Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Multimodal Mining to Drive Clinical Guidelines Development

We present exploratory investigations of multimodal mining to help designing clinical guidelines for antibiotherapy. Our approach is based on the assumption that combining various sources of data, such as the literature, a clinical datawarehouse, as well as information regarding costs will result in better recommendations. Compared to our baseline recommendation system based on a question-answering engine built on top of PubMed, an improvement of +16% is observed when clinical data (i.e. resistance profiles) are injected into the model. In complement to PubMed, an alternative search strategy is reported, which is significantly improved by the use of the combined multimodal approach. These results suggest that combining literature-based discovery with structured data mining can significantly improve effectiveness of decision-support systems for authors of clinical practice guidelines.
Optical Study of Ultrathin TiO2 Films for Photovoltaic and Gas Sensing Applications

TiO 2 ultrathin films of thickness below 20 nm were deposited by reactive RF magnetron sputtering. The optical properties of TiO 2 films were investigated by various optical techniques including UV-VIS-NIR spectroscopic ellipsometry. The Scanning Probe Microscopy (SPM) was used to determine thickness and surface roughness of the deposited films. The correlation between preparation conditions of ultrathin TiO 2 films and their physical properties has been studied. The analysis of optical data revealed the parameters of deposited films and intrinsic properties of TiO 2 material before and after annealing. We found that deposited layers were predominantly amorphous with high porosity at the top sample, and absence of porosity at the bottom of TiO 2 layer. Annealing considerably improves structural order of the studied samples and the film transforms to the polycrystalline anatase phase. Also we evaluated the energy bandgap (about 3.1 eV – 3.2 eV) which increases after annealing (above 3.3 eV) and it is close to the bandgap of anatase. DOI: http://dx.doi. org/10.5755/j01.ms.20.2. 6328
Functional Dependencies Unleashed for Scalable Data Exchange

We address the problem of efficiently evaluating target functional dependencies (fds) in the Data Exchange (DE) process. Target fds naturally occur in many DE scenarios, including the ones in Life Sciences in which multiple source relations need to be structured under a constrained target schema. However, despite their wide use, target fds' evaluation is still a bottleneck in the state-of-the-art DE engines. Systems relying on an all-SQL approach typically do not support target fds unless additional information is provided. Alternatively, DE engines that do include these dependencies typically pay the price of a significant drop in performance and scalability. In this paper, we present a novel chase-based algorithm that can efficiently handle arbitrary fds on the target. Our approach essentially relies on exploiting the interactions between source-to-target (s-t) tuple-generating dependencies (tgds) and target fds. This allows us to tame the size of the intermediate chase results, by playing on a careful ordering of chase steps interleaving fds and (chosen) tgds. As a direct consequence, we importantly diminish the fd application scope, often a central cause of the dramatic overhead induced by target fds. Moreover, reasoning on dependency interaction further leads us to interesting parallelization opportunities, yielding additional scalability gains. We provide a proof-of-concept implementation of our chase-based algorithm and an experimental study aimed at gauging its scalability and efficiency. Finally, we empirically compare with the latest DE engines, and show that our algorithm outperforms them.
The Wisconsin Wind Tunnel: virtual prototyping of parallel computers

We have developed a new technique for evaluating cache coherent, shared-memory computers. The Wisconsin Wind Tunnel (WWT) runs a parallel shared-memory program on a parallel computer (CM-5) and uses execution-driven, distributed, discrete-event simulation to accurately calculate program execution time. WWT is a virtual prototype that exploits similarities between the system under design (the target) and an existing evaluation platform (the host). The host directly executes all target program instructions and memory references that hit in the target cache. WWT's shared memory uses the CM-5 memory's error-correcting code (ECC) as valid bits for a fine-grained extension of shared virtual memory. Only memory references that miss in the target cache trap to WWT, which simulates a cache-coherence protocol. WWT correctly interleaves target machine events and calculates target program execution time. WWT runs on parallel computers with greater speed and memory capacity than uniprocessors. WWT's simulation time decreases as target system size increases for fixed-size problems and holds roughly constant as the target system and problem scale.
Transfer learning with probabilistic mapping selection

When transferring knowledge between reinforcement learning agents with different state representations or actions, past knowledge must be efficiently mapped to novel tasks so that it aids learning. The majority of the existing approaches use pre-defined mappings provided by a domain expert. To overcome this limitation and enable autonomous transfer learning, this paper introduces a method for weighting and using multiple inter-task mappings based on a probabilistic framework. Experimental results show that the use of multiple inter-task mappings, accompanied with a probabilistic selection mechanism, can significantly boost the performance of transfer learning relative to 1) learning without transfer and 2) using a single hand-picked mapping. We especially introduce novel tasks for transfer learning in a realistic simulation of the iCub robot, demonstrating the ability of the method to select mappings in complex tasks where human intuition could not be applied to select them. The results verified the efficacy of the proposed approach in a real world and complex environment.
The halo density profiles with non-standard N-body simulations

