia lose less Hb than children of normal genotype for a reduction in erythrocyte count of >1.1 × 1012/l as a result of the reduced mean cell Hb in homozygous α+-thalassaemia. In addition, children homozygous for α+-thalassaemia require a 10% greater reduction in erythrocyte count than children of normal genotype (p = 0.02) for Hb concentration to fall to 50 g/l, the cutoff for SMA. We estimated that the haematological profile in children homozygous for α+-thalassaemia reduces the risk of SMA during acute malaria compared to children of normal genotype (relative risk 0.52; 95% confidence interval [CI] 0.24–1.12, p = 0.09). Conclusions The increased erythrocyte count and microcytosis in children homozygous for α+-thalassaemia may contribute substantially to their protection against SMA. A lower concentration of Hb per erythrocyte and a larger population of erythrocytes may be a biologically advantageous strategy against the significant reduction in erythrocyte count that occurs during acute infection with the malaria parasite Plasmodium falciparum. This haematological profile may reduce the risk of anaemia by other Plasmodium species, as well as other causes of anaemia. Other host polymorphisms that induce an increased erythrocyte count and microcytosis may confer a similar advantage.
1 Characteristics of Small Social Networks

Two dozen networks are analyzed using three parameters that attempt to capture important properties of social networks: leadership L, member bonding B, and diversity of expertise D. The first two of these parameters have antecedents, the third is new. A key part of the analysis is to examine networks at multiple scales by dissecting the entire network into its n subgraphs of a given radius of two edge steps about each of the n nodes. This scale-based analysis reveals constraints on what we have dubbed “cognitive” networks, as contrasted with biological or physical networks. Specifically, “cognitive” networks appear to maximize bonding and diversity over a range of leadership dominance. Asymptotic relations between the bonding and diversity measures are also found when small, nearly complete subgraphs are aggregated to form larger networks. This aggregation probably underlies changes in a regularity among the LBD parameters; this regularity is a U-shaped function of networks size, n, which is minimal for networks around 80 or so nodes. 1.0 Overview One of the ways science advances is to discover non-trivial relations among measurements of an entity, which then give insights into the nature of that entity. Classic examples include: (a) the PV/T relation between pressure, P, temperature, T, and the volume, V, of a gas, (b) the Strahler number, Ni/(Ni+1) which characterizes the branching structure of rivers or trees, or (c) supply-demand relations. In the study of social networks, these kinds of discoveries of parametric relations are rare (but see Barabasi & Albert 2002, Watts & Strogatz 1998, Newman, 2003). Using a multi-scale analysis, we report three new properties that are characteristic of one important class of social networks. Although our specific measurement parameters might be questioned, they were chosen to have relevance to the building of “cognitive” social networks, as contrasted with networks such as the spread of infectious diseases or telecommunications infrastructure. Two of the three parameters chosen are closely allied with previous measures. These choices, however, are not the key issue. Rather, can these parameters lead to the discovery of new properties characteristic of a class of social networks? Will these discoveries of new relationships lead to insights about the structure of social networks?
Rectal Neuroendocrine Tumor G1 with a Solitary Hepatic Metastatic Lesion

Rectal neuroendocrine tumor (NET) is a relatively rare tumor. NET is classified as G1, G2, or G3 according to the degree of mitosis or Ki-67 proliferation index, which reflect the malignant potential of the tumor, such as metastasis. Advanced cases with metastasis are indicated for chemotherapy treatment. However, the efficacy of chemotherapy is limited. Therefore, resection is considered, even in metastatic cases, if complete resection is possible. We herein report a case of small rectal NET discovered with hepatic metastasis classified as G1. The patient showed good progress with no recurrence after undergoing hepatectomy and endoscopic resection of rectal NET.
A New Efficient Request-Based Comparable Encryption Scheme

Privacy-preserving comparisons over encrypted database is a hot topic in the current academic research. Recently, Furukawa [7] introduced a new primitive called request-based comparable encryption (comparable encryption for short) to achieve this target. Howver, one disadvantage of comparable encryption is that huge of the token and cipher text are required in the scheme and thus the computation and storage overload is heavy. In this paper, we propose an improved comparable encryption scheme by using the sliding window method, which is more efficient in the computation and storage workload than Furukawa's scheme. Besides, the proposed scheme allows the users to obtain a variable trade-off between security and efficiency through adaptively setting the window size.
Ontology-based Modeling of Clinical Practice Guidelines: A Clinical Decision Support System for Breast Cancer Follow-up Interventions at Primary Care Settings

Breast cancer follow-up care can be provided by family physicians after specialists complete the primary treatment. Cancer Care Nova Scotia has developed a breast cancer follow-up Clinical Practice Guideline (CPG) targeting family physicians. In this paper we present a project to computerize and deploy the said CPG in a Breast Cancer Follow-up Decision Support System (BCF-DSS) for use by family physicians in a primary care setting. We present a semantic web approach to model the CPG knowledge and employ a logic-based proof engine to execute the CPG in order to infer patient-specific recommendations. We present the three stages of the development of BCF-DSS--i.e., (a) Computerization of the paper-based CPG for Breast Cancer follow-up; (b) Development of three ontologies--i.e., the Breast Cancer Ontology, the CPG ontology based on the Guideline Element Model (GEM) and a Patient Ontology; and (c) Execution of the Breast Cancer follow-up CPG through a logic-based CPG execution engine.
Opinion Dynamics in Heterogeneous Networks: Convergence Conjectures and Theorems

