Miracle-dz/UniFAR-FTUs huggingface dataset:
What can be learned about carbon cycle climate feedbacks from the CO 2 airborne fraction

Abstract. The ratio of CO2 accumulating in the atmosphere to the CO2 flux into the atmosphere due to human activity, the airborne fraction AF, is central to predict changes in earth's surface temperature due to greenhouse gas induced warming. This ratio has remained remarkably constant in the past five decades, but recent studies have reported an apparent increasing trend and interpreted it as an indication for a decrease in the efficiency of the combined sinks by the ocean and terrestrial biosphere. We investigate here whether this interpretation is correct by analyzing the processes that control long-term trends and decadal-scale variations in the AF. To this end, we use simplified linear models for describing the time evolution of an atmospheric CO2 perturbation. We find firstly that the spin-up time of the system for the AF to converge to a constant value is on the order of 200–300 years and differs depending on whether exponentially increasing fossil fuel emissions only or the sum of fossil fuel and land use emissions are used. We find secondly that the primary control on the decadal time-scale variations of the AF is variations in the relative growth rate of the total anthropogenic CO2 emissions. Changes in sink efficiencies tend to leave a smaller imprint. Therefore, before interpreting trends in the AF as an indication of weakening carbon sink efficiency, it is necessary to account for trends and variations in AF stemming from anthropogenic emissions and other extrinsic forcing events, such as volcanic eruptions. Using atmospheric CO2 data and emission estimates for the period 1959 through 2006, and our simple predictive models for the AF, we find that likely omissions in the reported emissions from land use change and extrinsic forcing events are sufficient to explain the observed long-term trend in AF. Therefore, claims for a decreasing long-term trend in the carbon sink efficiency over the last few decades are currently not supported by atmospheric CO2 data and anthropogenic emissions estimates.
Sentence-based natural language plagiarism detection

With the increasing levels of access to higher education in the United Kingdom, larger class sizes make it unrealistic for tutors to be expected to identify instances of peer-to-peer plagiarism by eye and so automated solutions to the problem are required. This document details a novel algorithm for comparison of suspect documents at a sentence level and has been implemented as a component of plagiarism detection software for detecting similarities in both natural language documents and comments within program source-code. The algorithm is capable of detecting sophisticated obfuscation (such as paraphrasing, reordering, merging, and splitting sentences) as well as direct copying. The implemented algorithm has also been used to successfully detect plagiarism on real assignments at the university. The software has been evaluated by comparison with other plagiarism detection tools.
An Anti-Vascular Endothelial Growth Factor Receptor 2/Fetal Liver Kinase-1 Listeria monocytogenes Anti-Angiogenesis Cancer Vaccine for the Treatment of Primary and Metastatic Her-2/neu+ Breast Tumors in a Mouse Model1

Thirty years after angiogenesis was shown to play an enabling role in cancer, modern medicine is still trying to develop novel compounds and therapeutics to target the tumor vasculature. However, most therapeutics require multiple rounds of administration and can have toxic side effects. In this study, we use anti-angiogenesis immunotherapy to target cells actively involved in forming new blood vessels that support the growth and spread of breast cancer. Targeting a central cell type involved in angiogenesis, endothelial cells, we immunized against host vascular endothelial growth factor receptor 2 to fight the growth of Her-2/neu+ breast tumors. Using the bacterial vector, Listeria monocytogenes (Lm), we fused polypeptides from the mouse vascular endothelial growth factor receptor 2 molecule (fetal liver kinase-1) to the microbial adjuvant, listeriolysin-O, and used Lm to deliver the Ags and elicit potent antitumor CTL responses. Lm-listeriolysin-O-fetal liver kinase-1 was able to eradicate some established breast tumors, reduce microvascular density in the remaining tumors, protect against tumor rechallenge and experimental metastases, and induce epitope spreading to various regions of the tumor-associated Ag Her-2/neu. Tumor eradication was found to be dependent on epitope spreading to HER-2/neu and was not solely due to the reduction of tumor vasculature. However, vaccine efficacy did not affect normal wound healing nor have toxic side effects on pregnancy. We show that an anti-angiogenesis vaccine can overcome tolerance to the host vasculature driving epitope spreading to an endogenous tumor protein and drive active tumor regression.
View interpolation with structured depth from multiview video

