Miracle-dz/UniFAR-FTUs huggingface dataset:
Five-Year Follow-Up After Intracoronary Gamma Radiation Therapy for In-Stent Restenosis

Background—The Washington Radiation for In-Stent Restenosis Trial is a double-blinded randomized study evaluating the effects of intracoronary radiation therapy (IRT) in patients with in-stent restenosis (ISR). Methods and Results—One hundred thirty patients with ISR (100 native coronary and 30 vein grafts) underwent percutaneous transluminal coronary angioplasty, laser ablation, rotational atherectomy, or additional stenting (36% of lesions). Patients were randomized to either 192-Iridium IRT or placebo, with a prescribed dose of 15 Gy to a 2-mm radial distance from the center of the source. Angiographic restenosis (27% versus 56%, P =0.002) and target vessel revascularization (26% versus 68%, P <0.001) were reduced at 6 months in patients treated with IRT. Between 6 and 60 months, patients treated with IRT compared with placebo had more target lesion revascularization (IRT, 21.6% versus placebo, 4.7%; P =0.04) and target vessel revascularization (IRT, 21.5% versus placebo, 6.1%; P =0.03). At 5 years, the major adverse cardiac event rate was significantly reduced with IRT (46.2% versus 69.2%, P =0.008). Conclusions—In the Washington Radiation for In-Stent Restenosis Trial, patients with ISR treated with IRT using 192-Iridium had a reduction in the need for repeat target lesion and vessel revascularization at 6 months and 5 years.
Neuroimaging at 1.5 T and 3.0 T: Comparison of oxygenation‐sensitive magnetic resonance imaging

Noise properties, the signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), and signal responses were compared during functional activation of the human brain at 1.5 and 3.0 T. At the higher field spiral gradient‐echo (GRE) brain images revealed an average gain in SNR of 1.7 in fully relaxed and 2.2 in images with a repetition time (TR) of 1.5 sec. The tempered gain at longer TRs reflects the fact that the physiological noise depends on the signal strength and becomes a larger fraction of the total noise at 3.0 T. Activation of the primary motor and visual cortex resulted in a 36% and 44% increase of “activated pixels” at 3.0 T, which reflects a greater sensitivity for the detection of activated gray matter at the higher field. The gain in the CNR exhibited a dependency on the underlying tissue, i.e., an increase of 1.8× in regions of particular high activation‐induced signal changes (presumably venous vessels) and of 2.2× in the average activated areas. These results demonstrate that 3.0 T provides a clear advantage over 1.5 T for neuroimaging of homogeneous brain tissue, although stronger physiological noise contributions, more complicated signal features in the proximity of strong susceptibility gradients, and changes in the intrinsic relaxation times may mediate the enhancement. Magn Reson Med 45:595–604, 2001. © 2001 Wiley‐Liss, Inc.
A Research on Adaptive Neural Network Control Strategy of Vehicle Yaw Stability

An adaptive neural network control lerwitn both feed-forward and feedback is designed to improve the yaw stability of vehicle by compound control of direct yaw moment and active front steering. The feedback controller takes the deviation of actual yaw rate from objective yaw rate as its input by PD control strategy,whereas the feed-forward controller uses the output of feedback controller as the error for study through RBF neural network,so as to achieve adaptive control.The simulations show that using above mentioned compound control can effectively track the objective yaw rate and reduce the sideslip angle of mass center,enhancing the stability of vehicle in high-speed sharp turns.
Obsessive Compulsive Behaviors in Children with Developmental Disabilities: A Function-based Conceptual Framework and Single-case Application

Repetitive behaviors are prevalent in a number of neurodevelopmental disorders including autism spectrum disorder (ASD) and intellectual disability (ID). A subset of repetitive behaviors found in ASD and ID can be topographically similar to symptoms in obsessive compulsive disorder (OCD). Through two manuscripts in preparation (Chapters 2 and 4), this project aims to discuss a function-based approach to conceptualizing, assessing, and treating obsessive compulsive behaviors (OCBs) in ASD and ID. The first manuscript is a conceptual paper responding to the variety of approaches researchers use to categorize the behaviors (i.e., assigning a comorbid diagnosis or acknowledging overlapping symptoms of ASD). To date, a clear consensus has not yet been reached amongst the researchers in this field. This paper builds on the differential diagnosis guidelines of the DSM-5 and clinical experts in the field by providing a multidisciplinary, function-based approach to conceptualizing, assessing, and treating individual OCBs in ASD using clinical case examples. Obsessive compulsive behaviors can serve a variety of functions beyond the reduction of anxiety including automatic positive reinforcement or socially mediated reinforcement. The strengths of function-based treatment combined with cognitive behavioral therapy for working with complex obsessive compulsive behaviors in ASD are presented. A second manuscript demonstrates the applicability of this framework in a single case study of four-year-old boy with mild ID and obsessive compulsive behaviors. An intensive, adapted version of function-based cognitive behavioral therapy was administered in the boy’s preschool. The treatment successfully eliminated two OCBs while concomitantly teaching joint engagement with peers on work-related tasks. Results were maintained at a three-week followup. The implications of the treatment protocol, in addition to the broader importance of working
Static and Dynamic Spectral Features: Their Noise Robustness and Optimal Weights for ASR

