Miracle-dz/UniFAR-FTUs huggingface dataset:
A high probability bound on the mutual information across an observed Discrete Memoryless Channel

This paper introduces several new high probability bounds on the average mutual information, I (X; Y ) between the input and output of a Discrete Memoryless Channel (DMC) based on a set of observed input-output samples. We assume that channel-input probability mass function and channel transition probability mass functions are unknown. We compare a Bayesian-Dirichlet distribution model against a computationally-tractable distribution-free high-probability bound to determine (1) the rate of information possibly flowing across the channel and (2) the rate information could flow across the channel. The performance (as a function of N, the number of observed samples) is compared via simulation. The rate of convergence is towards to 'true' mutual information rate shown to be O (log(N)/N).
Dissipative cellular automata as minimalist distributed systems: a study on emergent behaviors

This paper describes the behavior observed in a class of cellular automata that we have defined as "dissipative", i.e., cellular automata for which the external environment can somehow inject "energy" to dynamically influence the evolution of the automata. In this class of cellular automata, we have observed that stable macro-level global structures emerge from local interactions among cells. Since dissipative cellular automata express characteristics strongly resembling those of open distributed systems, we expect that similar sorts of macro-level behaviors are likely to emerge in real world systems of the same nature and need to be studied, controlled, and possibly fruitfully exploited. A preliminary set of experiments reporting two ways of indirectly controlling the behavior of DCA are reported and discussed w.r. t. the possibility of applying similar sort of indirect control on open distributed systems.
Generation of polynomial inequalities as invariants

Embedded software in critical systems rise a need for software analysis, especially for guaranteeing safety properties. In the late seventies, Cousot & Cousot introduced a general framework, called abstract interpretation, dedicated to the conception of particular analyses: static analyses. Among the program properties of interest, discovering algebraic relationships between variables allows for proving the lack of run-time errors. While the inference of linear relationships is e fficiently solved, computing polynomial equalities as invariants is still a challenge, a fortiori is the inequality counterpart. After a brief overview of existing techniques for invariant inference, this report investigates the viability of a constraint-based method dedicated to the inference of polynomial inequalities as program invariants.
T–S-Fuzzy-Model-Based Approximation and Controller Design for General Nonlinear Systems

This paper presents a novel approach to control general nonlinear systems based on Takagi-Sugeno (T-S) fuzzy dynamic models. It is first shown that a general nonlinear system can be approximated by a generalized T-S fuzzy model to any degree of accuracy on any compact set. It is then shown that the stabilization problem of the general nonlinear system can be solved as a robust stabilization problem of the developed T-S fuzzy system with the approximation errors as the uncertainty term. Based on a piecewise quadratic Lyapunov function, the robust semiglobal stabilization and H∞ control of the general nonlinear system are formulated in the form of linear matrix inequalities. Simulation results are provided to illustrate the effectiveness of the proposed approaches.
Environment for hybrid simulation of information security solutions for Grid and Cloud-systems

Efficient and safe use of distributed information systems based on Grid and Cloud-computing technologies requires an assessment and optimization of their security level. For this purpose it seems appropriate to model the studied systems and their behavior under different working conditions. The paper presents an approach to modeling distributed systems in full-scale, simulation, virtual and analytical sense, and also describes the possibility of their simultaneous use in hybrid mode simulation. Such approach allows to investigate the behavior of distributed systems from different perspectives, taking into account features of the architecture, software, purposes of these systems and operation conditions. The paper also describes the mechanisms of the developed modeling software.
Confidence intervals should be used in reporting trials