In this paper, we propose a method for interpolating multiview imagery which uses structured depth maps and multiview video plus inter-view connection information to represent a three-dimensional (3D) scene. The structured depth map consists of an inter-view consistent principal depth map and auxiliary depth information. The structured depth maps address the inconsistencies among estimated depth maps which may degrade the quality of rendered virtual views. Generated from multiple depth observations, the structuring of the depth maps is based on tested and adaptively chosen inter-view connections. Further, the use of connection information on the multiview video minimizes distortion due to varying illumination in the interpolated virtual views. Our approach improves the quality of rendered virtual views by up to 4 dB when compared to the reference MPEG view synthesis software for emerging multimedia services like 3D television and free-viewpoint television. Our approach obtains first the structured depth maps and the corresponding connection information. Second, it exploits the inter-view connection information when interpolating virtual views.
Evaluation of ensemble-based sentiment classifiers for Twitter data

Social media are widely used worldwide and offer the possibility to users to post real time messages respecting their opinions on different topics, discuss everyday issues, complain and express positive, neutral or negative sentiments for anything that concerns them. As such, sentiment analysis has become a burning issue in the scientific literature. However, some researchers argue that Twitter sentiment classification performance may be elusive. To overcome this issue, in this paper, we evaluate the most common ensemble methods that can be used for effective sentiment analysis and the tested datasets used in this research proceed from Twitter. Experiment results reveal that the use of ensembles of multiple base classifiers can improve the accuracy of Twitter sentiment analysis. The discussion that is presented can clearly prove that such methods can surprisingly surpass the traditional algorithms in performance and can be seen as a beneficial tool in the field of sentiment analysis that can further enhance several other domains such as e-learning and web advertising.
Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs

Splicing remains an incompletely understood process. Recent findings suggest that chromatin structure participates in its regulation. Here, we analyze the RNA from subcellular fractions obtained through RNA-seq in the cell line K562. We show that in the human genome, splicing occurs predominantly during transcription. We introduce the coSI measure, based on RNA-seq reads mapping to exon junctions and borders, to assess the degree of splicing completion around internal exons. We show that, as expected, splicing is almost fully completed in cytosolic polyA+ RNA. In chromatin-associated RNA (which includes the RNA that is being transcribed), for 5.6% of exons, the removal of the surrounding introns is fully completed, compared with 0.3% of exons for which no intron-removal has occurred. The remaining exons exist as a mixture of spliced and fewer unspliced molecules, with a median coSI of 0.75. Thus, most RNAs undergo splicing while being transcribed: “co-transcriptional splicing. ” Consistent with co-transcriptional spliceosome assembly and splicing, we have found significant enrichment of spliceosomal snRNAs in chromatin-associated RNA compared with other cellular RNA fractions and other nonspliceosomal snRNAs. CoSI scores decrease along the gene, pointing to a “first transcribed, first spliced” rule, yet more downstream exons carry other characteristics, favoring rapid, co-transcriptional intron removal. Exons with low coSI values, that is, in the process of being spliced, are enriched with chromatin marks, consistent with a role for chromatin in splicing during transcription. For alternative exons and long noncoding RNAs, splicing tends to occur later, and the latter might remain unspliced in some cases.
Automatic Task-Cluster Generation based on Document Switching and Revisitation

