Miracle-dz/UniFAR-FTUs huggingface dataset:
Anisotropy Engineering in 3D Magnetoplasmonic Nanoantennas

The interaction between light and ferromagnetic nanostructures is investigated in this thesis. Studying the in uence of magnetic field on the surface plasmon properties and the associated enhancement of the magneto-optical (MO) response is becoming increasingly interesting due to advancements in nanofabrication and characterization [1]. Magnetoplasmonic devices find applications in sensing and telecommunication. In ferromagnetic materials the off-diagonal terms of the dielectric tensor can be activated (i.e., made non-zero) by the external magnetic field. These terms play a key role in the interaction of ferromagnets with light, producing interest- ing phenomena like Kerr and Faraday effects [2]. Ferromagnetic nanostructures of varying size, shape and thickness were fabricated in this work on glass substrates using hole mask colloidal lithography [3]. The optical and magnetic properties of these nanostructures can be controlled by these parameters. At the same time, their magneto-optical response can be effectively tuned by the localized plasmon excitations. Circular and elliptical nickel 2D nanostructures were investigated, followed by the increase in their height towards truly 3D magnetoplasmonic nanoantennas. The nanostructures were characterized by absorption spectroscopy and by the spec- troscopic magneto-optical Kerr effect (MOKE). Anisotropy in the particles played a key role in the tunability of the resulting Kerr polarization rotation. Different plasmon resonances (along x, y and z) of the nanostructures were engineered indi- vidually to achieve large enhancements in Kerr rotation. The out-of-plane plasmon resonance (along z) is shown to become more and more dominant in the definition of MO response as the structures grew in height,. This ultimately leads to a strong enhancement of MO response in these systems.
Overcoming Confounds of Stimulus Blocking: An Event-Related fMRI Design of Semantic Processing

The way in which meaning is represented and processed in the brain is a key issue in cognitive neuroscience, which can be usefully addressed by functional imaging techniques. In contrast to previous imaging studies of semantic knowledge, which have primarily used blocked designs, in this study we use an event-related fMRI (erfMRI) design, which has the advantage of enabling events to be presented pseudorandomly, thus reducing strategic processes and enabling more direct comparison with psychological behavioral studies. We used a semantic categorization task in which events were words representing either artifact or natural kinds concepts. Significant areas of activation for semantic processing included inferior frontal lobe bilaterally (BA 47) and left temporal regions, both inferior (BA 36 and 20) and middle (BA 21). These are areas that have been identified in previous neuroimaging studies of semantic knowledge. However, there were no significant differences between artifact and natural kinds concepts. These results are consistent with our previous imaging studies using blocked designs and suggest that conceptual knowledge is represented in a unitary, distributed neural system undifferentiated by domain of knowledge. These findings demonstrate that event-related designs can generate activations that are similar to those seen in blocked designs investigating semantics and, moreover, offer a greater capacity for interpretation free from the confounds of block effects.
TENSOR NORMS RELATED TO THE SPACE OF COHEN p-NUCLEAR MULTILINEAR MAPPINGS

In this paper we consider the ideal of Cohen p-nuclear multilinear mappings, which is a natural multilinear extension of the ideal of p-nuclear linear operators. The space of Cohen p-nuclear m-linear mappings is charac- terized by means of a suitable tensor norm up to an isometric isomorphism.
A STATISTICAL STUDY OF NEARBY GALAXIES. I. NIR GROWTH CURVES AND OPTICAL-TO-NIR COLORS AS A FUNCTION OF TYPE, LUMINOSITY AND INCLINATION

Growth curves of the near-infrared (NIR) magnitude as a function of the aperture have been built and used to derive NIR total magnitudes from aperture data taken from the literature. By cross-correlating with optical and redshift data, absolute magnitudes and optical-to-NIR colors have been computed for some 1000 galaxies of different types. Significant color gradients are observed, underlining that small aperture colors may lead to a biased picture of the stellar populations of galaxies. A statistical analysis, using various estimators taking into account the intrinsic scatter, has been performed to establish relations between the colors, the morphological type, the inclination or the shape, and the intrinsic luminosity. The combination of the optical and the NIR should obviously improve our understanding of the evolution of galaxies. Despite the intrinsic scatter, especially among star-forming galaxies, optical-to-NIR colors show a very well defined sequence with type, blueing by 1.35 mag from ellipticals to irregulars. The colors of spiral galaxies strongly redden with increasing inclination and put new constraints on the modeling of the extinction. No such effect is observed for lenticular galaxies. We also find that rounder ellipticals tend to be redder. A color-absolute magnitude relation is observed inside each type, with a slope significantly steeper for early and intermediate spirals than for ellipticals or late spirals. This stresses the importance of considering both the mass and the type to study the star formation history of galaxies.
A neonatal apnoea monitor for resource-constrained environments