Editor—The dispute between Barber and Thompson,1 who are advocating a t test, and Williams, Cohen, and Russell, who are advocating a Mann-Whitney U test,2 has its roots in the use of P values rather than confidence intervals. If Williams et al had reported their results as a confidence interval for the difference in mean cost, as recommended for the results of clinical trials published in the BMJ, the question would not have arisen. The sample size is surely large enough for the large sample Normal comparison, which does not require data to follow a normal approximation and to which the t method approximates, to be valid, even with such highly skewed data. This gives a confidence interval for the difference in cost of secondary treatment (routine minus open access) equal to –£180 to +£238, the point estimate being £29, and the same P value as the t test.
Combined antineoplastic and antiretroviral therapy for patients with hodgkin's disease and human immunodeficiency virus infection. A prospective study of 17 patients

Background. The optimal therapeutic approach for patients with Hodgkin's disease (HD) and human immunodeficiency virus (HIV) infection is unknown. In an attempt to improve the results obtained with standard chemotherapy and to decrease the occurrence of opportunistic infections (OI) during chemotherapy and follow‐up observed in a previous experience, the authors designed a prospective combined antineoplastic and antiretroviral approach.
The Use of the Multi-Island Vertical Rectus Abdominis Myocutaneous Flap in Head and Neck Reconstruction

BackgroundHead and neck cancer extirpation frequently creates complex 3-dimensional defects involving both intraoral and external tissues. Reconstruction can be accomplished either with 2 free flaps, a simultaneous free and pedicle flap, or a single double-island folded free flap. Herein, the outcomes and rationale for reconstruction of multilaminar head and neck defects using the multi-island vertical rectus abdominis myocutaneous (MI-VRAM) flap are reported. MethodsA retrospective review was performed of a prospectively collected database of all head and neck reconstructions completed by a single surgeon between 1992 and 2011. Forty-six patients were identified who underwent reconstruction of composite defects using MI-VRAM flaps. Oncologic defects were classified into 3 categories based on anatomic location, namely, the midface, lower face, and neck. Indications and outcomes were reviewed. ResultsPatients’ ages ranged from 7 to 84 years with two thirds being male; 45% received preoperative radiation. Average MI-VRAM skin paddle size was 9 × 25.5 cm. Of the 46 reconstructions, 27 (59%) were performed in the midface, 14 (30%) in the lower face, and 5 (11%) in the neck region. There were no complete flap loses; however, 1 patient lost the external skin island, requiring pectoralis flap coverage. Of these cases, 15% had surgically related complications. Ten patients required secondary contouring procedures to revise bulky flaps. ConclusionsThe MI-VRAM flap is a safe and reliable technique for reconstruction of complex defects throughout the head and neck region. Compared to 2 simultaneous free flaps or a free flap combined with a regional flap, it saves time and eliminates a second donor site. Level of EvidenceLevel IV. Clinical question addressed in this paper is whether a multi-island VRAM flap is a safe technique in head and neck reconstruction.
Effect of feeding hens regular or deodorized menhaden oil on production parameters, yolk fatty acid profile, and sensory quality of eggs.

An experiment was conducted to investigate whether feeding menhaden oil (MO) to hens affects egg weight, and whether using deodorized MO (DMO) could ameliorate decreased sensory quality of eggs (characteristic for hens fed high fish oil diets). Two-hundred twenty-four Single Comb White Leghorn hens were allocated to seven dietary treatments comprising either no fish oil, DMO, or regular MO (RMO) at 2, 4, and 6% in commercial-type diets from 19 to 55 wk of age. The data collected were analyzed in four experimental periods (i.e., 0, 2, 6, and 9 mo after feeding MO diets). The sensory evaluation of 2-wk stored eggs from hens fed the 2% RMO, 2% DMO, and control diets was undertaken. Egg weight decreased linearly with increasing MO in all periods tested (P < 0.05). The panelist's scores of aroma, taste, flavor, and acceptability of eggs from hens fed diets containing 2% of either RMO or DMO were lower (P < 0.05) than for control eggs. Greater aftertaste and off-flavors in these eggs were also detected. No differences in sensory quality (P > 0.05) for eggs from hens fed RMO vs. DMO were found. These results suggest that the deodorization of MO does not ameliorate the impaired sensory quality of eggs characteristic of hens fed MO.
"SAMs meet MEMS": surface modification with self-assembled monolayers for the dry-demolding of photoplastic MEMS/NEMS

