Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns, Trajectories, and Predictors of Functional Decline after Hospitalization for Acute Exacerbations in Men with Moderate to Severe Chronic Obstructive Pulmonary Disease: A Longitudinal Study

Background Hospitalization for acute exacerbations (AE) of chronic obstructive pulmonary disease (COPD) is common, but little is known about the impact of hospitalization on the development of disability. The purpose of this study was to determine the rate and time course of functional changes 3 months after hospital discharge for AE-COPD compared with baseline levels 2 weeks before admission, and to identify predictors of functional decline. Methods This was a prospective study including 103 patients (age mean, 71 years; standard deviation, 9.1 years) who were hospitalized with AE-COPD. Number of dependencies in Activities of Daily Living (ADLs) was measured at the preadmission baseline and at weeks 6 and 12 after discharge. Patterns of improvement, no change, and decline were defined over 3 consecutive intervals (baseline and weeks 6 and 12). Trajectories grouped patients with similar time courses of disability. Recovery was defined as returning to baseline function after functional decline. Univariate and multivariate multiple logistic regression was used to determine predictors of functional decline after week 12. Results Six trajectories of functional changes were found. From baseline to 12 weeks, 50% of patients continued to have the same function whereas 31% experienced functional decline after 6 weeks; 16.7% recovered over subsequent weeks. At week 12, as a consequence of all trajectories, 38% of patients showed functional declines compared with baseline function, 57% had not declined, and 6 improved. Length of stay (odds ratio [OR] = 1.12;95% [confidence interval] CI 1.03–1.22), dyspnea (OR = 1.85; 95% CI 1.05–3.26), and frailty (OR = 3.97; 95% CI 1.13–13.92) were independent predictors of functional decline after 12 weeks. Conclusions Hospitalization for AE-COPD is a risk factor for the progression of disability. More than one third of patients hospitalized for AE-COPD declined during the 12 weeks following discharge, with most of this decline occurring by week 6.
Automatic Detection of Expanding H i Shells Using Artificial Neural Networks

The identification of expanding H i shells is difficult because of their variable morphological characteristics. The detection of H i bubbles on a global scale has therefore never been attempted. In this paper, an automatic detector for expanding H i shells is presented. The detection is based on the more stable dynamical characteristics of expanding shells and is performed in two stages. The first one is the recognition of the dynamical signature of an expanding bubble in the velocity spectra, based on the classification of an artificial neural network. The pixels associated with these recognized spectra are identified on each velocity channel. The second stage consists of looking for concentrations of those pixels that were first pointed out and deciding if they are potential detections by morphological and 21 cm emission variation considerations. Two test bubbles are correctly detected, and a potentially new case of a shell that is visually very convincing is discovered. About 0.6% of the surveyed pixels are identified as part of a bubble. These may be false detections but still constitute regions of space with high probability of finding an expanding shell. The subsequent search field is thus significantly reduced. In the near future, we intend to conduct a large‐scale H i shell detection over the Perseus arm using our detector.
Robustly tracking objects via multi-task kernel dynamic sparse model

Recently, sparse representation has been successfully applied by some generative tracking methods. However, few methods consider the correlation between the representations of each particle in time domain and space domain. Additionally, most methods use the raw pixels as templates which can not well adapt to the sophisticated object changes. To solve these problems, we consider the sparse representation in kernel space and propose a multi-task kernel dynamic sparse tracking algorithm (MTKDST). As compared to previous methods, our method exploits the dependencies between particles in the space domain and the correlation of particle representation in the time domain to improve the tracking performance. Furthermore, we also adopt the multikernel fusion mechanism to utilize multiple complementary visual features (e.g., spatial color histogram and spatial gradient-orientation histogram) to enhance the robustness of the proposed method. The comprehensive experiments on several challenging image sequences demonstrate that the proposed method outperforms the state-of-the-art approaches in tracking accuracy.
Abrogation of Junctional Adhesion Molecule-A Expression Induces Cell Apoptosis and Reduces Breast Cancer Progression

