Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering

An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.
Patent renewals and R&D incentives

In a model with moral hazard and assymmetric information, we show that it can be welfare-improving to differentiate patent lives when firms have different R&D productivities. A uniform patent life provides excessive R&D incentive to low-productivity firms, and too little to high ones. The optimally differentiated patent scheme can be implemented through a menu of patent lives (or renewals) and associated fees. We characterize the optimal mechanism, and use simulation analysis to compare it with existing patent renewal systems and to illustrate the potential welfare gains from the optimal policy.
Regional Governance Infrastructure: The Positive Externality on the Inflow of Foreign Direct Investment

A standard neoclassical model of growth predicts convergence of income levels across countries and geographical regions. Recent empirical evidence, however, has shown that the gap between rich and poor countries has increased over the last thirty years. One of the key regularities that are at odds with the neoclassical theory is the lack of capital flows from rich to poor countries. Two competing, but not mutually exclusive theories that explain this regularity are new economic geography and institutional economics. The first theory emphasizes the importance of increasing returns and agglomeration to explain the divergence in economic development across different geographical regions. The second theory stresses the crucial role of good institutions that generate economic growth and improve investment climate inside the country. To investigate the role of institutions and geography on economic development, we look at inflows of foreign direct investment (FDI) in 24 transition countries from 1993 to 2003. We set up an econometric panel data model that takes into account spatial spillovers and spatially correlated error terms and estimated the model by applying a recently developed generalized method of moment (GMM) three-stage procedure. Our results show that the regional quality of institutions is an important factor that explains variations in FDI inflows. The positive effect of good regional governance dominates the effect of better developed regional markets. The effect of regional governance is both statistically significant and of the same order of magnitude as the effect of good governance inside the country. We also have found some evidence that EU membership and high oil and gas resources are important determinants of FDI inflows in transition countries.
Dynamic Compressive Tracking

Real-Time Compressive Tracking utilizes a very spare measurement matrix to extract the features for the appearance model. Such model performs well when the tracked objects are well defined. However, when the objects are low-grain, low-resolution, or small, a fixed size sparse measurement matrix is not sufficient enough to preserve the image structure of the object. In this work, we propose a Dynamic Compressive Tracking algorithm that employs adaptive random projections that preserve the image structure of the objects during tracking. The proposed tracker uses a dynamic importance ranking weight to evaluate the classification results obtained by each of the sparse measurement matrices and complete the tracking with the optimal sparse matrix. Extensive experimental results, on challenging publicly available data sets, shows that the proposed dynamic compressible tracking algorithm outperforms conventional compressive tracker.
All-optical clock recovery from NRZ data with simple NRZ-to-PRZ converter based on self-phase modulation of semiconductor optical amplifier

The feasibility of all-optical clock recovery (CR) from non-return-to-zero (NRZ) data with a simple NRZ-to-pseudo-RZ (PRZ) converter based on the self-phase modulation (SPM) of a semiconductor optical amplifier (SOA) is empirically presented. The SPM induces a frequency chirp at every leading edge of the NRZ data passing through the SOA. The SPM-induced chirp component is extracted by an optical grating bandpass filter and then transformed to PRZ data with a low polarisation dependency of ~0.7 dB. By transmitting the PRZ data into a mode-locked Er-doped fibre laser, all-optical CR has been achieved at 2.5 Gbit/s.
Gaussian mixture models with class-dependent features for speech emotion recognition

In this paper, we propose models for emotion recognition from speech based on class-dependent features and Gaussian mixture models (GMM). Seven emotions are identified (Happiness, Fear, Neutral, Disgust, Anger, Boredom and Sadness) with a small set of features for each class. Results show that our system outperforms the single-stage classifier, with a 82.41% (74.86% in single-stage) overall recognition rate for the male case and 81.28% (71.82% in single-stage) for the female case.
A unifying approach to product-forms in networks with finite capacity constraints

In queueing networks with blocking, stations wishing to transmit customers to a full queue are blocked and need to take alternative action on completing a service. In general, product-forms, i.e. separable solutions for such a network's equilibrium state probabilities, do not exist but some product-forms have been obtained over the years in special cases, using a variety of techniques. We show that the Reversed Compound Agent Theorem (RCAT) can obtain these diverse results in a uniform way by its direct application, so unifying product-forms in networks with and without blocking. New product-forms are also constructed for a type of blocking we call `skipping', where a blocked station sends its output-customers to the queue after the one causing the blocking in that customer's path. Finally, we investigate a novel congestion management scheme for networks of finite-capacity queues in which a station with a full queue transmits signals that delete customers from upstream queues in order to reduce incoming traffic.
Using a Mindset Intervention to Reduce Anxiety in the Statistics Classroom

