Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of the Surgical Fear Questionnaire in Adult Patients Waiting for Elective Surgery

Objectives Because existing instruments for assessing surgical fear seem either too general or too limited, the Surgical Fear Questionnaire (SFQ) was developed. The aim of this study is to assess the validity and reliability of the SFQ. Methods Based on existing literature and expert consultation the ten-item SFQ was composed. Data on the SFQ were obtained from 5 prospective studies (N = 3233) in inpatient or day surgery patients. These data were used for exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability analysis and validity analysis. Results EFA in Study 1 and 2 revealed a two-factor structure with one factor associated with fear of the short-term consequences of surgery (SFQ-s, item 1–4) and the other factor with fear of the long-term consequences of surgery (SFQ-l, item 5–10). However, in both studies two items of the SFQ-l had low factor loadings. Therefore in Study 3 and 4 the 2-factor structure was tested and confirmed by CFA in an eight-item version of the SFQ. Across all studies significant correlations of the SFQ with pain catastrophizing, state anxiety, and preoperative pain intensity indicated good convergent validity. Internal consistency (Cronbach's alpha) was between 0.765–0.920 (SFQ-total), 0.766–0.877 (SFQ-s), and 0.628–0.899 (SFQ-l). The SFQ proved to be sensitive to detect differences based on age, sex, education level, employment status and preoperative pain intensity. Discussion The SFQ is a valid and reliable eight-item index of surgical fear consisting of two subscales: fear of the short-term consequences of surgery and fear of the long-term consequences.
Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data

Abstract Model organism and human databases are rich with information about genetic and physical interactions. These data can be used to interpret and guide the analysis of results from new studies and develop new hypotheses. Here, we report the development of the Molecular Interaction Search Tool (MIST; http://fgrtools.hms.harvard.edu/MIST/). The MIST database integrates biological interaction data from yeast, nematode, fly, zebrafish, frog, rat and mouse model systems, as well as human. For individual or short gene lists, the MIST user interface can be used to identify interacting partners based on protein–protein and genetic interaction (GI) data from the species of interest as well as inferred interactions, known as interologs, and to view a corresponding network. The data, interologs and search tools at MIST are also useful for analyzing ‘omics datasets. In addition to describing the integrated database, we also demonstrate how MIST can be used to identify an appropriate cut-off value that balances false positive and negative discovery, and present use-cases for additional types of analysis. Altogether, the MIST database and search tools support visualization and navigation of existing protein and GI data, as well as comparison of new and existing data.
Optimal parallel algorithm for visibility of a simple polygon from a point

We present a parallel algorithm for computing the visible portion of a simple polygonal chain with <italic>n</italic> vertices from a point in the plane. The algorithm runs in <italic>&Ogr;</italic>(log <italic>n</italic>) time using <italic>&Ogr;</italic>(<italic>n</italic>/ log <italic>n</italic>) processors in the CREW-PRAM computational model, and hence is asymptomatically optimal.
Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA).
Antisense reductions in the PsbO protein of photosystem II leads to decreased quantum yield but similar maximal photosynthetic rates

Photosystem (PS) II is the multisubunit complex which uses light energy to split water, providing the reducing equivalents needed for photosynthesis. The complex is susceptible to damage from environmental stresses such as excess excitation energy and high temperature. This research investigated the in vivo photosynthetic consequences of impairments to PSII in Arabidopsis thaliana (ecotype Columbia) expressing an antisense construct to the PsbO proteins of PSII. Transgenic lines were obtained with between 25 and 60% of wild-type (WT) total PsbO protein content, with the PsbO1 isoform being more strongly reduced than PsbO2. These changes coincided with a decrease in functional PSII content. Low PsbO (less than 50% WT) plants grew more slowly and had lower chlorophyll content per leaf area. There was no change in content per unit area of cytochrome b6f, ATP synthase, or Rubisco, whereas PSI decreased in proportion to the reduction in chlorophyll content. The irradiance response of photosynthetic oxygen evolution showed that low PsbO plants had a reduced quantum yield, but matched the oxygen evolution rates of WT plants at saturating irradiance. It is suggested that these plants had a smaller pool of PSII centres, which are inefficiently connected to antenna pigments resulting in reduced photochemical efficiency.
Face and texture image analysis with quantized filter response statistics