Personal Information Management (PIM) research is challenging primarily due to the inherent nature of PIM. Studies have shown that people often adopt their own schemes when organising their personal collections, possibly because PIM tool-support is still lacking. In this paper we investigate the problem of automatic organisation of personal information into task-clusters by transparently exploiting the user’s behaviour while performing some tasks. We conduct a controlled experiment, with 22 participants, using three different task-execution strategies to gather clean data for our evaluation. We use our PiMx (PIM analytix) framework to analyse this data and understand better the issues associated with this problem. Based on this analysis, we then present the incremental density-based clustering algorithm, iDeTaCt, that is able to transparently generate task-clusters by exploiting document switching and revisitation. We evaluate the algorithm’s performance using the collected datasets. The results obtained are very encouraging and merit further investigation.
A fuzzy enhancement schema for density-typed mammograms

A number of fuzzy techniques have been introduced for image enhancement, with an unidentified criterion defining the variable success of each technique. Applying these algorithms to mammography demonstrates that the ability to accentuate regions of interest (ROI) through contrast improvement is inherently dependent on the breast tissue densities represented by the mammograms. Discrimination between different breast tissue density types can ensure that each class of mammogram is enhanced using the most effective fuzzy algorithm available, consequently providing the most accurate and precise results for contrast improvement, and thus, breast cancer detection.
An efficient inter-coding algorithm for H.264

An efficient inter-coding algorithm for H.264 video coding standard is presented that gives a simultaneous improvement of encoding time and bit rate without a loss in SNR. This is an extension of 3DRS algorithm for variable block sizes which was previously known for fixed block sizes. We use central difference to find boundaries in a consistent motion vector field to make macroblock partition decision. In comparison to Full Search (FS) algorithm the proposed algorithm on average takes 66.65% less computational time, producing 2.9% less number of bits with an average SNR gain of 0.053 dB. The proposed algorithm, on average, saves 6.31% of computational time and 1.5% number of bits as compared to Fast Motion Estimation (FME) reference algorithm with a gain of 0.072 dB of SNR. Experimental results using six different sequences are presented to demonstrate the advantage of using the proposed algorithm.
Blood-nourishing and Hard-softening Capsule Costs Less in the Management of Osteoarthritic Knee Pain: A Randomized Controlled Trial

The blood-nourishing and hard-softening (BNHS) capsule is a traditional Chinese formula used in the symptomatic treatment of inflammation and pain. We conducted this randomized controlled trial to compare the efficacy of BNHS with other commonly prescribed drugs. We recruited 120 patients from two teaching hospitals; 30 patients in each hospital were randomly assigned to receive BNHS. In one hospital, the 30 controls were given another traditional Chinese drug; whereas a Western medicine (chondroprotection drug/Viartril-s) was used as the control in the other hospital. Intervention was carried out over a period of 4 weeks. Primary outcome measures included self-reported pain level, and changes in stiffness and functional ability as measured by the Western Ontario McMaster Universities Osteoarthritis (WOMAC) index. Mixed models were used for statistical analysis. Substantial improvements in disease-specific symptoms were observed, after 4 weeks of treatment, in patients taking BNHS capsules. As assessed by the WOMAC index, pain level of the BNHS group decreased by 57% [95% confidence interval (CI) = 50, 63], stiffness by 63% (95% CI = 55, 71) and functional ability increased by 56% (95% CI = 50, 63). No significant differences were found in any of the outcome measures between the BNHS group and either of the comparison groups. No severe adverse effects were reported. However, this study lacked a placebo group; therefore, we conclude that BNHS appears to be as effective as commonly prescribed medicines for the relief of pain and dysfunction in knee osteoarthritis patients, but costs a lot less than other Western and herbal drugs in the study.
Current status of the use of antiadhesive agents for gastric cancer surgery: a questionnaire survey in South Korea

