Miracle-dz/UniFAR-FTUs huggingface dataset:
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions

Many deep Convolutional Neural Networks (CNN) make incorrect predictions on adversarial samples obtained by imperceptible perturbations of clean samples. We hypothesize that this is caused by a failure to suppress unusual signals within network layers. As remedy we propose the use of Symmetric Activation Functions (SAF) in non-linear signal transducer units. These units suppress signals of exceptional magnitude. We prove that SAF networks can perform classification tasks to arbitrary precision in a simplified situation. In practice, rather than use SAFs alone, we add them into CNNs to improve their robustness. The modified CNNs can be easily trained using popular strategies with the moderate training load. Our experiments on MNIST and CIFAR-10 show that the modified CNNs perform similarly to plain ones on clean samples, and are remarkably more robust against adversarial and nonsense samples.
Current-injection characterization of breakdown in AlGaN/GaN MODFETs

Al/sub 0.3/Ga/sub 0.7/N/GaN MODFETs grown on sapphire substrates by RF-assisted MBE demonstrate excellent DC as well as high-frequency characteristics. Power characteristics of these transistors measured on-wafer at 8 GHz showed excellent correlation with the biasing voltages and currents. Since the use of higher drain bias generally leads to higher output power and the maximum drain bias is limited by the drain-source and/or drain-gate breakdowns these need to be evaluated in order to investigate to what extent they limit the power performance of GaN-based MODFETs. The breakdown characteristics of AlGaN/GaN MODFETs were studied by employing the current-injection technique and the gate-drain and drain-source breakdown voltages were obtained as a function of the drain current and the substrate temperature. At room temperature devices demonstrated drain-source and gate-drain breakdown voltages of 70 and 80 V, respectively. Based on the gate bias and temperature dependence of the breakdown voltages, the source of breakdown is attributed to the thermionic emission in the gate-drain diode.
Feature-based quality evaluation of 3D heterogeneous data registration

The production of 3D realistic map databases is a matter of increasing interest. An approach based on the fusion of heterogeneous 3D representations was studied in previous works. Three methods of rigid registration were tested as well as the behaviors of those algorithms. One of the most difficult problems is the quantitative evaluation of 3D registration. In this term we propose a new quantitative evaluation method based on reference features comparison to improve and compare the accuracy of the registration methods.
Cardiovascular Disease Rates, Outcomes, and Quality of Care in Ontario Métis: A Population-Based Cohort Study

Background The burden of cardiovascular disease in the Métis, Canada’s fastest growing Aboriginal group, is not well studied. We determined rates of five cardiovascular diseases and associated outcomes in Ontario Métis, compared to the general Ontario population. Methods Métis persons were identified using the Métis Nation of Ontario Citizenship Registry. Métis citizens aged 20–105 were linked to Ontario health databases for the period of April 2006 to March 2011. Age- and sex-standardized prevalence and incidence of acute coronary syndromes (ACS), congestive heart failure (CHF), cerebrovascular disease (stroke), atrial fibrillation, and hypertension were compared between the Métis and the general population. Secondary outcome measures included one-year hospitalizations and mortality following the incident cardiovascular diagnosis, as well as quality-of-care measures. Results There were 12,550 eligible Métis persons and 10,144,002 in the general population. The adjusted prevalence of each disease was higher (p<0.05) among the Métis compared to the general population: ACS 5.3% vs. 3.0%; CHF 5.1% vs. 3.9%; stroke 1.4% vs. 1.1%; atrial fibrillation 2.1% vs. 1.4%; hypertension 34.9% vs. 29.8%. Incident ACS, stroke, and atrial fibrillation were also higher (p<0.05) among the Métis: ACS 2.4% vs. 1.5%; stroke 0.8% vs. 0.6%; atrial fibrillation 0.6% vs. 0.3%. One-year all-cause and cardiovascular-related mortality were not significantly different. Hospitalizations were higher for Métis persons with CHF (OR 1.93; 95% CI 1.34–2.78) and hypertension (OR 2.27; 95% CI 1.88–2.74). Métis with CHF made more emergency department (ED) visits in the year after diagnosis compared to non-Métis with CHF, while Métis aged ≥65 with ACS were more likely to be on beta-blockers following diagnosis. Conclusions The burden of cardiovascular disease was markedly higher in the Métis compared to the general population: prevalence rates for five cardiovascular conditions were 25% to 77% higher. Métis persons with CHF had more frequent hospitalizations and ED visits following their diagnosis.
Title : Relative Abundance of Full-Length and Truncated FOXP 1 Isoforms are Associated with NFκB Activity in Follicular Lymphoma