In this paper, we investigate the relative noise robustness of dynamic and static spectral features in speech recognition. It is found that the dynamic cepstrum is more robust to additive noise than its static counterpart. The results are consistent across different types of noise and over a wide range of noise levels. To exploit this unequal robustness, we propose a simple yet effective strategy of exponentially weighting the likelihoods that are contributed by the static and dynamic features during the decoding process. The optimal weights are discriminatively trained with a small amount of development data. This method is evaluated on two speaker-independent, connected digit databases, one in English (Aurora 2) and the other in Cantonese (CUDIGIT). For various types of noise at different signal-to-noise ratios (SNRs), the average relative word error rate reductions attained with the discriminatively trained weights are 36.6% and 41.9% for Aurora 2 and CUDIGIT, respectively. Noticeable performance improvement can be observed even when there is channel distortion. The proposed approach is appealing to practical applications because 1) noise estimation is not required, 2) model adaptation is not required, 3)only a minor modification of the decoding process is needed, and 4) only a few feature weights need to be trained
Adaptive Detection of Covert Communication in HTTP Requests

The infection of computer systems with malicious software is an enduring problem of computer security. Avoiding an infection in the first place is a hard task, as computer systems are often vulnerable to a multitude of attacks. However, to explore and control an infected system, an attacker needs to establish a communication channel with the victim. While such a channel can be easily established to an unprotected end host in the Internet, infiltrating a closed network usually requires passing an application-level gateway -- in most cases a web proxy -- which constitutes an ideal spot for detecting and blocking unusual outbound communication. This papers introduces DUMONT, a system for detecting covert outbound HTTP communication passing through a web proxy. DUMONT learns profiles of normal HTTP requests for each user of the proxy and adapts to individual web surfing characteristics. The profiles are inferred from a diverse set of features, covering the structure and content of outbound data, and allowing for automatically identifying tunnels and covert channels as deviations from normality. While this approach does not generally rule out sophisticated covert communication, it significantly improves on state-of-the-art methods and hardens networks against malware proliferation. This capability is demonstrated in an evaluation with 90 days of web traffic, where DUMONT uncovers the communication of malware, tunnels and backdoors with few false alarms.
Expression of Two Basic mRNA Biomarkers in Peripheral Blood of Patients with Non-Small Cell Lung Cancer Detected by Real-Time RT-PCR, Individually and Simultaneously

Introduction: Although extensive research has been conducted on lung cancer markers, a singular clinically applicable marker has not been found yet. The objective of this study was to evaluate the sensitivity and the specificity of carcinoembryonic antigen (CEA) mRNA and lung-specific X protein (LUNX) mRNA biomarkers in peripheral blood to detect lung cancer individually and simultaneously. Methods: Thirty patients affected by lung cancer and 30 healthy individuals were studied in this research. Three vials of cDNA were made from each sample after taking peripheral blood samples and extracting total RNA. Each sample was examined by the real-time RT-PCR technique. The result from each vial was then compared with the sensitivity of overall marker. Results: The CEA mRNA was positive in 24 out of 30 lung cancer patients. Hence, its sensitivity was determined at 80%, differing significantly from that observed in healthy individuals, where 11 positive cases were seen. The overall sensitivity of this marker was significantly associated with positivity in vials 2 and 3 but not in vial 1. The LUNX mRNA was positive in 21 out of 30 patients, indicating 70% sensitivity. This finding significantly differed from that in healthy individuals. The overall sensitivity of this marker was significantly associated with positivity in vials 1 and 3, but not in vial 2. In 93.3% of the patients, at least one positive marker was observed. Conclusion: The mentioned mRNA could be suggested as sensitive and specific markers in peripheral blood for primary diagnosis of lung cancer.
Improving Environmental Site Remediation Through Performance-Based Environmental Management (RPO-7)