Purpose The aim of this study was to investigate the current status of the use of antiadhesive agents (AAdAs) via a questionnaire and to discuss the availability of AAdAs. Methods The survey was sent to a list of members that was approved by the Korean Gastric Association. The survey included questions on AAdA use by surgeons, the type of AAdAs used, and the reasons for not using AAdAs. Surgeons were also asked to describe complications related to AAdAs, and the reliability of its use. Results The response rate was 21%. The rates of frequent use stratified by procedure were 26.9% (14/52) for open gastrectomy, 5.9% (3/51) for laparoscopic gastrectomy, and 31.5% (17/54) for surgery for postoperative bowel obstruction (P < 0.01). After including data from the occasional use group, the corresponding values were 51.9% (27/52), 19.6% (10/51), and 70.4% (38/54), respectively (P < 0.01). Sefrafilm and Guardix were most commonly used for open procedures. Guardix and Interceed were most commonly used for laparoscopic surgery. The primary reasons for nonuse of AAdAs were ineffectiveness and high cost. Ten percent (4/40) of surgeons observed complications associated with AAdAs. A minority (17.3%, 9/52) had positive attitudes toward AAdAs. The majority of respondents expressed neutral (73.1%, 38/52) or negative (9.6%, 5/52) attitudes toward AAdAs. Conclusion The low use rates of AAdAs in gastric cancer surgery may be attributable to perceptions that AAdAs are ineffective, unreliable, and costly. We anticipate the emergence of promising antiadhesive strategies that reach far beyond the limitations of current products.
High Affinity Recognition of Serotonin Transporter Antagonists Defined by Species-scanning Mutagenesis

Human and Drosophila melanogasterserotonin (5-HT) transporters (SERTs) exhibit similar 5-HT transport kinetics and can be distinguished pharmacologically by many, but not all, biogenic amine transporter antagonists. By using human andDrosophila SERT chimeras, major determinants of potencies of two transporter antagonists, mazindol and citalopram, were tracked to the amino-terminal domains encompassing transmembrane domains I and II. Species-scanning mutagenesis, whereby amino acid substitutions are made switching residues from one species to another, was employed on the eight amino acids that differ between human andDrosophila SERTs in this region, and antagonist potencies were reassessed in 5-HT uptake assays. A single mutation in transmembrane domain I of human SERT, Y95F, shifted both citalopram and mazindol to Drosophila SERT-like potencies. Strikingly, these potency changes were in opposite directions suggesting Tyr95 contributes both positive and negative determinants of antagonist potency. To gain insight into how the Y95F mutant might influence mazindol potency, we determined how structural variants of mazindol responded to the mutation. Our studies demonstrate the importance of the hydroxyl group on the heterocyclic nucleus of mazindol for maintaining species-selective recognition of mazindol and suggest that transmembrane domain I participates in the formation of antagonist-binding sites for amine transporters.
What Makes an LMS Effective - A Synthesis of Current Literature

There is a growing number of organizations and universities now utilising e-learning practices in their teaching and learning programs. These systems have allowed for knowledge sharing and provide opportunities for users to have access to learning materials regardless of time and place. However, while the uptake of these systems is quite high, there is little research into the effectiveness of such systems, particularly in higher education. This paper investigates the methods that are used to study the effectiveness of e-learning systems and the factors that are critical for the success of a learning management system (LMS). Five major success categories are identified in this study and explained in depth. These are the teacher, student, LMS design, learning materials and external support.
Efficient User Opt-Out from Block Stores

Massive collection of user or device data is a growing trend for personalizing services. Cheap and scalable storage is key to allow advanced analysis on the long run. In this context, block (HDFS) and columnar (Dremel, Parquet) stores are increasingly leveraged. Data protection acts, or simply the will for transparency to the user, impose to opt-out data on demand. Unfortunately, those data stores have departed from traditional databases, and do not provide efficient access and deletion to specific bits of data. In this paper, we study how to cost-efficiently opt-out user data from these stores. We apply two intuitive strategies (systematic erasure and encryption) to the context of big data systems. We model their respective costs and show that in the context of a service running atop Amazon Web Services, there is no general winner strategy (except in the special case where data cannot be compressed). Application constraints, such as data arrival and user opt-out rates, should then be considered to select the most cost-efficient opt-out strategy, while practical means of actions are the sharding policy and the careful setting of block sizes.
Pre-experimental Familiarization Increases Hippocampal Activity for Both Targets and Lures in Recognition Memory: An fMRI Study

