Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

Abstract Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning.
A T-S fuzzy based adaptive critic for continuous-time input affine nonlinear systems

This paper proposes a novel scheme of a Takagi-Sugeno (T-S) fuzzy based adaptive critic for the optimal control of the continuous-time input affine nonlinear system. A novel learning strategy is proposed to update the weights of critic network which resolves the issue of under-determined weight update equations discussed in [1]. The T-S Fuzzy based critic network approximates the global optimal cost as fuzzy average of local costs associated with local linear subsystems. This work clearly demonstrates that the optimal cost of a nonlinear system can be represented as the fuzzy cluster of optimal costs of locally valid linear models in a T-S framework. The proposed scheme has been simulated for four different dynamic systems. Simulation results clearly demonstrate that the T-S Fuzzy approximates the optimal cost, with subsystems in each fuzzy zone represents the optimal cost of locally valid linear model.
Concept for Client-initiated Selection of Cloud Instances for Improving QoE of Distributed Cloud Services

We introduce a concept for client-initiated selection of service location and service quality for improving the Quality of Experience (QoE) of general cloud services. It is loosely based on the HTTP adaptive streaming approach (e.g., MPEG DASH). A manifest file compiled by the cloud service provider specifies the available service locations and qualities, from which the user selects the optimal service instance based on contextual information obtained from client measurements and user preferences. The proposed concept is defined and is implemented in two client-based decision algorithms for improving the QoE of a simple picture gallery cloud service. These decision algorithms are evaluated and their impact on the service delivery is discussed. The evaluation shows that it is possible to improve the service location and quality selection by light-weight client-based algorithms.
Electrochemical responses of carbon fiber microelectrodes to dopamine in vitro and in vivo

Cylindrical, 7 μm in diameter carbon fiber (CF) microelectrodes were constructed and tested for their responses to dopamine in constant potential amperometry or fast scan cyclic voltammetry (FSCV). Amperometry was carried out in a miniature perfusion chamber whereas background subtracted FSCV was performed both in vitro and in vivo. For calibration performance of the microelectrodes, peak oxidation currents were determined using unmodified carbon tips of varying lengths and plotted against the tip lengths. A very close linear correlation (r = 0.997) was found between the two variables for tip lengths ranging from 25 to 300 μm. Also, a very close linear correlation was found between the oxidation current at a given carbon tip length in response to increasing dopamine concentrations measured by either amperometry or FSCV. In vivo experiments were carried out in the visual cortex of the anaesthetized rat to detect dopamine release in response to visual stimulation. Indeed, background subtracted cyclic voltammograms showed an increase in the current at 0.65 V which is the typical dopamine oxidation potential.
Aspects of Propeller Developments for a Submarine

Design and development of propellers for submarines are in some ways different from propellers for surface vessels. The most important demand is low acoustic signature that has priority over propeller efficiency, and the submarine propeller must be optimized with respect to acoustics rather than efficiency. Moreover the operating conditions of a submarine propeller are quite different. These aspects are discussed as well as the weighing of the various propeller parameters against the design objectives. The noise generated by the propeller can be characterized as thrust noise due to the inhomogeneous wake field of the submarine, trailing-edge noise and noise caused by turbulence in the inflow. The items discussed are demonstrated in a case study where a propeller of the Kappel type was developed. Three stages of the development are presented, including a design of an 8-bladed propeller where the thrust fluctuations as well as the thrust noise were significantly reduced relative to a 7-bladed propeller. Results of measurements are in good agreement with calculations.
Research in International Business and Finance Mean Reversion in Stock Market Prices: New Evidence Based on Bull and Bear Markets

In this paper we test whether mean reversion in stock market prices presents a different behavior in bull and bear markets. We date the US bull and bear periods using Bry and Boschan (1971) algorithm. We examine the order of integration in the S&P 500 stock market index covering a daily period from August 1929 to December 2006 in bull and bear phases. Our results indicate the existence of different episodes of mean reversion, which mainly correspond to bull market periods.
ROCK∗ — Efficient black-box optimization for policy learning