Intercellular junctions promote homotypic cell to cell adhesion and transfer intracellular signals which control cell growth and apoptosis. Junctional adhesion molecule-A (JAM-A) is a transmembrane immunoglobulin located at tight junctions of normal epithelial cells of mammary ducts and glands. In the present paper we show that JAM-A acts as a survival factor for mammary carcinoma cells. JAM-A null mice expressing Polyoma Middle T under MMTV promoter develop significantly smaller mammary tumors than JAM-A positive mice. Angiogenesis and inflammatory or immune infiltrate were not statistically modified in absence of JAM-A but tumor cell apoptosis was significantly increased. Tumor cells isolated from JAM-A null mice or 4T1 cells incubated with JAM-A blocking antibodies showed reduced growth and increased apoptosis which paralleled altered junctional architecture and adhesive function. In a breast cancer clinical data set, tissue microarray data show that JAM-A expression correlates with poor prognosis. Gene expression analysis of mouse tumor samples showed a correlation between genes enriched in human G3 tumors and genes over expressed in JAM-A +/+ mammary tumors. Conversely, genes enriched in G1 human tumors correlate with genes overexpressed in JAM-A−/− tumors. We conclude that down regulation of JAM-A reduces tumor aggressive behavior by increasing cell susceptibility to apoptosis. JAM-A may be considered a negative prognostic factor and a potential therapeutic target.
Efficient aggregation for graph summarization

Graphs are widely used to model real world objects and their relationships, and large graph datasets are common in many application domains. To understand the underlying characteristics of large graphs, graph summarization techniques are critical. However, existing graph summarization methods are mostly statistical (studying statistics such as degree distributions, hop-plots and clustering coefficients). These statistical methods are very useful, but the resolutions of the summaries are hard to control. In this paper, we introduce two database-style operations to summarize graphs. Like the OLAP-style aggregation methods that allow users to drill-down or roll-up to control the resolution of summarization, our methods provide an analogous functionality for large graph datasets. The first operation, called SNAP, produces a summary graph by grouping nodes based on user-selected node attributes and relationships. The second operation, called k-SNAP, further allows users to control the resolutions of summaries and provides the "drill-down" and "roll-up" abilities to navigate through summaries with different resolutions. We propose an efficient algorithm to evaluate the SNAP operation. In addition, we prove that the k-SNAP computation is NP-complete. We propose two heuristic methods to approximate the k-SNAP results. Through extensive experiments on a variety of real and synthetic datasets, we demonstrate the effectiveness and efficiency of the proposed methods.
Correct by construction components or: would Nasreddin use components?

We describe the component-based, architectural design of software systems, or service layer model (SLM) that perhaps offers a better approach to component based software development than the traditional approaches and all the drawbacks mentioned above. The development process defined in the SLM can roughly be described as follows: starting from a system specification, an initial design model is developed containing components that should be combined in the new system. This model is then step by step transformed into a description of the system that can directly be used to derive an implementation. This process is structured into different phases, which are supported by a model-checker that is used to evaluate correct specifications.
Financial crisis in Southeast Asia: dispelling illusion the Minskyan way

This paper extends Minsky's financial instability hypothesis to the case of the open, 'liberalised', economy, making it possible to put forward a specifically Minskyan account of the road to the financial crisis in Southeast Asia (1997-1998). The analysis suggests that the threats to growth and employment emanating from the financial sector which Minsky identified in the closed economy setting are much intensified in open, liberalised, developing economies. Financial liberalisation is an important key factor in this process. Rival explanations of the crisis are examined and rejected in favour of the extended Minskyan explanation. The policy implications are derived and discussed. Copyright 2002, Oxford University Press.
Application of Bat Algorithm for The Detection of Hidden Nodes in IEEE802.11ah Networks

Hidden node problem sometimes referred to as frequent packets collision that mostly leads to loss of packets is no longer new in wireless networks because it affects the previous IEEE802.11 standards. The new IEEE802.11ah standard which is also a sub-standard of IEEE 802.11 is no exemption. As a matter of fact, IEEE802.11ah suffers from a hidden node problem more than networks (IEEE 802.11a/b/n/ac) due to their wider coverage which is up to 1km, high number of devices they can support (over 8000 nodes to one AP) and frequent simultaneous sleeping and sending of the nodes (power saving mode). A few researchers have worked on this hidden node problem in IEEE802.11ah but could not get a lasting solution to it. Therefore, this paper proposes an algorithm which detects hidden nodes and also proposes a theoretical solution based on previous works which was also experimentally verified through the BIHD-CM.
N ov 2 00 1 A nonperturbative Real-Space Renormalization Group scheme

Based on the original idea of the density matrix renormalization group (DMRG) [1], i.e. to include the missing boundary conditions between adjacent blocks of the blocked quantum system, we present a rigorous and nonperturbative mathematical formulation for the realspace renormalization group (RG) idea invented by L.P. Kadanoff [2] and further developed by K.G. Wilson [3]. This is achieved by using additional Hilbert spaces called auxiliary spaces in the construction of each single isolated block, which is then named a superblock according to the original nomenclature [1]. On this superblock we define two maps called embedding and truncation for successively integrating out the small scale structure. Our method overcomes the known difficulties of the numerical DMRG, i.e. limitation to zero temperature and one space dimension. PACS: 75.10. Jm
Encoding of sequential translators in discrete-time recurrent neural nets

