Miracle-dz/UniFAR-FTUs huggingface dataset:
Antiepileptic drugs prescribed in pregnancy and prevalence of major congenital malformations: comparative prevalence studies

Objective The aim of this study was to examine the prevalence of major congenital malformations associated with antiepileptic drug (AED) treatment in pregnancy. Patients and methods Using data from The Health Improvement Network, we identified women who have given live birth and their offspring. Four subgroups were selected based on the AED treatment in early pregnancy, valproate, carbamazepine, lamotrigine and women not receiving AED treatment. We compared the prevalence of major congenital malformations within children of these four groups and estimated prevalence ratios (PRs) using Poisson regression adjusted for maternal age, sex of child, quintiles of Townsend deprivation score and indication for treatment. Results In total, 240,071 women were included in the study. A total of 229 women were prescribed valproate in pregnancy, 357 were prescribed lamotrigine and 334 were prescribed carbamazepine and 239,151 women were not prescribed AEDs. Fifteen out of 229 (6.6%) women prescribed valproate gave birth to a child with a major congenital malformation. The figures for lamotrigine, carbamazepine and women not prescribed AEDs were 2.7%, 3.3% and 2.2%, respectively. The prevalence of major congenital malformation was similar for women prescribed lamotrigine or carbamazepine compared to women with no AED treatment in pregnancy. For women prescribed valproate in polytherapy, the prevalence was fourfold higher. After adjustments, the effect of estimates attenuated, but the prevalence remained two- to threefold higher in women prescribed valproate. Conclusion The results of our study suggest that lamotrigine and carbamazepine are safer treatment options than valproate in pregnancy and should be considered as alternative treatment options for women of childbearing potential and in pregnancy.
Power Aware Fulfilment of Latency Requirements by Exploiting Heterogeneity in Wireless Sensor and Actuator Networks

One can imagine a lot of realistic scenarios, such as home or factory automation, where a wide variety of sensors and actuators form a wireless network in order to perform various tasks. There is still a lot of work to be done to ensure scalable and reliable functionality of such a heterogeneous network. We present a MAC layer algorithm that ensures end-to-end latency requirements while minimizing maintenance. We demonstrate that our algorithm scales down the duty cycles along constrained paths in such a way that nodes with more capabilities take on more responsibility in meeting the end-to-end application constraints.
Relaying technologies for smart grid communications

Wireless technologies can support a broad range of smart grid applications including advanced metering infrastructure and demand response. However, there are many formidable challenges when wireless technologies are applied to the smart gird, such as the trade-offs between wireless coverage and capacity, the high reliability requirement for communication, and limited spectral resources. Relaying has emerged as one of the most promising candidate solutions for addressing these issues. In this article, an introduction to various relaying strategies is presented, together with a discussion of how to improve spectral efficiency and coverage in relay-based information and communications technology infrastructure for smart grid applications. Special attention is paid to the use of unidirectional relaying, collaborative beamforming, and bidirectional relaying strategies.
Journal of Theoretical and Applied Information Technology Fuzzy Speed Control of Induction Motor with Dtc-based Neural Networks

In this paper, the authors present the induction motor speed control with DTC (direct torque control). The DTC in its conventional form uses algorithms to select the components of the voltage inverter. This paper proposes to replace the conventional selector switches statements of the voltage inverter by a selector based on neural networks ANN (Artificial Neural Network), which is able to manage in the same way the switches states, without resorting to complex programming. The speed loop regulation is carried out by a fuzzy controller giving exceeding performance in comparison with a classic PI regulator. The design of the controller is based on the experience without knowing the mathematical model of the Motor. The simulation results in Matlab/Simulink show that the speed static error and accelerating error is near zero, also the torque and flux ripples are respectively in the range of 2.5% and 5%.
A Level-Set Method for Skinning Animated Particle Data