As noted in the Interstate Technology & Regulatory Council (ITRC) Remediation Process Optimization (RPO) Team’s first technical regulatory guidance document, Remediation Process Optimization: Identifying Opportunities for Enhanced and More Efficient Site Remediation (ITRC 2004), federal, state, and private-sector organizations will continue—for the foreseeable future—to spend billions of dollars on the characterization and assessment of contaminated environmental media and on the selection, construction, operation, maintenance, and monitoring of environmental remediation systems. As numerous environmental cleanup statutes and their implementing regulations evolved, it was initially assumed that these programs could follow a basic “study, design, build” linear paradigm. However, years of experience have led to the realization that the significant uncertainty inherent in environmental cleanup requires more flexible, iterative approaches that manage uncertainty. Uncertainty, as demonstrated by frequently missed target dates, has forced the development of mechanisms that allow for both the systematic reevaluation of initial objectives and the continuous improvement and optimization of remediation technologies and techniques. These mechanisms and reevaluations are known collectively, or generally, as RPO. The team has identified a related concept—performancebased environmental management (PBEM), a method of project management that relies on establishing, and working towards, performance objectives rather that managing only the process. The ITRC RPO team developed this guide to respond to that realization. Schedules for projects in the operating and maintenance or long-term remedial action phase are frequently measured not in years but in decades. In such instances, RPO and PBEM are not just desirable; they are essential.
Creativity in Evolution: Individuals, Interactions and Environments (cid:3)

This paper addresses the nature of open-ended evolutionary processes, and the related, but more subtle, issue of how fundamental novelty (i.e. creativity) can arise in such processes. A number of existing artiﬁcial evolutionary systems, such as Tierra (Ray, 1991), are analysed in this context, but it is found that the theoretical grounding upon which they are based does not usually consider all of the relevant issues for creative evolution. The importance of considering the design of the environment, and of interactions between individuals, as well as the design of the individuals themselves, is emphasised. The properties of a hypothetical ‘proto-DNA’ structure—a suitable seed for an open-ended, and creative, evolutionary process—are discussed. A number of open questions relating to these issues are highlighted as useful areas of future research. Finally, a paradigm for an evolutionary process described by Waddington (1969) is described. It is suggested that this might represent a suitable starting place for a more uniﬁed and productive exploration of these issues using synthetic (artiﬁcial life) modelling techniques.
Boltzmann Machines and the EM algorithmPi erre

In this paper we formulate the Expectation Maximization (EM) algorithm for Boltzmann Machines and we prove that the Kullback distance is a Lyaponov function for the EM algorithm. As a result the EM algorithm yields the same solutions as the original learning rule of Ackley, Hinton and Sejnowski. We give an example of the EM algorithm applied to a special class of Boltzmann Machines (BM). This class of BM's includes feedforward networks, radial basis networks and unsupervised clustering and probability density estimation networks. For this Boltzmann Machine the EM algorithm gives a signiicant speed up compared to standard methods such as (conjugate) gradient descent.
Accounting ngrams and multi-word terms can improve topic models

The paper presents an empirical study of integrating ngrams and multi-word terms into topic models, while maintaining similarities between them and words based on their component structure. First, we adapt the PLSA-SIM algorithm to the more widespread LDA model and ngrams. Then we propose a novel algorithm LDA-ITER that allows the incorporation of the most suitable ngrams into topic models. The experiments of integrating ngrams and multiword terms conducted on five text collections in different languages and domains demonstrate a significant improvement in all the metrics under consideration.
Risk Factors for Medical Withdrawals in United States Tennis Association Junior National Tennis Tournaments

Background: There have been no large-scale epidemiologic studies designed to determine the risk factors related to medical withdrawal in United States Tennis Association junior national tennis tournaments. Hypothesis: Risk of medical withdrawal will increase on the basis of cumulative match volume within a tournament. Methods: A retrospective, cross-sectional analysis was performed of data collected for every match of all 4 United States Tennis Association supernational tournaments (spring, summer, fall, winter) for the boys and girls divisions and for all age divisions (12, 14, 16, 18) during a single year (2005). Logistic regression analysis was performed to determine relative risk of all risk factors. Additional analysis was performed to determine the threshold (ie, match number) at which a player would substantially increase his or her risk of medical withdrawal. Results: A total of 28 336 match exposures were analyzed, with an even distribution with respect to sex and age. The total medical withdrawal rate was 15.6 per 1000 match exposures. Every factor of interest was highly significant in predicting a higher rate of medical withdrawal per 1000 match exposures. The medical withdrawal rate was significantly higher in the fifth match or greater (26.3/1000) versus the first 4 matches (12.7/1000; P < .0001), even when analyzing main draw and singles matches. Conclusion: In United States Tennis Association national junior tennis tournaments, there is a significant increase in risk of medical withdrawals directly related to older age divisions, boys, singles matches, and main draw matches. Specifically, there is a significant increase in medical withdrawal rate beyond the fourth match in the tournament regardless of whether it is a main draw, consolation, or singles match. Clinical Relevance: Recommendations can be made to exercise caution in tournaments that involve boys, older age divisions, and singles. In addition, there may be sufficient evidence to suggest intervention within a tournament when players play beyond their fourth match.
Security in locally repairable storage

