Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of a two-stage model for detection of abnormalities in digital mammograms

Describes the characterization and development of a model for the detection of abnormalities in digital mammograms. The underlying detection model was previously introduced by Lai et al. (1989) for the detection of circumscribed masses in mammograms. This has been further developed into a two stage process whereby, in the first stage, the objective is to seek out all potential abnormalities present on the digitized mammogram while in the second stage these potential abnormalities are further investigated to establish whether they are genuine abnormalities or false alarms. This paper examines various spatial and textural techniques which have been developed in order to evaluate and then improve the performance of this detection model. The aim is ultimately to identify techniques which enhance the performance of this two-stage model of detection, and then to adapt the model for the purpose of analyzing mammograms for circumscribed masses and/or other masses.
Frequency of nosocomial infections with antibiotic resistant Acinetobacter spp. in intensive care unit (ICU) patients

Acinetobacter is a Gram-negative coccobacillus and one of the most nosocomial infections. Acinetobacter spp resistant strains in the world have created health problems. The aim of this study was to determine the prevalence and associated risk factors for infections with multi drug resistance (MDR) of Acinetobacter spp. in patients admitted to the ICU of Shahid Mostafa Khomeini Hospital, Tehran, Iran, 2008 to 2010. This descriptive-analytical and cross-sectional study was designed on all of the 130 patients with culture positive for Acinetobacter spp. Microbiologic and specific demographic data were extracted from patient’s laboratory and archive file. The data were analyzed by using SPSS16 statistical software and chi-square and Mann-Whitney test. The prevalence of infection with Acinetobacter spp. separately by years was: 21.5, 30.8 and 47.7% in 1386, 1387 and 1388, respectively. 100% isolates were resistance to Carbnicillin, Piperacillin, Cefotaxime and Cephalotin, 99.2% to Ciprofloxacin, Cotrimoxazole and Chloramphenicol, 97.7% to Imipenem, 95.4% to Tetracycline and 91.5% to Gentamicin. Highest percentage sensitivity was to Amikacin 14.6%. Nosocomial infections with Acinetobacter spp. during the three years, was a growing trend and all isolates were MDR and the highest susceptibility was to Amikacin. It seems that the incorrect diagnosis, use of antibiotics for viral infections, inappropriate doses and time of antibiotic therapy (less or more), inappropriate formulation and low quality of some of antibiotics, are the most important cause of MDR. The proper use of antibiotics to prevent MDR bacterial nosocomial infections is recommended. Key words: Acinetobacter spp, intensive care unit (ICU), multi drug resistance (MDR), nosocomial infections.
Características Morfológicas y Morfométricas de la Placenta de Término, en Recién Nacidos Pequeños para la Edad Gestacional (PEG) en la Ciudad de Temuco-Chile

El metabolismo placentario, el intercambio de sustancias y la produccion de hormonas son funciones vitales de la placenta para mantener y promover el desarrollo normal del feto. Existen factores de riesgo que alteran este patron en el caso del retardo del crecimiento intrauterino, cuyo resultado sera un recien nacido (RN) pequeno para la edad gestacional (PEG) que presentara una mayor morbilidad, crecimiento fisico e intelectual comprometido y una mayor probabilidad de desarrollar durante la vida adulta diferentes patologias. Los objetivos del presente trabajo son: 1. Reconocer las diferencias en los parametros morfometricos, como el area de las vellosidades, el area de los vasos, el numero de vasos y el area del sinciciotrofoblasto de las placentas de PEG en relacion con placentas de recien nacidos AEG y 2. Relacionar el diagnostico neonatal de PEG con las caracteristicas morfometricas. Se utilizaron 25 placentas de termino (37-42 semanas), 12 de recien nacidos adecuados a la edad gestacional (AEG), y 13 de recien nacidos pequenos para la edad gestacional (PEG). Las muestras fueron obtenidas de la maternidad del Hospital Hernan Henriquez Aravena de Temuco, IX Region Chile. De cada placenta se tomaron dos segmentos pericordonales, desde la placa subcorial hasta la placa basal y luego fueron fijadas en formalina tamponada al 10%. Las tecnicas histologicas utilizadas fueron H-E azul de Alcian, Tricromico de Masson, PAS-Hematoxilina y PAS-Diastasa. El area de las vellosidades mostraron diferencias significativas entre el grupo control (AEG) y el grupo PEG con p = 0,0194. En el grupo de PEG el area de los vasos fue significativamente mayor, con un valor de 234,05 |i,m2 en comparacion con el grupo control cuyo promedio fue de 150.99 |lm2 (p = 0,0001). El numero de vasos sanguineos por vellosidad libre no mostro diferencias significativas. En relacion con el area del sinciciotrofoblasto la diferencia no resulto ser significativamente diferente (p = 0,1410). Como conclusion se determino que las placentas de recien nacidos PEG presentaron diferencias significativas a nivel del area de los vasos y del area de las vellosidades coriales libres, en relacion con las placentas de AEG.
Gene Therapy – A Current Review.