In recent years, there has been a lot of interest in the use of discrete-time recurrent neural nets (DTRNN) to learn finite-state tasks, and in the computational power of DTRNN, particularly in connection with finite-state computation. This paper describes a simple strategy to devise stable encodings of sequential finite-state translators (SFST) in a second-order DTRNN with units having bounded, strictly growing, continuous sigmoid activation functions. The strategy relies on bounding criteria based on a study of the conditions under which the DTRNN is actually behaving as a SFST.
An Existence Proof for Interacting Quantum Field Theories with a Factorizing S-Matrix

Within the context of quantum field theories on two-dimensional Minkowski space with a factorizing S-matrix, a new construction scheme for relativistic model theories is carried out. We show that for a large class of such S-matrices, there exists an associated local quantum field theory in the sense of Haag and Kastler. The crucial step in this approach consists in proving the existence of observables localized in bounded spacetime regions, which is accomplished by verifying the modular nuclearity condition for wedge algebras.
Short-term load forecasting in large scale electrical utility using artificial neural network

This paper presents a novel method for short-term load forecasting (STLF), based on artificial neural network (ANN), targeted for use in large-scale systems such as distribution management system (DMS). The system comprises of a preprocessing unit (PPU) and a feed forward ANN ordered in a sequence. PPU prepares the data and feeds them as input to the ANN, which calculates the hourly load forecasts. Preprocessing of the entering data reduces the size of the input space to the ANN, which improves the generalization capability and shortens the training time of the network. Reduced dimension of the input space also diminishes the number of parameters to be set in a training procedure, allowing smaller training set, and thus online usage and adaptation. This is important for a real-world power system where a sufficient set of historical data (training points) may not always be available, for different reasons. Ease of use and fast adaptation are necessary when predictions need to carry out in a large number of nodes in the power grid. Functionality of the proposed method has been tested on recorded data from Serbian electrical utility. Results demonstrate that even with a simple configuration such as this one, fair accuracy can be achieved in forecasting the hourly load. The simplicity and reusability are very important factors for installation of the proposed system in a large-scale DMS, considering the technical requirements (e.g. training data availability, processing power and memory capacity).
Ebbs and Flows: A Longitudinal Study of an English Language Instructor’s Motivation to Learn Korean

ABSTRACT Native English speaking language teachers who live and work in various parts of the world often express a desire to learn the language of their host country. Without sustained levels of motivation, however, their desires are unlikely to be realized. This article reports the findings of a longitudinal case study of an American English language instructor working at a South Korean university. It examines the factors that impacted her motivation to learn Korean, and explores the ebbs and flows that characterized her motivation over a one-year period. Findings revealed that while the participant did express a desire to learn Korean and formulated learning goals, the action necessary to achieve these goals did not occur. This article draws on the L2 Motivational Self System and the Process Model of L2 Motivation to provide a dual theoretical perspective on the findings.
Advanced phenotyping and phenotype data analysis for the study of plant growth and development

Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. To solve these global challenges, novel approaches are required to identify quantitative phenotypes and to explain the genetic basis of agriculturally important traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments. Recently, plant phenomics has offered and integrated a suite of new technologies, and we are on a path to improve the description of complex plant phenotypes. High-throughput phenotyping platforms have also been developed that capture phenotype data from plants in a non-destructive manner. In this review, we discuss recent developments of high-throughput plant phenotyping infrastructure including imaging techniques and corresponding principles for phenotype data analysis.
Re-evaluating the responsiveness of DNS-based network control

For more than a decade, special purpose DNS [1] servers have been used to direct users to different IP addresses based on characteristics such as proximity, load balancing and failover. In this paper, we examine two of the mechanisms these special purpose DNS servers may use to control user traffic:-short time-to-lives (TTL) and time-splicing multiple responses. We find that although 2% of users are still using a stale DNS response after 15 minutes (despite a 20 second TTL), 90% of user requests will have switched to a new answer within 60 seconds. Further, when trying to direct users to two different locations in the ratio 90:10, we find time-splicing CNAME and A responses (corresponding to the two locations, respectively) is incompatible with a commonly deployed recursive resolver, and results in a ratio policy closer to 10:90. Overall we found that although DNS TTLs are not always respected, we are able manage load at a coarse granularity - provided time-spliced responses are of the same response type.
Chart Image Classification Using Multiple-Instance Learning