In this paper we extend the notion of locally repairable codes to secret sharing schemes. The main problem we consider is to find the optimal ways to distribute shares of a secret among a set of storage-nodes (participants) such that the content of each node (share) can be recovered by using contents of only few other nodes, and at the same time the secret can be reconstructed by only some allowable subsets of nodes. As a special case, an eavesdropper observing some set of a specified nodes (such as less than certain number of nodes) does not get any information. In other words, we propose to study a locally repairable distributed storage system that is secure against a passive eavesdropper that can observe some subsets of nodes. We provide a number of results related to such systems including upper-bounds and achievability results on the number of bits that can be securely stored with these constraints.
Parallel coupled microstrip filters with ground-plane aperture for spurious band suppression and enhanced coupling

Parallel coupled microstrip sections with a slotted ground plane are proposed as building blocks of coupled-line microstrip filters with enhanced performance. It is shown that, by proper adjustment of the ground-plane slot dimensions, the double frequency spurious band associated with unequal even/odd electrical lengths can be suppressed or meaningfully reduced. As an additional feature, this simple design relaxes tolerances of strip width and spacing in those cases where tightly coupled high-impedance sections are required. A rough preliminary design can be obtained within a few seconds using a fast optimization algorithm based on a quasi-TEM analysis of the coupled sections. Fine tuning is based on the use of a commercial electromagnetic simulator. Finally, experimental check of filter performance is provided.
Challenges in designing virtual environments training social skills for children with autism

The purpose of the study is to explore particular challenges faced when designing virtual environments for children with autism, with the purpose of training social skills. Our findings are based on studying autistic behaviour during three years (primary and secondary sources), analysis of related system and other computer mediated assistive technology, as well as general game design. From these studies we have identified eight critical design parameters that need to be adjustable in a system suitable for autistic persons. The parameters importance, their variation range, as well as the need for independent adjustment of these were estimated and verified by experienced expert pedagogues.
Optimal area covering using genetic algorithms

Path planning problems involve computing or finding a collision free path between two positions. A special kind of path planning is complete coverage path planning, where a robot sweeps all area of free space in an environment. There are different methods to cover the complete area; however, they are not designed to optimize the process. This paper proposes a novel method of complete coverage path planning based on genetic algorithms. In order to check the viability of this approach the optimal path is tested in a virtual environment. The simulation results confirm the feasibility of this method.
SEMANTIC SEGMENTATION OF RAILWAY TRACK IMAGES WITH DEEP CONVOLUTIONAL NEURAL NETWORKS

The condition of railway tracks needs to be periodically monitored to ensure passenger safety. Cameras mounted on a moving vehicle such as a hi-rail vehicle or geometry inspection car can generate large volumes of high resolution images. Extracting accurate information from those images has been challenging due to the clutter in the railroad environment. In this paper we describe a novel approach to visual track inspection using semantic segmentation with Deep Convolutional Neural Networks. We show that DCNNs trained end-to-end for material classiﬁcation are more accurate than shallow learning machines with hand-engineered features and are more robust to noise. Our approach results in a material classiﬁcation accuracy of 93.35% using 10 classes of materials. This allows for the detection of crumbling and chipped tie conditions at detection rates of 86.06% and 92.11%, respectively, at a false positive rate of 10 FP/mile on the 85-mile Northeast Corridor (NEC) 2012-2013 concrete tie dataset.
The Use of a Genetic Algorithm in Forecasting Air Carrier Financial Stress and Insolvency

While statistical and artificial intelligence methods such as Artificial Neural Networks (ANN) have been used successfully to classify organizations in terms of solvency or insolvency, they are limited in degree of generalization either by requiring linearly separable variables, lack of knowledge of how a conclusion is reached, or lack of a consistent approach for dealing with local optimal solution whether maximum or minimum. This research explores the use of a method that has the ability of the ANN method to deal with linearly inseparable variables and incomplete, noisy data; and resolves the problem of falling into a local optimum in searching the problems space. The paper applies a genetic algorithm to a sample of U.S. airlines and utilizes financial data from carrier income statements and balance sheets and ratios calculated from this data to assess air carrier solvency.