Gene therapy is replacement of defective gene with normal gene product. Various methods for it have been evolved and many more are in womb of future. Genetic involvement in affection of life on earth was found more than a century ago. But its application in medical field has been possible in last few years only. Still gene therapy is in its very infant stage and various clinical trials are going on. To make gene therapy available for mass population with feasibility and safety, medical fraternity has to keep patient till the time when research and trials in genetic field will approve for it. In this review little light has been focused on recent gene therapy trials which indicate the forthcoming era of human genome and their applicability in various genetic disorders.
Das Innenohr als elektrosensorisches Sinnesorgan

A model for cochlear perception is introduced based on the consideration that the organ of Corti is an electrosensory organ like that found in fish physiology during phylogenetic development. The inner hair cells work as electroreceptors, the outer hair cells as electrocytes. A layer of potassium ions on the lower surface of the tectorial membrane causes the excitation of the inner hair cells as soon as contact with a stereocilium occurs. A model for the basilar membrane motion, based on mathematical considerations and in accordance with the typical tuning curves of single auditory nerve fibres can serve as basis to explain the results of frequency and intensity analyses if we assume an electric excitation of the inner hair cells.
NEEDLESTICK INJURY: A REVIEW OF TWELVE THESES AMONG HEALTHCARE PERSONNEL IN MALAYSIA

Aims : The review was to determine the prevalence of needlestick injury especially among health care personnel and to determine the score of knowledge and compliance to the Standard Universal Precaution. Method : Twelve theses were reviewed from year 1996 to 2007 of Master in Community Health Science, Master in Community Health and Postgraduate Diploma in Occupational Health of Community Health Department Universiti Kebangsaan Malaysia Medical Center involving of 1645 respondents of health care personnel, support staff and student of nursing and medical. Eleven studies were cross sectional design and only one study was retrospective. Results : Respondents were dominated by female (74.6%) and Malay ethnicity (65%), as young as 19 years old to 56 years old of age. Majority had received Hepatitis B vaccination (79.4%) but only 37.1% had completed the 3 doses regime. The incidence of episodes of needle stick injury among health care personnel was double (53.7%) compared to final year medical students (20.9%). Needle stick injuries did not occur at random as there were a few health care personnel injured repeatedly. Those who had higher mean or median score for compliance to Standard Universal Precaution were non case of needlestick injury. Work practices had been highlighted in few studies of being risk factors for needlestick injury such as blood withdrawing related activities. Other risk factors were job category, predictive factor for compliance to Standard Universal Precaution, risk perception and training. Conclusion : Even though the review could not extrapolated to general population of healthcare personnel but it gave some illustrated pictures to what had happened in small clustered locations. Episodes of needle stick injury was 53.7% for past 12 years, it was double in comparison to final year medical students in year 2001. 13.2% were injured repeatedly. The score of knowledge was more the 50% of range but compliance to Standard Precaution made differences in being cases or non cases among respondents. The seroconversion status till date was unknown.
Regaining Control Over Cancer: A Health Communication Initiative Documenting Complementary and Alternative Medicine Use Among Cancer Patients and Survivors

Cancer is a complex chronic disease that has remained a paramount public health problem for the last 42 years. Systemic treatments, like chemotherapy, or local therapies, like radiation, are often used to treat metastatic cancer. Chemotherapy and radiation can cause a host of physical side effects including fatigue, hair loss, nausea, and vomiting. To combat these side effects of treatment, cancer patients and survivors turn to complementary and alternative medicine (CAM) both during and after cancer treatment, to improve their prognosis, symptoms, and quality of life. While CAM is often sought for symptom abatement, many patients fail to disclose their CAM use to their healthcare providers, despite the potential for adverse reactions with conventional treatments. To understand issues associated with CAM use, including the nondisclosure patterns, the National Cancer Institute Office of Cancer Complementary and Alternative Medicine’s (OCCAM) existing sources of qualitative data were reviewed along with the websites of several national cancer advocacy groups. A literature search on the motivations for CAM use and the barriers and facilitators to patient-provider communication about CAM was conducted to understand the current research on communication about CAM in different healthcare settings. Under the auspices of OCCAM, the author developed an interactive workbook to encourage patients’ participation in their health care by increasing their confidence to talk to their providers about CAM. The workbook contains different adaptations of health diaries to help patients track their motivations for CAM use, the frequency of their CAM use, and all of their medications, including vitamins, herbs, and dietary supplements. Two rounds of pretesting with cancer survivors and healthcare providers were conducted to determine the usability and functionality of the workbook. The healthcare providers and cancer survivors surveyed believed OCCAM’s initiative is an important resource because it empowers patients to help their healthcare providers understand their coping mechanisms during and after cancer care, including their use of CAM. This initiative has public health significance because there are limited patient education resources that encourage patients to talk to their providers about CAM and furthermore, when patients and providers communicate with each other, patients experience more positive health outcomes.
Order Matters at Fanatics Recommending Sequentially Ordered Products by LSTM Embedded with Word2Vec

