Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Frequency Analysis as Probabilistic Inference

This paper proposes a new view of time-frequency analysis framed in terms of probabilistic inference. Natural signals are assumed to be formed by the superposition of distinct time-frequency components, with the analytic goal being to infer these components by application of Bayes' rule. The framework serves to unify various existing models for natural time-series; it relates to both the Wiener and Kalman filters, and with suitable assumptions yields inferential interpretations of the short-time Fourier transform, spectrogram, filter bank, and wavelet representations. Value is gained by placing time-frequency analysis on the same probabilistic basis as is often employed in applications such as denoising, source separation, or recognition. Uncertainty in the time-frequency representation can be propagated correctly to application-specific stages, improving the handing of noise and missing data. Probabilistic learning allows modules to be co-adapted; thus, the time-frequency representation can be adapted to both the demands of the application and the time-varying statistics of the signal at hand. Similarly, the application module can be adapted to fine properties of the signal propagated by the initial time-frequency processing. We demonstrate these benefits by combining probabilistic time-frequency representations with non-negative matrix factorization, finding benefits in audio denoising and inpainting tasks, albeit with higher computational cost than incurred by the standard approach.
Distributed Multistage Cooperative-Social-Multicast-Aided Content Dissemination in Random Mobile Networks

A distributed multistage cooperative-social-multicast-protocol-aided content dissemination scheme is proposed, which is based on a self-organized ad hoc network of mobile stations (MSs) seeking the same content. In our content dissemination scheme, upon receiving the content, the content owners may further multicast it to their social contacts who are hitherto unserved content seekers. Then, we mathematically define the geographic social strength to describe the social relationships between a pair of MSs. By jointly considering the geographic social strength, the geographic distances, and the path loss, as well as the small-scale fading, we derive the closed-form formula of the average social unicast throughput. Furthermore, we model the content dissemination process by a discrete-time pure-birth-based Markov chain and derive the closed-form expressions for the statistical properties of the content dissemination delay. The proposed multistage cooperative social multicast protocol is capable of successfully delivering the content of common interest to all MSs in two transmission frames, provided that the density of the MSs is sufficiently high, as demonstrated both by our simulation and analytical results.
Quantitative Visualization of Transient Heat Transfer of Oblique Impinging Jet Using Thermographic Phosphor

The transient heat transfer and flow characteristics of oblique impinging jets, according to the impinging angle and distance from nozzle to the plate were studied. To investigate the temperature change of the hot plate, thermographic phosphor thermometry was used. For the transient heat transfer analysis, 1D semiinfinite solid model was used to obtain a coefficient of local heat transfer and Nusselt number. The peak heat transfer was occurring at the stagnation point, and secondary peak was also occurred at the low distance from nozzle to plate and high impinging angle condition. These secondary peaks are shown to be due to an abrupt increase in turbulence in the wall jet boundary layer. In order to verify this phenomenon, we conducted a flow visualization of oblique impinging jets on the hot plate
Prevalence and Transmission of Trypanosoma cruzi in People of Rural Communities of the High Jungle of Northern Peru

