Miracle-dz/UniFAR-FTUs huggingface dataset:
Distinguishing Fact from Fiction: Pattern Recognition in Texts Using Complex Networks

We establish concrete mathematical criteria to distinguish between different kinds of written storytelling, fictional and non-fictional. Specifically, we constructed a semantic network from both novels and news stories, with $N$ independent words as vertices or nodes, and edges or links allotted to words occurring within $m$ places of a given vertex; we call $m$ the word distance. We then used measures from complex network theory to distinguish between news and fiction, studying the minimal text length needed as well as the optimized word distance $m$. The literature samples were found to be most effectively represented by their corresponding power laws over degree distribution $P(k)$ and clustering coefficient $C(k)$; we also studied the mean geodesic distance, and found all our texts were small-world networks. We observed a natural break-point at $k=\sqrt{N}$ where the power law in the degree distribution changed, leading to separate power law fit for the bulk and the tail of $P(k)$. Our linear discriminant analysis yielded a $73.8 \pm 5.15%$ accuracy for the correct classification of novels and $69.1 \pm 1.22%$ for news stories. We found an optimal word distance of $m=4$ and a minimum text length of 100 to 200 words $N$.
The price of safety in an active network

Security is a major challenge for “Active Networking,” as accessible programmability creates numerous opportunities for mischief. The point at which programmability is exposed, e.g., through the loading and execution of code in network elements, must therefore be carefully crafted to ensure security. The SwitchWare active networking research project has studied the architectural implications of various tradeoffs between performance and security. Namespace protection and type safety were achieved with a module loader for active networks, ALIEN, which carefully delineated boundaries for privilege and dynamic updates. ALIEN supports two extensions, the Secure Active Network Environment (SANE), and the Resource Controlled Active Network Environment (RCANE). SANE extends ALIEN's node protection model into a distributed setting, and uses a secure bootstrap to guarantee integrity of the namespace protection system. RCANE provides resource isolation between active network node users, including separate heaps and robust time-division multiplexing of the node. The SANE and RCANE systems show that convincing active network security can be achieved. This paper contributes a measurement-based analysis of the costs of such security with an analysis of each system based on both execution traces and end-to-end behavior.
Learning to Parse and Ground Natural Language Commands to Robots

This paper describes a weakly supervised approach for understanding natural language commands to robotic systems. Our approach, called the combinatory grounding graph (CGG), takes as input natural language commands paired with groundings and infers the space of parses that best describe how to ground the natural language command. The command is understood in a compositional way, generating a latent hierarchical parse tree that involves relations (such as “to” or “by”) and categories (such as “the elevators” or “the doors”). We show an example parse-grounding tree and show that our system can successfully cluster the meanings of objects and locations.
Photonic sensors based on integrated reflectivity, ellipsometry and spectrometry measurements in submicron size geometries

We have developed micro-nano structures as photonic sensors based on the observation of external reflectivity profiles. Spectra interference patterns as a function of the angle of incidence for both s and p polarizations directions as well as a phase shift between s and p polarisation are obtained. Also the reflectivity of the photonic structures is calculated and analyzed over a wide range of wavelengths of light as well. The sub-micro holes of the photonic structure have been evaluated with several refractive indices. As a result, the change in the effective refractive index involved by the optical liquids produces variations in the phase shift and the interference reflectivity patterns, making the system suitable for chemical, biochemical and pharmaceutical applications. The theoretical results of the integrated reflectivity, ellipsometry and spectrometry measurements ensure sensitive, accurate and reliable optical sensing detection.
Evidence-based guidelines for weaning and discontinuing ventilatory support: a collective task force facilitated by the American College of Chest Physicians; the American Association for Respiratory Care; and the American College of Critical Care Medicine.

2001;120;375-396 Chest Neil R. MacIntyre Medicine and the American College of Critical Care American Association for Respiratory Care; American College of Chest Physicians; the Collective Task Force Facilitated by the Discontinuing Ventilatory Support : A Evidence-Based Guidelines for Weaning and http://chestjournal.org services can be found online on the World Wide Web at: The online version of this article, along with updated information and ). ISSN: 0012-3692. http://www.chestjournal.org/misc/reprints.shtml ( copyright holder reproduced or distributed without the prior written permission of the 60062. All rights reserved. No part of this article or PDF may be American College of Chest Physicians, 3300 Dundee Road, Northbrook IL It has been published monthly since 1935. Copyright 2007 by the CHEST is the official journal of the American College of Chest Physicians.
Predictors of end-stage renal disease and recurrence of lupus activity after initiation of dialysis in patients with lupus nephritis.

