Miracle-dz/UniFAR-FTUs huggingface dataset:
Intracavernous Aneurysms of the Internal Carotid Artery: Their Clinical Features and Natural History

In 1872, Roberts Bartholow, of Cincinnati, wrote a description of the clinical features of intracranial aneurysms arising from the internal carotid artery, which showed evidence of acute clinical observation. Jonathan Hutchinson, a London surgeon, described a case in 1875, and even considered carotid ligation. More recently, McKinney, Acree, and Soltz (1936) reported the clinical and radiological features of eight cases and called attention to the erosion of the optic foramen and widening of the superior orbital (sphenoidal) fissure which occurred in several of them. Jefferson (1938) made a detailed study of the clinical features of a series of 17 cases, which forms the basis of our present knowledge. At this period arteriography was in its infancy. Since then, relatively safe percutaneous angiography has become available and was undertaken in the majority of the present series of 15 patients which form the basis of the present contribution. Aneurysms arising from the
CAMP: A community-based approach to promoting safe sex behaviour in adolescence†

The primary goal of this study was to describe the development and assessment of the Community Awareness Motivation Partnership (CAMP) Teen Theatre intervention. Based on the behavioural ecological model, CAMP addressed the role of contraceptive use in safe sex behaviour through a brief informative, entertaining, and culturally responsive dramatization programme. Adolescents (N = 1613) between the ages of 11 and 18 (M = 13.31, SD = 0.76) received the intervention, and 961 participants completed both pre- and post-tests. The majority of participants were Latino (78.5%, n = 1039), many of whom were first generation immigrants. Results indicated that the intervention increased participants' reported intentions to delay sex and use contraceptives. The intervention produced short-term changes in safe-sex behavioural intentions and knowledge. Suggestions for producing long-term changes were discussed. Copyright © 2003 John Wiley & Sons, Ltd.
Web Services Architecture for M-Learning.

The academic environment is undergoing a major shift, as increasing numbers of schools are ready to offer courses using mobile technology for economic and other reasons both from an instructor and student perspective. The mobile learning (m-learning) approach would necessitate changes in pedagogy, educational roles, curricular content, and classroom practices. In addition, it would also require different system architecture because it would demand massive integration of software systems. This paper presents a method for exploiting web services architecture for m-learning.
Color atlas and synopsis of clinical dermatology : common and serious diseases

Part I: DISORDERS PRESENTING IN THE SKIN AND MUCOUS MEMBRANES. Part II:DERMATOLOGY AND INTERNAL MEDICINE. Part III: DISEASES DUE TO MICROBIAL AGENTS. Part IV: SKIN SIGNS OF HAIR, NAIL, AND MUCOSAL DISORDERS. Appendix A. Types of Skin Lesions. Appendix B. Special Clinical and Laboratory Aids to Dermatologic Diagnosis. Appendix C. Index of Infectious Diseases.
Comportamiento del fruto de uchuva Physalis peruviana L., bajo diferentes temperaturas de almacenamiento

El fruto de la uchuva Physalis peruviana L. es muy apetecido en los mercados internacionales, por sus caracteristicas nutraceuticas; sin embargo, por ser un fruto climaterico, los procesos de maduracion se dan de forma acelerada disminuyendo considerablemente su vida poscosecha. Teniendo en cuenta lo anterior se han desarrollado diferentes tecnicas que permiten desacelerar los procesos normales de maduracion, entre las que se encuentran, la modificacion de atmosferas o el almacenamiento a diferentes temperaturas, siendo este ultimo, muy utilizado por la facilidad de manejo y bajo costo. Por lo anterior, se evaluo el comportamiento del fruto de uchuva ecotipo Colombia sometido a diferentes temperaturas de almacenamiento, a los que se les evaluo la perdida de masa fresca, indice de color, firmeza, solidos solubles totales y acidez total. Se presentaron diferencias significativas para todas las variables evaluadas entre los tratamientos bajo refrigeracion y el testigo. La temperatura de almacenamiento, influyo sobre todas las caracteristicas fisicas y quimicas evaluadas de manera satisfactoria, ya que luego de 18 dias despues de almacenamiento los frutos refrigerados conservaron su calidad, siendo favorable para su comercializacion; sin embargo, se espera en estudios futuros realizar pruebas de vida en anaquel que permitan descartar fisiopatias que afecten la presentacion del producto asi como su consumo.
Nutritional status of akshara dasoha beneficiaries in Hubli city of Karnataka