A prototype Android application was designed to monitor for apnoea in neonates using a smartphone. The application receives data from a wireless pulse oximeter and uses machine learning techniques to detect apnoea. Distribution of the system requires only the pulse oximeter and a current mid-range smartphone. This work builds on previous research, but with a particular focus on classifying events accurately using a reduced set of information appropriate to a resource-constrained environment. This information consists only of the photoplethysmogram (PPG) and a set of PPG-derived physiological variables including heart rate and respiration rate. Various methods using the Support Vector Machine (SVM) were assessed using data from 27 annotated stays in a neonatal intensive care unit, divided approximately in half into training and test data. The best approach was found to be a combination of a feature selection method based on mutual information and an SVM with a radial basis function kernel, producing a classifier with a sensitivity of 98.7%, a specificity of 62.2% and a balanced accuracy of 80.5% on a training set of 796 events, and a sensitivity of 76.9%, a specificity of 52.0% and a balanced accuracy of 64.4% on a test set of 663 events.
Evolutionary Computation in Behavior Engineering

In the last few years we have used ALECSYS, a parallel learning classifier system based on the genetic algorithm, to develop behavioral modules for mobile robots, both simulated and real. In this paper we briefly report on our experience, and then reflect on various concepts stemming from the application of evolutionary computation to agent building. We propose a definition of agent, analyze the relationships holding between an agent and its external environment, and discuss some important similarities and differences between natural and artificial systems; in particular, we compare the concept of fitness of an organism with that of quality of an artifact. We then concentrate on adaptation, regarded as a basic process for the development of both biological organisms and artificial agents. We carry on our analysis trying to understand where and how Behavior Engineering (i.e., the discipline concerned with the development of artificial agents) might profit from the use of evolutionary strategies. We argue that an evolutionary approach might allow us to search the space of nonrational design, thus opening a whole new world of possibilities for the implementation of artificial systems.
Application of Open-Loop Stackelberg Equilibrium to Modeling a Driver's Interaction with Vehicle Active Steering Control in Obstacle Avoidance

The increasing use of active front steering (AFS) technology for obstacle avoidance raises the question of drivers’ interaction with vehicle automation. Mathematical models capable of representing such interaction are in demand for driver behavior study. This paper presents the application of open-loop Stackelberg equilibrium to modeling a driver's interaction with vehicle AFS control in an obstacle avoidance scenario, where both the driver and the AFS controller are exerting steering control to the vehicle. In this paper, such driver–AFS interactive steering control is modeled as a leader–follower game. Mathematical expressions of the driver's and the AFS controller's steering control strategies are derived using the linear quadratic dynamic optimization approach and the distributed model predictive control (DMPC) approach. These two approaches are found to give identical control gains, which suggest their equivalence in representing driver–AFS interaction. The DMPC approach is found to consume far less computation time due to its numerical nature. Mathematical modifications to the steering control strategies are then introduced to allow practical implementation for a future experimental study. Simulation results including time histories of steering angles and vehicle responses are illustrated and discussed.
Event-triggered control co-design for discrete-time systems subject to actuator saturation

In this paper we present a method to simultaneously design a static state feedback gain and the trigger function to stabilize discrete-time LTI systems subject to actuator saturation under an event-triggered strategy. We derive LMI-based conditions that ensure local (or global, when possible) asymptotic stability of the origin based on the Lyapunov theory. To tune the parameters, we propose convex optimization problems aiming at reducing the sampling activity. The efficiency of the proposed method is illustrated by numerical examples.
American Journal of Epidemiology Original Contribution Dietary Calcium and Magnesium Intake and Mortality: a Prospective Study of Men

