Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression

Background. Early technique failure has been a major limitation on the wider adoption of peritoneal dialysis (PD). The objectives of this study were to use data from a large, multi-centre, prospective database, the United Kingdom Renal Registry (UKRR), in order to determine the ability of an artificial neural network (ANN) model to predict early PD technique failure and to compare its performance with a logistic regression (LR)-based approach. Methods. The analysis included all incident PD patients enrolled in the UKRR from 1999 to 2004. The event of interest was technique failure. For both the ANN and LR analyses a bootstrap approach was used: the data were divided into 20 random training (75%) and validation (25%) sets. Models were derived on the latter and then used to make predictions on the former. Predictive accuracy was assessed by area under the ROC curve (AUROC). The 20 AUROC values and their standard errors were then averaged. Results. There were 3269 patients included in the analysis with a mean age of 59.9 years and a mean observation time of 430 days. Of the patients, 38.3% were female and 90.8% were Caucasian. 1458 patients (44.6%) suffered technique failure. The AUROC for the ANN model was 0.760 ± 0.0167 and the LR model was 0.709 and 0.0208. (P = 0.0164) Conclusions. Using UKRR data, both ANN and LR models predicted early PD technique failure with moderate accuracy. In this study, an ANN outperformed an LR-based approach. As the scope and the completeness of the UKRR increases, the question of whether more sophisticated ANN models will perform even better remains for further study.
Evolving intraday foreign exchange trading strategies utilizing multiple instruments price series

We propose a Genetic Programming architecture for the generation of foreign exchange trading strategies. The system's principal features are the evolution of free-form strategies which do not rely on any prior models and the utilization of price series from multiple instruments as input data. This latter feature constitutes an innovation with respect to previous works documented in literature. In this article we utilize Open, High, Low, Close bar data at a 5 minutes frequency for the AUD.USD, EUR.USD, GBP.USD and USD.JPY currency pairs. We will test the implementation analyzing the in-sample and out-of-sample performance of strategies for trading the USD.JPY obtained across multiple algorithm runs. We will also evaluate the differences between strategies selected according to two different criteria: one relies on the fitness obtained on the training set only, the second one makes use of an additional validation dataset. Strategy activity and trade accuracy are remarkably stable between in and out of sample results. From a profitability aspect, the two criteria both result in strategies successful on out-of-sample data but exhibiting different characteristics. The overall best performing out-of-sample strategy achieves a yearly return of 19%.
Text Mining from Social Media for Public Health Applications

Public Health is crucial to manage and monitor threats to the health of the population. In recent years, Twitter has been successfully applied to monitor diseases through its ability to provide near real-time data and proved to be an asset to the domain. This research aims to further explore capabilities of Twitter in the disease surveillance field by focusing on its geolocation feature and health mentions, identifiable through disease-specific language patterns present in Twitter messages.
Evaluation of anisotropic filters for diffusion tensor imaging

Diffusion tensor imaging (DTI) measures, such as fractional anisotropy (FA), and trace are very sensitive to noise contained in the acquired diffusion weighted images. Typical isotropic smoothing methods reduce the high spatial frequency image content and blur the image features. We hypothesized that the diffusion tensor would be an approximate anisotropic Gaussian filter function because the blur will tend to be oriented parallel to the white matter structures. Thus, we implemented and evaluated an anisotropic Gaussian kernel smoothing method based on the diffusion tensor for preserving diffusion tensor structural features while significantly reducing the noise. We compared the diffusion tensor anisotropic filtering with isotropic Gaussian filtering, and a Perona-Malik (PM) filtering algorithm, which was derived from the intensity gradients of diffusion weighted images. Human brain DTI data with high SNR was used as a gold standard for evaluation. Overall, the anisotropic filters performed similarly, with slightly better performance using the DT anisotropic filter across the whole brain
MRI/TRUS data fusion for brachytherapy

Prostate brachytherapy consists in placing radioactive seeds for tumour destruction under transrectal ultrasound imaging (TRUS) control. It requires prostate delineation from the images for dose planning. Because ultrasound imaging is patient‐ and operator‐dependent, we have proposed to fuse MRI data to TRUS data to make image processing more reliable. The technical accuracy of this approach has already been evaluated.
Influence of volcanic activity on the population genetic structure of Hawaiian Tetragnatha spiders: fragmentation, rapid population growth and the potential for accelerated evolution