Mid day meal programme known as Akshara dasoha programme in Karnataka is an ongoing school feeding programme. The objective of the present study is to assess the effect of Akshara dasoha programme on the nutritional status by anthropometry, and to assess the acceptability of Akshara dasoha food by beneficiaries. By multistage random sampling 900 school children (5-18) from 15 schools of Hubli city were selected for study. Height and weight of the children were recorded and categorized as per WHO 1983 classification. Results revealed that overall about 27.88 per cent of children were normal, 45.78 per cent were normally fed but having a past history of malnutrition or chronic malnutrition and only 2.33 per cent were overfed but not necessarily obese. Higher number of children in high school were in normal category (38.03%) compared to higher primary (25.36%) and lower primary (15.12%) schools. As there was increase in age there is shift in nutritional status of children towards normal category. Overall prevalence of abdominal obesity was 50.11 per cent, higher among girls (63.11%) as compared to boys (37.11). Thus the programme seemed to have achieved the nutritional benefit in the city. Early life malnutrition increased abdominal obesity promoting for metabolic disorders in later life.
Profil Pasien Kusta Baru pada Anak

Latar belakang: Kusta hingga saat ini masih menjadi masalah kesehatan di Indonesia. Kusta pada anak merupakan indeks epidemiologis untuk menentukan transmisi penyakit serta sebagai salah satu indikator dalam keberhasilan program pemberantasan penyakit kusta nasional. Tujuan: Mengevaluasi profil pasien kusta pada anak. Metode: Penelitian retrospektif terhadap semua kasus kusta baru pada anak (0-14 tahun) yang datang ke Unit Rawat Jalan (URJ) Kesehatan Kulit dan Kelamin RSUD Dr.Soetomo Surabaya mulai Januari 2009 sampai Desember 2011. Hasil: Didapatkan 37 pasien anak (5,5%) dari 677 kasus kusta baru. Kasus paling banyak (70,3%) terdapat pada kelompok usia 10-14 tahun. Laki-laki lebih banyak daripada perempuan. Berdasarkan klasifikasi WHO, sebanyak 29 kasus (78,4%) adalah kusta multibasiler (MB) dan 8 kasus (21,6%) adalah kusta pausibasiler (PB). Tipe terbanyak adalah kusta tipe BB (59,5%) pada kelompok MB dan diikuti tipe TT (16,2%) pada kelompok PB. Seluruh kasus mendapat pengobatan Multidrug Therapy (MDT) sesuai panduan WHO. Kontak serumah didapatkan 68,7% kasus. Simpulan: Ditemukan 37 kasus kusta baru pada anak di RSUD Dr. Soetomo Surabaya selama tiga tahun, terutama pada kelompok umur 10-14 tahun, dengan kasus terbanyak berupa kusta MB tipe BB. Kontak serumah masih memegang peranan yang penting pada transmisi kusta. Kata kunci: kusta, anak-anak, retrospektif.
Evaluation and Design of Robust Neural Network Defenses