Robotic learning on real hardware requires an efficient algorithm which minimizes the number of trials needed to learn an optimal policy. Prolonged use of hardware causes wear and tear on the system and demands more attention from an operator. To this end, we present a novel black-box optimization algorithm, Reward Optimization with Compact Kernels and fast natural gradient regression (ROCK*). Our algorithm immediately updates knowledge after a single trial and is able to extrapolate in a controlled manner. These features make fast and safe learning on real hardware possible. We have evaluated our algorithm on two simulated reaching tasks of a 50 degree-of-freedom robot arm and on a hopping task of a real articulated legged system. ROCK* outperformed current state-of-the-art algorithms in all tasks by a factor of three or more.
Protein kinase C inhibitors enhance the 5‐HT2A receptor‐mediated excitatory effects of serotonin on interneurons in rat piriform cortex

Previously it has been shown that excitatory effects of 5‐hydroxytryptamine (5‐HT) upon interneurons in the rat piriform cortex are mediated by 5‐HT2A receptors. This receptor is linked to phosphoinositide turnover, and one consequence of stimulating this receptor is the activation of protein kinase C (PKC). In the present study, the effect of PKC inhibitors on the 5‐HT excitation of piriform cortical interneurons was examined by extracellular recording in a rat brain slice preparation. Bath application of the selective PKC inhibitors, bisindolylmalemide and chelerythrine, and the nonselective protein kinase inhibitor, H‐7, all enhanced the excitatory effects of 5‐HT. Two other nonselective protein kinase inhibitors, H‐8 and HA 1004, which are 2.5‐fold and 6.7‐ fold less potent than H‐7 at inhibiting PKC, produced a slight or no enhancement, respectively, of the excitatory effect of 5‐HT. Bisindolylmalemide, chelerythrine, and H‐7 did not enhance the excitatory effects of norepinephrine or carbachol on the same interneurons. The PKC activator phorboll2,13‐diacetate (PDA) decreased the excitatory effect of 5‐HT this decrease was rapidly reversed by H‐7. As inhibitors of PKC selectively enhanced rather than blocked the excitation by 5‐HT mediated by 5‐HT2A receptors, we conclude that activation of PKC does not mediate the excitation by 5‐HT of piriform cortical interneurons. Instead, we propose that PKC may have a negative feedback role in modulating the excitation by 5‐HT of piriform cortical interneurons. © 1995 Wiley‐Liss, Inc.
Split-2 bisimilarity has a finite axiomatization over CCS with Hennessy's merge

This note shows that split-2 bisimulation equivalence (also known as timed equivalence) affords a finite equational axiomatization over the process algebra obtained by adding an auxiliary operation proposed by Hennessy in 1981 to the recursion free fragment of Milner's Calculus of Communicating Systems. Thus the addition of a single binary operation, viz. Hennessy's merge, is sufficient for the finite equational axiomatization of parallel composition modulo this non-interleaving equivalence. This result is in sharp contrast to a theorem previously obtained by the same authors to the effect that the same language is not finitely based modulo bisimulation equivalence.
Attentive Convolutional Neural Network Based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech

Speech emotion recognition is an important and challenging task in the realm of human-computer interaction. Prior work proposed a variety of models and feature sets for training a system. In this work, we conduct extensive experiments using an attentive convolutional neural network with multi-view learning objective function. We compare system performance using different lengths of the input signal, different types of acoustic features and different types of emotion speech (improvised/scripted). Our experimental results on the Interactive Emotional Motion Capture (IEMOCAP) database reveal that the recognition performance strongly depends on the type of speech data independent of the choice of input features. Furthermore, we achieved state-of-the-art results on the improvised speech data of IEMOCAP.
Secure streaming forensic data transmission for trusted cloud

The proliferation of cloud computing requires excessive forensic data to be acquired, transmitted, and analyzed in a timely manner. However, current digital forensic technology is ill-suited to cloud as it exposes the problems of trusted data acquisition and secure data transmission. In this paper, we present a novel scheme that secure forensic data collection and transmission. Our scheme allows forensic investigator to authenticate custodies by remotely attesting the platform that is run by custodies. To expedite data processing, forensic data is transmitted between trusted custodies as data stream. We develop a unified streaming data integrity verification algorithm, which allows a custody to verify the integrity of forensic data, detect and localize any malicious modification of data. To save communication bandwidth, data integrity information is transmitted as invisible watermark. We implement a proof-of-concept system. Our experimental results demonstrate that low performance overhead of data collection, node authentication, and data verification can be achieved with good scalability.
A complex of mammalian Ufd1 and Npl4 links the AAA‐ATPase, p97, to ubiquitin and nuclear transport pathways