In the present study, items pre-exposed in a familiarization series were included in a list discrimination task to manipulate memory strength. At test, participants were required to discriminate strong targets and strong lures from weak targets and new lures. This resulted in a concordant pattern of increased “old” responses to strong targets and lures. Model estimates attributed this pattern to either equivalent increases in memory strength across the two types of items (unequal variance signal detection model) or equivalent increases in both familiarity and recollection (dual process signal detection [DPSD] model). Hippocampal activity associated with strong targets and lures showed equivalent increases compared with missed items. This remained the case when analyses were restricted to high-confidence responses considered by the DPSD model to reflect predominantly recollection. A similar pattern of activity was observed in parahippocampal cortex for high-confidence responses. The present results are incompatible with “noncriterial” or “false” recollection being reflected solely in inflated DPSD familiarity estimates and support a positive correlation between hippocampal activity and memory strength irrespective of the accuracy of list discrimination, consistent with the unequal variance signal detection model account.
Classification using Deep Belief Nets Overview

This paper covers the implementation and testing of a Deep Belief Network (DBN) for the purposes of document classification. Document classification is important because of the increasing need for document organization, particularly journal articles and online information. Many popular uses of document classification are for email spam filtering, topic-specific search engine (where we only want to search documents that fall under a certain topic), or sentiment detection (to determine whether a document has more of a positive or negative tone) [1]. DBNs using a mostly unsupervised clustering algorithm for training are implemented. The clustering algorithm groups documents together that fall under the same category. Once the documents are grouped, a supervised learning algorithm (SVM in this case) is used to “assign” labels to the groups. More information and a background on DBNs is given in the following section. Testing of the DBN is primarily performed along three axes: 1) the number of hidden neurons in each layer of the DBN (which determines a “shape” for the overall network), 2) the number of layers in the DBN, and 3) the number of iterations that are used to train each layer of the DBN. Additionally, classification results of the DBN are compared with results obtained from using support vector machine (SVM) and Naïve Bayes (NB) classifiers. Some final experiments investigate how vocabulary size and word preprocessing affects performance. Preliminary results indicate that the DBN, given the implementations used in this paper, is not a viable alternative to other forms of document classifiers. The DBN has a significantly longer training time, and no matter how many training iterations are used, the DBN appears to have worse accuracy than either the SVM or NB classifiers. A number of suggestions for improvement are given in the final section, at least one of which should greatly increase the performance of the DBN.
Interact 2011 Workshop Building Bridges - HCI and Visualization

The fields, HCI and visualization, are usually practiced as two separate disciplines by researchers with different backgrounds and capabilities. However, these two disciplines, HCI and visualization, could complement each other and leveraging on the differences and complementary features of the two research fields could be beneficial for both. In this workshop, we are going to discuss the different approaches and capabilities of these two disciplines and layout a road map for a unified approach of research using both.
Financial Benefits, Travel Costs, and Bankruptcy

We are the first to show that the cost of personal bankruptcy filers traveling to their bankruptcy trustees affects bankruptcy choices. We use detailed balance sheet, income statement, and location data from 400,000 Canadian bankruptcies. To control for endogenous trustee selection, we use the location of local government offices as an instrument for the location of bankruptcy trustees (while filers interact with trustees, and trustees interact with local government, filers do not interact with the local government). We find that increased travel costs reduce the number of filings. Furthermore, for those individuals who do file, we find that their increased travel costs need to be compensated by increased financial benefits of bankruptcy. Filers without cars (higher travel costs), as well as those with jobs (higher opportunity costs), receive larger per-kilometer financial benefits from bankruptcy.