Miracle-dz/UniFAR-FTUs huggingface dataset:
A Rare Case of Primary Pulmonary Anaplastic Large Cell Lymphoma

Non-Hodgkin lymphomas are rare causes of primary lung neoplasms and most are B-cell in origin. Anaplastic large cell lymphoma is an exceedingly rare type of primary pulmonary lymphoma, with an aggressive clinical course. We present the case of an 85-year old male patient who attended our Emergency Department complaining of respiratory and constitutional symptoms, and who was found to have a bronchial mass causing subtotal atelectasis of the left lung. Histological examination showed an anaplastic large cell lymphoma and further investigation revealed that it was limited to the lung. To our knowledge, very few similar cases have been reported in the literature. LEARNING POINTS Non-Hodgkin lymphomas are rare causes of pulmonary lung neoplasms, with the majority of cases being marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue or diffuse large B-cell lymphoma. Anaplastic large cell lymphoma (ALCL) usually involves the lymph nodes, skin and soft tissue. It follows an aggressive clinical course and constitutional symptoms are frequent at presentation. Lung involvement may occur as a result of dissemination in up to 12% of cases. Primary ALCL of the lung is extremely rare. Anaplastic lymphoma kinase (ALK) expression is an important prognostic factor, with ALK+ ALCL patients experiencing better outcomes. Adverse prognostic factors also include advanced age, serum lactate dehydrogenase levels and early relapse after therapy.
Reactive Collision Avoidance - Safe navigation in a dynamic cluttered environment

Collision-free navigation in unknown environments is an essential quality for any autonomous vehicle. In this thesis, a reactive collision avoidance algorithm is presented for vehicles constrained by a unicycle nonholonomic model in a multi-agent environment. The agents navigate independently in a decentralized manner, without explicit communication. Restricted forward speed makes the model suitable for vehicles with heavy linear constraints such as marine vessels and unmanned aircraft. The sensor model is given by an integrated representation of the environment where only limited sensing is required. A new braking rule is created to cope with typical multi-agent challenges such as oscillation and deadlocks. Through rigorous mathematical analysis, sufficient conditions for collision-free navigation is derived by reducing the number of agents. Tests, simulating thousands of cluttered environments, is presented including scenarios with both multiple agents and passive obstacles. The simulations prove that agents safely navigates the environment even when ignoring the strict conditions made in the mathematical analysis. Furthermore, the algorithm shows promising results when compared to other well known multi-agent reactive algorithms, such as the Reciprocal Velocity Obstacles. The main contribution of this thesis is a computational efficient reactive algorithm suited for a wide range of vehicles. By merging two existing algorithms and adding a new breaking rule, the result is a fast and safe multi-agent algorithm. In addition, a literature review is carried out to investigate alternative approaches to collision-free navigation and present the most relevant prior research in the field.
Promoting Support for Public Health Policies Through Mediated Contact: Can Narrator Perspective and Self-Disclosure Curb In-Group Favoritism?

An online 2 × 2 factorial experiment ( N = 203) examined the effect of parasocial contact on support for public health policies in the context of opioid addiction. We hypothesize that because of an intergroup dynamic, individuals are less likely to engage with an out-group character than an in-group character featured in a news magazine article. Results support the in-group favoritism hypothesis. The study examines two narrative devices for overcoming this tendency: the narrator’s perspective and amount of insight into the character’s inner world through character self-disclosure. We find support for the narrator perspective but not for the self-disclosure effect. Finally, the study compares the effects of different types of character involvement with the in-group and the out-group character on support for social policies. The results indicate that readers identify with an in-group character to assist the out-group rather than empathizing with the out-group directly.
Advanced Global Illumination, Second Edition

Come with us to read a new book that is coming recently. Yeah, this is a new coming book that many people really want to read will you be one of them? Of course, you should be. It will not make you feel so hard to enjoy your life. Even some people think that reading is a hard to do, you must be sure that you can do it. Hard will be felt when you have no ideas about what kind of book to read. Or sometimes, your reading material is not interesting enough.
Comparison of the effectiveness of shared memory optimizations for stencil computations on NVIDIA GPU architectures