OBJECTIVES The present paper aims at identifying the predictors of end-stage renal disease (ESRD) and at determining the long-term outcome of ESRD patients according to renal replacement modality in Korean patients with lupus nephritis (LN). METHODS Between 1985 and 2010, 321 Korean patients with LN were enrolled in this study. We analysed the clinical and laboratory indices, the treatment responses and the biopsy findings. The events of interest were estimated by the Kaplan-Meier method and the risk factors were assessed by univariate and multivariate Cox proportional hazards regression analyses. RESULTS The median follow-up time after the diagnosis of LN was 84 months. During follow-up, twenty-nine patients evolved to ESRD. Renal survival rate at 5 and 10 years after LN onset was 95.9% and 91.1%, respectively. Deteriorated renal function (estimated glomerular filtration rate <60 ml/min/1.73m2) at LN onset (hazard ratio: 9.223) was found to be an independent risk factor for the development of ESRD. Recurrence of lupus nephritis in renal allograft and flare-ups of lupus activity were not observed among the patients undergoing kidney transplantation (KT) (n=11). In contrast, those with maintenance dialysis (n=18) developed 13 episodes of lupus flare in 10 patients and 5 died of either infection (n=2) or lupus flare (n=3). CONCLUSIONS The impaired renal function at baseline is an independent predictor of ESRD in Korean patients with LN. The benefits of KT on the control of lupus activity and survival should be emphasised.
A Hoare logic for the coinductive trace-based big-step semantics of While

In search for a foundational framework for reasoning about observable behavior of programs that may not terminate, we have previously devised a trace-based big-step semantics for While. In this semantics, both traces and evaluation (relating initial states of program runs to traces they produce) are defined coinductively. On terminating runs, it agrees with the standard inductive state-based semantics. Here we present a Hoare logic counterpart of our coinductive trace-based semantics and prove it sound and complete. Our logic subsumes both the partial correctness Hoare logic and the total correctness Hoare logic: they are embeddable. Since we work with a constructive underlying logic, the range of expressible program properties has a rich structure; in particular, we can distinguish between termination and nondivergence, e.g., unbounded total search fails to be terminating but is nonetheless nondivergent. Our metatheory is entirely constructive as well, and we have formalized it in Coq.
Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations

Segmentation is an important step in processing and analyzing time series. In this article, we present an approach to speed up some standard time series segmentation techniques. Often, time series segmentation is based on piecewise polynomial approximations of the time series (including piecewise constant or linear approximations as special cases). Basically, a least-squares fit with a polynomial has a computational complexity that depends on the number of observations, i.e., the length of the time series. To improve the computational complexity of segmentation techniques we exploit the fact that approximations have to be repeated in sliding (moving) or growing time windows. Therefore, we suggest to use update techniques for the approximations that determine the approximating polynomial in a sliding or growing time window from an already existing one with a computational complexity that is independent of the number of observations, i.e., the length of the window. For that purpose bases of orthogonal polynomials must be used instead of standard bases such as monomials. We take two standard techniques for segmentation - the on-line algorithm SWAB (Sliding Window And Bottom-up) and the off-line technique OptSeg (Optimal Segmentation) - and show that the run-times can be reduced substantially for a given polynomial degree. If run-time constraints are given, e.g., in real-time applications, it would also be possible to adapt the degree of the approximating polynomials. Higher polynomial degrees typically result in lower modeling errors or longer segments. The various properties of the new realizations of segmentation techniques are outlined by means of some benchmark time series. The experimental results show that, depending on the chosen parameterization, OptSeg can be accelerated by some orders of magnitude, SWAB by a factor of up to about ten.
Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains

Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Recently experimental evidence has been presented suggesting that neural information is encoded and transferred in packets, i.e., in stereotypical, correlated spiking patterns of neural activity. Due to their relevance to coherent spiking, synfire chains are one of the main theoretical constructs that have been appealed to in order to describe coherent spiking and information transfer phenomena. However, for some time, it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to propagate. This has limited the classical synfire chain’s ability to explain graded neuronal responses. Recently, we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes. In particular, we showed that it is possible to use one synfire chain to provide gating pulses and a second, pulse-gated synfire chain to propagate graded information. We called these circuits synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded information can rapidly cascade through a neural circuit, and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time. We show that SGSCs are robust in the presence of variability in population size, pulse timing and synaptic strength. Finally, we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained, spike-based, modular neural circuit that is triggered by streaming input, processes the input, then makes a decision based on the processed information and shuts itself down.
Semi-supervised NMF with Time-frequency Annotations for Single-channel Source Separation