An important step in chart image understanding is to identify the type of the input image so that corresponding interpretation can be performed. In this paper, we model the chart image classification as a multiple-instance learning problem. A chart image is treated as a bag containing a set of instances that are graphical symbols. For both training and recognition, shape detection is performed and general shape descriptors are used to form feature vectors. For the training images, the correlation factor (CF) of each shape is calculated for each chart type. The learnt CFs are then used to estimate the type of a new input image. Comparing with traditional multiple-instance learning algorithms, we allow negative examples to be less restrictive and hence easier to provide. Using our method, both the type and the data components of the chart image can be obtained in one-pass. The experimental results show that our approach works reasonably well
Open-Loop Planning in Large-Scale Stochastic Domains

We focus on effective sample-based planning in the face of underactuation, high-dimensionality, drift, discrete system changes, and stochasticity. These are hallmark challenges for important problems, such as humanoid locomotion. In order to ensure broad applicability, we assume domain expertise is minimal and limited to a generative model. In order to make the method responsive, computational costs that scale linearly with the amount of samples taken from the generative model are required. We bring to bear a concrete method that satisfies all these requirements; it is a receding-horizon open-loop planner that employs cross-entropy optimization for policy construction. In simulation, we empirically demonstrate near-optimal decisions in a small domain and effective locomotion in several challenging humanoid control tasks.
Mechanical wounding induces a nitrosative stress by down-regulation of GSNO reductase and an increase in S-nitrosothiols in sunflower (Helianthus annuus) seedlings

Nitric oxide (NO) and related molecules such as peroxynitrite, S-nitrosoglutathione (GSNO), and nitrotyrosine, among others, are involved in physiological processes as well in the mechanisms of response to stress conditions. In sunflower seedlings exposed to five different adverse environmental conditions (low temperature, mechanical wounding, high light intensity, continuous light, and continuous darkness), key components of the metabolism of reactive nitrogen species (RNS) and reactive oxygen species (ROS), including the enzyme activities L-arginine-dependent nitric oxide synthase (NOS), S-nitrosogluthathione reductase (GSNOR), nitrate reductase (NR), catalase, and superoxide dismutase, the content of lipid hydroperoxide, hydrogen peroxide, S-nitrosothiols (SNOs), the cellular level of NO, GSNO, and GSNOR, and protein tyrosine nitration [nitrotyrosine (NO2-Tyr)] were analysed. Among the stress conditions studied, mechanical wounding was the only one that caused a down-regulation of NOS and GSNOR activities, which in turn provoked an accumulation of SNOs. The analyses of the cellular content of NO, GSNO, GSNOR, and NO2-Tyr by confocal laser scanning microscopy confirmed these biochemical data. Therefore, it is proposed that mechanical wounding triggers the accumulation of SNOs, specifically GSNO, due to a down-regulation of GSNOR activity, while NO2-Tyr increases. Consequently a process of nitrosative stress is induced in sunflower seedlings and SNOs constitute a new wound signal in plants.
CourseVis: Externalising Student Information to Facilitate Instructors in Distance Learning

In this paper we present an approach for externalising student information to facilitate instructors involved in distance learning. Information visualization techniques have been used in CourseVis - a tool which obtains tracking data from course management systems, transforms the data into a form convenient for processing, and generates graphical represen tations that can be explored by course instructors to examine social, cognitive, and behavioural aspects of distance students. CourseVis is presented in the paper, and several examples of pictorial representations generated by the tool are discussed.
Explaining behavioural intentions toward co-branded products

The authors develop a conceptual model of factors that determine behavioural intentions to buy co-branded products. The model is an extension of the Simonin/Ruth model. Structural equation modelling serves to test the hypotheses. Product fit has the strongest effect (of the exogenous factors) on behavioural intention. Marketers should gain a detailed understanding of consumers' perceptions of product and brand fit with partner brands and establish positive associations before leveraging a brand as a co-branded product. Buying intentions for co-branded products can be increased by marketing to brand-conscious consumers, consumers with variety-seeking tendencies, and highly involved consumers.
Weibo, and a tale of two worlds

Weibo is the Twitter counterpart in China that has attracted hundreds of millions of users. We crawled an almost complete Weibo user network that contains 222 million users and 27 billion links in 2013. This paper analyzes the structural properties of this network, and compares it with a Twitter user network. The topological properties we studied include the degree distributions, reciprocity, clustering coefficient, PageRank centrality, and degree assortativity. We find that Weibo users have a higher diversity index, higher Gini index, but a lower reciprocity and clustering coefficient for most of the nodes. A surprising observation is that the reciprocity of Weibo is only about a quarter of the reciprocity of the Twitter user network. We also show that Weibo adoption rate correlates with economic development positively, and Weibo network can be used to quantify the connections between provinces and regions in China. In particular, point-wise mutual information is shown to be accurate in quantifying the strength of connections.