Miracle-dz/UniFAR-FTUs huggingface dataset:
Widely Linear Sphere Decoder in MIMO Systems by Exploiting the Conjugate Symmetry of Linearly Modulated Signals

This paper investigates the widely linear processing (WLP) for the detection of circular signals, such as M-ary phaseshift keying (MPSK) signals and M-ary quadrature amplitude modulation (MQAM) signals. First, a unified mathematical model is derived to describe the conjugate symmetry of general MPSK/MQAM signals. In the unified model, a phase-rotation matrix (PRM) is introduced to partition the constellation of multiple-input multiple-output (MIMO) signals into subsets. Signals in a subset share the same PRM. Second, a widely linear receiver is proposed in each subset for MIMO detection. To avoid repetitive WLP in each subset, a widely linear sphere decoder (WLSD) is further proposed for MIMO systems. WLSD transforms the traditional sphere decoder (SD) searching for a true transmitted vector into a shrunk one by searching for the corresponding phase-rotation vector. Finally, the diversity order of WLSD is proven to be more than NR - NT -1/2 and less than NR, where NT (or NR) denotes the number of transmitting (or receiving) antennas. Additional performance analysis is also conducted to quantify the signal-to-noise ratio improvement. The complexity analysis reveals that the candidate phase-rotation vectors of WLSD are no more than (1/2)NT of the SD candidates. Simulation results show that the proposed WLSD can achieve quasi-optimal bit error rate performance, while the computational complexity is reduced by more than a half compared with the Schnorr-Euchner sphere decoder.
All four Sendai Virus C proteins bind Stat1, but only the larger forms also induce its mono-ubiquitination and degradation.

Sendai virus infection strongly induces interferon (IFN) production and has recently been shown to interdict the subsequent IFN signaling through the Jak/Stat pathway. This anti-IFN activity of SeV is due to its "C" proteins, a nested set of four proteins (C', C, Y1, Y2) that carry out a nested set of functions in countering the innate immune response. We previously reported that all four C proteins interact with Stat1 to prevent IFN signaling through the Jak/Stat pathway. Nevertheless, only the longer C proteins reduced Stat1 levels and prevented IFN from inducing an antiviral (VSV) state, or apoptosis, in IFN-competent murine cells. Here, we investigate the mechanism by which the various C proteins differentially affect the host antiviral defenses. All four C proteins were found to physically associate with Stat1 during cell culture infections, and in vitro in the absence of other viral gene products (as evidenced by co-immunoprecipitation). In addition, the inability of a null mutant (C(F170S)) to bind Stat1 suggests that this interaction is physiologically relevant. We have also shown that the proteasomal inhibitor MG132 can prevent the C protein-induced dismantling of the antiviral (VSV) state in murine cells; thus, the turnover of Stat1 correlates with the C protein-mediated counteraction of the antiviral (VSV) state. The C protein-induced instability of Stat1 was accompanied by a clear increase in the level of mono-ubiquinated Stat1, an unexpected hallmark of protein degradation. Finally, we show that a rSeV with mutant C proteins but wild-type Y proteins (CDelta10-15, that does not counteract the endogenous antiviral (VSV) state of MEFs even though their C proteins bind Stat1 and prevent its activity) is also unable to decrease bulk Stat1 levels or to increase the level of ubiquinated Stat1.
Analysis of Pre-Processing Methods for Music Information Retrieval in Noisy Environments using Mobile Devices

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.
FUSING DATA-MINING AND KNOWLEDGE-ELICITATION FOR THE SUPERVISORY CONTROL OF AN ALUMINIUM PLATE MILL