The AAA‐ATPase, p97/Cdc48p, has been implicated in many different pathways ranging from membrane fusion to ubiquitin‐dependent protein degradation. Binding of the p47 complex directs p97 to act in the post‐mitotic fusion of Golgi membranes. We now describe another binding complex comprising mammalian Ufd1 and Npl4. Yeast Ufd1p is required for ubiquitin‐dependent protein degradation whereas yeast Npl4p has been implicated in nuclear transport. In rat liver cytosol, Ufd1 and Npl4 form a binary complex, which exists either alone or bound to p97. Ufd1/Npl4 competes with p47 for binding to p97 and so inhibits Golgi membrane fusion. This suggests that it is involved in another cellular function catalysed by p97, the most likely being ubiquitin‐dependent events during mitosis. The fact that the binding of p47 and Ufd1/Npl4 is mutually exclusive suggests that these protein complexes act as adapters, directing a basic p97 activity into different cellular pathways.
WITH EFFICIENT STRUCTURED BOOSTING

In this paper, we propose a novel robust action recognition framework with the following capabilities: 1) online encoding motions to multi-label sequence where the output in each frame is a tuple of labels rather than a single label, 2) providing efficient automatic relevant motion selection framework, 3) learning systems so as to be optimal for online multilabel sequence classification. As for multi-label classification, our approach incorporates contextual information about action not only temporal information but hierarchical information of actions. Inference tends to be complex so as to achieve such complex recognition scheme, however, we propose an efficient Viterbi-like decoding algorithm which integrates forward algorithm and loopy message passing algorithm. As for the learning process, the algorithm optimizes the parameters so as to maximize log likelihood of the model. Boosting, ensemble approach of machine learning, is leveraged to provide efficient feature selection framework in the training process. The experimental results show that the proposed method successfully exploits the impact of contextual information then significantly outperforms the traditional approaches in dynamic gait motion classification.
Application to the gelation of an alginate

A new time-optimal rheometry technique is presented. It consists in applying to the material a continuous exponential frequency sweep and analyzing its response by means of Fourier transforms. The prop- erties of this method are that it takes the least possible time to perform the linear viscoelastic measurement in a given frequency range, and also presents an optimal signal to noise ratio in Fourier space. After validation against classical methods, it is used to characterize the gelation of a dental alginate. Properly time-resolved measurements of the frequency dependent viscoelastic modulii are presented. This allows to evaluate rigor- ously the gel point, using the Winter and Chambon criterion. Analysis of the data shows that a fractal networks forms inside the material after a lag time of about 350s, with a subsequent fast evolution (less than two minutes) towards the final structure.
Source-Pull and Load-Pull Characterization of Graphene FET

This paper presents the characterization of a GFET transistor using a source-pull/load-pull test set. The characterization shows that despite the good fT and fMAX, it is hard to achieve power gain using the GFET device within a circuit configuration. This is due to the very high impedance at the gate making impedance matching at the input extremely difficult. S-parameter characterization is performed and the associated small signal model is developed in order to further analyse and extrapolate the source-pull and load-pull measurement results. A good agreement is observed between small signal model simulation results and source-pull/load-pull measurements. Finally, the model is used to evaluate the optimum power gain of the transistor in a circuit configuration under matched conditions.
Plenoptic stitching: a scalable method for reconstructing 3D interactive walk throughs

Interactive walkthrough applications require detailed 3D models to give users a sense of immersion in an environment. Traditionally these models are built using computer-aided design tools to define geometry and material properties. But creating detailed models is time-consuming and it is also difficult to reproduce all geometric and photometric subtleties of real-world scenes. Computer vision attempts to alleviate this problem by extracting geometry and photogrammetry from images of the real-world scenes. However, these models are still limited in the amount of detail they recover. Image-based rendering generates novel views by resampling a set of images of the environment without relying upon an explicit geometric model. Current such techniques limit the size and shape of the environment, and they do not lend themselves to walkthrough applications. In this paper, we define a parameterization of the 4D plenoptic function that is particularly suitable for interactive walkthroughs and define a method for its sampling and reconstructing. Our main contributions are: 1) a parameterization of the 4D plenoptic function that supports walkthrough applications in large, arbitrarily shaped environments; 2) a simple and fast capture process for complex environments; and 3) an automatic algorithm for reconstruction of the plenoptic function.
Upper and lower limb muscle power relationships in mobility-limited older adults.