Michael R. Green, Pali Verma, Maher K. Gandhi, Colm Keane, Kirk Morris, Paula Marlton, Lyn Griffiths 1 Genomics Research Centre, Griffith University, Queensland, Australia Griffith Medical Research College, a joint program of Griffith University and the Queensland Institute of Medical Research, Queensland, Australia 3 Clinical Immunohaematology Laboratory, The Queensland Institute of Medical Research, Queensland, Australia 4 The Princess Alexandra Hospital, Queensland Health, Queensland, Australia
Towards Unsupervised Spoken Language Understanding: Exploiting Query Click Logs for Slot Filling

In this paper, we present a novel approach to exploit user queries mined from search engine query click logs to bootstrap or improve slot filling models for spoken language understanding. We propose extending the earlier gazetteer population techniques to mine unannotated training data for semantic parsing. The automatically annotated mined data can then be used to train slot specific parsing models. We show that this method can be used to bootstrap slot filling models and can be combined with any available annotated data to improve performance. Furthermore, this approach may eliminate the need for populating and maintaining in-domain gazetteers, in addition to providing complementary information if they are already available. Index Terms: spoken language understanding, slot filling, data mining, named entity extraction, unsupervised learning
3D technologies applied to architectural teaching

We present this paper as the result of an educational research project at the School of Architecture of Barcelona, in which we demonstrate, by means of a study case focused on the Host Course, HC, that using a reverse strategy, opposed to the traditional pedagogical strategy, in which future architecture students start to use first ICT tools and 3D technologies instead of the traditional drawing, their graphic training, visual education, understanding of the space, academic performance and satisfaction as users of computer applications, are superior, with shorter periods for learning.
An Access-Control Framework for WS-BPEL

Business processes, the next-generation workflows, have attracted considerable research interest in the last 15 years. More recently, several XML-based languages have been proposed for specifying and orchestrating business processes, resulting in the WS-BPEL language. Even if WS-BPEL has been developed to specify automated business processes that orchestrate activities of multiple Web services, there are many applications and situations requiring that people be considered as additional participants who can influence the execution of a process. Significant omissions from WS-BPEL are the specification of activities that require interactions with humans to be completed, called human activities, and the specification of authorization information associating users with human activities in a WS-BPEL business process and authorization constraints, such as separation of duty, on the execution of human activities. In this article, we address these deficiencies by introducing a new type of WS-BPEL activity to model human activities and by developing RBAC-WS-BPEL, a role-based access-control model for WS-BPEL, and BPCL, a language to specify authorization constraints.
Dark Matter and Baryons in the X-Ray Luminous Merging Galaxy Cluster RX J1347.5–1145

The galaxy cluster RX J1347.5–1145 is one of the most X-ray luminous and most massive clusters known. This makes it a prime target for studying issues addressing cluster formation and cosmology. Despite the naive expectation that mass estimation for this cluster should be straightforward (high mass and favorable redshift make it an efficient lens; it is bright in X-rays and appears to be fairly relaxed), some studies have reported very discrepant mass estimates. In this paper we present new high-resolution HST ACS and Chandra X-ray data. The high resolution and sensitivity of ACS enabled us to detect and quantify several new multiple images; we use a total of eight for the strong-lensing analysis. Combining it with shape measurements of weak-lensing sources in the central regions of the cluster, we derive a high-resolution, absolutely calibrated mass map. This map provides the best available quantification of the total mass of the central part of the cluster to date. We compare the reconstructed mass with that inferred from the new Chandra X-ray data. Both mass estimates agree extremely well in the observed region (within 400 h−170 kpc of the cluster center). In addition we study the major baryonic components (gas and stars) and hence derive the dark matter distribution in the center of the cluster. We find that the dark matter and baryons are both centered on the BCG within the uncertainties (alignment is better <10 kpc). We measure the corresponding dark matter profile and find it consistent with both NFW and cored profiles, indicating that a more extended radial analysis is needed to pinpoint the concentration parameter, and hence the inner slope of the dark matter profile.
A Comparison of Two Approaches to Pure-Inertial and Doppler-Inertial Error Analysis

Error equations for inertial navigation systems are derived using a perturbation (or true frame) approach and a psi angle (or computer frame) approach in a manner which shows the underlying as sumptions and allows direct comparison of the two methods. The comparison is general since the analysis is not associated with any particular mechanization. Different definitions of velocity errors and misalignment angles result from the two methods of error analysis, and, consequently, have significance in testing and analysis of pure-inertial systems, Doppler-inertial systems, and inertially aided weapon delivery systems. Examples and numerical results are presented for a local-level north-pointing mechanization.
Social Psychology Emotional transfer in goal systems q