Background Vector-borne transmission of Trypanosoma cruzi is seen exclusively in the Americas where an estimated 8 million people are infected with the parasite. Significant research in southern Peru has been conducted to understand T. cruzi infection and vector control, however, much less is known about the burden of infection and epidemiology in northern Peru. Methodology A cross-sectional study was conducted to estimate the seroprevalence of T. cruzi infection in humans (n=611) and domestic animals [dogs (n=106) and guinea pigs (n=206)] in communities of Cutervo Province, Peru. Sampling and diagnostic strategies differed according to species. An entomological household study (n=208) was conducted to identify the triatomine burden and species composition, as well as the prevalence of T. cruzi in vectors. Electrocardiograms (EKG) were performed on a subset of participants (n=90 T. cruzi infected participants and 170 age and sex-matched controls). The seroprevalence of T. cruzi among humans, dogs, and guinea pigs was 14.9% (95% CI: 12.2 – 18.0%), 19.8% (95% CI: 12.7- 28.7%) and 3.3% (95% CI: 1.4 – 6.9%) respectively. In one community, the prevalence of T. cruzi infection was 17.2% (95% CI: 9.6 - 24.7%) among participants < 15 years, suggesting recent transmission. Increasing age, positive triatomines in a participant's house, and ownership of a T. cruzi positive guinea pig were independent correlates of T. cruzi infection. Only one species of triatomine was found, Panstrongylus lignarius, formerly P. herreri. Approximately forty percent (39.9%, 95% CI: 33.2 - 46.9%) of surveyed households were infested with this vector and 14.9% (95% CI: 10.4 - 20.5%) had at least one triatomine positive for T. cruzi. The cardiac abnormality of right bundle branch block was rare, but only identified in seropositive individuals. Conclusions Our research documents a substantial prevalence of T. cruzi infection in Cutervo and highlights a need for greater attention and vector control efforts in northern Peru.
Flat vs. symbiotic evolutionary subspace clusterings

Subspace clustering coevolves the attribute space supporting clusters at the same time as parameterizing the cluster location and combination. Typically, a 'flat' representation is pursued in which individuals describe both the property of individual clusters as well as the combination of clusters used to define the overall solution; hereafter F-ESC. Conversely, a symbiotic approach was recently proposed in which candidate clusters and the combination of clusters are coevolved from independent populations; hereafter S-ESC. In this work a common framework is pursued in order for flat and symbiotic evolutionary subspace clustering to be compared directly. We show that F-ESC might match S-ESC results for data sets with high proportions of cluster support, however, the gap between the two algorithm increases as cluster support decreases.
Yeast Nanobiotechnology

: Yeast nanobiotechnology is a recent ﬁeld where nanotechniques are used to manipulate and analyse yeast cells and cell constituents at the nanoscale. The aim of this review is to give an overview and discuss nanobiotechnological analysis and manipulation techniques that have been particularly applied to yeast cells. These techniques have mostly been applied to the model yeasts Saccharomyces cerevisiae and Schizosaccaromyces pombe , and the pathogenic model yeast Candida albicans . Nanoscale imaging techniques, such as Atomic Force Microscopy (AFM), super-resolution ﬂuorescence microscopy, and electron microscopy (scanning electron microscopy (SEM), transmission electron microscopy (TEM), including electron tomography) are reviewed and discussed. Other nano-analysis methods include single-molecule and single-cell force spectroscopy and the AFM-cantilever-based nanomotion analysis of living cells. Next, an overview is given on nano/microtechniques to pattern and manipulate yeast cells. Finally, direct contact cell manipulation methods, such as AFM-based single cell manipulation and micropipette manipulation of yeast cells, as well as non-contact cell manipulation techniques, such as optical, electrical, and magnetic cells manipulation methods are reviewed.
Applications of Ramsey's theorem to decision tree complexity

Combinatorial techniques for extending lower bound results for decision trees to general types of queries are presented. Problems that are defined by simple inequalities between inputs, called <italic>order invariant</italic> problems, are considered. A decision tree is called <italic>k-bounded</italic> if each query depends on at most <italic>k</italic> variables. No further assumptions on the type of queries are made. It is proved that one can replace the queries of any <italic>k</italic>-bounded decision tree that solves an order-invariant problem over a large enough input domain with <italic>k</italic>-bounded queries whose outcome depends only on the relative order of the inputs. As a consequence, all existing lower bounds for comparison-based algorithms are valid for general <italic>k</italic>-bounded decision trees, where <italic>k</italic> is a constant. An &OHgr;(<italic>n</italic> log <italic>n</italic>) lower bound for the element uniqueness problem and several other problems for any <italic>k</italic>-bounded decision tree, such that <italic>k</italic> = <italic>O</italic>(<italic>n<supscrpt>c</supscrpt></italic>) and <italic>c</italic> < 1/2 is proved. This lower bound is tight since there exist <italic>n</italic><supscrpt>1/2</supscrpt>-bounded decision trees of complexity <italic>O</italic>(<italic>n</italic>) that solve the element-uniqueness problem. All the lower bounds mentioned above are shown to hold for nondeterministic and probabilistic decision trees as well.
Steady-State Performance Analyses of Adaptive Filters