BACKGROUND Lower limb muscle power impairments are modifiable factors underlying mobility limitations in older adults. This study examined relationships between upper and lower limb muscle power and their role in predicting mobility performance among community-dwelling older adults. METHODS A cross-sectional analysis was conducted. Participants included 37 mobility-limited adults (24 women, 13 men), aged 65 to 93 years. Measures included upper (elbow extension) and lower limb (double leg press) one repetition maximum (1RM), and muscle power at both 40% and 70% one repetition maximum. Physical performance measures included stair climb time, the Short Physical Performance Battery, and 4-meter walk time. Factors commonly mediating the relationship between impairments and physical performance were analyzed as covariates. RESULTS Participants had a mean age of 76 years, had five chronic medical conditions, and manifested moderate mobility limitations. Although the associations between the upper and lower limbs were strong (p <.001), the magnitude of association was greater for power (r =.88-.89) as compared to strength (r =.69). Multivariate regression analyses revealed consistently strong relationships between limb muscle power and mobility performance measures. Substituting upper for lower limb power within these models did not materially weaken the relationships. CONCLUSION Muscle power appears to be a more generalized attribute between the upper and lower limbs than is muscle strength, suggesting that mechanisms underlying velocity of movement, as opposed to force production, may be important factors underlying muscle power in elderly persons. Additionally, upper limb muscle power measures may serve as a useful surrogate measure of limb power having implications for clinicians and researchers.
A survey on automated dynamic malware-analysis techniques and tools

Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.
Impact Study of PMSG-Based Wind Power Penetration on Power System Transient Stability Using EEAC Theory

Wind turbines with direct-driven permanent magnet synchronous generators (PMSGs) are widely used in wind power generation. According to the dynamic characteristics of PMSGs, an impact analysis of PMSG-based wind power penetration on the transient stability of multi-machine power systems is carried out in this paper based on the theory of extended equal area criterion (EEAC). Considering the most severe PMSG integration situation, the changes in the system’s equivalent power-angle relationships after integrating PMSGs are studied in detail. The system’s equivalent mechanical input power and the fault period electrical output power curves are found to be mainly affected. The analysis demonstrates that the integration of PMSGs can cause either detrimental or beneficial effects on the system transient stability. It is determined by several factors, including the selection of the synchronous generators used to balance wind power, the reactive power control mode of PMSGs and the wind power penetration level. Two different simulation systems are also adopted to verify the analysis results.
Minimum-energy broadcast routing in static ad hoc wireless networks

Energy conservation is a critical issue in ad hoc wireless networks for node and network life, as the nodes are powered by batteries only. One major approach for energy conservation is to route a communication session along the routes which requires the lowest total energy consumption. This optimization problem is referred to as minimum-energy routing. While minimum-energy unicast routing can be solved in polynomial time by shortest-path algorithms, it remains open whether minimum-energy broadcast routing can be solved in polynomial time, despite the NP-hardness of its general graph version. Previously three greedy heuristics were proposed in Wieselthier et al. (2000): MST (minimum spanning tree), SPT (shortest-path tree), and BIP (broadcasting incremental power). They have been evaluated through simulations in Wieselthier et al.], but little is known about their analytical performance. The main contribution of this paper is the quantitative characterization of their performances in terms of approximation ratios. By exploring geometric structures of Euclidean MSTs, we have been able to prove that the approximation ratio of MST is between 6 and 12, and the approximation ratio of BIP is between /sup 13///sub 3/ and 12. On the other hand, the approximation ratio of SPT is shown to be at least /sup n///sub 2/, where n is the number of receiving nodes. To our best knowledge, these are the first analytical results for minimum-energy broadcasting.
The Hybrid Artisans

We present an approach to combining digital fabrication and craft, demonstrating a hybrid interaction paradigm where human and machine work in synergy. The FreeD is a hand-held digital milling device, monitored by a computer while preserving the makers freedom to manipulate the work in many creative ways. Relying on a pre-designed 3D model, the computer gets into action only when the milling bit risks the objects integrity, preventing damage by slowing down the spindle speed, while the rest of the time it allows complete gestural freedom. We present the technology and explore several interaction methodologies for carving. In addition, we present a user study that reveals how synergetic cooperation between human and machine preserves the expressiveness of manual practice. This quality of the hybrid territory evolves into design personalization. We conclude on the creative potential of open-ended procedures within this hybrid interactive territory of manual smart tools and devices.