In this paper, we present a straightforward, easy to implement method for particle skinning-generating surfaces from animated particle data. We cast the problem in terms of constrained optimization and solve the optimization using a level-set approach. The optimization seeks to minimize the thin-plate energy of the surface, while staying between surfaces defined by the union of spheres centered at the particles. Our approach skins each frame independently while preserving the temporal coherence of the underlying particle animation. Thus, it is well-suited for environments where particle skinning is treated as a post-process, with each frame generated in parallel. Moreover, our approach is integrated with the OpenVDB library and the underlying partial differential equation is amenable to implicit time integration. We demonstrate our method on data generated by a variety of fluid simulation techniques and simple particle systems.
Massively Multi-target Tracking for Objects in Clutter

We propose a method for tracking large numbers of objects in clutter in an open system. Sequences of video may contain hundreds of objects. Measurements of the state of an object may be absent, even when an object is present. Extrane­ ous measurements (clutter) may appear when no object is present. Objects may appear or disappear at any time in a sequence. We use recursive Bayesian filters in conjunction with a probabilistic data association method for tracking objects. We use hypothesis testing for handling measurement irregularities. We quantitatively evaluate our method using simulated data. We also show the effectiveness of our method in a number of difficult imaging situations. Our method works in near realtime, after we employ simple pruning techniques which make the running time of our algorithm practically O(n) in the number of objects in the system.
An Architecture for Logging and Searching Chat Messages

Instant Messaging has become an important means of communication between people around the globe, providing an alternative to telephone and email conversations. The number of users using Instant Messenger products has been increasing over recent years. Messenger services provide a perfect environment for private and personal chat. People of all ages log into messenger service or chat rooms to spend time chatting with known and unknown persons. As a form of entertainment for children, there may be danger lurking from adults chatting with teens, camouflaged as teens. This has created a desire to monitor chat activity on a system. The goals of this thesis are to provide an archival system for a chat client, an incremental indexing system and a retrieval system that supports a variety of queries. The archival system is designed to be independent of the messaging software so it can be adopted more widely. The indexing and retrieval systems are portable to both Windows and Linux with minimal changes and the system is to be scalable to continuous operation. Chat sessions can be archived on either the server side (chat rooms) or client side (Instant Messengers). This thesis has focused on client applications, but the development has been coordinated with a related server side project. One of the popular chat clients, Microsoft MSN Messenger, has been chosen to provide the text streams for archiving. The text is indexed and a search capability is incorporated. The retrieval system can support queries based on keyword, username, date, and other criteria.
Query Sphere Indexing for Neighborhood Requests

This is an algorithm for finding neighbors for point objects that can freely move and have no predefined position. The query sphere consists of a center location and a given radius within which nearby objects must be found. Space is discretized in cubic cells. This algorithm introduces an indexing scheme that gives the list of all the cells making up the query sphere, for any radius and any center location. It can additionally take into account both cyclic and noncyclic regions of interest. Finding only the k-nearest neighbors naturally benefits from the query sphere indexing by running through the list of cells from the center in increasing distance and prematurely stopping when the k neighbors have been found. Source code is available online.
A Simple Introduction to Maximum Entropy Models for Natural Language Processing

Many problems in natural language processing can be viewed as linguistic classification problems, in which linguistic contexts are used to predict linguistic classes. Maximum entropy models offer a clean way to combine diverse pieces of contextual evidence in order to estimate the probability of a certain linguistic class occurring with a certain linguistic context. This report demonstrates the use of a particular maximum entropy model on an example problem, and then proves some relevant mathematical facts about the model in a simple and accessible manner. This report also describes an existing procedure called Generalized Iterative Scaling , which estimates the parameters of this particular model. The goal of this report is to provide enough detail to re-implement the maximum entropy models described in [Ratnaparkhi,1996, Reynar and Ratnaparkhi,1997, Ratnaparkhi, 1997] and also to provide a simple explanation of the maximum entropy formalism.
Symbol dictionary design for the JBIG2 standard