The authors examined the association of dietary calcium and magnesium intake with all-cause, cardiovascular disease (CVD), and cancer mortality among 23,366 Swedish men, aged 45–79 years, who did not use dietary supplements. Cox proportional hazards regression models were used to estimate the multivariate hazard ratios and 95% confidence intervals of mortality. From baseline 1998 through December 2007, 2,358 deaths from all causes were recorded in the Swedish population registry; through December 2006, 819 CVD and 738 cancer deaths were recorded in the Swedish cause-of-death registry. Dietary calcium was associated with a statistically significant lower rate of all-cause mortality (hazard ratio (HR) ¼ 0.75, 95% confidence interval (CI): 0.63, 0.88; P trend < 0.001) and a nonsignificantly lower rate of CVD (HR ¼ 0.77, 95% CI: 0.58, 1.01; P trend ¼ 0.064) but not cancer mortality (HR ¼ 0.87, 95% CI: 0.65, 1.17; P trend ¼ 0.362) when the highest intake tertile (mean ¼ 1,953 mg/ day; standard deviation (SD), 334) was compared with the lowest (990 mg/day; SD, 187). Dietary magnesium intake (means of tertiles ranged from 387 mg/day (SD, 31) to 523 mg/day (SD, 38) was not associated with all-cause, CVD, or cancer mortality. This population-based, prospective study of men with relatively high intakes of dietary calcium and magnesium showed that intake of calcium above that recommended daily may reduce all-cause mortality. Although the role of calcium and magnesium in homeo-stasis has been examined extensively, the optimal intake of the nutrients and balance between them is not clear. Calcium and magnesium may antagonize each other in terms of absorption and metabolism, particularly when intake of one is significantly higher than intake of the other (1, 2). Associations between intakes of these 2 micronutrients and incidence of major chronic diseases such as hyperten-sion, type 2 diabetes, cardiovascular disease (CVD), and specific cancers have been investigated in many epidemi-ologic studies (2–12). However, the association of calcium intake with mortality has been addressed in few investigations , with no consistent inverse associations observed with CVD mortality (13–15) and an adverse association observed with prostate cancer mortality (16, 17); to our knowledge, there are no studies of dietary magnesium intake and mortality. Most studies were limited to only drinking water as a source of calcium and magnesium when evaluating the association with CVD (18–21) or cancer (22–24) mortality. Therefore, we conducted a prospective study of dietary intakes of calcium and magnesium in relation …
Intrinsic Image Extraction from a Single Image

An image is often modeled as a product of two principal components: illumination and reflectance components. The former is related to the amount of light incident on the scene and the latter is associated with the scene characteristics. The images formed from the two components are referred to as the illumination and the reflectance images; both are called the intrinsic images of the original image. The illumination components of the images of a fixed scene vary from image to image, while the reflectance components of the images in principle remain constant. Both reflectance and illumination images have their own applications. Intrinsic image extraction has long been an important task for computer vision applications. However, this task is not at all simple because it is an illconditioned problem. The proposed approach convolves an input image with a prescribed set of derivative filters. The pixels of the derivative images are next classified as being reflectance or illumination according to three measures: chromatic, intensity contrast and edge sharpness, which are calculated in advance for each pixel from the input image. Finally, a de-convolution process is applied to the classified derivative images to obtain the intrinsic images. The results reveal the feasibility of the proposed technique in both rapidly and effectively decomposing intrinsic images from one single image.
Using malware analysis to improve security requirements on future systems

In this position paper, we propose to enhance current software development lifecycle models by including use cases, based on previous cyberattacks and their associated malware, and to propose an open research question: Are specific types of systems prone to specific classes of malware exploits? If this is the case, developers can create future systems that are more secure, from inception, by including use cases that address previous attacks.
Repression of Human Telomerase Reverse Transcriptase Using Artificial Zinc Finger Transcription Factors

Telomerase activation is a key step in the development of human cancers. Expression of the catalytic subunit, human telomerase reverse transcriptase (hTERT), represents the limiting factor for telomerase activity. In this study, we have used artificial zinc finger protein (ZFP) transcription factors (TF) to repress the expression of hTERT in human cancer cell lines at the transcriptional level. We have constructed four-fingered ZFPs derived from the human genome which binds 12-bp recognition sequences within the promoter of the hTERT gene and fused them with a KRAB repressor domain to create a potent transcriptional repressor. Luciferase activity was decreased by >80% in all of the transcriptional repressors with luciferase reporter assay. When they were transfected into the telomerase-positive HEK293 cell line, a decrease of mRNA level and telomerase activity together with shortening of telomere length was observed. Actual growth of HEK293 cells was also inhibited by transfection of artificial ZFP-TFs. The repression was maintained for 100 days of culture. The repression of telomerase expression by artificial ZFP-TFs targeting the promoter region of the hTERT presents a new promising strategy for inhibiting the growth of human cancer cells. Mol Cancer Res; 8(2); 246–53
A supplier selection and order allocation problem with stochastic demands

We consider a system comprising a retailer and a set of candidate suppliers that operates within a finite planning horizon of multiple periods. The retailer replenishes its inventory from the suppliers and satisfies stochastic customer demands. At the beginning of each period, the retailer makes decisions on the replenishment quantity, supplier selection and order allocation among the selected suppliers. An optimisation problem is formulated to minimise the total expected system cost, which includes an outer level stochastic dynamic program for the optimal replenishment quantity and an inner level integer program for supplier selection and order allocation with a given replenishment quantity. For the inner level subproblem, we develop a polynomial algorithm to obtain optimal decisions. For the outer level subproblem, we propose an efficient heuristic for the system with integer-valued inventory, based on the structural properties of the system with real-valued inventory. We investigate the efficiency of the proposed solution approach, as well as the impact of parameters on the optimal replenishment decision with numerical experiments.
A Particle Filtering Scheme for Processing Time Series Corrupted by Outliers