Adaptive filters have become a vital part of many modern communication and control systems, which can be used in system identification, adaptive equalization, echo cancellation, beamforming, and so on [l]. The least mean squares (LMS) algorithm, which is the most popular adaptive filtering algorithm, has enjoyed enormous popularity due to its simplicity and robustness [2] [3]. Over the years several variants of LMS have been proposed to overcome some limitations of LMS algorithm by modifying the error estimation function from linearity to nonlinearity. Sign-error LMS algorithm is presented by its computational simplicity [4], least-mean fourth (LMF) algorithm is proposed for applications in which the plant noise has a probability density function with short tail [5], and the LMMN algorithm achieves a better steady state performance than the LMS algorithm and better stability properties than the LMF algorithm by adjusting its mixing parameter [6], [7]. The performance of an adaptive filter is generally measured in terms of its transient behavior and its steady-state behavior. There have been numerous works in the literature on the performance of adaptive filters with many creationary results and approaches [3]-[20]. In most of these literatures, the steady-state performance is often obtained as a limiting case of the transient behavior [13]-[16]. However, most adaptive filters are inherently nonlinear and time-variant systems. The nonlinearities in the update equations tend to lead to difficulties in the study of their steady-state performance as a limiting case of their transient performance [12]. In addition, transient analyses tend to require some more simplifying assumptions, which at times can be restrictive. Using the energy conservation relation during two successive iteration update , N. R. Yousef and A. H. Sayed re-derived the steady-state performance for a large class of adaptive filters [11],[12], such as sign-error LMS algorithm, LMS algorithm, LMMN algorithm, and so on, which bypassed the difficulties encountered in obtaining steady-state results as the limiting case of a transient analysis. However, it is generally observed that most works for analyzing the steady-state performance study individual algorithms separately. This is because different adaptive schemes have different nonlinear update equations, and the particularities of each case tend to require different arguments and assumptions. Some authors try to investigate the steadystate performance from a general view to fit more adaptive filtering algorithms, although that is a challenge task. Based on Taylor series expansion (TSE), S. C. Douglas and T. H. Meng obtained a general expression for the steady-state MSE for adaptive filters with error
Lag time determination in DEC measurements with PTR-MS

Abstract. The disjunct eddy covariance (DEC) method has emerged as a popular technique for micrometeorological flux measurements of volatile organic compounds (VOCs). It has usually been combined with proton transfer reaction mass spectrometry (PTR-MS), an online technique for VOC concentration measurements. However, the determination of the lag time between wind and concentration measurements has remained an important challenge. To address this issue, we studied the effect of different lag time methods on DEC fluxes. The analysis was based on both actual DEC measurements with PTR-MS and simulated DEC data derived from high frequency H2O measurements with an infrared gas analyzer. Conventional eddy covariance fluxes of H2O served as a reference in the DEC simulation. The individual flux measurements with PTR-MS were rather sensitive to the lag time methods, but typically this effect averaged out when the median fluxes were considered. The DEC simulation revealed that the maximum covariance method was prone to overestimation of the absolute values of fluxes. The constant lag time methods, one based on a value calculated from the sampling flow and the sampling line dimensions and the other on a typical daytime value, had a tendency to underestimate. The visual assessment method and our new averaging approach utilizing running averaged covariance functions did not yield statistically significant errors and thus fared better than the habitual choice, the maximum covariance method. Given this feature and the potential for automatic flux calculation, we recommend using the averaging approach in DEC measurements with PTR-MS. It also seems well suited to conventional eddy covariance applications when measuring fluxes near the detection limit.
Evaluating Uncertainty to Strengthen Epidemiologic Data for Use in Human Health Risk Assessments