Image appearance descriptors are needed for different computer vision applications dealing with, for example, detection, recognition and classification of objects, textures, humans, etc. Typically, such descriptors should be discriminative to allow for making the distinction between different classes, yet still robust to intra-class variations due to imaging conditions, natural changes in appearance, noise, and other factors. The purpose of this thesis is the development and analysis of photometric descriptors for the appearance of real life images. The two application areas included in this thesis are face recognition and texture classification. To facilitate the development and analysis of descriptors, a general framework for image description using statistics of quantized filter bank responses modeling their joint distribution is introduced. Several texture and other image appearance descriptors, including the local binary pattern operator, can be presented using this model. This framework, within which the thesis is presented, enables experimental evaluation of the significance of each of the components of this three-part chain forming a descriptor from an input image. The main contribution of this thesis is a face representation method using distributions of local binary patterns computed in local rectangular regions. An important factor of this contribution is to view feature extraction from a face image as a texture description problem. This representation is further developed into a more precise model by estimating local distributions based on kernel density estimation. Furthermore, a face recognition method tolerant to image blur using local phase quantization is presented. The thesis presents three new approaches and extensions to texture analysis using quantized filter bank responses. The first two aim at increasing the robustness of the quantization process. The soft local binary pattern operator accomplishes this by making a soft quantization to several labels, whereas Bayesian local binary patterns make use of a prior distribution of labelings, and aim for the one maximizing the a posteriori probability. Third, a novel method for computing rotation invariant statistics from histograms of local binary pattern labels using the discrete Fourier transform is introduced. All the presented methods have been experimentally validated using publicly available image datasets and the results of experiments are presented in the thesis. The face description approach proposed in this thesis has been validated in several external studies, and it has been utilized and further developed by several research groups working on face analysis.
Blind Detectors in Spread Spectrum Watermarking for Interference Reduction

In this paper, we propose novel blind detectors for spread spectrum watermarking systems. There exists host signal interference in blind spread spectrum watermarking systems where host signal is not available to the end-users. Our aim is to reduce the interference since it limits the capacity of watermarking systems and increases the bit error rate of the recovered watermark even if there is no distortion or attack. The novel blind detectors, which are based on the proposed normalization method, is presented in this work. The proposed normalization method reduces host signal interference by using local statistics of 8x8 discrete cosine transform blocks of the watermarked signal. Simulation results demonstrates that the proposed method considerably reduce the bit error rate of the recovered watermarks in case of existing correlation, covariance and maximum likelihood estimation based detectors are employed at the receiver. Consequently, the proposed detectors reduce the host signal interference and increase the capacity of the system.
Tempest and Typhoon: user-level shared memory

Future parallel computers must efficiently execute not only hand-coded applications but also programs written in high-level, parallel programming languages. Today's machines limit these programs to a single communication paradigm, either message-passing or shared-memory, which results in uneven performance. The authors address this problem by defining an interface, Tempest, that exposes low-level communication and memory-system mechanisms so programmers and compilers can customize policies for a given application. Typhoon is a proposed hardware platform that implements these mechanisms with a fully-programmable, user-level processor in the network interface. The authors demonstrate the utility of Tempest with two examples. First, the Stache protocol uses Tempest's fine-grain access control mechanisms to manage part of a processor's local memory as a large, fully-associative cache for remote data. The authors simulated Typhoon on the Wisconsin Wind Tunnel and found that Stache running on Typhoon performs comparably (/spl plusmn/30%) to an all-hardware Dir/sub N/NB cache-coherence protocol for five shared-memory programs. Second, they illustrate how programmers or compilers can use Tempest's flexibility to exploit an application's sharing patterns with a custom protocol. For the EM3D application, the custom protocol improves performance up to 35% over the all-hardware protocol. <<ETX>>
Efficient background subtraction for tracking in embedded camera networks