Volcanic activity on the island of Hawaii results in a cyclical pattern of habitat destruction and fragmentation by lava, followed by habitat regeneration on newly formed substrates. While this pattern has been hypothesized to promote the diversification of Hawaiian lineages, there have been few attempts to link geological processes to measurable changes in population structure. We investigated the genetic structure of three species of Hawaiian spiders in forests fragmented by a 150‐year‐old lava flow on Mauna Loa Volcano, island of Hawaii: Tetragnatha quasimodo (forest and lava flow generalist), T. anuenue and T. brevignatha (forest specialists). To estimate fragmentation effects on population subdivision in each species, we examined variation in mitochondrial and nuclear genomes (DNA sequences and allozymes, respectively). Population subdivision was higher for forest specialists than for the generalist in fragments separated by lava. Patterns of mtDNA sequence evolution also revealed that forest specialists have undergone rapid expansion, while the generalist has experienced more gradual population growth. Results confirm that patterns of neutral genetic variation reflect patterns of volcanic activity in some Tetragnatha species. Our study further suggests that population subdivision and expansion can occur across small spatial and temporal scales, which may facilitate the rapid spread of new character states, leading to speciation as hypothesized by H. L. Carson 30 years ago.
Time Use, Time Pressure, Personal Stress, Mental Health, and Life Satisfaction from a Life Cycle Perspective

Abstract The article examines relationships between the use of time, subjectively perceived time pressure, life stress, mental health, and life satisfaction from a life cycle perspective, using data collected as part of the 1986 and 1992 Canadian General Social Surveys, and the 1994 Canadian National Population Health Survey. Analyses reported in the article suggest that subjective sense of time pressure is grounded in objective reality. Respondents and life cycle groups reporting higher levels of perceived time pressure carry heavier loads of paid and unpaid work, and are limited in their access to leisure time resources. Low as well as excessive levels of time pressure seem to correlate negatively with mental health. Life‐cycle situation strongly affects respondents’ sense of life satisfaction and emotional well‐being. Employed married respondents in the 25 to 44 group, and particularly the 45 to 64 age group, with or without children at home, report the highest levels of emotional well being, in spite .
Time Stamp Detection and Recognition in Video Frames

Time stamp superimposed on the video frames can be used as indices for video indexing and retrieval systems. Some applications use it for speed detection of objects. In this paper, we propose a time stamp detection approach based on edge detection method. Furthermore, we apply temporal properties of video frames so that time stamp can be located quickly. We also propose our digit recognition module to test the performance. Experimental results show that our approach can detect time stamp location accurately. The proposed approach can discriminate between date and time, and it can extract time with sub-second of precision. The recognition rate is also high enough to be used practically.
Numerical estimation of the relative entropy of entanglement

We propose a practical algorithm for the calculation of the relative entropy of entanglement (REE), defined as the minimum relative entropy between a state and the set of states with positive partial transpose. Our algorithm is based on a practical semidefinite cutting plane approach. In low dimensions the implementation of the algorithm in matlab provides an estimation for the REE with an absolute error smaller than 10{sup -3}.
2016 Ieee International Conference on Big Data (big Data) Inferring Relations in Knowledge Graphs with Tensor Decompositions

—Multi-relational data, like knowledge graphs, are generated from multiple data sources by extracting entities and their relationships. We often want to include inferred, implicit or likely relationships that are not explicitly stated, which can be viewed as link-prediction in a graph. Tensor decomposition models have been shown to produce state-of-the-art results in link-prediction tasks. We describe a simple but novel extension to an existing tensor decomposition model to predict missing links using similarity among tensor slices, as opposed to an existing tensor decomposition models which assumes each slice to contribute equally in predicting links. Our extended model performs better than the original tensor decomposition and the non-negative tensor decomposition variant of it in an evaluation on several datasets.
Analyzing BitTorrent and related peer-to-peer networks

We analyze protocols for disseminating a collection of data blocks over a network of peers with a view towards Bit-Torrent and related peer-to-peer networks. Unlike previous work, we accurately model the distribution of the individual data blocks, a process which is critical to the parallelism that makes BitTorrent successful in practice. We also consider multiple network topologies and routing algorithms. We first demonstrate several routing algorithms that distribute <i>b</i> data blocks on a network with diameter <i>d</i> and maximum degree <i>D</i> in <i>O</i>(<i>D</i>(<i>b + d</i>)) phases of concurrent downloads with high probability. This is tight within a factor of <i>D</i>. We also specialize to the networks used by BitTorrent and we improve this bound to <i>O</i>(<i>b</i> ln <i>n</i>) phases where <i>n</i> is the number of clients. Finally, we discuss several practical extensions to BitTorrent, one of which improves the bound to a near-optimal <i>O</i> (<i>b</i> + (ln <i>n</i>)<sup><i>2</i></sup>) phases.
Attachment Styles and Attraction to Death: Diversities Among Eating Disorder Patients