Background: There is a recognized need to improve the application of epidemiologic data in human health risk assessment especially for understanding and characterizing risks from environmental and occupational exposures. Although there is uncertainty associated with the results of most epidemiologic studies, techniques exist to characterize uncertainty that can be applied to improve weight-of-evidence evaluations and risk characterization efforts. Methods: This report derives from a Health and Environmental Sciences Institute (HESI) workshop held in Research Triangle Park, North Carolina, to discuss the utility of using epidemiologic data in risk assessments, including the use of advanced analytic methods to address sources of uncertainty. Epidemiologists, toxicologists, and risk assessors from academia, government, and industry convened to discuss uncertainty, exposure assessment, and application of analytic methods to address these challenges. Synthesis: Several recommendations emerged to help improve the utility of epidemiologic data in risk assessment. For example, improved characterization of uncertainty is needed to allow risk assessors to quantitatively assess potential sources of bias. Data are needed to facilitate this quantitative analysis, and interdisciplinary approaches will help ensure that sufficient information is collected for a thorough uncertainty evaluation. Advanced analytic methods and tools such as directed acyclic graphs (DAGs) and Bayesian statistical techniques can provide important insights and support interpretation of epidemiologic data. Conclusions: The discussions and recommendations from this workshop demonstrate that there are practical steps that the scientific community can adopt to strengthen epidemiologic data for decision making. Citation: Burns CJ, Wright JM, Pierson JB, Bateson TF, Burstyn I, Goldstein DA, Klaunig JE, Luben TJ, Mihlan G, Ritter L, Schnatter AR, Symons JM, Yi KD. 2014. Evaluating uncertainty to strengthen epidemiologic data for use in human health risk assessments. Environ Health Perspect 122:1160–1165; http://dx.doi. org/10.1289/ehp.1308062
Investigation of the antibiofilm capacity of peptide-modified stainless steel

Biofilm formation on surfaces is an important research topic in ship tribology and medical implants. In this study, dopamine and two types of synthetic peptides were designed and attached to 304 stainless steel surfaces, aiming to inhibit the formation of biofilms. A combinatory surface modification procedure was applied in which dopamine was used as a coupling agent, allowing a strong binding ability with the two peptides. X-ray photoelectron spectroscopy (XPS), elemental analysis, contact angle measurement and surface roughness test were used to evaluate the efficiency of the peptide modification. An antibiofilm assay against Staphylococcus aureus was conducted to validate the antibiofilm capacity of the peptide-modified stainless steel samples. XPS analysis confirmed that the optimal dopamine concentration was 40 µg ml−1 in the coupling reaction. Element analysis showed that dopamine and the peptides had bound to the steel surfaces. The robustness assay of the modified surface demonstrated that most peptide molecules had bound on the surface of the stainless steel firmly. The contact angle of the modified surfaces was significantly changed. Modified steel samples exhibited improved antibiofilm properties in comparison to untreated and dopamine-only counterpart, with the peptide 1 modification displaying the best antibiofilm effect. The modified surfaces showed antibacterial capacity. The antibiofilm capacity of the modified surfaces was also surface topography sensitive. The steel sample surfaces polished with 600# sandpaper exhibited stronger antibiofilm capacity than those polished with other types of sandpapers after peptide modification. These findings present valuable information for future antifouling material research.
Genetic analysis of CAV 1 gene in hypertension and metabolic syndrome

Antonio Grilo1, Maria Luisa Fernandez1, Manuel Beltrán1, Reposo Ramirez-Lorca2, María Angeles González1, Jose Luis Royo2, Reyes Gutierrez-Tous3, Francisco Jesús Morón2, Carmen Couto3, Manuel Serrano-Rios4, Maria Eugenia Saez2, Agustin Ruiz2, Luis Miguel Real2 1Servicio de Medicina Interna, Hospital deValme, Sevilla, 2Departmento de Genómica Estructural, Neocodex, Sevilla, 3Servicio de Hematología, Hospital deValme, Sevilla, 4Departmento de Medicina Interna II, Hospital Clínico San Carlos, Madrid; Spain
Bayesian Poisson tree processes and multispecies coalescent models shed new light on the diversification of Nawab butterflies in the Solomon Islands (Nymphalidae, Charaxinae, Polyura)