The JBIG2 standard for lossy and lossless bi-level image coding is a very flexible encoding strategy based on pattern matching techniques. The encoder collects a set of symbols in a dictionary and encodes a page by reference to the dictionary symbols. JBIG2 allows the encoder to view all symbols and choose a good set for the dictionary. We propose a two-pass technique for choosing the dictionary entries, and discuss the trade-offs in bit allocation that these choices entail. The proposed dictionary design technique outperforms simpler dictionary formation techniques by up to 7-9% and 10-18% for lossless and lossy compression, respectively. In the lossy case, we further point out one way to significantly speed up the symbol bitmap compression procedure.
The N-terminus of mature human frataxin is intrinsically unfolded

Frataxin is a highly conserved nuclear‐encoded mitochondrial protein whose deficiency is the primary cause of Friedreich’s ataxia, an autosomal recessive neurodegenerative disease. The frataxin structure comprises a well‐characterized globular domain that is present in all species and is preceded in eukaryotes by a non‐conserved N‐terminal tail that contains the mitochondrial import signal. Little is known about the structure and dynamic properties of the N‐terminal tail. Here, we show that this region is flexible and intrinsically unfolded in human frataxin. It does not alter the iron‐binding or self‐aggregation properties of the globular domain. It is therefore very unlikely that this region could be important for the conserved functions of the protein.
Hyperfast Perspective Cone--Beam Backprojection

Tomographic image reconstruction, such as the reconstruction of CT projection values, of tomosynthesis data, PET or SPECT events, is computational very demanding. The most time-consuming step is the backprojection which is often limited by the memory bandwidth. Recently, a novel general purpose architecture optimized for distributed computing became available: the Cell Broadband Engine (CBE). Its eight synergistic processing elements (SPEs) currently allow for a theoretical performance of 192 GFlops (3 GHz, 8 units, 4 floats per vector, 2 instructions, multiply and add, per clock). To maximize image reconstruction speed we modified our parallel-beam backprojection algorithm that is highly optimized for standard PCs, and optimized the code for the cell processor. Data mining techniques and double buffering of source data were extensively used to optimally utilize both the memory bandwidth and the available local store of each SPE. The pixel-driven backprojection code uses floating point arithmetic and either linear interpolation (LI) or nearest neighbor (NN) interpolation between neighboring detector channels. Performance was measured using simulated data with 512 parallel beam projections per half rotation and 1024 detector elements. The data were backprojected into an image of 512 by 512 pixels using our PC-based approach and the new cell-based algorithm. Both the PC and the CBE were clocked at 3 GHz. Images obtained were found to be identical with both approaches. A throughput of 11 fps (LI) and 15 fps (NN) was measured on the PC whereas the CBE achieved 126 fps (LI) and 165 fps (NN). Thereby, the cell greatly outperforms today's top-notch backprojections based on graphical processing units (GPU). Using both CBEs of our dual cell-based blade (Mercury Computer Systems) one can backproject 252 images per second with LI and and 330 images per second with NN.
Robotic pitching by rolling ball on fingers for a randomly located target

This study considers a robotic pitching task with the goal of implementing dynamic manipulation. The strategy used to control the pitching direction of the ball is based on human pitching action, and involves rolling the fingers on the ball at release. The effect of varying the time of release in the pitching direction is analyzed based on ball dynamics. Experiments to test the proposed method featured a high-speed manipulator, composed of a robotic arm with four degrees of freedom (DoFs) and a hand with 10 DoFs, throwing a ball toward a randomly located target recognized by using high-speed vision.
A research on automatic human fall detection method based on wearable inertial force information acquisition system