We propose a new numerical procedure to simulate a single dark halo of any size and mass in a hierarchical framework coupling the extended Press‐Schechter formalism (EPSF) to N-body simulations. The procedure consists of assigning cosmological initial conditions to the particles of a single halo with the EPSF technique and following only the dynamical evolution using a serial N-body code. The computational box is fixed with a side of 0.5 h −1 Mpc. This allows us to simulate galaxy cluster haloes using appropriate scaling relations, to ensure savings in computing time and code speed. The code can describe the properties of haloes composed of collisionless or collisional dark matter. For collisionless cold dark matter particles, the Navarro‐ Frenk‐White profile is reproduced for galactic haloes as well as galaxy cluster haloes. Using this numerical technique, we study some characteristics of haloes assumed to be isolated or placed in a cosmological context in the presence of weak self-interacting dark matter: the soft core formation and the core collapse. The self-interacting dark matter cross-section per unit mass is assumed to be inversely proportional to the particle collision velocity:σ/mx ∝ 1/v.
Home-environment adaptation of phoneme factorial hidden Markov models

We focus on the problem of speech recognition in the presence of nonstationary sudden noise, which is very likely to happen in home environments. To handle this problem, a model compensation method based on a factorial hidden Markov model (FHMM) has been recently introduced. In this architecture, speech and noise processes are modeled in parallel by a phoneme FHMM that is built by combining a clean-speech phoneme hidden Markov model (HMM) and a sudden noise HMM. Here, to increase the robustness of this method further, we apply supervised and unsupervised home-environment adaptation of phoneme FHMMs. A database recorded by a personal robot PaPeRo in home environments was used for the evaluation of the proposed method under noisy conditions. The phoneme home-dependent FHMM achieved better recognition accuracy than the clean-speech home-independent HMM, reducing the overall relative error by 16.2% and 12.3% on average for supervised and unsupervised adaptation, respectively.
Channel estimation for MC-CDMA uplink transmissions with combined equalization

Combined equalization has recently been proposed to enhance the error rate performance of conventional multicarrier code-division multiple-access (MC-CDMA) systems. This technique applies pre-equalization at the transmitter in conjunction with post-equalization at the receiver, thereby splitting the overall equalization process into two separate parts. In this way, efficient power allocation over the available subcarriers is possible at the transmitter, while leaving the interference cancellation task at the receiver. In this paper, we consider the uplink of an MC-CDMA system employing combined equalization. As the users transmit from different locations, the uplink signals arrive at the base station after passing through different multipath channels and the goal is to estimate the pre-equalized channel frequency response of each user. This is pursued following two different approaches. The first operates in the frequency-domain and treats the channel gains over adjacent subcarriers as independent unknown parameters. The second operates in the time-domain and achieves better performance by reducing the number of unknown parameters. Both schemes are based on maximum-likelihood reasoning and require knowledge of the transmitted symbols. Numerical examples are given to highlight the effectiveness of the proposed methods.
Risk prediction using genome‐wide association studies

Over the last few years, many new genetic associations have been identified by genome‐wide association studies (GWAS). There are potentially many uses of these identified variants: a better understanding of disease etiology, personalized medicine, new leads for studying underlying biology, and risk prediction. Recently, there has been some skepticism regarding the prospects of risk prediction using GWAS, primarily motivated by the fact that individual effect sizes of variants associated with the phenotype are mostly small. However, there have also been arguments that many disease‐associated variants have not yet been identified; hence, prospects for risk prediction may improve if more variants are included. From a risk prediction perspective, it is reasonable to average a larger number of predictors, of which some may have (limited) predictive power, and some actually may be noise. The idea being that when added together, the combined small signals results in a signal that is stronger than the noise from the unrelated predictors. We examine various aspects of the construction of models for the estimation of disease probability. We compare different methods to construct such models, to examine how implementation of cross‐validation may influence results, and to examine which single nucleotide polymorphisms (SNPs) are most useful for prediction. We carry out our investigation on GWAS of the Welcome Trust Case Control Consortium. For Crohn's disease, we confirm our results on another GWAS. Our results suggest that utilizing a larger number of SNPs than those which reach genome‐wide significance, for example using the lasso, improves the construction of risk prediction models. Genet. Epidemiol. 34: 643‐652, 2010. © 2010 Wiley‐Liss, Inc.
Clock Synchronization in Wireless Sensor Networks: An Overview