Butterflies of the genus Polyura form a widespread tropical group distributed from Pakistan to Fiji. The rare endemic Polyura epigenes Godman & Salvin, 1888 from the Solomon Islands archipelago represents a case of marked island polymorphism. We sequenced museum specimens of this species across its geographic range to study the phylogeography and genetic differentiation of populations in the archipelago. We used the Bayesian Poisson tree processes and multispecies coalescent models, to study species boundaries. We also estimated divergence times to investigate the biogeographic history of populations. Our molecular species delimitation and nuclear DNA network analyses unambiguously indicate that Malaita populations form an independent metapopulation lineage, as defined in the generalized lineage concept. This lineage, previously ranked as a subspecies, is raised to species rank under the name Polyura bicolor Turlin & Sato, 1995 stat. nov. Divergence time estimates suggest that this lineage split from its sister taxon in the late Pleistocene. At this time, the bathymetric isolation of Malaita from the rest of the archipelago probably prevented gene flow during periods of lower sea level, thereby fostering allopatric speciation. The combination of molecular species delimitation methods, morphological comparisons, and divergence time estimation is useful to study lineage diversification across intricate geographic regions.
Novel Approaches in the Design of Donor-Acceptor Oligomeric and Polymeric Materials for Photovoltaic Applications: D/A Blend versus Self-assembly of D/A by Covalent or Non-Covalent Interaction

Organic semiconducting materials involving small -conjugated molecules as well as polymers are fast growing members in the family of potential materials for harvesting non-conventional source of energy like the solar radiation.1-8 Organic photovoltaic cells (OPVs) are a revolutionary technology which converts the solar radiations into electrical energy using organic/polymeric semiconducting material (active layer). The technology has become very promising due to its low fabrication cost, lightweight, large area and mechanical flexibility. The major breakthrough in OPV was created by Tang et al. who reported the first hetero junction organic solar cell with power conversion efficiency (PCE) ~ 1 %. 1,9Although intensely researched for more than two decades, the efficiency of organic solar cell remains lower compared to efficiency based on inorganic crystalline silicon which has reached up to ~ 25 %. However, promising breakthroughs in terms of better device performance efficiencies, device designs as well as longer outdoor performance of test modules have kept the field ongoing.10-13 The basic OPV device architecture consists of a photo-active layer of organic /polymeric semiconductor sandwiched between indium tin oxide (ITO) electrode (anode) and metal electrode (cathode). 3 Tang implemented modifications involving a bilayer heterojunction OPV device comprising of p-type donor and n-type acceptor as active layer, which improved the device performance.1,9 The working principle of such device involves the light absorption by active layer which lead to creation of electron-hole pair i.e. exciton. The next step is the dissociation of excitons; which recombined by emitting photon or non-radioactive recombination if eventual separation to its component electron and hole did not occur within the short exciton diffusion lenght.1,3, 6-8 Once the charges are separated into electrons and hole, they move towards the respective electrode and finally charge collection leads to electric current. The charge extraction is driven by internal electric field in cell caused by different work function of electrodes. It is widely accepted that the exciton diffusion at D/A interface is the most important step that depends upon how the donor and acceptor are arranged in the nanoscopic length scale of about 10-20 nm which is critical to the exciton life time. Generally, the life time of exciton is very short with diffusion length limited to 5-20 nm after which they will recombine and fail to generate free charges.3, 6, 8 The exciton diffusion is considered to be the most important aspect during design of active layer of solar cell. Therefore, morphology of the active layer component (donor-acceptor) plays a vital role in the device efficiency of solar cells.14