Frequent and high-risk fall accidents of the elders have become a serious medical and social problem. In this paper, a wireless automatic human fall detection device based on tri-axis accelerator is designed and realized; and a novel method to distinguish fall events from other daily activities is proposed also, including multi-impact falls and rolling down falls. In the real application, the device is worn on upper trunk of human body, with the algorithm focusing on the impact during a fall and the orientation of trunk before the fall and after it. At last, experiment is performed on some typical daily activities, such as walking, stand-to-sit, stand-to-squat, fall frontward, sideways, etc, and it has shown good results in high detection rate and low positive false rate.
Robust Impulsive Synchronization of Delayed Dynamical Networks and Its Applications

This brief paper further investigates robust impulsive synchronization of complex delayed dynamical networks. Based on impulsive control theory on delayed dynamical systems, some simple yet less conservative criteria ensuring robust impulsive synchronization of coupled delayed dynamical networks are derived analytically. Furthermore, the theoretical results are applied to a typical scale-free (SF) network composing of the representative chaotic delayed Hopfield neural network nodes, and numerical results are presented to demonstrate the effectiveness of the proposed control techniques.
Construction and Application of Elastin Like Polypeptide Containing IL-4 Receptor Targeting Peptide

Various human solid tumors highly express IL-4 receptors which amplify the expression of some of anti-apoptotic proteins, preventing drug-induced cancer cell death. Thus, IL-4 receptor targeted drug delivery can possibly increase the therapeutic efficacy in cancer treatment. Macromolecular carriers with multivalent targeting moieties offered great advantages in cancer therapy as they not only increase the plasma half-life of the drug but also allow delivery of therapeutic drugs to the cancer cells with higher specificity, minimizing the deleterious effects of the drug on normal cells. In this study we designed a library of elastin like polypeptide (ELP) polymers containing tumor targeting AP1 peptide using recursive directional ligation method. AP1 was previously discovered as an atherosclerotic plaque and breast tumor tissue homing peptide using phage display screening method, and it can selectively bind to the interleukin 4 receptor (IL-4R). The fluorescently labeled [AP1-V12]6, an ELP polymer containing six AP1 enhanced tumor-specific targeting ability and uptake efficiency in H226 and MDA-MB-231 cancer cell lines in vitro. Surface plasmon resonance analysis showed that multivalent presentation of the targeting ligand in the ELP polymer increased the binding affinity towards IL-4 receptor compared to free peptide. The binding of [AP1-V12]6 to cancer cells was remarkably reduced when IL-4 receptors were blocked by antibody against IL-4 receptor further confirmed its binding. Importantly, the Cy5.5-labeled [AP1-V12]6 demonstrated excellent homing and longer retention in tumor tissues in MDA-MB-231 xenograft mouse model. Immunohistological studies of tumor tissues further validated the targeting efficiency of [AP1-V12]6 to tumor tissue. These results indicate that designed [AP1-V12]6 can serve as a novel carrier for selective delivery of therapeutic drugs to tumors.
Conditional Survival: A Useful Concept to Provide Information on How Prognosis Evolves over Time

Conditional survival (CS) is defined as the probability of surviving further t years, given that a patient has already survived s years after the diagnosis of a chronic disease. It is the simplest form of a dynamic prediction in which other events in the course of the disease or biomarker values measured up to time s can be incorporated. CS has attracted attention in recent years either in an absolute or relative form where the latter is based on a comparison with an age-adjusted normal population being highly relevant from a public health perspective. In its absolute form, CS constitutes the quantity of major interest in a clinical context. Given a clinical cohort of patients with a particular type of cancer, absolute CS can be estimated by conditional Kaplan–Meier estimates in strata defined, for example, by age and disease stage or by a conditional version of the Cox and other regression models for time-to-event data. CS can be displayed as a function of the prediction time s in parametric as well as nonparametric fashion. We illustrate the use of absolute CS in a large clinical cohort of patients with multiple myeloma. For investigating CS, it is necessary to ensure almost complete long-term follow-up of the patients enrolled in the clinical cohort and to consider potential age–stage migration as well as changing treatment modalities over time. CS provides valuable and relevant information on how prognosis develops over time. It also serves as a starting point for identifying factors related to long-term survival. Clin Cancer Res; 21(7); 1530–6. ©2015 AACR.
A survey on optimal trajectory planning for train operations