The primary goal of this article is to provide detailed instructions as to how to run a mindset intervention in a psychology statistics course. A secondary goal is to provide preliminary data on such an intervention’s relationship to students’ statistics anxiety and course grades. Large randomized studies have demonstrated that using a one-time mindset intervention can have positive benefits for students’ course outcomes. We adapted this approach to design an intervention that includes a 75-minute presentation on what mindsets are, how are they are related to learning, and strategies for students to learn statistics with a growth mindset and, thereby, reduce their anxiety. Data from N = 75 students split into an experimental and comparison group suggested that receiving the intervention resulted in students’ mindsets becoming more growth oriented and that this was related to a decrease in anxiety and an increase in course grade.
Traitor Deterring Schemes: Using Bitcoin as Collateral for Digital Content

We put forth a new cryptographic primitive called a Traitor Deterring Scheme (TDS). A TDS is a multi-recipient public-key encryption scheme where an authority issues decryption keys to a set of users. The distinguishing feature of a TDS is that secret-keys are issued only after the users provide some private information as a form of collateral. The traitor deterring property ensures that if a malicious coalition of users (aka "traitors") produces an unauthorized (aka "pirate") decryption device, any recipient of the device will be able to recover at least one of the traitors' collaterals with only black-box access to the device. On the other hand, honest users' collaterals are guaranteed to remain hidden. In this fashion a TDS deincentivizes malicious behavior among users. We model, construct and analyze TDS's based on various cryptographic assumptions and we show how bitcoin can be used as collateral for real world deployment of TDS's for the distribution of digital content. Along the way, we present cryptographic building blocks that may be of independent interest, namely fuzzy lockers, and comparison predicate encryption schemes for exponentially large domains. We also compare TDS with previous primitives specifically traitor tracing schemes (TTS) introduced by Chor et al. [9] and digital signets for self enforcement introduced by Dwork et al. [12]. A TDS constitutes a strict strengthening of a TTS and, when modeled in what we call the "known ciphertext model", it is a reformulation of digital signets in the public-key, black-box secure setting. In digital signets the adversary attempts to transmit a pirate copy at a favorable "space rate", i.e., without having to send the whole plaintext (and without revealing the traitor collaterals). It is an open question from [12] to construct o(1) space rate schemes under a falsifiable assumption. With our TDS constructions we resolve this open question showing feasibility for space rates O(log λ / λ) and infeasibility for space rates Ω(log2λ/ λ).
Social Networks and Mobile Devices in Higher Education: Pilot Project

Reflecting latest trends in higher education, social networks and mobile devices have been detected to be important tools. Collecting the appropriate data the main objective of the research was to consider whether mobile-assisted learning could be used within the faculty. The survey monitoring their use for private and education purposes was held at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. Data were collected by the questionnaire from the sample of 203 respondents. Totally twelve questions were included monitoring (1) the types and access frequency to 19 social networks and (2) the ownership and purpose of use of 12 types of mobile devices. The results showed students were sufficiently equipped with the mobile devices and using them for private purposes they reached high level of literacy. This result entitled us to start the pilot process of mobile-assisted learning using the Blackboard Mobile LearnTM version 4.0 for Apple and Android devices.
Tagging Chinese microblogger via sparse feature selection

In new media era, users post messages to record their daily lives and express their opinions via social media platforms, such as microblog. Recently, it is an attractive topic to tag users from the users generation contents. Tags for a microblog user, as the description for his/her interests, concerns or occupational characteristics, are playing an important role in user indexing, personalized recommendation, and so on. Previous works apply keyword extraction methods to present the interests of users. However, it is hard for keyword extraction to give accurate results when the data is deficient and noisy. In this paper, we propose a novel method to tag the users. Firstly, we apply feature selection via sparse classifier to generate preliminary tags for users. Then we also apply feature selection method to extend the tags. Finally, we refine the tags with a reranking strategy. We conduct our experiments on the data of the most popular Chinese microblog (Sina Weibo). The experimental results show that our method improves the performance significantly over other methods.
Efficiency of Ferritin as an MRI Reporter Gene in NPC Cells Is Enhanced by Iron Supplementation