The current research was aimed at examining how attachment styles are connected to attraction to death among eating disorder (ED) patients. A sample of 34 anorexic and 34 bulimic patients was matched by age, sex, and socioeconomic status to 37 normal controls. They were administered the Adult Attachment Scale, the Beck Depression Inventory, and the Multi-Attitude Suicidal Tendency Scale. Results show that ED patients scored significantly higher on the insecure attachment scale and were less attracted to and more repulsed by life than controls. No significant differences were found on death variables. A significant difference between the three attachment styles was found beyond depression. The results suggest a model that may explain the diversities of attachment styles and the relationship with death among ED patients. Insecure attachment style may explain the repulsion by life, while the illness itself may serve as a false “secure base” and may protect from the fear of death. Clinicians may thus attempt to replace the “illness secure base” by enriching the attraction to life.
Factors associated with survival in a contemporary adult sickle cell disease cohort

In this study, the relationship of clinical differences among patients with sickle cell disease (SCD) was examined to understand the major contributors to early mortality in a contemporary cohort. Survival data were obtained for 542 adult subjects who were enrolled since 2002 at three university hospitals in the southeast United States. Subjects were followed up for a median of 9.3 years. At enrollment, clinical parameters were collected, including hemoglobin (Hb) genotype, baseline laboratory values, comorbidities, and medication usage. Levels of soluble adhesion molecules were measured for a subset of 87 subjects. The relationship of clinical characteristics to survival was determined using regression analysis. Median age at enrollment was 32 years. Median survival was 61 years for all subjects. Median survival for Hb SS and Sβ0 was 58 years and for Hb SC and Sβ+ was 66 years. Elevated white blood count, lower estimated glomerular filtration rate, proteinuria, frequency of pain crises, pulmonary hypertension, cerebrovascular events, seizures, stroke, sVCAM‐1, and short‐acting narcotics use were significantly associated with decreased survival. Forty‐two percent of subjects were on hydroxyurea therapy, which was not associated with survival. SCD continues to reduce life expectancy for affected individuals, particularly those with Hb Sβ0 and SS. Not only were comorbidities individually associated with decreased survival but also an additive effect was observed, thus, those with a greater number of negative endpoints had worse survival (P < 0.0001). The association of higher sVCAM‐1 levels with decreased survival suggests that targeted therapies to reduce endothelial damage and inflammation may also be beneficial. Am. J. Hematol. 89:530–535, 2014. © 2014 Wiley Periodicals, Inc.
Is alliteration mnemonic without awareness-raising?

English phraseology abounds with alliterative multiword units (e.g., slippery slope), which suggests that alliterative word strings have a comparative advantage to become stock phrases. One plausible explanation for this advantage is that alliterative word strings are relatively memorable, all else being equal, although there is little directly pertinent empirical evidence. In the context of instructed second language acquisition, it has been suggested that alliteration has a good potential to facilitate the learning of multiword units (MWU), at least when learners are made aware of the presence of alliteration in these units. In the present article, we investigate whether alliteration also makes MWUs comparatively memorable also in the absence of awareness-raising. English alliterative phrases and matched controls were dictated to EFL learners whose ability to recall the phrases was subsequently gauged in unannounced tests. We found a modest, short-lived mnemonic effect of alliteration. This points to an important role for awareness-raising if teachers or materials writers wish to help learners capitalise on the mnemonic advantage afforded by alliteration.
Switching Latency Impact on Star-Coupled WDM Photonic Network Pre-Allocation Protocol Performance

This paper evaluates the performance of two pre-allocation protocols when the switching latency of the wavelength tunable optical devices required for WDM networks is not negligible. The channels are pre-allocated to the nodes with the approach, where each node has a home channel that it uses either for all data packet transmissions or all data packet receptions. This approach reduces the resulting system complexity since both tunable transmitters and receivers are not required, and also has the advantage of being applicable to systems where there are many more nodes than wavelength channels. Switching latency is the time required by the tunable optical devices to tune to the required destination wavelength. The performance impact of switching latency on a generalized random access protocol is compared to an approach based on interleaved time multiplexing. Both protocols are designed to operate in a multiple-channel multiple-access environment and require each node to possess a tunable transmitter and a fixed (or slow tunable) receiver. Semi-markov analytic models are developed to investigate the performance of the two protocols. The analytic models are validated through extensive simulation. The performance is evaluated in terms of network throughput and packet delay with variations in the number of nodes, data channels, packet generation rate, and switching latency.
The Rademacher Complexity of Co-Regularized Kernel Classes