Processing of Aluminium Plate through a four high, reversing mill, requires complex supervisory control in order to obtain quality product. Alcoa, Europe, Flat Rolled Products, currently employ a state-of-the-art model-based, adaptation system (HMAS), supplied by VAI. It was considered that useful knowledge contained within routinely collected plant data and within the expertise of plant personnel was not being fully exploited. The aim of this work is to obtain benefit from this knowledge. This paper shows how data-mining and knowledge-elicitation techniques were combined in an advanced supervisory control (level II) system, which has successfully rolled aluminium plates of exacting specifications.
Série Scientifique Scientific Series Forecasting Financial Volatility with Combined Qml and Lad-arch Estimators of the Garch Model Forecasting Financial Volatility with Combined Qml and Lad-arch Estimators of the Garch Model *

Le CIRANO collabore avec de nombreux centres et chaires de recherche universitaires dont on peut consulter la liste sur son site web. Les cahiers de la série scientifique (CS) visent à rendre accessibles des résultats de recherche effectuée au CIRANO afin de susciter échanges et commentaires. Ces cahiers sont écrits dans le style des publications scientifiques. Les idées et les opinions émises sont sous l'unique responsabilité des auteurs et ne représentent pas nécessairement les positions du CIRANO ou de ses partenaires. This paper presents research carried out at CIRANO and aims at encouraging discussion and comment. The observations and viewpoints expressed are the sole responsibility of the authors. They do not necessarily represent positions of CIRANO or its partners. Résumé / Abstract GARCH models and their variants are usually estimated using quasi-Maximum Likelihood (QML). Recent work has shown that by using estimates of quadratic variation, for example from the daily realized volatility, it is possible to estimate these models in a different way which incorporates the additional information. Theory suggests that as the precision of estimates of daily quadratic variation improves, such estimates (via LAD-ARCH approximation) should come to equal and eventually dominate the QML estimators. The present paper investigates this using a five-year sample of data on returns from all 466 S&P 500 stocks which were present in the index continuously throughout the period. The results suggest that LAD-ARCH estimates, using realized volatility on five-minute returns over the trading day, yield measures of 1-step forecast accuracy comparable or slightly superior to those obtained from QML estimates. Combining the two estimators, either by equal weighting or weighting based on cross-validation, appears to produce a clear improvement in forecast accuracy relative to either of the two different forecasting methods alone. * The support of the Fonds québécois de la recherche sur la société et la culture (FQRSC) is gratefully acknowledged. We also thank CIRANO (Centre Interuniversitaire de recherche en analyse des organisations) for providing research facilities. Data were provided by Tick Data (www.tickdata.com).
Interactive and guided architectural refactoring with search-based recommendation

Architectural refactorings can contain hundreds of steps and experienced developers could carry them out over several weeks. Moreover, developers need to explore a correct sequence of refactorings steps among many more incorrect alternatives. Thus, carrying out architectural refactorings is costly, risky, and challenging. In this paper, we present Refactoring Navigator: a tool-supported and interactive recommendation approach for aiding architectural refactoring. Our approach takes a given implementation as the starting point, a desired high-level design as the target, and iteratively recommends a series of refactoring steps. Moreover, our approach allows the user to accept, reject, or ignore a recommended refactoring step, and uses the user's feedback in further refactoring recommendations. We evaluated the effectiveness of our approach and tool using a controlled experiment and an industrial case study. The controlled experiment shows that the participants who used Refactoring Navigator accomplished their tasks in 77.4% less time and manually edited 98.3% fewer lines than the control group. The industrial case study suggests that Refactoring Navigator has the potential to help with architectural refactorings in practice.
Genome Sequence of a Versatile Aromatic Hydrocarbon-Degrading Bacterium, Arthrobacter sp. W1

ABSTRACT Arthrobacter sp. W1 is a versatile aromatic-degrading strain which can directly or cometabolically degrade various organic pollutants, such as phenol, naphthalene, carbazole, dibenzofuran, and dibenzothiophene. Here, we present a 3.8-Mb draft genome sequence of strain W1, which may provide comprehensive genetic information for the application in environmental pollution remediation.
Combining Structural Information and the Use of Priors in Mixed Named-Page and Homepage Finding