We formulate a novel extension of nonnegative matrix factorization (NMF) to take into account partial information on source-speciﬁc activity in the spectrogram. This information comes in the form of masking coefﬁcients, such as those found in an ideal binary mask. We show that state-of-the-art results in source separation may be achieved with only a limited amount of correct annotation, and furthermore our algorithm is robust to incorrect annotations. Since in practice ideal annotations are not observed, we propose several supervision scenarios to estimate the ideal mask- ing coefﬁcients. First, manual annotations by a trained user on a dedicated graphical user interface are shown to provide satisfactory performance although they are prone to errors. Second, we investigate simple learning strate- gies to predict the Wiener coefﬁcients based on local information around a given time-frequency bin of the spec- trogram. Results on single-channel source separation show that time-frequency annotations allow to disambiguate the source separation problem, and learned annotations open the way for a completely unsupervised learning procedure for source separation with no human intervention.
Efficient and flexible object sharing

DiSOM is a software-based distributed shared memory (DSM) system, which supports intra- and inter-application sharing in heterogeneous networks of microprocessor workstations. Unlike previous DSM systems, DiSOM provides fine-grained control over communication while retaining a simple shared memory model. It achieves this by using an update-based implementation of entry consistency, semaphores, remote object invocation, dynamic decomposition of objects and object-oriented language mechanisms. These techniques allow programmers to exploit application-specific knowledge to improve performance. A comparison between DiSOM and TreadMarks, a state-of-the-art DSM system, shows that on average DiSOM executes 33% faster and sends 69% fewer messages and 38% less data.
Estimation of respiratory information from the built-in pressure sensors of a dialysis machine

The purpose of the present study is to determine the feasibility of estimating respiratory information from the built-in pressure sensors of a dialysis machine. The study database consists of simultaneous recordings of pressure signals and capnographic signals from 6 patients during 7 hemodialysis treatment sessions. Respiration rates were estimated using respiratory induced variations in the beat-to-beat interval series of the cardiac component of the pressure signal and respiratory induced baseline variations in the pressure signal, respectively. The estimated respiration rates were compared to a reference respiration rate determined from the capnograhpic signal. The root-mean-square error of the estimated respiration rate from the baseline variations of the pressure signal was 2.10 breaths/min; the corresponding error of the estimated respiration rate from the beat-to-beat interval series of the cardiac component was 4.95 breaths/min. The results suggest that it is possible to estimate respiratory information from the pressure sensors.
Cardiovascular , Pulmonary , and Renal Pathology Autophagy Guards Against Cisplatin-Induced Acute Kidney Injury

Autophagy is a highly conserved bulk protein degradation pathway involved in cellular homeostasis. Although emerging evidence indicates involvement of autophagy in various conditions, efforts to clarify the role of autophagy in renal tubules are beginning to be elucidated. In the present study, we examined the hypothesis that autophagy guards against acute kidney injury (AKI) by modulating several deteriorative pathways that lead to tubular cell death using a cisplatin-induced model of AKI. Cisplatin treatment of GFP-LC3 (green fluorescent protein–microtubule-associated protein 1 light chain 3) transgenic mice induced autophagy in kidney proximal tubules in a time-dependent manner. Proximal tubule–specific autophagy-deficient mice exhibited more severe cisplatin-induced AKI than did control mice, as assessed via kidney function and morphologic findings. In addition, cisplatin induced more severe DNA damage and p53 activation, concomitant with an increase in apoptotic cell number, and a massive accumulation of protein aggregates in autophagy-deficient proximal tubules. Cisplatin treatment significantly increased reactive oxygen species–producing damaged mitochondria in immortalized autophagy-deficient proximal tubular cells when compared with autophagyretrieved control cells. In conclusion, autophagy guards kidney proximal tubules against AKI, possibly by alleviating DNA damage and reactive oxygen species production and by eliminating toxic protein aggregates. Enhancing autophagy may provide a novel therapeutic option to minimize AKI. (Am J Pathol 2012, 180:517–525; DOI: 10.1016/j.ajpath.2011.11.001)
Image Segmentation for Object Detection using CRFs with Robust Higher Order Clique Potentials

Object recognition is a fundamental problem in computer vision. In this work, an approach for object recognition that combines detection and segmentation is explored. Using the result of segmentation in the detection process leads to significant improvements in the recognition accuracies. Rather than considering a simple pairwise CRF model for the segmentation process, the use of higher-order clique potentials is explored. The results are presented on the PASCAL Visual Object Classes Challenge dataset.
Power-law Correlations of Connectivities in Biomolecular Networks