Stencil computations are commonly used in for example scientific simulations of heat distribution, fluid dynamics and seismic activity. The effectiveness of shared memory optimization in stencil computations on different NVIDIA GPU architectures is investigated in this thesis. Experiments on the previous GPU architectures Fermi and Kepler show that shared memory optimizations result in increasing efficiency as expected based on previous work, where the Kepler architecture GPU gets a greater performance increase than the Fermi architecture GPU. For two newer GPU architectures, Maxwell and Pascal, extensive GPU analysis is done to discover why the Maxwell GPUs almost doubles in speed with shared memory optimization while the Pascal architecture GPU only shows a speedup of about 10%. A conclusive explanation for this difference is not found which calls for future research to be done on the use of shared memory in stencil kernels on Pascal architecture GPUs.
On Efficient Processing of Group and Subsequent Queries for Social Activity Planning

Three essential criteria are important for social activity planning: (1) finding attendees familiar with the initiator, (2) ensuring most attendees have tight social relations with each other, and (3) selecting an activity period available to all. In this paper, we propose the Social-Temporal Group Query (STGQ) to find suitable time and attendees with minimum total social distance. We first prove that the problem is NP-hard and inapproximable within any ratio. Next, we design two algorithms, SGSelect and STGSelect, which include effective pruning techniques to substantially reduce running time. Moreover, as users may iteratively adjust query parameters to fine tune the results, we study the problem of Subsequent Social Group Query (SSGQ). We propose the Accumulative Search Tree and Social Boundary, to cache and index intermediate results of previous queries in order to accelerate subsequent query processing. Experimental results indicate that SGSelect and STGSelect are significantly more efficient than baseline approaches. With the caching mechanisms, processing time of subsequent queries can be further reduced by 50-75 percent. We conduct a user study to compare the proposed approach with manual activity coordination. The results show that our approach obtains higher quality solutions with lower coordination effort, thereby increasing the users’ willingness to organize activities.
Radionavigation field monitoring in the landing area using software-defined radio receiver

The growth of aviation and the urgent need to reduce fuel consumption and emissions demand increased airspace and airport capacity and a focus on providing the preferred trajectory to each aircraft. Aircraft operators also require efficiency gains via approaches with the lowest possible minima and the significant safety benefits of vertical guidance. GNSS-based services can meet these goals and have already provided significant safety and efficiency benefits to many aircraft operators worldwide (USA, and EU-members mainly). Following this trend, Ukraine started GNSS implementation in aviation as well. Paper [1] addresses main aspects of Performance Based Navigation (PBN) implementation in Ukraine. According the requirements and methodology provided in the Manual on Testing of Radio Navigation Aids [2,3], GNSS monitoring and testing should be performed, to confirm the ability of GNSS signals to support flight procedures in accordance with Annex 10 [4]. The safe operation of an aircraft landing based on GNSS signals depends on the performance of navigation receiver and accuracy of its coordinates determination, which depends on the accuracy of atmospheric parameters estimation [5] and surrounding electromagnetic environment [4]. Therefore, an important scientific and technical problem is monitoring and control of the radionavigation field and electromagnetic environment in the approach and landing area in order to increase the safety of aircrafts using GNSS technology [6-9]. Currently, multichannel scanning receivers are used for radio monitoring. They perform constant automatic control and radio signals search at predetermined frequencies. It should be noted that the efficiency of radio monitoring depends not only on the availability of expensive equipment (frequency meters, field indicators, spectrum analyzers, broadband antennas, band filters, low noise antenna amplifiers, high-frequency cables with small losses), proper installation, but also on the methods of data analysis, qualification and experience of radio operators. Radio broadcast monitoring is a constant and intense work of qualified specialists in identification, measurements of radio signals parameters, recording, storing and processing of the obtained information. Radio monitoring is able to solve the following main tasks for providing information security of the object: detection of unauthorized radiation devices and their localization (jamming devices);
Advanced model updating strategies for structural dynamic systems