This paper presents Carnegie Mellon University’s experiments on the mixed named-page and homepage finding task of the TREC 12 Web Track. Our results were strong; we achieved the success using language models estimated from combining information from document text, in-link text, and information present in the structure of the documents. We also present experiments using expectations about posterior distributions to create class-based prior probabilities. We find that priors do provide a large gain for our official runs, but we do further experiments that show the priors do not always help. Some preliminary analysis shows that the prior probabilities are not providing the desired posterior distributions. In cases where applying the priors harm performance, the observed posterior distributions in the rankings are far off of the desired posterior distributions.
Low Cost Artificial Planar Target Measurement Techniques for Terrestrial Laser Scanning

SUMMARY This paper presents both an internal and external accuracy assessment of four different methods for measuring the centroid of a signalized planar target captured by a terrestrial laser scanner. The planar targets used in this project are composed of a black background and a white circle printed on 8½ by 11 inches plain sheet of paper using a consumer level LaserJet printer. The first two methods tested define the centroid of a target to be the mean and median of the cluster of points belonging to the white circle in the point cloud. The latter two methods are more advanced, and they take advantage of the planar nature of the target as well as the intensity difference between the circle and the background to strengthen the centroid derivation through a combination of least-squares plane fitting and circle fitting. The main benefit of the four presented methodologies is that no specialized and/or laser scanner dependent targets need to be utilized. And it will be demonstrated in this paper that using the two advanced methods can yield position measurement precision and accuracy far superior to the simple mean or median computations. In fact, sub-millimetre precision and accuracy is achievable from using low cost paper targets provided that an appropriate target measurement algorithm like the latter two methodologies proposed in this paper is adopted.
Optimized Wireless Web Browsing Using Mobile Agents

The use of wireless data comunications is increasing rapidly despite limited bandwidth and reliability. Algorithms have been developed to optimize the available bandwidth (e.g. through compression) but the widespread adoption of wireless data communications has been slowed by the need to agree on optimization standards and to implement and deploy code to implement such standards. In this paper we propose a framework that uses mobile agents to dynamically deploy application-specific, bandwidth optimization code. No agreement is required on the set of available optimizations only that base stations support a specific mobile agent execution environment. If a given base station does not support mobile agents, it can still be used but in an unoptimized fashion. In our system, the mobile unit “downloads” an agent to its base station that dynamically loads optimization code as needed over the fixed network. The optimizations loaded are determined by the mobile unit (via the agent) and correspond to optimizations the mobile unit supports. This framework supports optimization specialization at very low cost since little code must be downloaded from the mobile unit. A prototype implementation of the framework using the Java-based Aglets mobile agent system is presented.
Widespread acetaldehyde near the galactic centre

We present Giant Meterwave Radio Telescope images of the 1065 MHz emission from the 1(11) --> 1(10) rotational transition of acetaldehyde ( CH3CHO) in the molecular cloud complex Sgr B2. Our observations are unique in that they have a high spatial resolution (similar to4"), while still being sensitive to large-scale emission. Most complex organic molecules in this cloud (e.g. acetone, methyl formate, acetic acid) are concentrated in a very small core, similar to0.1 pc across. In contrast, acetaldehyde is found to be spread over a region at least 100 times larger in extent. The line emission is confined to regions with radio continuum emission and correlates well ( in both position and velocity) with formaldehyde absorption towards this continuum; this is consistent with earlier single dish results suggesting that it is likely to be weakly mased. Our observations also suggest that grain mantle destruction by shocks plays an important role in the observed gas phase abundance of CH3CHO in Sgr B2.
Condition Monitoring of Combustion Processes Through Flame Imaging and Kernel Principal Component Analysis