A unique challenge for e-commerce recommendation is that customers are often interested in products that are more advanced than their already purchased products, but not reversed. The few existing recommender systems modeling unidirectional sequence output a limited number of categories or continuous variables. To model the ordered sequence, we design the first recommendation system that both embed purchased items with Word2Vec, and model the sequence with stateless LSTM RNN. The click-through rate of this recommender system in production outperforms its solely Word2Vec based predecessor. Developed in 2017, it was perhaps the first published real-world application that makes distributed predictions of a single machine trained Keras model on Spark slave nodes at a scale of more than 0.4 million columns per row.
Efficient Drone Mobility Support Using Reinforcement Learning

Flying drones can be used in a wide range of applications and services from surveillance to package delivery. To ensure robust control and safety of drone operations, cellular networks need to provide reliable wireless connectivity to drone user equipments (UEs). To date, existing mobile networks have been primarily designed and optimized for serving ground UEs, thus making the mobility support in the sky challenging. In this paper, a novel handover (HO) mechanism is developed for a cellular-connected drone system to ensure robust wireless connectivity and mobility support for drone-UEs. By leveraging tools from reinforcement learning, HO decisions are dynamically optimized using a Q-learning algorithm to provide an efficient mobility support in the sky. The results show that the proposed approach can significantly reduce (e.g., by 80%) the number of HOs, while maintaining connectivity, compared to the baseline HO scheme in which the drone always connects to the strongest cell.
Rare events in stochastic processes with sub-exponential distributions and the big jump principle

Rare events in stochastic processes with heavy-tailed distributions are controlled by the big jump principle, which states that a rare large fluctuation is produced by a single event and not by an accumulation of coherent small deviations. The principle has been rigorously proved for sums of independent and identically distributed random variables and it has recently been extended to more complex stochastic processes involving Lévy distributions, such as Lévy walks and the Lévy–Lorentz gas, using an effective rate approach. We review the general rate formalism and we extend its applicability to continuous time random walks and to the Lorentz gas, both with stretched exponential distributions, further enlarging its applicability. We derive an analytic form for the probability density functions for rare events in the two models, which clarify specific properties of stretched exponentials.
Rethinking Normalization and Elimination Singularity in Neural Networks

In this paper, we study normalization methods for neural networks from the perspective of elimination singularity. Elimination singularities correspond to the points on the training trajectory where neurons become consistently deactivated. They cause degenerate manifolds in the loss landscape which will slow down training and harm model performances. We show that channel-based normalizations (e.g. Layer Normalization and Group Normalization) are unable to guarantee a far distance from elimination singularities, in contrast with Batch Normalization which by design avoids models from getting too close to them. To address this issue, we propose BatchChannel Normalization (BCN), which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models. Unlike Batch Normalization, BCN is able to run in both large-batch and micro-batch training settings. The effectiveness of BCN is verified on many tasks, including image classification, object detection, instance segmentation, and semantic segmentation. The code is here: this https URL.
Identify the cells' nuclei based on the deep learning neural network

Identify the cells' nuclei is the important point for most medical analyses. To assist doctors finding the accurate cell' nuclei location automatically is highly demanded in the clinical practice. Recently, fully convolutional neural network (FCNs) serve as the back-bone in many image segmentation, like liver and tumer segmentation in medical field, human body block in technical filed. The cells' nuclei identification task is also kind of image segmentation. To achieve this, we prefer to use deep learning algorithms. we construct three general frameworks, one is Mask Region-based Convolutional Neural Network (Mask RCNN), which has the high performance in many image segmentations, one is U-net, which has the high generalization performance on small dataset and the other is DenseUNet, which is mixture network architecture with Dense Net and U-net. we compare the performance of these three frameworks. And we evaluated our method on the dataset of data science bowl 2018 challenge. For single model without any ensemble, they all have good performance.
Anaphora Resolution in Dialogue Systems for South Asian Languages