In conjunction with increasing computational power, numerical models gained importance in virtually all engineering sciences during the last decades. Sophisticated modeling approaches are employed to simulate the physical behavior of the investigated structures for many practical problems. However, many of these models do not match with data measured in real-world structures to a certain degree. These inaccuracies are often caused by parameters needed for sophisticated modeling being imprecise, determined using engineering judgment or even completely unknown. Structural systems are a particular case with many unknown parameters due to varying environmental conditions, approximated loads and imprecise knowledge of boundary conditions. This thesis aims to modify parameterized numerical models of structural systems automatically to increase conformity between simulation results and measured data. This process is known as model updating from the relevant literature. The deviation between numerical results and measured data is minimized using optimization methods via modifications of parameters of the numerical model. A further aim is to apply the same methodology to damage localization. If a model building a good representation of measurement data is found using a first model updating step, the methodology is applied again after a damage event to localize these damages. This technique is useful in structural systems, where assemblies may be inaccessible, and ongoing visual inspections are likely to be costly. Different metrics formulated in both frequency and time domain are investigated for their application in model updating within this text. A new scheme for the automated adjustment of numerical models is presented, enabling a comprehensive view of methods and techniques needed to perform iterative model updating. The aim is to minimize the outcome of these metrics, which results in nonlinear optimization problems providing several local minima. A new two-step algorithm employing state-of-the-art optimization methods is introduced to minimize the metrics, constituting a possible implementation of the scheme. The two steps consist of the global optimization method Simulated Quenching and the local Sequential Quadratic Programming. The need for a global algorithm is demonstrated using the concept of convexity and practical examples. Both methods can handle constraints to keep parameters within a specific user-defined range. Since the al-
NUMERICAL STABILITY OF A HYBRID METHOD FOR PRICING OPTIONS

We develop and study stability properties of a hybrid approximation of functionals of the Bates jump model with stochastic interest rate that uses a tree method in the direction of the volatility and the interest rate and a finite-difference approach in order to handle the underlying asset price process. We also propose hybrid simulations for the model, following a binomial tree in the direction of both the volatility and the interest rate, and a space-continuous approximation for the underlying asset price process coming from a Euler–Maruyama type scheme. We test our numerical schemes by computing European and American option prices.
Grid‐connected control strategy of modular multilevel converter–battery energy storage system based on VSG

Modular multilevel converter-battery energy storage system (MMC-BESS) has a good engineering application. When MMC-BESS is connected to the grid, the real-time phase angle of grid is an important parameter. When MMC-BESS is connected to the grid, a simulation model based on virtual synchronous generator (VSG) is built in MATLAB. The results show the control algorithm based on VSG can improve the dynamic response of the system and stably detect the phase angle of the grid.
Study of Hemolysin Gene “aspHS” and Its Phenotype in Aspergillus Fumigatus

AIM: The main goal of this study was to analysis the “aspHS” gene and its phenotype in A. fumigatus. METHODS: Fifty-three A. fumigatus strains, including environmental, clinical and reference isolates, were used in this research. PCR was carried out based on Asp-hemolysin gene sequence. Two restriction enzymes TagI and NcoI were employed for digestion of PCR products. RESULTS: PCR products of 180 and 450 bp were generated for all A. fumigatus isolates. Digestion of the aspHS gene 180 bp amplicons with TagI and 450 bp amplicons with TagI and NcoI produced the expected bands for most isolates. Hemolysin production of A. fumigatus isolates was evaluated on sheep blood agar (SBA). CONCLUSION: In conclusion, our results provide evidence hemolysin activity and analysis of aspHS gene of A. fumigatus. These data may be useful in early diagnosis of A. fumigatus infections.
On Solving Fuzzy investment Problem using Dynamic Programming

Investment problem is considered as one of the most important and interesting optimization problem. This problem becomes more difficult when we deal with it in an uncertain and vague environment with fuzzy data. The aim of this paper is to modify the investment problem with Interval-valued fuzzy number instead of normal fuzzy numbers. While the Interval-valued fuzzy number investment problem introduced, a dynamic programming is applied to obtain the optimal policy and the corresponding best return. A numerical example is given to illustrate the aspects of the considered problem.
Social and Economic Aspects in City Management in Context of Sustainable Development

The article presents the issue of implementing sustainable development in cities with particular emphasis on the economic and social aspects, which together with the environmental one constitute the three pillars of this very concept. The most important social and economic problems of the world's largest cities are presented, as well as the current strategies of cities such as Singapore, London, New York and Dubai, which perform particularly well in these categories. A section analysing the operating effectiveness of cities on the basis of the Arcadis Sustainable Cities Index is presented.
Are Price-Earnings Ratios Mean Reverting? An Empirical Study