This article presents a methodology for the diagnosis of abnormal conditions in a combustion process through flame imaging and kernel principal component analysis (KPCA). A digital imaging system is used to capture real-time flame images and radiation signals, from which flame characteristics such as flame area, brightness, non-uniformity, and oscillation frequency are quantified. These characteristics are used as the variables to establish the KPCA model of the combustion process. With the use of Hotelling's T2 and Q statistics, the monitoring of abnormal conditions of the combustion process is achieved. Unlike the traditional principal component analysis (PCA) method, the KPCA method is capable of dealing with nonlinear data via nonlinear mapping, which projects the original nonlinear input space into a high-dimensional linear feature space. The effectiveness of the methodology is demonstrated by applying the approach to processing the data obtained on a 9MWth heavy oil fired combustion test facility. Experimental results obtained show that the KPCA method outperforms the traditional PCA in discriminating between the normal and abnormal combustion conditions, even in cases where the number of training samples is limited.
The Fungus Candida albicans Tolerates Ambiguity at Multiple Codons

The ascomycete Candida albicans is a normal resident of the gastrointestinal tract of humans and other warm-blooded animals. It occurs in a broad range of body sites and has high capacity to survive and proliferate in adverse environments with drastic changes in oxygen, carbon dioxide, pH, osmolarity, nutrients, and temperature. Its biology is unique due to flexible reassignment of the leucine CUG codon to serine and synthesis of statistical proteins. Under standard growth conditions, CUG sites incorporate leucine (3% of the times) and serine (97% of the times) on a proteome wide scale, but leucine incorporation fluctuates in response to environmental stressors and can be artificially increased up to 98%. In order to determine whether such flexibility also exists at other codons, we have constructed several serine tRNAs that decode various non-cognate codons. Expression of these tRNAs had minor effects on fitness, but growth of the mistranslating strains at different temperatures, in medium with different pH and nutrients composition was often enhanced relatively to the wild type (WT) strain, supporting our previous data on adaptive roles of CUG ambiguity in variable growth conditions. Parallel evolution of the recombinant strains (100 generations) followed by full genome resequencing identified various strain specific single nucleotide polymorphisms (SNP) and one SNP in the deneddylase (JAB1) gene in all strains. Since JAB1 is a subunit of the COP9 signalosome complex, which interacts with cullin (Cdc53p) to mediate degradation of a variety of cellular proteins, our data suggest that neddylation plays a key role in tolerance and adaptation to codon ambiguity in C. albicans.
Liver Cirrhosis: Evaluation, Nutritional Status, and Prognosis

The liver is the major organ for the metabolism of three major nutrients: protein, fat, and carbohydrate. Chronic hepatitis C virus infection is the major cause of chronic liver disease. Liver cirrhosis (LC) results from different mechanisms of liver injury that lead to necroinflammation and fibrosis. LC has been seen to be not a single disease entity but one that can be graded into distinct clinical stages related to clinical outcome. Several noninvasive methods have been developed for assessing liver fibrosis and these methods have been used for predicting prognosis in patients with LC. On the other hand, subjects with LC often have protein-energy malnutrition (PEM) and poor physical activity. These conditions often result in sarcopenia, which is the loss of skeletal muscle volume and increased muscle weakness. Recent studies have demonstrated that PEM and sarcopenia are predictive factors for poorer survival in patients with LC. Based on these backgrounds, several methods for evaluating nutritional status in patients with chronic liver disease have been developed and they have been preferably used in the clinical field practice. In this review, we will summarize the current knowledge in the field of LC from the viewpoints of diagnostic method, nutritional status, and clinical outcomes.
Improvement of white matter fiber tracking based on diffusion-tensor MR imaging data using modified speed functions.