Author(s): Carlini, Nicholas | Advisor(s): Wagner, David | Abstract: Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to test-time evasion attacks adversarial examples): inputs specifically designed by an adversary to cause a neural network to misclassify them. This makes applying neural networks in security-critical areas concerning.In this dissertation, we introduce a general framework for evaluating the robustness of neural network through optimization-based methods. We apply our framework to two different domains, image recognition and automatic speech recognition, and find it provides state-of-the-art results for both. To further demonstrate the power of our methods, we apply our attacks to break 14 defenses that have been proposed to alleviate adversarial examples.We then turn to the problem of designing a secure classifier. Given this apparently-fundamental vulnerability of neural networks to adversarial examples, instead of taking an existing classifier and attempting to make it robust, we construct a new classifier which is provably robust by design under a restricted threat model. We consider the domain of malware classification, and construct a neural network classifier that is can not be fooled by an insertion adversary, who can only insert new functionality, and not change existing functionality.We hope this dissertation will provide a useful starting point for both evaluating and constructing neural networks robust in the presence of an adversary.
Minimization of PAPR for DFT-Spread OFDM With BPSK Symbols

In this paper, the problem of reducing the peak-to-average power ratio (PAPR) of a discrete Fourier transform spread orthogonal frequency-division multiplexing signal is considered for the transmission of BPSK symbols. In particular, a joint optimization of the constellation-rotation angle and the pulse-shaping vector is conducted with or without the allocation of additional sub-carriers. To avoid the exponential complexity required to evaluate the PAPR, a computationally efficient upper bound on the worst-case PAPR is derived and adopted as the objective function. Unlike conventional upper bounds, the proposed upper bound exploits the structure in the relative phase among symbol waveforms and incorporates the constellation-rotation effect. The signal-to-interference-plus-noise ratio (SINR) at the output of a constellation-derotating matched filter is also derived along with a generalized Nyquist criterion and adopted as the inequality constraint function. Then, joint optimizations are conducted through numerical search for various signal parameters and target SINRs. Surprisingly, the optimal rotation angle is neither 0 nor $\pi /2$. It is shown that the proposed combinations significantly outperform the existing combinations of the rotation angle and the shaping vector when the signal is amplified by a nonlinear power amplifier.
Performance Analysis of Secure Communications Over Dual Correlated Rician Fading Channels

Wireless secrecy under dual correlated Rician fading environments is theoretically studied for correlated source–destination and source–wiretapper channels. Without considering wiretapper active jamming capability, expressions for: 1) the ergodic secrecy capacity when the wiretapper is active and 2) secrecy outage probability when the wiretapper is passive are obtained. To further improve system security, new secrecy analyses employing an adaptive encoder with ON/OFF transmission are also given. The new findings exactly reduce to existing findings under dual correlated Rayleigh fading and advance theoretical developments for wireless secrecy under dual correlated Rician fading. Monte Carlo simulation results are given, which match theoretical predictions. Under wireless secrecy context, the disadvantage of line-of-sight (LoS) power and channel correlation is evidently shown, revealing the non-usefulness of LoS power and establishing a tradeoff between the transmission security and the transmission success. Benefits of a high-average transmit signal-to-noise ratio are discussed.
NOMA-Assisted Multi-Access Mobile Edge Computing: A Joint Optimization of Computation Offloading and Time Allocation

Multi-access mobile edge computing (MEC), which enables mobile users (MUs) to offload their computation-workloads to the computation-servers located at the edge of cellular networks via multi-access radio access, has been considered as a promising technique to address the explosively growing computation-intensive applications in mobile Internet services. In this paper, by exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, we study the NOMA-enabled multi-access MEC. We aim at minimizing the overall delay of the MUs for finishing their computation requirements, by jointly optimizing the MUs’ offloaded workloads and the NOMA transmission-time. Despite the non-convexity of the formulated joint optimization problem, we propose efficient algorithms to find the optimal offloading solution. For the single-MU case, we exploit the layered structure of the problem and propose an efficient layered algorithm to find the MU's optimal offloading solution that minimizes its overall delay. For the multi-MU case, we propose a distributed algorithm (in which the MUs individually optimize their respective offloaded workloads) to determine the optimal offloading solution for minimizing the sum of all MUs’ overall delay. Extensive numerical results have been provided to validate the effectiveness of our proposed algorithms and the performance advantage of our NOMA-enabled multi-access MEC in comparison with conventional orthogonal multiple access enabled multi-access MEC.
Multi-layer fusion techniques using a CNN for multispectral pedestrian detection