The literature in engineering and statistics is abounding in techniques for detecting and properly processing anomalous observations in the data. Most of these techniques have been developed in the framework of static models and it is only in recent years that we have seen attempts that address the presence of outliers in nonlinear time series. For a target tracking problem described by a nonlinear state-space model, we propose the online detection of outliers by including an outlier detection step within the standard particle filtering algorithm. The outlier detection step is implemented by a test involving a statistic of the predictive distribution of the observations, such as a concentration measure or an extreme upper quantile. We also provide asymptotic results about the convergence of the particle approximations of the predictive distribution (and its statistics) and assess the performance of the resulting algorithms by computer simulations of target tracking problems with signal power observations.
Robotic versus Open Thyroidectomy for Differentiated Thyroid Cancer: An Evidence-Based Review

While open thyroidectomy (OT) is advocated as the gold standard treatment for differentiated thyroid cancer, the contemporary use of robotic thyroidectomy (RT) is often controversial. Although RT combines the unique benefits of the surgical robot and remote access thyroidectomy, its applicability on cancer patients is challenged by the questionable oncological benefits and safety. This review aims to analyze the current literature evidence in comparing RT to OT on thyroid cancers for their perioperative and oncological outcomes. To date, no randomized controlled trial is available in comparing RT to OT. All published studies are nonrandomized or retrospective comparisons. Current data suggests that RT compares less favorably than OT for longer operative time, higher cost, and possibly inferior oncological control with lower number of central lymph nodes retrieved. In terms of morbidity, quality of life outcomes, and short-term recurrence rates, RT and OT are comparable. While conventional OT continues to be appropriate for most thyroid cancers, RT should better be continued by expert surgeons on selected patients who have low-risk thyroid cancers and have high expectations on cosmetic outcomes. Future research should embark on prospective randomized studies for unbiased comparisons. Long-term follow-up studies are also needed to evaluate outcomes on recurrence and survival.
Interactive construction: interactive fabrication of functional mechanical devices

Personal fabrication tools, such as laser cutters and 3D printers allow users to create precise objects quickly. However, working through a CAD system removes users from the workpiece. Recent interactive fabrication tools reintroduce this directness, but at the expense of precision. In this paper, we introduce constructable, an interactive drafting table that produces precise physical output in every step. Users interact by drafting directly on the workpiece using a hand-held laser pointer. The system tracks the pointer, beautifies its path, and implements its effect by cutting the workpiece using a fast high-powered laser cutter. Constructable achieves precision through tool-specific constraints, user-defined sketch lines, and by using the laser cutter itself for all visual feedback, rather than using a screen or projection. We demonstrate how constructable allows creating simple but functional devices, including a simple gearbox, that cannot be created with traditional interactive fabrication tools.
Psychosocial predictors of death for low-risk patients after a first myocardial infarction: a 7-year follow-up study.

PURPOSE This study aimed to identify whether psychosocial variables predicted mortality 7 years after a first myocardial infarction among patients who did not undergo revascularization. METHODS The study population consisted of 222 men aged 30 to 60 years who entered an inpatient rehabilitation program a mean of 7 weeks after a first myocardial infarction. Medical data were obtained from the patients and their physicians at entry to the rehabilitation center and 1 year later. Psychosocial and medical variables were obtained from the patient by questionnaires at entry 1 and 7 years later. Follow-up evaluation was 96% complete. Statistical analyses are reported using odds ratios (OR). RESULTS The 7-year mortality rate was 10.4%; the reinfarction rate was 9%; the hospital readmission rate was 32.3%; and 39.2% of the patients underwent a subsequent revascularization procedure, either coronary artery bypass grafting (CABG) or percutaneous transluminal coronary angioplasty (PTCA). The most important psychosocial predictors for death at 7 years identified with logistic regression analyses were lack of partnership (OR, 3.46), anxiety (OR, 1.19), and depression (OR, 1.04). Medical predictors of 7-year mortality were severity of infarction (OR, 2.86) and the number of coronary risk factors (OR, 1.77). CONCLUSIONS Survival in a group of low-risk male patients 7 years after a first myocardial infarction was predicted by the following psychological and social variables: lack of partnership, depression, and anxiety. Although it is unknown whether the risk of dying 7 years after an initial myocardial infarction can be reduced by therapeutic interventions, these data reinforce the importance of special attention for patients with these psychosocial characteristics.