The development of tiny, low-cost, low-power and multifunctional sensor nodes equipped with sensing, data processing, and communicating components, have been made possible by the recent advances in micro-electro-mechanical systems (MEMS) technology. Wireless sensor networks (WSNs) assume a collection of such tiny sensing devices connected wirelessly and which are used to observe and monitor a variety of phenomena in the real physical world. Many applications based on these WSNs assume local clocks at each sensor node that need to be synchronized to a common notion of time. This paper reviews the existing clock synchronization protocols for WSNs and the methods of estimating clock offset and clock skew in the most representative clock synchronization protocols for WSNs.
Individual and Domain Adaptation in Sentence Planning for Dialogue

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations.
Ductile to brittle transition depths for CVD silicon carbide and quartz

Scratching experiments were performed on highly brittle materials (Chemically Vapour Deposited (CVD) Silicon Carbide (SiC) and quartz) to determine if a ductile response was possible and then to further determine the Ductile to Brittle Transition (DBT) depth, that is, critical depth of cut or penetration. Scratches were made by moving a diamond stylus and a single crystal diamond flat nose tool over a polished sample surface. RAPT Inc. had previously polished the surface to a roughness of less than 10 nm rms. During the scratching operation the horizontal forces and the Acoustic Emission (AE) signals were recorded. In post experimental analysis the depth and width of the scratch was measured using a white light interference microscope (Wyko, RST).
A multi-level approach for understanding I/O activity in HPC applications

I/O has become one of the determining factors of HPC application performance. Understanding an application's I/O activity requires a multi-level view of the I/O function flow that includes high-level I/O libraries. We have developed a tracing framework, called Recorder, that captures I/O function calls at multiple layers of the parallel I/O stack without requiring source code modifications. In this paper, we show how Recorder's trace output can be used to investigate I/O activity and identify performance inefficiencies in two I/O benchmarks running on a leading edge HPC platform. Future work to organize and present the collected information more intuitively will further increase the value of Recorder's capabilities. We believe that a multi-level I/O tracing framework can provide key insights to end users and I/O library developers working to improve I/O on HPC platforms.
In-Depth Glycoproteomic Characterization of γ-Conglutin by High-Resolution Accurate Mass Spectrometry

The molecular characterization of bioactive food components is necessary for understanding the mechanisms of their beneficial or detrimental effects on human health. This study focused on γ-conglutin, a well-known lupin seed N-glycoprotein with health-promoting properties and controversial allergenic potential. Given the importance of N-glycosylation for the functional and structural characteristics of proteins, we studied the purified protein by a mass spectrometry-based glycoproteomic approach able to identify the structure, micro-heterogeneity and attachment site of the bound N-glycan(s), and to provide extensive coverage of the protein sequence. The peptide/N-glycopeptide mixtures generated by enzymatic digestion (with or without N-deglycosylation) were analyzed by high-resolution accurate mass liquid chromatography–multi-stage mass spectrometry. The four main micro-heterogeneous variants of the single N-glycan bound to γ-conglutin were identified as Man2(Xyl) (Fuc) GlcNAc2, Man3(Xyl) (Fuc) GlcNAc2, GlcNAcMan3(Xyl) (Fuc) GlcNAc2 and GlcNAc 2Man3(Xyl) (Fuc) GlcNAc2. These carry both core β1,2-xylose and core α1-3-fucose (well known Cross-Reactive Carbohydrate Determinants), but corresponding fucose-free variants were also identified as minor components. The N-glycan was proven to reside on Asn131, one of the two potential N-glycosylation sites. The extensive coverage of the γ-conglutin amino acid sequence suggested three alternative N-termini of the small subunit, that were later confirmed by direct-infusion Orbitrap mass spectrometry analysis of the intact subunit.
Stroke Risk Calculators in the Era of Electronic Health Records Linked to Administrative Databases