Mean reversion in stock prices is a highly studied area in the financial literature with controversial findings. While some economists have found evidence of mean reverting processes in stock prices, many argue in favor of the Efficient Market Hypothesis which states stock prices are random walk processes. This paper seeks to add to the literature on mean reversion but testing for evidence in price-earnings ratios rather than stock prices. The study employs a robust regression model controlling for company-specific and general market factors that influence price-earnings ratio deviations. After correcting for heteroskedasticity, serial correlation, and unit root processes, the results indicate mean reverting behavior does exist in US equities from 2008-2017 and mean reversion in price-earnings ratios may occur more quickly than mean reversion of stock prices. The outcome of this paper also implies some level of endogeneity in the Three-Factor-Model proposed by Fama and French (1992). Abstract Mean reversion in stock prices is a highly studied area in the financial literature with controversial findings. While some economists have found evidence of mean reverting processes in stock prices, many argue in favor of the Efficient Market Hypothesis which states stock prices are random walk processes. This paper seeks to add to the literature on mean reversion but testing for evidence in price-earnings ratios rather than stock prices. The study employs a robust regression model controlling for company-specific and general market factors that influence price-earnings ratio deviations. After correcting for heteroskedasticity, serial correlation, and unit root processes, the results indicate mean reverting behavior does exist in US equities from 2008- 2017 and mean reversion in price-earnings ratios may occur more quickly than mean reversion of stock prices. The outcome of this paper also implies some level of endogeneity in the Three- Factor-Model proposed by Fama and French (1992).
Translating Terminological Expressions in Knowledge Bases with Neural Machine Translation

Our work presented in this paper focuses on the translation of terminological expressions represented in semantically structured resources, like ontologies or knowledge graphs. The challenge of translating ontology labels or terminological expressions documented in knowledge bases lies in the highly specific vocabulary and the lack of contextual information, which can guide a machine translation system to translate ambiguous words into the targeted domain. Due to these challenges, we evaluate the translation quality of domain-specific expressions in the medical and financial domain with statistical as well as with neural machine translation methods and experiment domain adaptation of the translation models with terminological expressions only. Furthermore, we perform experiments on the injection of external terminological expressions into the translation systems. Through these experiments, we observed a significant advantage in domain adaptation for the domain-specific resource in the medical and financial domain and the benefit of subword models over word-based neural machine translation models for terminology translation.
Computer-Aided Classification of Liver Lesions Using Contrasting Features Difference

Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.
Data Encryption using Geometric Range

Geometric range scan is a key primitive for spatial information analysis in SQL and NoSQL databases. It has broad applications in area based administrations, PC aided design, and computational geometry. Due to the dramatic increase in information size, it is fundamental for organizations and associations to outsource their spatial informational indexes to outsider cloud administrations (e.g., Amazon) so as to decrease stockpiling and inquiry handling costs, at the same time, then, with the guarantee of no security spillage to the outsider. Searchable encryption is a system to perform significant inquiries on encoded information without uncovering security. In any case, geometric range seek on spatial information has not been completely researched nor upheld by existing searchable encryption plans. In this paper, we outline a symmetric-key searchable encryption plot that can bolster geometric range questions on encoded spatial information. One of our real commitments is that our plan is a general approach, which can bolster distinctive sorts of geometric range questions. At the end of the day, our plan on scrambled information is autonomous from the states of geometric range questions. In addition, we additionally develop our plan with the extra utilization of tree structures to accomplish look multifaceted nature that is speedier than straight. We formally characterize and demonstrate the security of our plan with indistinguishable capacity under specific picked plaintext assaults, and show the execution of our plan with trials in a genuine cloud stage (Amazon EC2).
User identification via neural network based language models

Identifying compromised accounts on online social networks that are used for phishing attacks or sending spam messages is still one of the most challenging problems of cyber security. In this research, the authors explore an artificial neural network‐based language model to differentiate the writing styles of different users on short text messages. In doing so, the aim is to be able to identify compromised user accounts. The results obtained indicate that one can learn the language model on one dataset and can generalize it to different datasets to identify users with high accuracy and low false alarm rates without any modification to the language model.
Music Data Analysis: Foundations and Applications

This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology.