Miracle-dz/UniFAR-FTUs huggingface dataset:
DNA sequencing of cytopathologically inconclusive EUS-FNA from solid pancreatic lesions suspicious for malignancy confirms EUS diagnosis

Background and Objectives: EUS-FNA is inconclusive in up to 10%–15% of patients with solid pancreatic lesions (SPLs). We aimed to investigate whether supplementary genetic analyses with whole-exome sequencing add diagnostic value in patients with SPLs suspicious of malignancy but inconclusive EUS-FNA. Patients and Methods: Thirty-nine patients, who underwent EUS-FNA of an SPL were retrospectively included. Three groups were defined: 16 (41.0%) had suspected malignancy on EUS confirmed by cytology (malignant), 13 (33.3%) had suspected malignancy on EUS but benign cytology (inconclusive), and 10 (25.6%) had benign EUS imaging and cytology (benign). Areas with the highest epithelial cell concentrations were macro-dissected from the FNA smears from each patient, and extracted DNA was used for whole-exome sequencing by next-generation sequencing of a selected gene panel including 19 genes commonly mutated in cancer. Results: Pathogenic mutations in K-RAS, TP53, and PIK3CA differed significantly between the three groups (P < 0.001, P = 0.018, and P = 0.026, respectively). Pathogenic mutations in KRAS and TP53 were predominant in the inconclusive (54% and 31%, respectively) and malignant groups (81.3% and 50%, respectively) compared to the benign group (0%). Malignant and inconclusive diagnoses correlated strongly with poor overall survival (P < 0.001). Conclusion: Whole-exome sequencing of genes commonly mutated in pancreatic cancer may be an important adjunct in patients with SPLs suspicious for malignancy on EUS but with uncertain cytological diagnosis.
Phonetic Transcription for Speech-Language Pathology in the 21st Century

The past few decades have seen rapid changes in speech-language pathology in terms of technology, information on speech production and perception, and increasing levels of multilingualism in communities. This tutorial provides an overview of phonetic transcription for the modern world, both for work with clients, and for research and training. The authors draw on their backgrounds in phonetics, phonology and speech-language pathology, and their crosslinguistic project in the phonological acquisition of children with typical versus protracted phonological development. Challenges and solutions are presented, as well as resources for further training of students, clinicians and researchers.
Evaluating Retention in Medical Care and its Impact on the Health Outcomes of Individuals Living with Human Inmmunodeficiency Virus

OF DISSERTATION EVALUATING RETENTION TO HIV MEDICAL CARE AND ITS IMPACT ON THE HEALTH OUTCOMES OF INDIVIDUALS LIVING WITH HUMAN IMMUNODEFICIENCY VIRUS In the last few years, engagement in medical care among individuals living with HIV has become a major priority among HIV medical providers and public health researchers. Engagement in medical care is an important concept as it involves the process of linking newly diagnosed individuals into medical care and retaining those individuals in care throughout the course of their infection. Although there have been major advances in the management of HIV, like the advent of Highly Active Antiretroviral Therapy, morbidity and mortality due to HIV cannot be fully reduced if the individual does not optimally retain in care. Retention in HIV medical care has become an emerging topic in HIV research, but there still remains a scarce amount of research on how to properly define retention, understand its predictors, and how it impacts HIV outcomes. The purpose of this dissertation was to evaluate retention in HIV medical care among individuals diagnosed with HIV and seeking care at an urban infectious disease clinic in Kentucky. The three specific aims of this dissertation were to: (1) compare methods in measuring retention in HIV medical care; (2) determine the predictors of poor retention in care and assess the effect of non-HIV related comorbidities have on retention over time; and (3) determine the impact early retention to medical care has on time to viral load suppression and rebound among individuals initiating Highly Active Antiretroviral Therapy. A retrospective cohort study was conducted employing a medical chart review, and patients who sought HIV care at the Bluegrass Care Clinic between January 1st 2003 and May 1st 2011 were eligible for the study. There were 1,358 patients included in the study and these individuals were followed until December 31st, 2011. The results suggested that individuals living with HIV should seek care at least once every six months (visit constancy) and that only 48.6% of the study population obtained optimal retention over time. Over time the rate of retention decreased among the study sample and those with optimal retention were more likely to suppress their viral loads compared to poor retainers.
Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints

An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, “Trims cash” is an anagram of “Christmas”. Existing automatic anagram generation methods can find possible combinations of words form an anagram. However, they do not pay much attention to the naturalness of the generated anagrams. In this paper, we show that simple depth-first search can yield natural anagrams when it is combined with modern neural language models. Human evaluation results show that the proposed method can generate significantly more natural anagrams than baseline methods.
SGXPool: Improving the performance of enclave creation in the cloud