Background subtraction is often the first step in many computer vision applications such as object localisation and tracking. It aims to segment out moving parts of a scene that represent object of interests. In the field of computer vision, researcherswlon have dedicated their efforts to improve the robustness and accuracy of such segmentations but most of their methods are computationally intensive, making them nonviable options for our targeted embedded camera platform whose energy and processing power is significantly more con-strained. To address this problem as well as maintain an acceptable level of performance, we introduce Compressive Sensing (CS) to the widely used Mixture of Gaussian to create a new background subtraction method. The results show that our method not only can decrease the computation significantly (a factor of 7 in a DSP setting) but remains comparably accurate.
A decentralized scheduling algorithm for time synchronized channel hopping

Time Synchronized Channel Hopping (TSCH) is an existing Medium Access Control scheme which enables robust communication through channel hopping and high data rates through synchronization. It is based on a time-slotted architecture, and its correct functioning depends on a schedule which is typically computed by a central node. This paper presents, to our knowledge, the first scheduling algorithm for TSCH networks which both is distributed and which copes with mobile nodes. Two variations on scheduling algorithms are presented. Aloha-based scheduling allocates one channel for broadcasting advertisements for new neighbors. Reservationbased scheduling augments Aloha-based scheduling with a dedicated timeslot for targeted advertisements based on gossip information. A mobile ad hoc motorized sensor network with frequent connectivity changes is studied, and the performance of the two proposed algorithms is assessed. This performance analysis uses both simulation results and the results of a field deployment of floating wireless sensors in an estuarial canal environment. Reservation-based scheduling performs significantly better than Aloha-based scheduling, suggesting that the improved network reactivity is worth the increased algorithmic complexity and resource consumption.
Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target

The Cramer-Rao Lower Bounds (CRLBs) of the joint time delay and Doppler stretch estimation for an extended target is derived in this paper. The results indicate that the CRLB of the Doppler stretch is inversely proportional to the effective time-bandwidth product of the transmitted signal. The estimator using wideband ambiguity function (WBAF) is evaluated for the extended target case through numerical experiments. Our results indicate that the WBAF estimator, which is derived under the single point scatterer assumption, is not appropriate to the parameter estimation for an extended target.
Hybrid Algorithm of Optimal Control of a Class of Nonlinear Systems

This paper describes the use of a hybrid algorithm of optimal control for a class of mechanical, under-actuated, nonlinear systems having fewer control inputs than degrees of freedom, like Inverted Pendulum (IP), when sampling frequency of the measurable state variables is near the frequency in which folding phenomenon happens. In this algorithm the online adaptive recognition of the system is essential and to implement the algorithm on a digital controller a computational algorithm of inverse Z-transform is presented to apply on the microprocessor-based controllers.
Self-organizing map clustering based on continuous multiresolution entropy

The detection of changes in the parameter values of a nonlinear dynamic system is a branch of study with multiple applications. In this paper, we explore a variant of an automatic detector and clustering of slight parameter variations in nonlinear dynamic systems proposed by Torres et al. [Automatic detection of slight changes in nonlinear dynamical systems using multiresolution entropy tools, Int. J. Bifurc. Chaos 11(4) (2001) 967–981]. The new method takes the advantages of the continuous multiresolution entropy to localize slight changes in the parameters, and uses self-organizing maps to quantify and cluster these changes. We discuss the performance of this method while applied to automatic segmentation of natural see front matter r 2005 Elsevier B.V. All rights reserved. physa.2005. 05.073 nding author. Tel. : +54343 4975078/77x122; fax: +54 343 4975100/01x105. dress: metorres@ceride.gov. ar (M.E. Torres). rch has been carried out with the support of the Consejo Nacional de Investigaciones Técnicas, Argentina. by Universidad Nacional de Entre Rı́os (UNER) and Agencia Nacional de Promoción ecnológica, Argentina, under Project PICT-2002 11-12700, and PID-UNER 07F/6060.
Non‐adjacent Dependency Learning in Humans and Other Animals