In this study, a novel multi-layer fused convolution neural network (MLF-CNN) is proposed for detecting pedestrians under adverse illumination conditions. Currently, most existing pedestrian detectors are very likely to be stuck under adverse illumination circumstances such as shadows, overexposure, or nighttime. To detect pedestrians under such conditions, the authors apply deep learning for effective fusion of the visible and thermal information in multispectral images. The MLF-CNN consists of a proposal generation stage and a detection stage. In the first stage, they design an MLF region proposal network and propose to use summation fusion method for integration of the two convolutional layers. This combination can detect pedestrians in different scales, even in adverse illumination. Furthermore, instead of extracting features from a single layer, they extract features from three feature maps and match the scale using the fused ROI pooling layers. This new multiple-layer fusion technique can significantly reduce the detection miss rate. Extensive evaluations of several challenging datasets well demonstrate that their approach achieves state-of-the-art performance. For example, their method performs 28.62% better than the baseline method and 11.35% better than the well-known faster R-CNN halfway fusion method in detection accuracy on KAIST multispectral pedestrian dataset.
Optimal Training for Residual Self-Interference for Full-Duplex One-Way Relays

Channel estimation and optimal training sequence design for full-duplex one-way relays are investigated. We propose a training scheme to estimate the residual self-interference (RSI) channel and the channels between nodes simultaneously. A maximum likelihood estimator is implemented with the Broyden–Fletcher–Goldfarb–Shanno algorithm. In the presence of RSI, the overall source-to-destination channel becomes an inter-symbol-interference (ISI) channel. With the help of estimates of the RSI channel, the destination is able to cancel the ISI through equalization. We derive and analyze the Cramer–Rao bound (CRB) in closed-form by using the asymptotic properties of Toeplitz matrices. The optimal training sequence is obtained by minimizing the CRB. Extensions for the fundamental one-way relay model to the frequency-selective fading channels and the multiple relays case are also considered. For the former, we propose a training scheme to estimate the overall channel, and for the latter the CRB and the optimal number of relays are derived when the distance between the source and the destination is fixed. Simulations using LTE parameters corroborate our theoretical results.
A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

Fault diagnosis plays an important role in modern industry. With the development of smart manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods have two shortcomings: 1) their performances depend on the good design of handcrafted features of data, but it is difficult to predesign these features and 2) they work well under a general assumption: the training data and testing data should be drawn from the same distribution, but this assumption fails in many engineering applications. Since deep learning (DL) can extract the hierarchical representation features of raw data, and transfer learning provides a good way to perform a learning task on the different but related distribution datasets, deep transfer learning (DTL) has been developed for fault diagnosis. In this paper, a new DTL method is proposed. It uses a three-layer sparse auto-encoder to extract the features of raw data, and applies the maximum mean discrepancy term to minimizing the discrepancy penalty between the features from training data and testing data. The proposed DTL is tested on the famous motor bearing dataset from the Case Western Reserve University. The results show a good improvement, and DTL achieves higher prediction accuracies on most experiments than DL. The prediction accuracy of DTL, which is as high as 99.82%, is better than the results of other algorithms, including deep belief network, sparse filter, artificial neural network, support vector machine and some other traditional methods. What is more, two additional analytical experiments are conducted. The results show that a good unlabeled third dataset may be helpful to DTL, and a good linear relationship between the final prediction accuracies and their standard deviations have been observed.
Joint Access and Fronthaul Radio Resource Allocation in PD-NOMA-Based 5G Networks Enabling Dual Connectivity and CoMP