Deploying user data or programs in cloud risks divulging their privacy because the cloud‐side supervisors, such as system administrators, can leverage the higher privilege to snoop the user data. Based on the trusted execution environment (TEE) technology, Intel Software Guard eXtension (SGX) is a practical remedy to user privacy, which employs hardware‐assisted enclave to wrap the sensitive data, preventing them from the disclosure. However, the application performance is hurt due to the CPU‐expensive and frequent operations on the enclave creation and destruction. In this paper, we propose SGXPool, an application‐level framework of resource management to relieve the above issue. SGXPool first uses a preallocated resource pool to assign/revoke the enclave on demand and avoids its cost of dynamical creation and destruction. Then, SGXPool utilizes another resource pool to constrain the threading overhead such as thread initialization, scheduling, and destruction. In addition, SGXPool exposes the simple and clear interface, allowing users or programmers to apply SGXPool without any intrusive modifications on the original SGX application. We implement SGXPool in the typical web servers which use the multithreading to handle the concurrent user requests. The evaluation results show that SGXPool can improve the performance of the original multithreading system up to 19 times. Meanwhile, the original security of SGX is maintained.
Holistic Abstraction for Distributed Network Debugging

ions are a powerful conceptual tool that allow the discovery of order in chaos. A modern computer network is a dynamic system whose operations on a minute scale might appear chaotic. To create order out of this chaos new abstractions are needed. This dissertation presents one such concept, to reduce the complexity of managing and understanding a network. This dissertation presented a topology-agnostic abstraction for troubleshooting a network. In this abstraction a network administrator can pose a query to the entire network to troubleshoot a problem. Given the magnitude of modern networks, this is a non-trivial task due to the speed of different operations and the number of entities in a network. The query posed by the network administrator is combined with the network topology to determine where each operation placement, depending on their type and data dependency. This mapping of the global state semantics to local execution engines constitutes the second significant contribution of this work. By distributing the state and computation to different network elements, a more efficient and lean mechanism was achieved reducing the burden of computation and state maintenance on individual network nodes, resulting in an increased amount of
Learning to Plan Semantic Free-Space Boundary

Recently, free-space detection has attracted widespread attention. Most existing methods treat free-space detection as a semantic segmentation task. In this paper, we propose a novel approach to directly infer the boundary of the semantic free-space from a single image. Firstly, we design a multistage CNN to produce 2D belief maps with high resolution for boundary segments of different semantic classes, such as road boundary, vertical obstacles on road and so on. The proposed CNN architecture can implicitly learn boundary structure and long-range spatial context. Then, based on the 2D belief maps we address the semantic free-space detection as a dynamic programming problem to ensure the spatial smoothness of the predicted boundary. The experimental results on our dataset show that our method has a convincing performance on various quantitative metrics.
The Closing Sequences and Ritual Expressions of Informal Mobile Phone Calls Between Saudis: A Conversational Analysis

Although much work has been conducted on studying conversational openings of telephone and ritual expressions, conversational closings and ritual expressions have received less attention by researchers due to the complexity and difficulty of identifying the beginning of closings in telephone conversations. The parts of closing and ritual expressions on telephone have been examined in some languages, but Arabic has not been studied in landline telephone or mobile phone. Therefore, this study seeks to identify the sequences and ritual expressions between Saudi friends and relatives to explore the strategies of closing informal mobile phone calls by using a conversation analysis approach. Thirty audio-recorded and transcribed mobile phone conversations served as the data source for this study. The results found that the majority of mobile phone closing conversations include three parts: pre-closing, leave taking, and terminal exchange that are similar to many languages such as English, Japanese, and German. Also, various expressions were used in pre-closing and leave taking sequences, but the expressions of using prayers were used more frequently in the sequences. Finally, closing conversation is affected by various external and internal social factors in the sequences and the use of ritual expressions.
Rust AV1 encoder (rav1e) project

Last year, the Alliance for Open Media released its next-generation video codec AV1. It achieves better compression than proprietary competitors, while its patents can be licensed via a royalty-free, open-source friendly license. With a broad array of industry support including all major browser vendors, many hardware partners, internet streaming and video conferencing providers, we think this is our best chance yet to create a successful video codec that achieves wide deployment. However publishing the AV1 standard is just the first step towards broad adoption. Now that the bitstream is finalized research has shifted to encoder algorithms for use in real-world, production environments. The Rust AV1 Encoder (rav1e) project at Mozilla is a clean room AV1 implementation targeting a variety of operating points. Where the standardization effort focused on tools that improved objective metrics, the rav1e project is focused on algorithms that improve perceived video quality.
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic

The emergence of XNOR networks seek to reduce the model size and computational cost of neural networks for their deployment on specialized hardware requiring real-time processes with limited hardware resources. In XNOR networks, both weights and activations are binary, bringing great benefits to specialized hardware by replacing expensive multiplications with simple XNOR operations. Although XNOR convolutional and fully-connected neural networks have been successfully developed during the past few years, there is no XNOR network implementing commonly-used variants of recurrent neural networks such as long short-term memories (LSTMs). The main computational core of LSTMs involves vector-matrix multiplications followed by a set of non-linear functions and element-wise multiplications to obtain the gate activations and state vectors, respectively. Several previous attempts on quantization of LSTMs only focused on quantization of the vector-matrix multiplications in LSTMs while retaining the element-wise multiplications in full precision. In this paper, we propose a method that converts all the multiplications in LSTMs to XNOR operations using stochastic computing. To this end, we introduce a weighted finite-state machine and its synthesis method to approximate the non-linear functions used in LSTMs on stochastic bit streams. Experimental results show that the proposed XNOR LSTMs reduce the computational complexity of their quantized counterparts by a factor of 86x without any sacrifice on latency while achieving a better accuracy across various temporal tasks.
LTE‐DSRC hybrid vehicular communication system using DFT‐spread OFDM in uplink

Technologies are advancing at a rapid rate in the current era. People are advancing with the advancement of technologies, be it in education industry, health industry, and providing luxury. With the advent of autonomous luxury‐in‐motion car that provides in‐car‐entertainment (ICE), vehicular communication is the prerequisite towards achieving this goal. Dedicated short range communication (DSRC) is a matured vehicular communication standard, and Long‐Term Evolution (LTE) is the most competing technologies in the cellular communication. In this paper, we focus on the uplink performance of the LTE‐DSRC hybrid infrastructure. The basic transmission scheme for uplink direction is based on single carrier transmission in the form of discrete Fourier transform (DFT)‐spread Orthogonal Frequency Division Multiplexing (OFDM) with a minimum mean square error (MMSE) receiver at the LTE Evolved Node B (eNodeB) and a DSRC OFDM transmitter. A comprehensive bit error rate (BER) performance simulative study has been made on a color image transmission in uplink hybrid LTE‐DSRC system, and the results obtained are encouraging that the proposed convergence is possible, as it provides a substantial decrease in the BER with a gradual increase in the signal ‐to‐ noise ratio Signal to Noise Ratio (SNR).
Implementation of Doppler Radar-Based Vehicle Speed Detection System

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by /4.0) ABSTRACT Nowadays, vehicular accidents have been increasing from day to day. Most of them are the cause of over speeding. This thesis describes the Arduino based vehicle speed detection system using Doppler radar. The primary goal of this system is to design of the vehicle speed detector using Doppler radar which is used to reduce the amount of accidents caused by over speeding. In this system, continuous wave (CW) Doppler radar which is a special case that only provides a velocity output is used as a sensor and the microcontroller is used to calculate the speed of the vehicle. Moreover, the amplifier is used to amplify the voltage level to drive the microcontroller as the sensor output is in micro volts. It is also included LCD display to indicate the speed level. A Doppler radar that can determine the frequency shift that occurs in electromagnetic waves due to the motion of scatters toward or away from the observer through measurement of the phase change that occurs in electromagnetic waves during a series of pulses. The Doppler frequency is negative for objects receding from the radar. The Doppler frequency is positive for objects approaching the radar. This system can be acceptable in real-time applications because it is independent of temperature, humidity, noise airflow, dust, light, etc. The results of design are tested and follow through realization. Each of the implementation is evaluated and these evaluations lead to the conclusion that the design is able to achieve high accuracy of the speed. The major components of the design are Doppler radar (HB100), Arduino Uno, LCD display and LM324. C++ programming language is developed in this system. The results of this work can improve the performance of automobile safety system.
Pre-Training BERT on Domain Resources for Short Answer Grading