Background. An emerging MRI reporter, ferritin heavy chain (FTH1), is recently applied to enhance the contrast and increase the sensitivity of MRI in the monitoring of solid tumors. However, FTH1-overexpression-related cytotoxicity is required to be explored. Methods. By using the Tet-Off system, FTH1 overexpression was semi-quantitativiely and dynamicly regulated by doxycycline in a NPC cell line. Effects of FTH1 overexpression on the proliferation, cytotoxicity, apoptosis and migration of NPC cells were investigated in vitro, and MR relaxation rate was measured in vitro and in vivo. Results. In vitro and in vivo overexpression of FTH1 significantly increased the transverse relaxivity (R2), which could be enhanced by iron supplementation. In vitro, overexpression of FTH1 reduced cell growth and migration, which were not reduced by iron supplementation. Furthermore, cells were subcutaneously inoculated into the nude mice. Results showed FTH1 overexpression decreased tumor growth in the absence of iron supplementation but not in the presence of iron supplementation. Conclusion. To maximize R2 and minimize the potential adverse effects, supplementation of iron at appropriate dose is recommended during the application of FTH1 as a reporter gene in the monitoring of NPC by MRI.
Demand-side Value for Ecosystem Services and Implications for Innovative Markets: Experimental Perspectives on the Possibility of Private Markets for Public Goods

Environmental economists invest in measuring the value of the environment but put less effort toward integrating that value directly into the economy. Experimental economists evaluate the performance of mechanisms to fund public goods but in some cases offer limited insight into practical implications for developing markets. This discussion presents initial insights into applying mechanisms for private provision of public goods based on demand-side values rather than regulatory-based market incentives such as cap-and-trade policies. Consideration of mechanisms to generate revenue inspires field tests that could direct experiments using threshold public goods and Lindahl's framework toward applications that transform value into revenue.
Step Integration based Information Fusion for Multimodal Biometrics

The present day needs of the society for an efficient and more reliable identification system has given birth to the biometric matcher technology. Over the years, efforts have been made to improve the detection and recognition performance of the biometric systems, and at the same time reduce the processing time of the systems for a faster identification. One of the approaches for performance optimization is to fuse two or more biometric matcher technologies to add on to the performance of the individual systems. In the present paper, we propose a new step integration based fusion method for multimodal biometric technologies, based upon eliminative machine learning. The individual matcher systems are integrated in steps eliminating unwanted user classes at each matcher step. The method achieves high accuracies and recognition rates, achieving low processing times at the same time. We compare our approach with simple sum fusion technique because of the simplicity and high accuracy rates associated with the simple sum method.
Want Drugs? Use Python

We describe how Python can be leveraged to streamline the curation, modelling and dissemination of drug discovery data as well as the development of innovative, freely available tools for the related scientific community. We look at various examples, such as chemistry toolkits, machine-learning applications and web frameworks and show how Python can glue it all together to create efficient data science pipelines.
Marketing orientation and its determinants: an empirical analysis

While a strong association between Marketing Orientation development and company performance has been established, the understanding of the Marketing Orientation remains unclear since some studies have suggested a philosophical nature for Marketing Orientation and some other studies concluded that Marketing Orientation represents a behavioural notion. As a result of this antithesis, research has not proceeded in the investigation on the factors that determine the degree of Marketing Orientation development. Shows that Marketing Orientation should be conceptualised synthetically since it represents the integration of a certain culture with specific behaviour. Closely examines the major determinants of Marketing Orientation development. Although exploratory in nature, suggests that Marketing Orientation development is determined by company‐specific, as well as by market‐specific factors with the former having a facilitating effect and the latter a coercive effect.
Processes of oscillatory basin filling and excavation in a tectonically active orogen: Quebrada del Toro Basin, NW Argentina