In this contribution we demonstrate the use of self-assembled monolayers (SAMs) as anti-adhesion coating to assist the removal of photoplastic MEMS/NEMS with a patterned metal layer from the surface without wet chemical sacrificial layer etching, so-called 'dry-demolding'. The SAMs functionality here is to reduce the stiction between the surface and a thin evaporated metal film. The double-layer SAM/metal provides enough stability to support subsequent micromachining steps.
Community detection in heterogenous networks using incremental seed expansion

Mining communities is fundamental to network science as it facilitates the analysis of modern information infrastructure. Though community detection in networks is a very active research area, most of the algorithms focus on homogenous networks. Homogeneous networks consist of only one type of nodes and one type of edges. However, real-world networks mostly exhibit multi-typed objects and relationships thus forming heterogeneous information networks. There have been only a few studies in the past which aim at revealing the hidden communities in heterogeneous information networks. In this paper, we propose an effective community detection algorithm based on incremental seed expansion mechanism. Our algorithm utilizes the concept of weighted path matrix and transforms the network from heterogeneous to a homogenous network. The central idea behind our algorithm is to identify good seeds and then iteratively expand them such that the conductance score of detected communities is minimized. Experiment on a real-world network establish the viability of proposed approach.
Learning to give route directions from human demonstrations

For several applications, robots and other computer systems must provide route descriptions to humans. These descriptions should be natural and intuitive for the human users. In this paper, we present an algorithm that learns how to provide good route descriptions from a corpus of human-written directions. Using inverse reinforcement learning, our algorithm learns how to select the information for the description depending on the context of the route segment. The algorithm then uses the learned policy to generate directions that imitate the style of the descriptions provided by humans, thus taking into account personal as well as cultural preferences and special requirements of the particular user group providing the learning demonstrations. We evaluate our approach in a user study and show that the directions generated by our policy sound similar to human-given directions and substantially more natural than directions provided by commercial web services.
Cloud Platform for Scientific Advances in Earth Surface Interferometric SAR Image Analysis

The advanced Differential SAR Interferometers (DInSAR) methodologies are widely used for the investigation of Earth's surface deformation phenomena. In particular, the advanced DInSAR approach referred to as Small Baseline Subset (SBAS) technique is able to produce deformation velocity maps and the corresponding displacement time-series from a temporal sequence of space borne SAR acquisitions. Considering the already huge SAR data archives as well the upcoming massive data flow coming from the SENTINEL satellite constellation, cloud computing can be a valid solution to carry out DInSAR analyses thanks to its scalability and flexibility features. In this paper, the focus is given on the migration of the whole parallel version of the SBAS technique, namely P-SBAS, to a cloud environment by taking into account different parameters that influence processing time. Experimental tests that have been performed using both private and public cloud are also presented.
A switched-capacitor inverter using series/parallel conversion

A novel switched-capacitor inverter is proposed. The proposed inverter outputs larger voltage than the input voltage by switched capacitors in series and in parallel. The maximum output voltage is decided by the number of the capacitors. The proposed inverter, which does not need any inductors, can be smaller than a conventional unit which consists of a boost converter and an inverter. Its output harmonics is reduced compared to the conventional single phase inverter. The proposed inverter is driven by the multicarrier PWM method. In this paper, the circuit configuration, the theoretical operation, the simulation results with MATLAB/SIMULINK and the experimental results are shown. The experimental results accorded with the theoretical calculation and the simulation results.
A Sequential Learning Resource Allocation Network for Image Processing Applications

Online adaptation is a key requirement for image processing applications when used in dynamic environments. In contrast to batch learning, where retraining is required each time a new observation occurs, sequential learning algorithms offer the ability to iteratively adapt the existing classifier. In this paper, we present a neural network architecture and a fast online learning algorithm that allow to use the class of resource allocation networks for such adaptive image processing applications. The network is based on receptive fields that are processed by RBF sub-nets. The learning algorithm builds such networks online by adding new units to the sub-nets each time novel input data is observed. For this, we define a global and a local novelty criterion. Experimental results show that the proposed network outperforms existing RAN algorithms when used for face detection and recognition and is competitive with existing classifiers.
Recent Advances in Bioinformatics Reveal the Molecular Heterogeneity of Hepatocellular Carcinoma

Bioinformatics is an academic field that manages information about materials and substances essential for the biological activities of genes, proteins, and RNA. Bioinformatics enables vast amounts of biological data to be made available for statistical analysis to improve our understanding, diagnosis, and treatment of diseases. In recent years, genome-wide association studies (GWAS) and next-generation DNA sequencing technology have made it possible to generate large amounts of data very quickly. Consequently, the biggest challenge today is to extract clinically useful data from overwhelmingly large datasets, and this challenge has become a major research objective in the field of bioinformatics. A key example is that these techniques made it possible to achieve the long-held goal of molecular classification of hepatocellular carcinoma (HCC) [1, 2](fig. 1). In 2011, the National Cancer Center of Japan achieved a world first by the whole-genome sequencing of HCC using next-generation sequencers [3], and this was successively followed by reports of whole-genome and exome sequencing of HCC by both Japanese and overseas research groups [4–8]. However, in contrast to the well-known high-frequency mutations of epidermal growth factor receptor genes and the anaplastic-lymphoma-kinase fusion gene in lung adenocarcinoma, the whole-genome sequence of HCC revealed the presence of lowfrequency mutations in various genes but no single driver mutation, thereby practically ruling out the possibility that HCC is caused by a single driver mutation. However, since such vast amounts of bioinformatic data are now readily available to clinicians, we believe that if the correct approaches are taken, it is only a matter of time before a paradigm-shattering concept buried deep in bioinformatic data will be found that will revolutionize medical care. In the field of bioinformatics, a very wide range of applications is possible; for example, it can be used to predict tumor malignancy, optimal treatments, and prognosis. Also, GWAS data are useful for predicting drug sensitivity and adverse effects. This is truly the dawn of the era of individualized medicine, and the application of bioinformatics in the field of oncology is already forging a path to this goal. © 2014 S. Karger AG, Basel 2235-1795/14/0032-0068$39.50/0 www.karger. com/lic Liver Cancer 2014;3:68–70
Simultaneous optical spectral loss and chromatic dispersion measurements of fiber Bragg grating using the phase-shift technique

We present a novel measurement setup that can measure the optical spectral loss (OSL) from the phase shift induced in the transmission mode and the chromatic dispersion in the reflection mode simultaneously for the chirped fiber Bragg grating. A comparison has been made between the OSL of the new method and the typical-OSL.
Surface reconstruction based on a dynamical system †

We present an efficient algorithm that computes a manifold triangular mesh from a set of unorganized sample points in . The algorithm builds on the observation made by several researchers that the Gabriel graph of the sample points provides a good surface description. However, this surface description is only one‐dimensional. We associate the edges of the Gabriel graph with index 1 critical points of a dynamical system induced by the sample points. Exploiting also the information contained in the critical points of index 2 provides a two‐dimensional surface description which can be easily turned into a manifold.
Preoperative Aspiration Culture for Preoperative Diagnosis of Infection in Total Hip or Knee Arthroplasty

ABSTRACT This meta-analysis evaluated preoperative aspiration culture for diagnosing prosthetic joint infection (PJI) in total hip arthroplasty (THA) and total knee arthroplasty (TKA). The pooled sensitivity and specificity were 0.72 (95% confidence interval, 0.65 to 0.78) and 0.95 (0.93 to 0.97), respectively. Subgroup analyses revealed nonsignificant worse diagnostic performance for THA than for TKA (sensitivity, 0.70 versus 0.78; specificity, 0.94 versus 0.96). Preoperative aspiration culture has moderate to high sensitivity and very high specificity for diagnosing PJI.
Zbed3 contributes to malignant phenotype of lung cancer via regulating β‐catenin and P120‐catenin 1

Our previous studies indicate that abnormal expression of several Wnt signaling molecules including Axin, Dvl and β‐catenin are involved in proliferation, invasion and metastasis of lung cancer. Zbed3 was found to inhibit function of Axin‐GSK3β complex and thus lead to accumulation of β‐catenin in NIH3T3 and HEK293T cells. However its function in malignant tumors is largely unknown. Here we investigate the clinico‐pathological significance of Zbed3 expression and its function in non‐small cell lung cancer. We use immunohistochemistry and Western blotting to examine Zbed3 expression in non‐small cell lung cancer and lung tissues. Transfection of siRNA and plasmid was used to study the function of Zbed3 in lung cancer cells in vitro. We found Zbed3 expression was elevated in cancer tissues compared to normal lung tissues. Increased Zbed3 expression is significantly associated with lymph node metastasis, advanced TNM stages, higher Ki67 status and patients' poor clinical outcome. Higher Zbed3 expression was also found in lung cancer cell lines compared to bronchial epithelial cell line HBE. Downregulation of Zbed3 by siRNA significantly inhibits cancer cell proliferation and invasion in vitro. Downregulation of Zbed3 also significantly inhibits expression of β‐catenin, downstream molecules of Wnt signaling and P120ctn‐1 in lung cancer cells. These results suggest that Zbed3 may contribute to lung cancer cell invasion through regulating β‐catenin and p120ctn‐1 and may be a promissing cancer marker in non‐small cell lung cancer. © 2014 Wiley Periodicals, Inc.
Mechanical reliability of optical fibre for strain sensors

The reliability of optical fibre for sensor applications is an important issue since they are embedded in structures. The production of these sensors sometimes requires that the coating is stripped from the optical fibres with hot sulphuric acid. Tests conducted on the static fatigue of optical fibres under water, where the time to failure is measured at different stresses showed a deviation from the power-law equation. This indicates an increase in the stress corrosion parameter, n, at lower stress. Tests done using a two-point bend machine to measure the fracture strength of fibre at different time intervals after previous loading to a fixed stress showed a slight increase with time before the strength starts decreasing. This can be explained by the suggestion that the silica bonds deform by twisting to decrease the stretch in the bonds themselves before cracking. However, this might be a problem for optical fibres strain sensors used at high stress.
Lowpass filtering method for heart rate variability analysis

Various methods have been proposed for the spectral analysis of heart rate variability (HRV). Such methods always require constant period data in time series. However, since the R-R interval of HRV is, in general, irregular, somehow the experimental data must be converted to constant period data. Assuming an integral pulse frequency modulation (IPFM) model, constant period data can be obtained by passing the heartbeat impulse train through a digital filter using the alias-free method proposed by French and Holden. However, the forementioned method has two problems; that is: (1) the frequency characteristics of the lowpass filter are assumed to be ideal, hence the impulse response function of the filter spreads infinitely with respect to time; and (2) only pulses inside the analyzing section are considered while those outside the analyzing section in the convolution integral are ignored. To solve those problems, the method of French and Holden has been modified in the following three points: (1) such a finite impulse response digital filter was used that the sidelobe of the filter decays rapidly in its impulse response function; (2) the analyzing section as well as its impulse response time were lengthened, thus the pulses outside the analyzing section were considered; and (3) an appropriate window was used. With these improvements, the truncation error and the computation time could be reduced. Furthermore, as a result of simulation, a much higher accuracy in the demodulation of the heartbeat signal was obtained.