Five experimental studies explored the phenomenon of affective transfer in goal systems. We find that affect associated with goal attainment may be transferred to means cognitively associated with such goal-events, and that factors affecting the dimensions of transfer include the magnitude of affect invested in the goal, the quality of invested affect and the strength of association between a given means and the goal-event. Accordingly, the transfer mechanism was shown to impact the magnitude of affect experienced in regard to the means in question, as well as its kind (involving, e.g., promotion-type affect or prevention-type affect), and was shown to influence the interpersonal feelings toward others perceived as helpful to the attainment of various goals. 2004 Elsevier Inc. All rights reserved.
Using collision cones to assess biological deconfliction methods

Biological systems consistently outperform autonomous systems governed by engineered algorithms in their ability to reactively avoid collisions. To better understand this discrepancy, a collision avoidance algorithm was applied to frames of digitized video trajectory data from bats, swallows and fish (Myotis velifer, Petrochelidon pyrrhonota and Danio aequipinnatus). Information available from visual cues, specifically relative position and velocity, was provided to the algorithm which used this information to define collision cones that allowed the algorithm to find a safe velocity requiring minimal deviation from the original velocity. The subset of obstacles provided to the algorithm was determined by the animal's sensing range in terms of metric and topological distance. The algorithmic calculated velocities showed good agreement with observed biological velocities, indicating that the algorithm was an informative basis for comparison with the three species and could potentially be improved for engineered applications with further study.
Noninvasive Assessment of Myocardial Inflammation by Cardiovascular Magnetic Resonance in a Rat Model of Experimental Autoimmune Myocarditis

Background— Limited availability of noninvasive and biologically precise diagnostic tools poses a challenge for the evaluation and management of patients with myocarditis. Methods and Results— The feasibility of cardiovascular magnetic resonance (CMR) imaging with magneto-fluorescent nanoparticles (MNPs) for detection of myocarditis and its effectiveness in discriminating inflammation grades were assessed in experimental autoimmune myocarditis (EAM) (n=65) and control (n=10) rats. After undergoing CMR, rats were administered with MNPs, followed by a second CMR 24 hours later. Head-to-head comparison of MNP-CMR with T2-weighted, early and late gadolinium enhancement CMR was performed in additional EAM (n=10) and control (n=5) rats. Contrast-to-noise ratios were measured and compared between groups. Flow cytometry and microscopy demonstrated that infiltrating inflammatory cells engulfed MNPs, resulting in altered myocardial T2* effect. Changes in contrast-to-noise ratio between pre- and post-MNP CMR were significantly greater in EAM rats (1.08±0.10 versus 0.48±0.20; P<0.001). In addition, contrast-to-noise ratio measurement in MNP-CMR clearly detected the extent of inflammation (P<0.001) except for mild inflammation. Compared with conventional CMR, MNP-CMR provided better image contrast (CNR change 8% versus 46%, P<0.001) and detectability of focal myocardial inflammation. Notably, MNP-CMR successfully tracked the evolution of myocardial inflammation in the same EAM rats. Conclusions— Magneto-fluorescent nanoparticle CMR permitted effective visualization of myocardial inflammatory cellular infiltrates and distinction of the extent of inflammation compared with conventional CMR in a preclinical model of EAM. Magneto-fluorescent nanoparticle CMR performs best in EAM rats with at least moderate inflammatory response.
Characteristics of Childhood Experiences in Women with Remitted Major Depression

Research into the relationships between psychiatric disorders and childhood adversities (e.g. neglect, physical abuse, sexual abuse) has been limited by the use of self-report assessments, which may provide incomplete or biased information. The Childhood Experience of Care and Abuse (CECA; Bifulco, Brown, & Harris, 1994) is a rigorous semi-structured interview, but is limited in utility because of its length. The goal of this project was to more efficiently classify detailed reports of childhood events gathered from the CECA. An exploratory factor analysis was performed using a sample of women (N=142) with remitted, recurrent major depression. One definable factor was found, Noxious Environment, which accounted for a large proportion of the variance in the CECA. This suggests that the development of a briefer CECA rating method is warranted. Additional exploratory analyses indicated that childhood experiences might be significantly influenced by the composition of the household and any caregiver transitions that occur. These findings suggest the CECA can be condensed, making it less time-consuming and burdensome to score and thus more widely usable in research settings. Additionally, these findings reinforce the advantages of using the CECA over self-report measures of adverse experiences by allowing for changes in ratings over time and accounting for changes in household composition.
Advances in Brief Specific Inhibition of Kras Expression and Tumorigenicity of Lung Cancer Cells by Antisense RNA 1

A human lung cancer cell line (H460a) with a homozygous spontaneous k-rav mutation was transfected with a recombinant plasmiti that synthe sizes a 2-kilobase genomic segment of the K-ra.vprotooncogene in antisense orientation. Translation of the mutated k-rav inKNA in H460a cells was specifically inhibited, whereas expression of I l-rav and N-ras was unchanged. A 3-fold growth inhibition occurred in H460a cells when expression of the mutated ras p21 protein was down-regulated by antisense RNA. However, cells remained viable despite the absence of K-rav expression. The growth of H460a tumors in nu/nu mice was substantially reduced by expressed K-rav antisense RNA.
Data-Driven Sales Leads Prediction for Everything-as-a-Service in the Cloud