Abstract Learning and processing natural language requires the ability to track syntactic relationships between words and phrases in a sentence, which are often separated by intervening material. These nonadjacent dependencies can be studied using artificial grammar learning paradigms and structured sequence processing tasks. These approaches have been used to demonstrate that human adults, infants and some nonhuman animals are able to detect and learn dependencies between nonadjacent elements within a sequence. However, learning nonadjacent dependencies appears to be more cognitively demanding than detecting dependencies between adjacent elements, and only occurs in certain circumstances. In this review, we discuss different types of nonadjacent dependencies in language and in artificial grammar learning experiments, and how these differences might impact learning. We summarize different types of perceptual cues that facilitate learning, by highlighting the relationship between dependent elements bringing them closer together either physically, attentionally, or perceptually. Finally, we review artificial grammar learning experiments in human adults, infants, and nonhuman animals, and discuss how similarities and differences observed across these groups can provide insights into how language is learned across development and how these language‐related abilities might have evolved.
Exploring Optimism and Pessimism in Twitter Using Deep Learning

Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ. In many cases, these different distributions can be modeled as different contexts of a single underlying system, in which each distribution corresponds to a different perturbation of the system, or in causal terms, an intervention. We focus on a class of such causal domain adaptation problems, where data for one or more source domains are given, and the task is to predict the distribution of a certain target variable from measurements of other variables in one or more target domains. We propose an approach for solving these problems that exploits causal inference and does not rely on prior knowledge of the causal graph, the type of interventions or the intervention targets. We demonstrate our approach by evaluating a possible implementation on simulated and real world data.
Measuring the Effectiveness of Volatility Auctions

We propose a method for event studies based on synthetic portfolios that provides a robust data-driven approach to build a credible counterfactual. The method is used to evaluate the effectiveness of volatility auctions using intraday data from the Colombian Stock Exchange. The results indicate that the synthetic portfolio method provides an accurate way to build a credible counterfactual that approximates the behavior of the asset if the auction had not taken place. The main results indicate that the volatility auction mitigates the volatility of the asset, but its effect on liquidity and trading activity is ambiguous at best.
Reduce the False Positive and False Negative from Real Traffic with Intrusion Detection in Zigbee Wireless Networks

Denial-of-Service attack in particular is a threat to zigbee wireless networks. It is an attack in which the primary goal is to deny the legitimate users access to the resources. A node is prevented from receiving and sending data packets to its destinations.Typically the traffic through the network is heterogeneous and it flows from multiple utilities and applications Considering todays threats in network there is yet not a single solution to solve all the issues because the traditional methods of port-based and payload-based with machine learning algorithm suffers from dynamic ports and encrypted application.Many international network equipment manufactures like cisco, juniper also working to reduce these issues in the hardware side.Here this paper presents a new approach considering the idea based on SOTC.This method adapts the current approaches with new idea based on service-oriented traffic classification(SOTC) and it can be used as an efficient alternate to existing methods to reduce the false positive and false negative traffic and to reduce computation and memory requirements.By evaluating the results on real traffic it confirm that this method is effective in improving the accuracy of traffic classification considerably,and promise to suits for a large number of applications.Finally, it is also possible to adopt a service database built offline, possibly provided by a third party and modeled after the signature database of antivirus programs,which in term reduce the work of training procedure and overfitting of parameters in case of parameteric classifier of supervised traffic classification. Index Terms: Network operations, traffic classification, security,zigbee ,wireless networks.
6 Periodic Event-Triggered Control

Recent developments in computer and communication technologies are leading to an increasingly networked and wireless world. This raises new challenging questions in the context of networked control systems, especially when the computation, communication, and energy resources of the system are limited. To efficiently use the available resources, it is desirable to limit the control actions to instances when the system really needs attention. Unfortunately, the classical time-triggered control paradigm is based on performing sensing and actuation actions periodically in time (irrespective of the state of the system) rather than when the system needs attention. Therefore, it is of interest to consider event-triggered control (ETC) as an alternative paradigm as it is more natural to trigger control actions based on the system state, output, or other available information. ETC can thus be seen as the introduction of feedback in the sensing, communication, and actuation processes. To facilitate an easy implementation of ETC, we propose to combine the principles and particularly the benefits of ETC and classical periodic time-triggered control. The idea is to periodically evaluate the triggering condition and to decide, at every sampling instant, whether the feedback loop needs to be closed. This leads to the periodic event-triggered control (PETC) systems. In this chapter, we discuss PETC strategies, their benefits, and two analysis and design frameworks for linear and nonlinear plants, respectively.