Background/Objective: White matter tractography is a non-invasive method, which reconstructs three-dimensional trajectories of the brain tracts using diffusion-tensor imaging (DTI) data. Due to the partial volume effect of DTI data, some of tractography algorithms are unable to follow the correct pathways after the crossing and branching regions. The main challenge for tractography methods has been the ability to detect these regions. Fast marching techniques are capable of tracking the fibers with wide spreading. Materials and Methods: In order to detect true fibers, an adaptive functional anisotropy (FA) weighted function is proposed to modify the speed function of these algorithms. The performance of the proposed tractography method is assessed using synthetic data and its feasibility is showed by extracting some well-known tracts using healthy human DTI datasets. Result: The percentage of the length of whole tracts extracted by our proposed method is above 85% even for a signal to noise ratio (SNR) level equal to 16. The ability of this method to detect the fiber crossing in simulation data is above 90%. Furthermore, the tractography results of some well-known tracts demonstrate the ability of the proposed methods to extract the correct pathways from the anatomical point of view. Conclusion: This method has led to great impact on the fast-marching fiber-tracking method in propagating the tractography front in an adaptive manner. The suggested speed function can make the speed of front propagation adapted to the type of brain’s environments such as isotropic and anisotropic regions.
A Verified Decision Procedure for Pseudo-Boolean Formulas

Pseudo-Boolean formulas consist of constraints of the form ∑i=1 wi · xi k, where xi are propositional literals, wi ∈ Z, k ∈ Z, and arise in planning, scheduling and optimization problems. We describe an efficient and easily verifiable decision procedure for pseudo-Boolean formulas, that is based on encoding PB formulas into the propositional satisfiability problem with the cutting-edge sequential weighted counter encoding. State-of-the-art SAT solvers that emit unsatisfiability proofs are used to solve the resulting instances. The combination of a verified translation to SAT, and certified SAT solvers leads to a verified decision procedure for PB formulas. The verification of the encoding is carried out in the Coq proof assistant.
Canonical Tree Representation of Distance Hereditary Graphs with Applications ∗ Ryuhei Uehara †

The class of distance hereditary graphs consists of the isometric graphs. That is, for each pair of vertices, its distance is invariant for any induced path in a distance hereditary graph. In the paper, a canonical tree representation of a distance hereditary graph is proposed. A linear time algorithm for computing the tree representation is also presented. Hence the recognition problem and the graph isomorphism problem for the graph class can be solved in linear time, thanks to linear time isomorphism algorithm for labeled trees. The tree representation takes O(|V |) space for a distance hereditary graph G = (V, E). Hence it can be used as a compact data structure for the graph. It is so informative that all pruning sequences, which is a previously known characterization based on a vertex ordering, can be generated from the tree representation efficiently.
BayesMendel: an R Environment for Mendelian Risk Prediction

Several important syndromes are caused by deleterious germline mutations of individual genes. In both clinical and research applications it is useful to evaluate the probability that an individual carries an inherited genetic variant of these genes, and to predict the risk of disease for that individual, using information on his/her family history. Mendelian risk prediction models accomplish these goals by integrating Mendelian principles and state-of-the-art statistical models to describe phenotype/genotype relationships. Here we introduce an R library called BayesMendel that allows implementation of Mendelian models in research and counseling settings. BayesMendel is implemented in an object-oriented structure in the language R and distributed freely as an open source library. In its first release, it includes two major cancer syndromes: the breast-ovarian cancer syndrome and the hereditary non-polyposis colorectal cancer syndrome, along with up-to-date estimates of penetrance and prevalence for the corresponding genes. Input genetic parameters can be easily modified by users. BayesMendel can also serve as a generic tool for genetic epidemiologists to flexibly implement their own Mendelian models for novel syndromes and local subpopulations, without reprogramming complex statistical analyses and prediction tools.
Designing nearest neighbour classifiers by the evolution of a population of prototypes

A new evolutionary algorithm to design nearest neightbour classi(cid:12)ers is presented in this paper. Main design topics of this sort of classi(cid:12)ers are the number of prototypes used and their position. This algorithm is based on the evolution of a population of prototypes that try to achieve an equilibrium by (cid:12)nding the right size of the population and the position of each prototype in the environment, solving at the same time both design topics above. A biological point of view is given to explain most of the concepts introduced, as well as the operators used in evolution.