Miracle-dz/UniFAR-FTUs huggingface dataset:
Lung Metastasectomy for Pulmonary Metastatic Breast Carcinoma Lungenmetastasenchirurgie beim pulmonal metastasierten Mammakarzinom

Abstract Breast carcinoma with pulmonary metastasis can be treated locally or systemically. Following primary tumour resection patients with isolated, completely resectable pulmonary nodules and definite functional operability can be offered lung metastasis resection. Following metastasectomy a median survival of 32 to 96.6 months can be achieved with corresponding five-year survival rates between 30.8 and 54.4%. The procedure is associated with a mortality rate of 0 to 3%. The most important independent prognostic factor for long-term survival is complete resection of all lung lesions. The configuration and pattern of metastasis as well as disease-free interval, hormone and HER2/neu receptor status also appear to influence prognosis, but are of lesser importance. Intrapulmonary recurrence of metastases may, after careful selection on a case-by-case basis, also be treated operatively. In some cases this is associated with a favourable long-term prognosis. Pulmonary metastasectomy should be the treatment of choice for selected patients with metastatic breast carcinoma. Zusammenfassung Das pulmonal metastasierte Mammakarzinom kann systemisch oder lokal therapiert werden. Nach erfolgter Primärtumorresektion können Patientinnen mit isolierten, vollständig resektablen Lungenrundherden und sichergestellter funktioneller Operabilität einer Lungenmetastasektomie zugeführt werden. Nach Metastasenresektion wird ein medianes Überleben von 32 bis 96,6 Monaten erreicht, korrespondierende 5-Jahres-Überlebensraten liegen zwischen 30,8 und 54,4%. Der Eingriff ist mit einer Mortalität von 0 – 3% verbunden. Der wichtigste unabhängige Prognosefaktor für das Langzeitüberleben ist die vollständige Resektion aller Lungenmetastasen. Die Metastasenkonfiguration und das Metastasierungsmuster scheinen ebenso wie das krankheitsfreie Intervall, der Hormon- und HER2/neu-Rezeptorstatus für die Prognose zwar relevant, jedoch von nachrangiger Bedeutung zu sein. Intrapulmonale Metastasenrezidive können nach sorgfältiger Indikationsstellung ebenfalls reseziert werden. Dies kann in Einzelfällen ein günstiges Langzeitüberleben ermöglichen. Die Lungenmetastasenchirurgie sollte die Therapie der Wahl für selektionierte Patientinnen mit pulmonal metastasiertem Mammakarzinom sein.
Path Planning for Data Collection Multiagent System in a Sensing Field with Obstacles

Using mobile robots to collect data from wireless sensor network can reduce energy dissipation and this way improve network lifetime. Our problem is to plan paths for multiagent system to visit a set of sensor nodes in a sensing field with obstacles while minimizing the data collection time. Reconstructing the path of an intruder in a guarded area is also a possible application of this technology. This paper applied a new approach for construct clusters of nodes that each robot must visit. To design the clusters and the visiting sequence of nodes a new algorithm based on Ant Colony Optimalization is developed.
The introduction of species as a result of human activities is a major concern in the maintenance of biodiversity in marine systems

Recurrent introductions of non-indigenous species generate novel interactions that vary with local conditions and the composition of the receiving community. Most studies examine relationships of newcomers with native species, but interactions among introduced species could also affect community shifts. As early ontogenetic stages are particularly vulnerable to biotic interactions, we explored direct and indirect interactions across early life-history stages in space-dominating marine invertebrates. We used introduced ascidians and both native and introduced mussels. To increase generality, we ran our experiments in two distant locations, one in the northern and one in the southern hemisphere (Mediterranean and South Africa). We found no sperm interference between the ascidians, nor were there interspecific effects on settlement or metamorphosis success. However, larvae of the ascidian species reacted to each other by shifting from aggregated to random settlement. Juvenile mussels consumed large numbers of ascidian larvae, though larvae that avoided mussel predation showed higher settlement success. Mussel species in the southern hemisphere locality (native Perna perna and introduced Mytilus galloprovincialis) consumed more ascidian larvae than mussels in the northern locality (native M. galloprovincialis) with a tendency for ascidian larvae to avoid settling close to mussels in the latter locality. We conclude that larval consumption by mussels affects the establishment of ascidians, but that the magnitude of this effect is context dependant. These results emphasize the importance of the composition of the receiving community in determining its susceptibility to invasion. Whether the species comprising this community are native or introduced is, however, less important than what manner of species they are.
Domain-Aware Attention FC yDomain Feature Extractor and Classifier Sentiment Feature Extractor and ClassifierDomain-Aware Query VectorBiLSTM BiLSTM Domain-Aware Word Embedding