Because of the rising energy prices and environmental concerns, the calculation of energy-optimal reference trajectories for trains is significant for energy saving. On the other hand, with the development automatic train operation (ATO), the optimal trajectory planning is significant to the performance of train operation. In this paper, we present an integrated survey of this field. First, a nonlinear continuous-time train model and a continuous-space model of train operations are described, after which the optimal trajectory planning problem is formulated based on these two models. The various approaches in the literature to calculate the reference trajectory are reviewed and categorized into two groups: analytical solutions and numerical optimization. Finally, a short discussion of some open topics in the field of optimal trajectory planning for train operations are given.
Pre-Release Movie Piracy and Box Office Sales : Estimates and Policy Implications

The impact of piracy on digital media sales is an important academic, managerial, and policy question. Answers to this question are particularly salient for theatrical piracy where rights-holders and policy-makers are struggling to determine both the size of the problem and the appropriate legislative and managerial responses, and where there are few if any rigorous academic studies to guide their decisions. The importance of this question became particularly clear earlier this year when a pirated copy of the movie “X-Men Origins: Wolverine” appeared on Internet weeks before its official theatrical release, generating many hundreds of thousands of downloads before the movie was available in theaters.
Parameterized Unit Testing in the Open Source Wild

With recent advances in test generation research, powerful test generation tools are now at the ngertips of developers in software industry. For example, Microsoft Research Pex, a state-of-the-art tool based on dynamic symbolic execution, has been shipped as IntelliTest in Visual Studio 2015. For test inputs automatically generated by such tool, to supply test oracles (beyond just uncaught runtime exceptions or crashes), developers can write formal speci cations such as code contracts in the form of preconditions, postconditions, and class invariants. However, just like writing other types of formal speci cations, writing code contracts, especially postconditions, is challenging. In the past decade, parameterized unit testing has emerged as a promising alternative to specify program behaviors under test in the form of unit tests. Developers can write parameterized unit tests (PUTs), unit-test methods with parameters, in contrast to conventional unit tests, without parameters. PUTs have been popularly supported by various unit testing frameworks for . NET along with the recent JUnit framework. However, there exists no study to o er insights on how PUTs are written by developers in either proprietary or open source development practices, posing barriers for various stakeholders to bring PUTs to widely adopted practices in software industry. To ll this gap, in this paper, we present the rst empirical study of parameterized unit testing conducted on open source projects. We study hundreds of parameterized unit tests that open source developers wrote for these open source projects. Our study ndings provide valuable insights for various stakeholders such as current or prospective PUT writers (e.g., developers), PUT framework designers, test-generation tool vendors, testing researchers, and testing educators.
A Call Admission Control Scheme Using NeuroEvolution Algorithm in Cellular Networks

This paper proposes an approach for learning call admission control (CAC) policies in a cellular network that handles several classes of traffic with different resource requirements. The performance measures in cellular networks are long term revenue, utility, call blocking rate (CBR) and handoff failure rate (CDR). Reinforcement Learning (RL) can be used to provide the optimal solution, however such method fails when the state space and action space are huge. We apply a form of NeuroEvolution (NE) algorithm to inductively learn the CAC policies, which is called CN (Call Admission Control scheme using NE). Comparing with the Q-Learning based CAC scheme in the constant traffic load shows that CN can not only approximate the optimal solution very well but also optimize the CBR and CDR in a more flexibility way. Additionally the simulation results demonstrate that the proposed scheme is capable of keeping the handoff dropping rate below a pre-specified value while still maintaining an acceptable CBR in the presence of smoothly varying arrival rates of traffic, in which the state space is too large for practical deployment of the other learning scheme.