Intramontane basins may act as important sediment storage areas, serve as recorders of the history of deformation, record syntectonic deposition, and document the evolution of climatic conditions during deposition. We document the timing, cyclicity, and processes that led to the filling and reexcavation of the intramontane Quebrada del Toro basin in NW Argentina. Geomorphic and geologic observations indicate that the basin was filled with sediment that has been subsequently excavated at least two times in the last ∼8 m.y. The last filling and excavation cycle occurred within the last 0.98 m.y. and has led to the deposition and removal of ∼61.4 km 3 of material from the basin, leading to a basin-wide averaged minimum denudation rate of 0.16 mm/yr. Aggradation within the basin took place due to channel steepening of the downstream fluvial system that connects the intramontane basin to the foreland. This portion of the fluvial system is actively incising through an uplifting bedrock zone. We use observations within the Toro to test a quasi-physically based model of channel aggradation behind a rising base level that rises due to downstream channel steepening. Our work shows that the bedrock incision rate constant required to reproduce conditions observed within the Toro basin is consistent with values measured independently in similar rock types. Therefore, in intramontane basins that experience similar processes of filling and evacuation, this model may be used to assess the relative importance of tectonic rock uplift, bedrock resistance to fluvial incision, and climate in determining the geomorphic and sedimentologic history of these basins.
Control techniques for power factor correction converters

The aspects regarding control strategies for Power Factor Correction (PFC) converters are investigated. The major control techniques to absorb sinusoidal input currents in boost PFC's are reviewed and analyzed. Their extension to other converter topologies is discussed and some experimental results for a PFC based on the Sepic topology are reported, which allow comparison of converter performance with different control techniques. Lastly, some considerations regarding dynamic operation are given, together with information about control IC's specifically developed for PFC applications.
Perception and Prevalence of Plagiarism among Postgraduate Medical Students in Twin Cities of Pakistan

Objective: To compare the perception and prevalence of plagiarism in doctors pursuing academic postgraduate qualification versus clinical postgraduate qualification Methods: All doctors pursuing a postgraduate qualification in six institutes of Islamabad and Rawalpindi, Pakistan were included in the study from April to October 2018. Data was collected through a validated, self-administered and structured questionnaire. Results: Out of 126 participants, the reported prevalence of plagiarism was 57.14% (n=72) with a higher proportion among clinically oriented specialty students (65.43%, n = 53) as compared to their counterparts doing academic degrees (42.22 %; n=19). Majority of respondents (80.24%) knew about plagiarism, but only 35.80% were aware of its legal consequences. 67.9 % of the MD/MS/ FCPS students did not consider plagiarism as unprofessional or unethical as compared to 40% of the M.Phil./PhD students (p
BUFFER REQUIREMENT ANALYSES FOR MULTIVIEW VIDEO CODING

Multiview video coding (MVC), which is becoming an extension of H.264/AVC, is currently under development by the Joint Video Team (JVT). Compared to H.264/AVC, the main new compression tool in MVC is inter-view prediction, which, among others, causes a substantial increase of the decoded picture buffer (DPB) size. Therefore to have an efficient buffer management for MVC is highly desirable. In this paper, we provide analyses of minimum buffer requirements for typical MVC coding structure with two coding methods, viewfirst coding and time-first coding. The analysis results are helpful in designing reference picture management or reference picture marking methods.
A community based authentication and authorisation mechanism for digital ecosystem

In this paper, we present a distributed mechanism for elevating resource protection in a Digital Ecosystem environment. The dynamic interaction and collaboration between Digital Ecosystem entities poise several main challenges in protecting the resources and information. First, a strong mechanism is needed to ensure only the authentic entities that are able to access the resources. Second, this mechanism must also maintain the confidentiality and integrity of resources over the untrusted network. Unfortunately, the existing mechanisms which focus on providing a centralized protection facade several issues ranging from single point failure to huge administrative burden. Therefore, we present a distributed mechanism to address these challenges. The inclusivity of community based trust approach in our mechanism further promotes its applicability to the Digital Ecosystem environment, on which it is heavily driven by the interacting entities. Public Key Infrastructure is employed to provide a strong protection during its access workflow.
A magnetic-less DC-DC converter for dual voltage automotive systems

The automotive industry is moving toward 42 volt to meet the more electric needs. Several dual voltage (42 and 14 volt) architectures have been proposed for the transition and accommodation of 14-volt loads. A DC-DC converter that connects the 42 and 14 volt architectures is one key device in any dual voltage architecture. This paper presents a compact, efficient, magnetic-less bi-directional DC-DC converter for dual voltage (42/14 Volt) automotive systems. The DC-DC converter is based on the generalized multilevel converter topology having the ability to balance battery voltages, emit zero or low electromagnetic interference (EMI), and have low cost by using low-voltage metal oxide field effect transistors (MOSFETs). The main circuit of the DC-DC converter is analyzed and its control scheme is presented in the paper. A self-powered gate drive circuit is developed for the DC-DC converter to reduce costs, signal connections, and circuit complexity. A prototype has been built and experimental results are presented.