Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution Modeling and Stabilization Control for Discrete-Time Linear Random Dynamical Systems Using Ensemble Kalman Filter

This paper studies an output feedback stabilization control framework for discrete-time linear systems with stochastic dynamics determined by an independent and identically distributed (i.i.d.) process. The controller is constructed with an ensemble Kalman filter (EnKF) and a feedback gain designed with our earlier result about state feedback control. The EnKF is also used for modeling the distribution behind the system, which is required in the feedback gain synthesis. The effectiveness of our control framework is demonstrated with numerical experiments. This study will become the first step toward the realization of learning type control using our stochastic systems control theory.
Postmarketing surveillance of the oxidative stability for cooking oils, frying oils, and vanaspati supplied in the retail market

Abstract In this study, postmarketing surveillance (PMS) was conducted in terms of the parameters which are reliable indicators of the oxidative stability of cooking oils, frying oils, and vanaspati samples. The analyzed parameters were fatty acid composition, peroxide value (PV), free fatty acids (FFA), p‐anisidine value (p‐AV), induction period at 110°C (IP110) determined by Rancimat test, and TOTOX value. For this purpose, different samples from four highly popular brands of mentioned products were randomly collected from Iran's market during 2016–2018. All monitored products had trans fatty acid <1.0%. In the case of FFA and IP110, the ranges of 0.03–0.08 (%) and 9.3–17.2 hr were obtained, respectively, being mostly in conformity with the National Standard of Iran (FFA < 0.1% and IP110 > 15 hr). The ranges of PV of cooking oils, frying oils, and vanaspati samples were 1.2–2.7, 0.93–2, and 0.84–1.6 meq/kg, respectively. Our results revealed that p‐AV of frying oils and cooking oils was mostly outside of legal limits of Iran (p‐AV > 6) with the ranges of 4.2–12.5 and 4.3–12.3, respectively. In terms of TOTOX value, monitored products had a range from 5.2 to 13.0 (mostly <10) being nearly acceptable.
Diffusion tensor imaging: A diagnostic tool for neuromuscular conditions

This thesis describes the development and application of different MRI techniques which can be used for diagnostic work-up in a broad spectrum of diseases or injuries affecting the neuromuscular system. The largest portion of this thesis is aimed at one specific technique which is called diffusion tensor imaging (DTI). This technique enables noninvasive and in-vivo assessment of the diffusion characteristics of water molecules in living tissues such as skeletal muscles and peripheral nerves. These diffusion properties strongly depend on the intra- and extracellular constituents of muscle and nerve tissue, rendering it therefore highly sensitive to (patho-)physiological changes of these constituents. The thesis is divided into two parts, of which the first is aimed at the application of DTI in the skeletal muscles. The second part subsequently utilizes lessons learned from the first part to apply DTI to the peripheral nerves located between the neck and shoulder, the so called brachial plexus. Finally, the techniques developed in this thesis are applied to patients with different neuropathies to explore the diagnostic properties of these techniques.
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification

In this paper, we propose a variational approach to weakly supervised document-level multi-aspect sentiment classification. Instead of using user-generated ratings or annotations provided by domain experts, we use target-opinion word pairs as “supervision. ” These word pairs can be extracted by using dependency parsers and simple rules. Our objective is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment polarity classifier to predict the sentiment polarity of each aspect given a document. By introducing a latent variable, i.e., the sentiment polarity, to the objective function, we can inject the sentiment polarity classifier to the objective via the variational lower bound. We can learn a sentiment polarity classifier by optimizing the lower bound. We show that our method can outperform weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to the state-of-the-art supervised method with hundreds of labels per aspect.
Unification in Description Logic El without Top Constructor

In recent years, the description logic EL has received a significant interest. The description logic EL is a knowledge representation formalism used e.g in natural language processing, configuration of technical systems, databases and biomedical ontologies. Unification is used there as a tool to recognize equivalent concepts. It has been proven that unification in EL is NP-complete. This result was based on a locality property of certain EL unifiers. Here we show that a similar locality holds for EL without top (EL-top), but decidability of EL-top unification does not follow immediately from locality as it does in the case of unification in EL. However, by restricting further the locality property, we prove that EL-top unification is decidable and construct an NExpTime decision procedure for the problem. Furthermore, we show that unification in EL-top is PSPACE-hard by reducing the Finite State Automata Intersection problem to EL-top unification problem.
On designing a mobile robot for RoboCup

The Roobots are a robot soccer team which participated in the RoboCup smallsized robot league competition in the years 2000, 2001 and 2002, when they finished in fourth place. This thesis describes the design of the robots in the 2002 team. Design issues for mobile robots in the RoboCup small-sized robot league are reviewed. The design decisions are presented. Finally, some lessons learnt for system design and project management from the three years of competition are presented.
Visual Tracking for Augmented Reality

In Augmented Reality applications, the real environment is annotated or enhanced with computer-generated graphics. These graphics must be exactly registered to real objects in the scene and this requires AR systems to track a user's viewpoint. This book shows that visual tracking with inexpensive cameras (such as those now often built into mobile computing devices) can be sufficiently robust and accurate for AR applications. Visual tracking has previously been applied to AR, however this has used artificial markers placed in the scene; this is undesirable. This thesis explores edge-based tracking as an efficient and accurate alternative, and explored methods to improve the robustness of edge-based tracking against rapid user motions. This thesis also shows how CAD models of the scene can be used to correctly clip virtual graphics, improving the perceived realism of the scene.
Development of a Complex Intervention for the Maintenance of Postpartum Smoking Abstinence: Process for Defining Evidence-Based Intervention

Relapse to tobacco smoking for pregnant women who quit is a major public health problem. Evidence-based approaches to intervention are urgently required. This study aimed to develop an intervention to be integrated into existing healthcare. A mixed methods approach included a theory-driven systematic review identifying promising behaviour change techniques for targeting smoking relapse prevention, and qualitative focus groups and interviews with women (ex-smokers who had remained quit and those who had relapsed), their partners and healthcare professionals (N = 74). A final stage recruited ten women to refine and initially test a prototype intervention. Our qualitative analysis suggests a lack, but need for, relapse prevention support. This should be initiated by a trusted ‘credible source’. For many women this would be a midwife or a health visitor. Support needs to be tailored to individual needs, including positive praise/reward, novel digital and electronic support and partner or social support. Advice and support to use e cigarettes or nicotine replacement therapy for relapse prevention was important for some women, but others remained cautious. The resulting prototype complex intervention includes face-to-face support reiterated throughout the postpartum period, tailored digital and self-help support and novel elements such as gifts and nicotine replacement therapy (NRT).
Latent Variable Autoencoder

Learning to discover hidden variables from unlabeled data is an important task. Traditional generative methods model the generation process of the observed variables as well as the hidden variables. However, tractable inference and learning on these models requires strong conditional independence assumptions being made among observed and hidden variables. To tackle this limitation, we propose an autoencoder framework. The encoder produces an intermediate representation from the observed variables, and the decoder is a generative latent variable model conditioned on the intermediate representation that tries to generate the hidden variables as well as to reconstruct the observed variables. We introduce three variant models of our framework with either a deterministic or a stochastic encoding process. To optimize our model, we propose an algorithm similar to the classic expectation–maximization (EM) algorithm that supports online learning for large-scale datasets. The flexibility of our framework allows us to apply it to various scenarios where the explicit inference of hidden variables is desired. We discuss the applications of our framework to the perceptual grouping task and the part-of-speech (POS) induction task. Our experiments on the two tasks demonstrate that our framework can achieve better performance than vanilla latent variable generative models.
PedJointNet: Joint Head-Shoulder and Full Body Deep Network for Pedestrian Detection

Pedestrian detection when occlusions exist represents a great challenge in real-world applications, including urban autonomous driving and surveillance systems. However, the head–shoulder feature of pedestrians, which is more stable and less likely to be occluded than other areas of the body, can be used as a complement to full body prediction to boost pedestrian detection accuracy. In this paper, we investigate the unique features of the head–shoulder and full body features belonging to pedestrians. Then, instead of using a popular general object detection framework like R-CNN series, SSD, or YOLO, we propose a novel pedestrian detection network, called PedJointNet, that simultaneously regresses two bounding boxes to localize the head–shoulder and full body regions based on a feasible object detection backbone. Moreover, unlike the traditional strategy of keeping the weights fixed for each attribute, we design an inbuilt mechanism to dynamically and adaptively adjust the relationships of the head–shoulder and full body predictions for more accurate pedestrian localization. We validate the effectiveness of the proposed method using the CUHK-SYSU, TownCentre, and CityPersons datasets. Overall, our two-pronged prediction approach achieves excellent performance in detecting both non-occluded and occluded pedestrians, especially under circumstances involving occlusion, as compared to other state-of-the-art methods.
Small Sample Motor Imagery Classification Using Regularized Riemannian Features

Motor imagery-based electroencephalogram brain–computer interface (BCI) performance suffers from huge variations within and across subjects. This is due to different spatial and temporal characteristics among the subjects. To address these variabilities, a large number of labeled subject specific training trials are collected to calibrate systems for new subjects. This results in long calibration time that limits the BCI usage in practice. One major challenge in the development of brain computer interface is to reduce calibration time or completely eliminate it. The existing approaches rise up to this challenge by incorporating inter-subject and intra-subject variations through covariance matrices from other subject’s training trials. Current approaches use covariance matrices but do not consider the geometry of the covariance matrices, which lies in the space of symmetric positive definite matrices. This inevitably limits their performance. We focus on reducing calibration time by introducing a Riemannian approach. However, in Riemannian approach the performance degrades in small sample scenario as the dimensionality of covariance matrices is large in comparison to the number of trials. To overcome this limitation, we proposed a new framework that transforms covariance matrices into a lower dimension through spatial filter regularized by data from other subjects. The efficacy of the proposed approach was validated on the small sample scenario dataset IVa from BCI competition III. The proposed approach has achieved 87.21% mean accuracy and 0.74 mean kappa on dataset IVa. The proposed method outperforms the conventional method and other existing studies on dataset IVa. To ensure the robustness of the proposed method we evaluated on dataset IIIa from BCI competition III and dataset IIa from BCI competition IV. The proposed method has achieved mean accuracy 90.93% and 80.98% on dataset IIIa and dataset IIa, respectively.
Securing the Smart Grid: A Comprehensive Compilation of Intrusion Detection and Prevention Systems

The smart grid (SG) paradigm is the next technological leap of the conventional electrical grid, contributing to the protection of the physical environment and providing multiple advantages such as increased reliability, better service quality, and the efficient utilization of the existing infrastructure and the renewable energy resources. However, despite the fact that it brings beneficial environmental, economic, and social changes, the existence of such a system possesses important security and privacy challenges, since it includes a combination of heterogeneous, co-existing smart, and legacy technologies. Based on the rapid evolution of the cyber-physical systems (CPS), both academia and industry have developed appropriate measures for enhancing the security surface of the SG paradigm using, for example, integrating efficient, lightweight encryption and authorization mechanisms. Nevertheless, these mechanisms may not prevent various security threats, such as denial of service (DoS) attacks that target on the availability of the underlying systems. An efficient countermeasure against several cyberattacks is the intrusion detection and prevention system (IDPS). In this paper, we examine the contribution of the IDPSs in the SG paradigm, providing an analysis of 37 cases. More detailed, these systems can be considered as a secondary defense mechanism, which enhances the cryptographic processes, by timely detecting or/and preventing potential security violations. For instance, if a cyberattack bypasses the essential encryption and authorization mechanisms, then the IDPS systems can act as a secondary protection service, informing the system operator for the presence of the specific attack or enabling appropriate preventive countermeasures. The cases we study focused on the advanced metering infrastructure (AMI), supervisory control and data acquisition (SCADA) systems, substations, and synchrophasors. Based on our comparative analysis, the limitations and the shortcomings of the current IDPS systems are identified, whereas appropriate recommendations are provided for future research efforts.
Arc Flash Hazard—When Overestimating Underestimates a Problem

When one undertakes an arc flash study, one must develop a model based on the power system of interest. The model requires estimates of conductor lengths and other data. To ensure that the arc flash study that one is performing does not underestimate the magnitudes of the short circuit currents present within the power system, and to allow for limitations in the procedure by which one develops the model, typically visual inspection, one must carefully estimate the lengths of conductors. Computing “safety margins” can help to avoid errors, and maintenance switches can address the uncertainties involved. The final solution may be software that permits the percentage estimates for impedance elements to be built into the model for various equipment and used to compute arc flash levels on the secondaries of low-voltage circuit breakers.
System-Level Efficiency Evaluation of Isolated DC/DC Converters in Power Electronics Transformers for Medium-Voltage DC Systems

This paper presents system-level efficiency evaluation of the suitable isolated dc/dc converters in input-series-output-parallel power electronics transformers (PET) applied for medium-voltage dc systems and provides engineering insights into the design of PETs from a system perspective. To this end, three candidates—a single-stage dual active bridge (DAB) converter, a boost converter plus a DAB with a constant boost output voltage, and a boost converter plus a DAB with a constant DAB voltage gain—are selected and evaluated from a system-level aspect. Comprehensive system-level efficiency comparisons are done with the chosen candidates, considering numbers of normal and hot standby submodules, different working conditions, rated voltages and power of the PET, switching frequencies, and commercial devices. Analysis with detailed loss breakdowns clearly shows the distribution of losses and elaborates the profound reasons behind the efficiency differences. In the comparison, the boost and DAB with a constant boost output voltage have higher or similar efficiency than the other two candidates, verified by simulation and experimental results. Considering its advantages in redundancy in the PET, the boost and DAB with constant boost output voltage are a preferable choice for submodules in input-series-output-parallel PETs.
Association between different sequences of vessel ligation during video-assisted thoracoscopic lobectomy and survival in patients with non-small cell lung cancer.

Background The aim of this retrospective study was to assess the inﬂuence of the sequence of pulmonary vessel ligation during video-assisted thoracoscopic lobectomy on long-term survival in patients with non-small cell lung cancer (NSCLC). Methods This retrospective study included 60 patients treated surgically with lobectomy and standard lymphadenectomy between 2012 and 2013. Patients had primary ligation of the pulmonary vein or veins (PV group, 33 patients) or of the pulmonary artery or arteries (PA group, 27 patients). Patients were excluded if they had undergone pulmonary wedge resection before lobectomy. Subgroup and sensitivity analyses were also used to investigate the effect of clinical characteristics of interest on survival. Results Median follow-up was 54.5 months. Baseline characteristics of the two groups were statistically comparable regarding gender, histology, type of resection, T stage, and overall stage (all P>0.05). Overall, 5-year survival reached 66.67% in the PV group and 44.44% in the PA group (P=0.084). There were no differences between two groups regarding overall survival (OS) (P=0.063, HR: 2.093; 95% CI: 0.960-4.564), disease-free survival (DFS) (P=0.180, HR: 1.539; 95% CI: 0.820-2.889), or cancer-specific deaths (P=0.227, 14/33 vs. 17/27). Subgroup analyses showed no significant difference of OS (P=0.374, HR: 1.541; 95% CI: 0.594-3.997) or DFS (PLog-rank=0.746) for isolated adenocarcinoma, but significant differences in OS (PLog-Rank=0.036; HR: 3.992; 95% CI: 0.987-16.139) and DFS (P=0.044, HR: 3.011; 95% CI: 1.031-8.795) between the two groups of patients in whom squamous cell carcinomas. Sensitivity analysis showed that the differences were not statistically significant between the two groups regarding OS (P=0.140, HR: 1.944; 95% CI: 0.804-4.700) and DFS (P=0.190, HR: 1.605; 95% CI: 0.791-3.255) for patients with a tumour diameter of greater than 3 cm. Conclusions In summary, the sequence of pulmonary vessel ligation during video-assisted thoracoscopic lobectomy for NSCLC has no different effects on long-term survival, but for patients with squamous cell carcinoma, venous ligation should be preferred first since it may bring survival advantage after surgery.
The Social Life of Class Clowns: Class Clown Behavior Is Associated With More Friends, but Also More Aggressive Behavior in the Classroom

A dimensional rather than a typological approach to studying class clown behavior was recently proposed (Ruch et al., 2014). In the present study, four dimensions of class clown behavior (class clown role, comic talent, disruptive rule-breaker, and subversive joker) were used to investigate the associations between class clown behavior and indicators of social status and social functioning in the classroom in a sample of N = 300 students attending grades 6 to 9 (mean age: 13.09 years, 47.7% male). Participants and their teachers completed measures of class clown behavior, and peer nominations of peer acceptance, mutual friends as well as social behavior in the classroom (popular-leadership, aggressive-disruptive, sensitive-isolated, and prosocial behaviors) were collected. The results showed that overall, class clown behavior was positively related to peer acceptance, the number of mutual friends in the classroom and peer-perceived social status. Overall, it was also positively related to peer-rated popular-leadership and aggressive-disruptive behaviors, as well as negatively related to prosocial behaviors. When considering the four dimensions of class clown behavior, comic talent was particularly relevant for the relationship with social status and with popular-leadership behaviors, but also with aggressive-disruptive behaviors. Aggressive-disruptive behaviors were also particularly related to the class clown dimension disruptive rule-breaker. The results underline the significance of class clown behavior for the social status and functioning of students and may help further understand the phenomenon in its multidimensional nature.
Half-mass Radii for ∼7000 Galaxies at 1.0 ≤ z ≤ 2.5: Most of the Evolution in the Mass–Size Relation Is Due to Color Gradients

Radial mass-to-light ratio gradients cause the half-mass and half-light radii of galaxies to differ, potentially biasing studies that use half-light radii. Here we present the largest catalog to date of galaxy half-mass radii at z > 1: 7006 galaxies in the CANDELS fields at 1.0 ≤ z ≤ 2.5. The sample includes both star-forming and quiescent galaxies with stellar masses . We test three methods for calculating half-mass radii from multiband PSF-matched Hubble Space Telescope (HST) imaging: two based on spatially resolved spectral energy distribution modeling, and one that uses a rest-frame color profile. All three methods agree, with scatter ≲0.3 dex. In agreement with previous studies, most galaxies in our sample have negative color gradients (the centers are redder than the outskirts, and ). We find that color gradient strength has significant trends with increasing stellar mass, half-light radius, U − V color, and stellar mass surface density. These trends have not been seen before at z > 1. Furthermore, color gradients of star-forming and quiescent galaxies show a similar redshift evolution: they are flat at z ≳ 2, then steeply decrease as redshift decreases. This affects the galaxy mass–size relation. The normalizations of the star-forming and quiescent relations are 10%–40% smaller than the corresponding relations; the slopes are ∼0.1–0.3 dex shallower. Finally, the half-mass radii of star-forming and quiescent galaxies at M* = 1010.5 M⊙ only grow by ∼1% and ∼8% between z ∼ 2.25 and z ∼ 1.25. This is significantly less than the ∼37% and ∼47% size increases found when using the half-light radius.