Sentiment classification is a fundamental task in NLP. However, as revealed by many researches, sentiment classification models are highly domaindependent. It is worth investigating to leverage data from different domains to improve the classification performance in each domain. In this work, we propose a novel completely-shared multidomain neural sentiment classification model to learn domain-aware word embeddings and make use of domain-aware attention mechanism. Our model first utilizes BiLSTM for domain classification and extracts domain-specific features for words, which are then combined with general word embeddings to form domain-aware word embeddings. Domain-aware word embeddings are fed into another BiLSTM to extract sentence features. The domain-aware attention mechanism is used for selecting significant features, by using the domainaware sentence representation as the query vector. Evaluation results on public datasets with 16 different domains demonstrate the efficacy of our proposed model. Further experiments show the generalization ability and the transferability of our model.
You Say , We Say : Using Spoken Language to Model Socio-Cognitive Processes during Computer-Supported Collaborative Problem Solving

Collaborative problem solving (CPS) is a crucial 21st century skill; however, current technologies fall short of effectively supporting CPS processes, especially for remote, computer-enabled interactions. In order to develop next-generation computer-supported collaborative systems that enhance CPS processes and outcomes by monitoring and responding to the unfolding collaboration, we investigate automated detection of three critical CPS process – construction of shared knowledge, negotiation/coordination, and maintaining team function – derived from a validated CPS framework. Our data consists of 32 triads who were tasked with collaboratively solving a challenging visual computer programming task for 20 minutes using commercial videoconferencing software. We used automatic speech recognition to generate transcripts of 11,163 utterances, which trained humans coded for evidence of the above three CPS processes using a set of behavioral indicators. We aimed to automate the trained human-raters’ codes in a team-independent fashion (current study) in order to provide automatic real-time or offline feedback (future work). We used Random Forest classifiers trained on the words themselves (bag of n-grams) or with word categories (e.g., emotions, thinking styles, social constructs) from the Linguistic Inquiry Word Count (LIWC) tool. Despite imperfect automatic speech recognition, the n-gram models achieved AUROC (area under the receiver operating characteristic curve) scores of . 85, . 77, and . 77 for construction of shared knowledge, negotiation/coordination, and maintaining team function, respectively; these reflect 70%, 54%, and 54% improvements over chance. The LIWC-category models achieved similar scores of . 82, . 74, and . 73 (64%, 48%, and 46% improvement over chance). Further, the LIWC model-derived scores predicted CPS outcomes more similar to human codes, demonstrating predictive validity. We discuss embedding our models in collaborative interfaces for assessment and dynamic intervention aimed at improving CPS outcomes.
Expression Analysis of Amylase Gene and Starch Degradation during Fruit Development and Ripening Stages of Exotic Cultivars of Banana

Trend in expression of amylase gene along with changes in starch and total sugar contents in different banana cultivars during fruit ripening stages were examined. Five exotic cultivars of banana were selected. The ripening stages of fingers were designated by changes in color from green to yellow. RT-PCR analysis revealed that expression of amylase gene was barely detectable during first three stages (6 weeks), but gradually increased at 4th stage and became very strong during 6th to 7th stage (ripening stage). A significant decrease in starch content was observed in all the cultivars at ripening stage while sugar contents increased correspondingly. In all the cultivars, the starch contents ranged from 21.8% to 24.67% at first stage and decreased to almost zero (0.32 to 0.60%) at 11th stage. On the other hand sugar contents were increased from 1% to 1.90% (23.2 to 24.2) from 1st to 11th stage. These findings have future implications in developing functional food products having desired range of sugar and starch contents in the fruit.
Evaluating STT-RAM as an Energy-Efficient Main Memory Alternative

In this paper, we use the possibility of STTRAM technology to entirely replace DRAM in main memory. Our goal is to make STT-RAM performance comparable to DRAM while providing significant power savings. Towards this goal, we first analyze the performance and energy of STT-RAM, and then identify key optimizations that can be employed to improve its characteristics. Specifically, using partial write and row buffer write bypass, we show that STT-RAM main memory performance and energy can be significantly improved. Our experiments indicate that an optimized, like capacity STT-RAM main memory can provide performance comparable to DRAM main memory, with an usual 60% reduction in main memory energy.
From Conference Paper to Journal Article : The long and Winding Road Les Worrall

This study developed a framework for qualitative inquiry and administration of social systems. It describes the mechanisms that decision-makers, such as the police detectives, military commanders, and transformative managers use in their problem solving initiatives. First, the framework was reviewed and constructed for: (1) the theory of qualitative data analysis; (2) problem solving process; and (3) the correlation matrices. Then, based on 14 Points, an extracted framework and its methodology are presented. Finally, a manifest (praxis) is introduced for the framework developed for a project in development planning. This innovative framework can be used for policy-making, qualitative data analysis, or problem solving and administration of social systems.
PEST analysis of wind energy in the world: From the worldwide boom to the emergent in Colombia

This article presents the analysis of the political, economic, social and technological aspects of the countries with the greatest development and generation of wind energy in the world, with the aim of studying the successes and disadvantages over the years and serve as a reference to a country like Colombia, which is in full technological development and is very interested in investing in new alternatives to the imminent ecological disaster resulting from polluting emissions. Colombia signs the Paris Agreement and immediately creates a large number of opportunities supported by state policies and economic investment, specifically in the construction of wind farms that guarantee a real alternative to the country’s energy demand. Wind energy supplies 5% of the world’s electricity, a contribution in which countries such as the United States, China, and Germany are the main producers. For this reason, it is necessary to study and analyze the factors that prevent the technological development of wind energy in Colombia, if it is considered a country with abundant resources in renewable energy sources and zero cost, a country that aspires to be a protagonist in the next 5 years according to the Ministry of Mines and Energy.
Automating the Generation of Production Rules for Intelligent Tutoring Systems

Intelligent Tutoring Systems that adapt to an individual student have shown great promise; yet have failed to become ubiquitous in education. The main reason these systems have been slow to be embraced is the high level of time and expert knowledge that are needed to create successful student models. Much of the time involves experts creating the production rules that will cover all of the ways a student might solve a problem. In this research, we propose a method of automatically generating production rules using previous student data on a proposed problem set. In addition, our proposed method will continue to refine the production rules as new data is available. We compare the results of our method run on several different data sets and with the production rules generated by domain experts.
Classifying Disney Characters from Commercial Merchandise using Convolutional Neural Networks

The ultimate task of this project is to classify Disney merchandises after only training on images of cartoon images, using convolutional neural networks. In order to test how well training images generalize to test images, we first do a preliminary test on whether it is better to train with some product images. However, ConvNet models did not work very well, as there are many challenges, including small dataset, intraclass variations among merchandises, and scale variability between training and test images. Therefore, we used transfer learning to use pretrained ConvNet models to leverage cutting-edge ConvNet models and object detection to obtain region proposals that may contain characters in images. By using object detection model, we were able to achieve a much higher top-3 accuracy at 82%, as object detection model could contribute to finding a region that would lead to a higher score for a particular character class.
Towards Uncertainty-aware Remaining Useful Life Prediction via Domain Adaptation

Predicting accurate remaining useful life (RUL) is quite necessary in many industrial cases. Recently, data-driven method has been widely used for RUL prediction because of its promising performance and ease of use. However, one practical challenging issue of the data-driven method is lack of labeled data. One way for dealing with this issue is domain adaptation. With domain adaptation technique, a RUL prediction model is built using labeled data of source domain and then the trained model is used to predict RUL of target domain where labeled data is unavailable. In this paper, we propose our method for RUL prediction through domain adaptation. Another practical issue of RUL prediction is that a single RUL prediction value may not be enough for successful maintenance of target systems. In other words, we need to know how uncertain RUL prediction is. In this paper, we study Monte Carlo (MC) dropout-based uncertainty metric for realizing better RUL prediction result. Through experiment, we first show that our domain adaptation technique shows reasonable performance. We also show that MC dropout-based approach generates the uncertainty metric only meaningful for source domain.
Simulation of ultrasound signals for the study of velocity estimation techniques

Fabio Rizental Coutinho, Cesar Yutaka Ofuchi, Lucia Valeria Ramos de Arruda, Flavio Neves Jr and Rigoberto Eleazar Melgarejo Morales 1 Department of Electronic Engineering, Federal University of Technology-Paraná (UTFPR) Campus Toledo-PR 85902-490 Brazil 2 Graduate School of Electrical Engineering and Computer Science (CPGEI), Federal University of TechnologyParaná (UTFPR), Avenida 7 de Setembro 3165, Curitiba, Paraná, Brazil 3 Mechanical & Materials Engineering Postgraduate Program (PPGEM), Federal University of TechnologyParaná (UTFPR), Avenida 7 de Setembro 3165, Curitiba, Paraná, Brazil
Deep Learning Systems for Identifying and Localizing Objects in Real-World Environments

The intersection of deep learning and mobile/embedded systems has received significant attention in recent years. Development in the areas of deep learning and mobile architecture has made possible an entirely new ecosystem of technological solutions. We propose one of those solutions, a mobile system to predict the whereabouts of objects in the environment. Using recently developed techniques, we are able to efficiently utilize computer vision and mobile processing to assist in the task of interacting with the natural environment. This solution provides a means of assisting those with memory impairments and those without memory impairment alike.
I T ] 2 8 A ug 2 01 9 1 Binary MDS Array Codes with Optimal Repair

Consider a binary maximum distance separable (MDS) array code composed of an m × (k + r) array of bits with k information columns and r parity columns, such that any k out of k + r columns suffice to reconstruct the k information columns. Our goal is to provide optimal repair access for binary MDS array codes, meaning that the bandwidth triggered to repair any single failed information or parity column is minimized. In this paper, we propose a generic transformation framework for binary MDS array codes, using EVENODD codes as a motivating example, to support optimal repair access for k + 1 ≤ d ≤ k + r − 1, where d denotes the number of non-failed columns that are connected for repair; note that when d < k + r − 1, some of the chosen d columns in repairing a failed column are specific. In addition, we show how our transformation framework applies to an example of binary MDS array codes with asymptotically optimal repair access of any single information column and enables asymptotically or exactly optimal repair access for any column. Furthermore, we present a new transformation for EVENODD codes with two parity columns such that the existing efficient repair property of any information column is preserved and the repair access of parity column is optimal. Index Terms Binary MDS array codes, EVENODD codes, repair bandwidth, repair access.
SPAMMER DETECTION ON ONLINE SOCIAL NETWORKS A Dissertation by AMIT ANAND AMLESHWARAM

Twitter with its rising popularity as a micro-blogging website has inevitably attracted attention of spammers. Spammers use myriad of techniques to lure victims into clicking malicious URLs. In this thesis, we present several novel features capable of distinguishing spam accounts from legitimate accounts in real-time. The features exploit the behavioral and content entropy, bait-techniques, community-orientation, and profile characteristics of spammers. We then use supervised learning algorithms to generate models using the proposed features and show that our tool, spAmbush, can detect spammers in real-time. Our analysis reveals detection of more than 90% of spammers with less than five tweets and more than half with only a single tweet. Our feature computation has low latency and resource requirement. Our results show a 96% detection rate with only 0.01% false positive rate. We further cluster the unknown spammers to identify and understand the prevalent spam campaigns on Twitter.
A MOLECULAR APPROACH TO THE ELECTRICAL AGING OF XLPE CABLES

The relation between electrical aging and breakdown of XLPE and its molecular characteristics is examined. We show that our new aging concept describes the electrical aging process of XLPE and, in fact applies to all polymers with free volume. The physical significance of the basic parameters (activation energy and volume) is discussed and it is shown that XLPE cable aging directly depends on the breaking of C-C bonds. The influence of samples size is briefly discussed as well as the influence of temperature cycles. Testing under high frequency might be reliably translated to power frequency condition with our model.
A Fast Filter Feature Selection Algorithm for Ranking ( Technical Report )

Learning to rank (L2R) algorithms optimize search relevance by tuning the weights among large pools of ranking features. However, including more features in the ranking model also implies a larger implementation effort, more processing time to rank documents, and in some cases, more index space to store the features. In addition, as shown in this paper, more features do not necessarily lead to better relevance. We analyzed feature selection heuristics to find the best relation between the number of features and relevance. As a result, we propose a new heuristic for predicting the upper bound improvement that an additional feature can add to a feature set. We show that it is more precise, robust and faster than previous heuristics. Its application selects at most 15% of the ranking features used in three LETOR datasets, and even so, the MAP and NDCG@10 metrics are equally good.
Numerical Methods for Fluid Dynamical Simulation of Hydro Power Plants

Hydro power plants of any size – be it large or small hydro are always quite complicated arrangements as they cannot be designed for a definite duty point of operation but in fact the duty point varies strongly with the flow rate and in many cases also with the pressure head available. The optimization of these plants has always been a challenge to the design engineers from all faculties involved, in the course of this presentation mechanical or civil engineering are concerned. Due to the more or less and sometimes extremely – variable duty points the dimensioning and the set-up in detail are remarkably different from on plant to another – and so is the stationary and transient operation. Both the specific arrangement as well as the varying duty points often cause a number of problems as any hydro power plant exist exactly once and in so far is always a prototype thus often causing unforeseeable difficulties. On the example of a number of components fluid dynamical optimizations by means of numerical methods are outlined for intakes, valves and different turbine designs. Specific problems as they can frequently occur in hydro power and remedy to solve the problems are described. On the example of a cavitating Francis runner a new way for cavitation simulation will be presented.
Hyaluronidase in Clinical Isolates of Propionibacterium acnes Harmony

Objectives. We sought to describe the prevalence of a hyaluronidase gene and hyaluronidase production in 197 clinical isolates of P. acnes; we assessed kinetics of hyaluronidase production in a subset of three isolates.Methods. The hyaluronidase gene was detected using polymerase chain reaction. Hyaluronidase production was detected by growing isolates on BHI agar containing 400μg/mL hyaluronic acid and 1% albumin and flooding plates with 2N glacial acetic acid to precipitate unbound hyaluronic acid, with a zone of clearing representing a positive phenotype. Hyaluronidase production kinetics were measured as a function of hyaluronic acid digestion over time in a liquid medium. Results. A hyaluronidase gene and hyaluronidase production were detected in 100 and 97% of P. acnes isolates, respectively. Hyaluronidase production in liquid medium was detectable after 96 hours of growth. Conclusions. Hyaluronidase production is nearly universal among P. acnes isolates.Three days appear to be required for significant hyaluronidase production in a liquid medium. Detection of hyaluronidase in tissue specimens may be a strategy to differentiate P. acnes infection from colonization when P. acnes is isolated from a clinical specimen.