A cloud platform website, offering a catalog of services, operates under a freemium business model or a free trial business model, aggressively marketing to customers who have previously visited. In such a cloud platform or service business, accurate identification of high profile customers is central to the success for the business. However, there are several limitations of existing approaches because of the following challenges: (1) heavy customer traffic flows, (2) the noise in user behaviors, (3) a lack of collaboration across stakeholders, (4) class imbalanced customer data (few paying customers vs. high numbers of freemium or trial customers), and (5) unpredictable business environments. In this paper, we propose a data-driven iterative sales lead prediction framework for cloud everything as a service (XaaS), including a cloud platform or software. In this framework, from the BizDevOps process we collaborate to extract business insights from multiple business stakeholders. From these business insights, we calculate service usage scores using our RFDL (Recency, Frequency, Duration, and Lifetime) analysis and estimate sales lead prediction based on the usage scores in a supervised manner. Our framework adapts to a continuously changing environment through iterations of the whole process, maintains its performance of sales lead prediction, and finally shares the prediction results to the sales or marketing team effectively. A three-month pilot implementation of the framework led to more than 300 paying customers and more than $200K increase in revenue. We expect our scalable, iterative sales lead prediction approach to be widely applicable to online or cloud business domains where there is a constant flux of customer traffic.
Crack Initiation and Progressive Damage of Three Gorges Granite

Based on the uniaxial and triaxial compression tests of Three Gorges granites，the crack initiation stress under different confining pressures can be obtained by studying the crack strain curves. The results show that the crack initiation stress changes at the same extent with confining pressure, and is generally located between 25% and 50% of the peak strength. Moreover, the crack initiation criteria and progressive damage model are established, and some beneficial conclusions are drawn. The tension concentration model of elliptic crack can be used to explain cracking mechanism of Three Gorges granite at relatively low confinement, and Three Gorges granite mainly occurs lateral damage in the microfracturing process and exhibits the similar damage evolutionary rule under different confining stress. The damage model can be used to describe the crack propagation process.
FPGA-based PID controller for DC-DC converter

Proportional Integral Derivative (PID) controller is the most preferable controller in industries that does not require precise analytical model of the system to be controlled. This paper presents a design and implementation of PID (Proportional-Integral-Derivative) controller based on FPGA (Field-Programmable Gate Arrays) for low voltage synchronous buck Converter. Matlab/Simulink environment is used for the PID controller design to generate a set of coefficients associated with the desired controller characteristics. These controller coefficients are then included in VHDL that implements the PID controller on to FPGA. The two architectures of PID controller are considered with their device utilization and power dissipation reports to show the resource utilization and power dissipation of selected FPGA. The architectures are implemented in FPGA Virtex-5(ML505) XC5VLX50T-1FF1136 (-1 speed grade) device.
An efficient alternative approach for home furniture detection and localization by an autonomous mobile robot

In order for service robots to help humans in daily home tasks, they need to have a better understanding of their environment. Therefore, detection and localization of home furniture becomes a very important capability for them. This paper presents an approach to detect typical home furniture by an RGB-D sensor mounted on a mobile robot. The approach is based on the analysis of discriminant features extracted from very easy to compute measures distributions. Over an offline learning phase, each piece of furniture is modeled according to their distributions of: height, color (H and S components) and normals. Then a sequence of distributions analysis are applied to the scene for selecting, according to learned models, the pieces of furniture with a high probability of being present. The point cloud is segmented according to model analysis and then segmented points are projected to the floor plane for clustering and noise removal. According to previous analysis and footprints of segmented points clouds, regions are then classified as a possible piece of furniture. Finally, the orientation and localization of the detected furnitures are obtained, using the footprint and their neighbor regions. The proposed approach has been proved to be very efficient in order to be incorporated on mobile robotic platforms.
Trustworthiness Management in the Social Internet of Things

The integration of social networking concepts into the Internet of things has led to the Social Internet of Things (SIoT) paradigm, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability in information/service discovery. Within this scenario, we focus on the problem of understanding how the information provided by members of the social IoT has to be processed so as to build a reliable system on the basis of the behavior of the objects. We define two models for trustworthiness management starting from the solutions proposed for P2P and social networks. In the subjective model each node computes the trustworthiness of its friends on the basis of its own experience and on the opinion of the friends in common with the potential service providers. In the objective model, the information about each node is distributed and stored making use of a distributed hash table structure so that any node can make use of the same information. Simulations show how the proposed models can effectively isolate almost any malicious nodes in the network at the expenses of an increase in the network traffic for feedback exchange.