Recently, significant attention has been dedicated to the models of opinion dynamics in which opinions are described by real numbers and agents update their opinions synchronously by averaging their neighbors' opinions. The neighbors of each agent can be defined as either (1) those agents whose opinions are in its “confidence range” or (2) those agents whose “influence range” contain the agent's opinion. The former definition is employed in Hegselmann and Krause's bounded confidence model, and the latter is novel here. As the confidence and influence ranges are distinct for each agent, the heterogeneous state-dependent interconnection topology leads to a poorly-understood complex dynamic behavior. In both models, we classify the agents via their interconnection topology and, accordingly, compute the equilibria of the system. Then, we define a positive invariant set centered at each equilibrium opinion vector. We show that if a trajectory enters one such set, then it converges to a steady state with consta.
The NESPOLE ! Multimodal Speech-to-Speech Translation System : User Based System Improvements

This work discusses the results of two user studies aiming to evaluate the NESPOLE! speech-to-speech translation system, which provides for multil ingual and multimodal communication in the tourism and in the medical domain, allowing users to interact through the Internet by sharing maps, web-pages and pen-based gestures. The purpose is to investigate the overall effectiveness of the combination of multimodality and multilinguality in respect to a speech-only system. Specifically, we will examine the improvement in usability offered by the multimodal system, which is gauged by evaluating the interaction between novice users and the system at several stages of development. Additionally, we will discuss how data collection contributed to system improvement.
DWT analysis of sEMG for muscle fatigue assessment of dynamic motion flexion-extension of elbow joint

Fatigue is a state that the muscle could not able to maintain the contraction. Electromyography signals can be used to determine the state of muscle fatigue. Quantization of muscle fatigue needs to be defined clearly so that it can be used as an indicator or a compensator in the control system. Electromyography signal which is produced during dynamic motion during muscle fatigue assessment is a non-stationary signal. In this study, the discrete wavelet transforms method was used to analyze electromyography signals. The electromyography signal was decomposed up to 5th scale. Features extraction, root mean square, mean average, standard deviation and power signal were used to observe the correlation coefficient and the slope of the regression line. Our finding from the results of the analysis with multi-resolution discrete wavelet transforms showed that the frequency ranged 62.5 Hz up to 125 Hz was the dominant frequency of the EMG signal and feature of power was better among other features to observe the muscle fatigue. In this study, the correlation coefficient was 0.852 and the best slope was 0.728 mV/s. These value can also be used as an indicator or an estimator of the state of the fatigue and used as a compensator in the system based myoelectric control.
Fast logo detection based on morphological features in document images

In this paper, a novel fast logo detection approach in document images is presented. Logos with separated parts usually can affect the logo detection process. To overcome this problem, some specifications of logos are considered. Our proposed method divided in three main sections. In the first section, a horizontal dilation operator is used to merge separated parts of logo in horizontal direction. In the second section, a simple decision classifier is applied for classifying logo and non-logo. In the final section, a modifying operation for detecting separated-part-logo, logo which has separated part, based on two specifications is used. These specifications include centroid coordinate and intersection of each logo's separated part bounding box. The proposed approach is evaluated on a public document image database and international logos. Experimental results show its performance in logo detection problem.
Gravitational Lensing

Gravitational lensing of the cosmic microwave background by large-scale structure in the late universe is both a source of cosmological information and a potential contaminant of primordial gravity waves. Because lensing imprints growth of structure in the late universe on the CMB, measurements of CMB lensing will constrain parameters to which the CMB would not otherwise be sensitive, such as neutrino mass. In CMB polarization, gravitational lensing is the largest guaranteed source of Bmode (or curl-like) polarization. Future CMB polarization experiments with sufficient sensitivity to measure B-modes on small angular scales (£ ~ 1000) can measure lensing with better sensitivity, and on different scales, than could be achieved by measuring CMB temperature alone. If the instrumental noise is sufficiently small (< 5 /xK-arcmin), the gravitational lensing contribution to the large-scale B-mode will be the limiting source of contamination when constraining a stochastic background of gravity waves in the early universe, one of the most exciting prospects for future CMB polarization experiments. High-sensitivity measurements of small-scale B-modes can reduce this contamination through a lens reconstruction technique that separates the lensing and primordial contributions to the B-mode on large scales. A fundamental design decision for a future CMB polarization experiment such as CMBpol is whether to have coarse angular resolution so that only the large-scale Bmode (and the large-scale E-mode from reionization) is measured, or high resolution to additionally measure CMB lensing. The purpose of this white paper is to evaluate the science case for CMB lensing in polarization: constraints on cosmological parameters, increased sensitivity to the gravity wave B-mode via lens reconstruction, expected level of contamination from non-CMB foregrounds, and required control of beam systematics. kmsmith@ast.cam. ac.uk CP1141, CMB Polarization Workshop: Theory and Foregrounds, CMBPol Mission Concept Study edited by S. Dodelson, D. Baumann, A. Cooray, J. Dunkley, A. A. Fraisse, M. G. Jackson, A. Kogut, L. M. Krauss, K. M. Smith, and M. Zaldarriaga © 2009 American Institute of Physics 978-0-73 54-0678-0/09/S25.00 t