Anaphora resolution is a challenging task which has been the interest of NLP researchers for a long time. Traditional resolution techniques like eliminative constraints and weighted preferences were successful in many languages. However, they are ineffective in free word order languages like most SouthAsian languages.Heuristic and rule-based techniques were typical in these languages, which are constrained to context and this http URL this paper, we venture a new strategy us-ing neural networks for resolving anaphora in human-human dialogues. The architecture chiefly consists of three components, a shallow parser for extracting features, a feature vector generator which produces the word embed-dings, and a neural network model which will predict the antecedent mention of an anaphora.The system has been trained and tested on Telugu conversation corpus we generated. Given the advantage of the semantic information in word embeddings and appending actor, gender, number, person and part of plural features the model has reached an F1-score of 86.
RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving

In this paper, we strive for solving the ambiguities arisen by the astoundingly high density of raw PseudoLiDAR for monocular 3D object detection for autonomous driving. Without much computational overhead, we propose a supervised and an unsupervised sparsification scheme of PseudoLiDAR prior to 3D detection. Both the strategies assist the standard 3D detector gain better performance over the raw PseudoLiDAR baseline using only ~5% of its points on the KITTI object detection benchmark, thus making our monocular framework and LiDAR-based counterparts computationally equivalent (Figure 1). Moreover, our architecture agnostic refinements provide state-of-the-art results on KITTI3D test set for "Car" and "Pedestrian" categories with 54% relative improvement for "Pedestrian". Finally, exploratory analysis is performed on the discrepancy between monocular and LiDAR-based 3D detection frameworks to guide future endeavours.
Class-specific residual constraint non-negative representation for pattern classification

Abstract. Representation-based classification methods (RBCM) remain one of the hottest topics in the community of pattern recognition, and the recently proposed non-negative representation-based classification (NRC) achieved impressive recognition results in various classification tasks. However, NRC ignores the relationship between the coding and classification stages. Moreover, there is no regularization term other than the reconstruction error term in the formulation of NRC, which may result in an unstable solution leading to misclassification. To overcome these drawbacks of NRC, we propose a class-specific residual constraint non-negative representation (CRNR) for pattern classification. CRNR introduces a class-specific residual constraint into the formulation of NRC, which encourages training samples from different classes to competitively represent the test sample. Based on the proposed CRNR, we develop a CRNR-based classifier (CRNRC) for pattern classification. Experimental results on several benchmark datasets demonstrate the superiority of CRNRC over conventional RBCM as well as the recently proposed NRC. Moreover, CRNRC works better than or comparable to some state-of-the-art deep approaches on diverse challenging pattern classification tasks.
Covering Codes for Insertions and Deletions

A covering code is a set of codewords with the property that the union of balls, suitably defined, around these codewords covers an entire space. Generally, the goal is to find the covering code with the minimum size codebook. While most prior work on covering codes has focused on the Hamming metric, we consider the problem of designing covering codes defined in terms of insertions and deletions. First, we provide new sphere-covering lower bounds on the minimum possible size of such codes. Then, we provide new existential upper bounds on the size of optimal covering codes for a single insertion or a single deletion that are tight up to a constant factor. Finally, we derive improved upper bounds for covering codes using R≥ 2 insertions or deletions. We prove that codes exist with density that is only a factor O(R log R) larger than the lower bounds for all fixed R. In particular, our upper bounds have an optimal dependence on the word length, and we achieve asymptotic density matching the best known bounds for Hamming distance covering codes.
SparseTrain: Leveraging Dynamic Sparsity in Training DNNs on General-Purpose SIMD Processors

Our community has greatly improved the efficiency of deep learning applications, including by exploiting sparsity in inputs. Most of that work, though, is for inference, where weight sparsity is known statically, and/or for specialized hardware. We propose a scheme to leverage dynamic sparsity during training. In particular, we exploit zeros introduced by the ReLU activation function to both feature maps and their gradients. This is challenging because the sparsity degree is moderate and the locations of zeros change over time. We also rely purely on software. We identify zeros in a dense data representation without transforming the data and performs conventional vectorized computation. Variations of the scheme are applicable to all major components of training: forward propagation, backward propagation by inputs, and backward propagation by weights. Our method significantly outperforms a highly-optimized dense direct convolution on several popular deep neural networks. At realistic sparsity, we speed up the training of the non-initial convolutional layers in VGG16, ResNet-34, ResNet-50, and Fixup ResNet-50 by 2.19x, 1.37x, 1.31x, and 1.51x respectively on an Intel Skylake-X CPU.
Noise Induces Loss Discrepancy Across Groups for Linear Regression

We study the effect of feature noise (measurement error) on the discrepancy between losses across two groups (e.g., men and women) in the context of linear regression. Our main finding is that adding even the same amount of noise on all individuals impacts groups differently. We characterize several forms of loss discrepancy in terms of the amount of noise and difference between moments of the two groups, for estimators that either do or do not use group membership information. We then study how long it takes for an estimator to adapt to a shift in the population that makes the groups have the same mean. We finally validate our results on three real-world datasets.