Complex networks which describe biological systems such as gene regulations and metabolisms indicate several striking statistical properties such as power-law degree distribution P (k)∼k−γ that is a scalefree property and high clustering coefficient, yielding nontrivial interpretations for topologies and signal transductions of the networks [1]. In this report, we forcus on degree correlation k̄nn(k) [3] denotes average degree (number of edges) of nearest neighbors of nodes with degree k to examine features of neighbor connectivities in the networks. We show that various biomolecular networks exhibit conserved correlations of connectivities; the correlations were initially found in the protein-protein interaction and the regulatory networks in yeast [2]. Furthermore, we propoase a growth network model which reproduces the correlations and provides insights into topologies of the biomolecular networks and several evolutionary mechanisms.
Variational Gaussian mixtures for blind source detection

Bayesian algorithms have lately been used in a large variety of applications. This paper proposes a new methodology for hyperparameter initialization in the Variational Bayes (VB) algorithm. We employ a dual expectation-maximization (EM) algorithm as the initialization stage in the VB-based learning. In the first stage, the EM algorithm is used on the given data set while the second EM algorithm is applied on distributions of parameters resulted from several runs of the first stage EM. The graphical model case study considered in this paper consists of a mixture of Gaussians. Appropriate conjugate prior distributions are considered for modelling the parameters. The proposed methodology is applied on blind source separation of modulated signals.
Spirometry Reference Values for Women and Men Aged 65–85 Living in Southern Europe: The Effect of Health Outcomes

Background: Spirometry is more and more frequently used for the diagnosis of respiratory diseases in the elderly people. Unfortunately, reliable predictive values for the spirometric variables in the elderly are lacking. Objectives: Data obtained from 429 subjects participating in the multicenter Italian study SARA on respiratory health in the elderly were analyzed in order to derive reference values for spirometry in the age range of 65–85 years. The second aim of this study was to assess the influence of variables describing many dimensions of the health status on the prediction of spirometric variables in that age range. Methods: Subjects were recruited as consecutive outpatients referred during the study period to 24 geriatric or respiratory institutions distributed throughout Italy. Spirometries were performed using the same equipment by specifically trained and certified personnel supervised by a real-time quality control program. In addition to the standard anthropometric variables, covariates describing the affective, mental, and general health conditions were investigated through standardized questionnaires, to improve the modeling of spirometric outcomes. Results: Reliable predictive values for forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) were produced (coefficient of determination, for FVC: R2 = 0.64; for FEV1: R2 = 0.54). The effect of general health conditions on spirometric outcomes accounted for the most part of the between-gender difference in the ageing-dependent decrease in lung function. Conclusions: We suggest that a multidimensional approach to health problems should be used for a better interpretation of spirometric results in the elderly.
Emissivity measurement of coated copper and aluminum samples at 80 K

Accelerator cryomodules reduce the radiative thermal load to the 1.8 – 4.5 K components by enclosing them within low-emissivity shields cooled to 70–100 K. These heat shields are not continuous and have many penetrations, or holes, to allow for the connection of support structures and the many subsystems running from room temperature. The penetrations have thermal baffles to reduce, or eliminate if possible, the direct line-of-sight between room temperature and the lower temperature components but leave many, low emissivity, paths with only a single reflection required for the room temperature photons to strike the 1.8 – 4.5 K surfaces. To reduce this we are coating our baffles such that the surfaces which face lower temperatures have a low-emissivity while the surfaces which the photons may reflect from but do not have line-of-sight to lower temperature surfaces have a high-absorption. We measure emissivities of different coatings from the heat balance between absorption, from background radiation and the heater attached on the sample, and emission powers. In this paper, design details of our experiment and measurement results will be presented.
Identifying the community roles of social capitalists in the Twitter network

In the context of Twitter, social capitalists are users trying to increase their number of followers and interactions by any means. They are not healthy for the service, because they introduce a bias in the way user influence and visibility are perceived. Understanding their behavior and position in the network is thus of important interest. In this work, we propose to do so by focusing on the community structure level. We first extend an existing method based on the notion of community role, on three different points: 1) handling of directed networks, 2) more precise modeling of the community-related connectivity and 3) unsupervised role identification. We then take advantage of an existing tool to detect social capitalists, and apply our method to analyze their organization and how their links spread across the network. The specific community roles they hold in the network let us know that they reach to obtain high visibility.