Pre-trained BERT contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data. While there has been a lot of focus on task-specific fine-tuning, there has been limited work on improving the pre-trained representations. In this paper, we explore ways of improving the pre-trained contextual representations for the task of automatic short answer grading, a critical component of intelligent tutoring systems. We show that the pre-trained BERT model can be improved by augmenting data from the domain-specific resources like textbooks. We also present a new approach to use labeled short answering grading data for further enhancement of the language model. Empirical evaluation on multi-domain datasets shows that task-specific fine-tuning on the enhanced pre-trained language model achieves superior performance for short answer grading.
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings

Word embeddings are increasingly used for the automatic detection of semantic change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking. We propose a new evaluation framework for semantic change detection and find that (i) using the whole time series is preferable over only comparing between the first and last time points; (ii) independently trained and aligned embeddings perform better than continuously trained embeddings for long time periods; and (iii) that the reference point for comparison matters. We also present an analysis of the changes detected on a large Twitter dataset spanning 5.5 years.
ESTIMATING BASE STATION-BASED INDOOR AND OUTDOOR ELECTRIC FIELD LEVELS BY ARTIFICIAL NEURAL NETWORKS

The measurement of the electric field and the magnetic field is significant in order to determine electromagnetic pollution level compared to standards. In Turkey, electric field limit value, which is emitted by base station is 13.5 V/m for one mobile communication operator. In addition, according to the regulation of medical devices, the limit of electric field value inside the hospital, where the medical devices are located, is 3 V/m. In this study, the measurement and evaluation of electromagnetic pollution inside and outside the Bursa Uludag University hospital building are performed and its compliance with national and international standards is examined. Moreover, the distribution of electric field in the environment is estimated by the artificial neural network and fuzzy logic methods considering the measurement results. The measured electric field values, estimated electric field values, and national standard values are compared.
Analysis of power module reliability exposed to real operation conditions observed in electrified vehicles

Power modules equipped in electrified vehicles often operate at harsh conditions. Thermal stress in power modules are known to be one of the major factors resulting performance degradations or damages in solder joints in power modules. In this paper, the temperature distribution analysis and the lifetime analysis were conducted with consideration of real operation conditions, especially ambient temperatures and coolant temperatures. Simulation results reveal that the effects of ambient temperature increment depends on the state of power semiconductors and also influenced by coolant temperature. The increased temperature also increases thermal stress, resulting reduced lifetime of IGBT solder joint (7% reduction) at an ambient temperature of 100°C, which is often observed in vehicle engines. In addition, the IGBT solder joint exhibits shorter (~25% reduction) lifetime compared to diode solder joint because of higher power loss. Therefore, power module design should simultaneously consider heat generated from devices and given by operating
Optimal Capacitor Placement in Radial Distribution System Using Moth Flame Optimization Algorithm

The environment friendly designing and running of electricity distribution structures are becoming vaster with the conjunction of renewable electricity options into power distribution systems. This study presents Moth Flame Optimization (MFO) approach to minimize total losses kW and energy cost and maximize net savings with solving the voltage deviation problem through optimal site and size of capacitor banks in electrical radial distribution networks. Two various load profiles are utilized to take a look at the modified algorithm on IEEE 123-, and 13-bus check networks. The OpenDSS software is utilized to resolve the power flowing via MATLAB programming interface. The results obtained are compared with IEEE normal state and other two heuristic approaches in same study, namely, imperialist competitive algorithm and particle swarm optimization. The proposed algorithm is more effective to minimise the total power losses and total cost and maximise net savings and to enhance the voltage profile for various distribution systems.
COMPARISON OF IOT DEVICES ARCHITECTURE IN HOME NETWORK

Recently, the number of IoT devices has increased, and this trend has been extended to home appliances (e.g. air conditioners, washing machines, refrigerators, etc.) and equipment (e.g. storage batteries, photovoltaics, etc.). There are mainly two architectures regarding to IoT conversion of the appliances installed in these houses. Specifically, they are "IoT devices connect to a server on the Internet via a controller" and "IoT devices connect to a server on the Internet directly". In this paper, these architectures are compared and it is shown that what kind of usage each architecture is effective.
Deep Copycat Networks for Text-to-Text Generation

Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output. We introduce Copycat, a transformer-based pointer network for such tasks which obtains competitive results in abstractive text summarisation and generates more abstractive summaries. We propose a further extension of this architecture for automatic post-editing, where generation is conditioned over two inputs (source language and machine translation), and the model is capable of deciding where to copy information from. This approach achieves competitive performance when compared to state-of-the-art automated post-editing systems. More importantly, we show that it addresses a well-known limitation of automatic post-editing - overcorrecting translations - and that our novel mechanism for copying source language words improves the results.