In this paper, fifth-generation (5G) cellular networks under three promising technologies, namely, dual connectivity, coordinated multi-point transmission (CoMP), and power domain non orthogonal multiple access (PD-NOMA) are investigated. The main aim is to maximize the downlink energy efficiency (EE) by using both millimeter wave (mmW) and micro wave (<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>) links in access and fronthaul, while employing CoMP and PD-NOMA. In this regard, joint access and fronthaul radio resource allocation for a downlink heterogeneous cloud radio access network is considered. The proposed optimization is a mixed integer non-convex problem with a high computational complexity solution, and hence, the alternate search method based on a successive convex approximation approach using fractional programing is exploited. Furthermore, the convergence of the proposed iterative resource allocation method is proved and its computational complexity is investigated. As the numerical results show, via dual connectivity through receiving signals from both mmW and <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> transmitters, the system EE is improved by approximately 50%, in contrast to using only <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> subcarriers (e.g., as in local thermal equilibrium). In addition, by applying both PD-NOMA and CoMP technologies on the <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> subcarriers, the EE of the system increases by approximately 45%.
Investigation of Vehicle Positioning by Infrared Signal-Direction Discrimination for Short-Range Vehicle-to-Vehicle Communications

A method to locate the position of the target vehicle with the aid of infrared signal-direction discrimination for short-range vehicle-to-vehicle communications is proposed in this paper. With the use of two one-dimensional (1-D) signal-direction discriminators mounted on a vehicle with a lateral separation between them to measure the coming directions of the signal emitted from another target vehicle, the position of the target vehicle relative to this detecting vehicle can be located by triangulation. The aforementioned 1-D signal-direction discriminator is composed of two planar receiving modules forming a specific geometric structure, which leads to different responsivities of these individual receiving modules for a signal incident from a definite direction. The coming direction of the signal is determined by comparing the signal strengths received by these individual receiving modules. With a proposed hardware and software implementation, the position of the target vehicle can be accurately determined within a longitudinal range of 50 m. Owing to the restriction of the width of the vehicle, the separation of the signal-direction discriminators mounted on the vehicle cannot exceed 1.8 m. This leads to the consequence that in the remote region any small inaccuracy of the measured signal directions may cause very large position error. However, our measured results reveal a specific characteristic of the distribution of the measured positions for the target vehicle at a definite location. This allows us to design deliberate statistical algorithms to accurately extract the position of the target vehicle in the remote region. Using the simplest statistical algorithm, we are able to locate the position of the target vehicle with acceptable accuracy within a longitudinal range of 80 m. In this paper, we show the feasibility and applicability of this method. Furthermore, improvement is foreseeable with more deliberate design of hardware and software.
Target Localization in NLOS Environments Using RSS and TOA Measurements

This letter addresses the problem of target localization in adverse non-line-of-sight environments. By utilizing integrated received signal strength and time of arrival measurements, a novel alternating algorithm is proposed. The new algorithm is derived by converting the original nonconvex problem into a generalized trust region sub-problem framework, which can be solved exactly by just a bisection procedure. Therefore, the proposed algorithm is very light in terms of computational cost, and its excellent estimation accuracy is validated through computer simulations.
Persymmetric GLRT Detection in MIMO Radar

The problem of target detection in colocated multiple-input multiple-output radar with unknown disturbance covariance matrix is considered. According to the generalized likelihood ratio test, we design an adaptive detector by exploiting the persymmetric structure of the covariance matrix. In particular, training data are not required in the proposed adaptive detector. Analytical expressions for the probability of false alarm and detection probability are derived, which are confirmed by Monte Carlo simulations. The expression for the probability of false alarm shows that the proposed detector exhibits a constant false alarm rate property against the disturbance covariance matrix. Numerical examples based on both simulated and experimental data confirm that the proposed detector has better detection performance than its counterparts.
OmniArt

Baselines are the starting point of any quantitative multimedia research, and benchmarks are essential for pushing those baselines further. In this article, we present baselines for the artistic domain with a new benchmark dataset featuring over 2 million images with rich structured metadata dubbed OmniArt. OmniArt contains annotations for dozens of attribute types and features semantic context information through concepts, IconClass labels, color information, and (limited) object-level bounding boxes. For our dataset we establish and present baseline scores on multiple tasks such as artist attribution, creation period estimation, type, style, and school prediction. In addition to our metadata related experiments, we explore the color spaces of art through different types and evaluate a transfer learning object recognition pipeline.