In the multi-view approach to semisupervised learning, we choose one predictor from each of multiple hypothesis classes, and we co-regularize our choices by penalizing disagreement among the predictors on the unlabeled data. We examine the co-regularization method used in the co-regularized least squares (CoRLS) algorithm, in which the views are reproducing kernel Hilbert spaces (RKHS's), and the disagreement penalty is the average squared difference in predictions. The final predictor is the pointwise average of the predictors from each view. We call the set of predictors that can result from this procedure the co-regularized hypothesis class. Our main result is a tight bound on the Rademacher complexity of the co-regularized hypothesis class in terms of the kernel matrices of each RKHS. We find that the co-regularization reduces the Rademacher complexity by an amount that depends on the distance between the two views, as measured by a data dependent metric. We then use standard techniques to bound the gap between training error and test error for the CoRLS algorithm. Experimentally, we find that the amount of reduction in complexity introduced by co regularization correlates with the amount of improvement that co-regularization gives in the CoRLS algorithm.
Involvement of a visual blackboard architecture in imagery

We discuss a visual blackboard architecture that could be involved in imagery. In this architecture, networks that process identity information interact with networks that process location information, in a manner that produces structural (compositional) forms of representation. Architectures of this kind can be identified in the visual cortex, but perhaps also in prefrontal cortex areas related with working memory.
Multiple Human Tracking Based on Multi-view Upper-Body Detection and Discriminative Learning

This paper focuses on the problem of tracking multiple humans in dense environments which is very challenging due to recurring occlusions between different humans. To cope with the difficulties it presents, an offline boosted multi-view upper-body detector is used to automatically initialize a new human trajectory and is capable of dealing with partial human occlusions. What is more, an online learning process is proposed to learn discriminative human observations, including discriminative interest points and color patches, to effectively track each human when even more occlusions occur. The offline and online observation models are neatly integrated into the particle filter framework to robustly track multiple highly interactive humans. Experiments results on CAVIAR dataset as well as many other challenging real-world cases demonstrate the effectiveness of the proposed method.
Source Free Transfer Learning for Text Classification

Transfer learning uses relevant auxiliary data to help the learning task in a target domain where labeled data is usually insufficient to train an accurate model. Given appropriate auxiliary data, researchers have proposed many transfer learning models. How to find such auxiliary data, however, is of little research so far. In this paper, we focus on the problem of auxiliary data retrieval, and propose a transfer learning framework that effectively selects helpful auxiliary data from an open knowledge space (e.g. the World Wide Web). Because there is no need of manually selecting auxiliary data for different target domain tasks, we call our framework Source Free Transfer Learning (SFTL). For each target domain task, SFTL framework iteratively queries for the helpful auxiliary data based on the learned model and then updates the model using the retrieved auxiliary data. We highlight the automatic constructions of queries and the robustness of the SFTL framework. Our experiments on 20NewsGroup dataset and a Google search snippets dataset suggest that the framework is capable of achieving comparable performance to those state-of-the-art methods with dedicated selections of auxiliary data.
Tackling Racist Offending: A Generalised Or Targeted Approach?

Liz Dixon discusses the development of programmes for racist and racially motivated offenders with particular reference to the targeted programme developed in London. She examines the Accreditation Panel’s recent decision to recommend the use of generalised cognitive behavioural programmes with this group, arguing that this takes insufficient account of existing evidence and the complex challenges of racist offending.
Architectures and Performance of Multichannel Multihop Packet Radio Networks

Packet radio networks that employ several parallel multiple-access channels are considered. An architecture for such a network dictates the selection of channels for packet transmission. We propose and analyze two multichannel architectures. In the first, each node employs a single radio and is assigned a channel on which it listens when it does not transmit. To transmit a packet, the node tunes its radio to the channel of the intended receiver for that transmission only. The second architecture requires each radio to use a single channel for both transmission and reception, but provides some of the nodes with more than one radio each, allowing them to serve as bridges between channels. Within these architectures, one can further select the amount of routing information held by each node and the channel-access protocol, both of which greatly affect the network performance. To ascertain the effects of the various parameters, we calculate the throughput in both architectures. The channel-access protocols we consider are slotted ALOHA and CSMA with and without capture. We also evaluate the effect of increasing the amount of routing information held by the nodes.
An Unsupervised Method for Word Sense Tagging using Parallel Corpora

We present an unsupervised method for word sense disambiguation that exploits translation correspondences in parallel corpora. The technique takes advantage of the fact that cross-language lexicalizations of the same concept tend to be consistent, preserving some core element of its semantics, and yet also variable, reflecting differing translator preferences and the influence of context. Working with parallel corpora introduces an extra complication for evaluation, since it is difficult to find a corpus that is both sense tagged and parallel with another language; therefore we use pseudo-translations, created by machine translation systems, in order to make possible the evaluation of the approach against a standard test set. The results demonstrate that word-level translation correspondences are a valuable source of information for sense disambiguation.