Medicine is a science of uncertainty and an art of probability . —Sir William Osler, 1904 Electronic health records (EHRs) linked to administrative databases can unlock the potential of clinical data by overcoming limitations of current cohort-derived risk calculators. EHRs can facilitate implementation of risk-based approaches, whereas current manual or web-based risk calculators pose prohibitive practical barriers to widespread use. We discuss how EHRs can be used: (1) To derive increasingly precise risk equations calibrated to the populations in which they will be applied; (2) To facilitate communication of individual risk to patients; and (3) To provide population-based risk information for researchers, administrators, and policymakers. Several risk calculators directly predict the outcome of stroke.1–3 Stroke is also included as part of a composite outcome in other calculators,4 an approach that may grow in popularity as the concept of stroke as a cardiovascular disease (CVD) risk equivalent becomes more widely accepted. Guidelines state that the level of stroke and CVD risk should inform decision-making about initiating treatments such as aspirin or lipid-lowering agents.5 These strategies are based on the observation that the respective relative risk reduction of aspirin and statin therapies is similar for most subpopulations, and therefore the absolute benefit of treatment is proportional to the absolute risk of stroke or coronary heart disease. Because clinicians do not accurately estimate cardiovascular risk,6,7 adhering to these guidelines requires the use of explicit risk calculators. At least 110 stroke and CVD risk scoring methods exist.8 Early systems typically relied on a points-based system that required clinicians to manually calculate the sum of points associated with various risk factors. More recent calculators allow clinicians to input parameters directly into a website that uses a multivariate equation to predict risk. A recent study comparing 2 …
Elevator Control as a Planning Problem

The synthesis of elevator control commands is a difficult, problem when new service requirements such as VIP service, access restrictions, nonstop travel etc. have to be individually tailored to each passenger. AI planning technology offers a very elegant and flexible solution because the possible actions of a control system can be made explicit and their preconditions and effects can be specified using expressive representation formalisms. Based on the specification, a planner can flexibly synthesize the required control and changes in the specification do not require any reimplementation of the control software. In this paper, we describe the application and investigate how currently available domain-independent planning formalisms can cope with it.
A novel method to measure the sidewall angle of MEMS structures

A good profile of the sidewall in key structure of MEMS (Micro-electromechanical Systems) is becoming a critical problem to enhance the performance. Therein, measurement of the sidewall angle has the direct bearing on the improvement of process and design of structures. This paper presents a novel method to measure the sidewall angle. Compared with the traditional method-microscopic measurement, this approach only requires a simple microstructure based on a micromechanical latch switch and an optical profiler. By means of measuring the out-of-plane motion of the structure, the angle of the sidewall can be calculated. In addition, positive or negative sign of this angle can be deduced from the relevance of the directions between the load and the out-of-plane motion. The effectiveness of this method is verified by simulations and experiments.
Distribution of muscle strength impairments following stroke

Objective: The purpose of this study was to quantify the distribution of strength impairments soon after stroke. We were specifically interested in differences in impairments between proximal and distal actions, flexion and extension actions, and upper and lower limb actions. Design: We conducted a retrospective chart review of strength scores of patients with acute stroke. Setting: Inpatient rehabilitation unit. Subjects: Forty-eight patients with a primary diagnosis of stroke underwent initial testing on admission; 31 of the same patients underwent final testing prior to discharge. Main outcome measures: The strength of eight muscle actions was assessed bilaterally using hand-held dynamometry. Force measurements obtained were expressed in newtons and as a percentage of normal. Results: Strength was impaired bilaterally but more so on the side contralateral to the brain lesion. Distal muscle actions were less impaired than proximal muscle actions on the stronger side. Extension actions were less impaired than flexion actions bilaterally but primarily in the upper limbs. Upper limb actions were less impaired than lower limb actions only on the stronger side. Conclusions: With a few exceptions, our results do not support common clinical assumptions regarding the distribution of strength impairments following stroke.
Performance Analysis of Cognitive Relay Networks Over Nakagami-  $m$ Fading Channels

In this paper, we present performance analysis for underlay cognitive decode-and-forward relay networks with the Nth best relay selection scheme over Nakagami-m fading channels. Both the maximum tolerated interference power constraint and the maximum transmit power limit are considered. Specifically, exact and asymptotic closed-form expressions are derived for the outage probability of the secondary system with the Nth best relay selection scheme. The selection probability of the Nth best relay under limited feedback is discussed. In addition, we also obtain the closed-form expression for the ergodic capacity of the secondary system with a single relay. These expressions facilitate in effectively evaluating the network performance in key operation parameters and in optimizing system parameters. The theoretical derivations are extensively validated through Monte Carlo simulations. Both theoretical and simulation results show that the fading severity of the secondary transmission links has more impact on the outage performance and the capacity than that of the interference links does. Through asymptotic analysis, we show that the diversity order for the Nth best relay selection scheme is min(m1, m3) × (M - N) + m3, where M denotes the number of cognitive relays, and m1 and m3 represent the fading severity parameters of the first-hop transmission link and the second-hop transmission link, respectively.