Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Water Activity Testing to Replace Karl Fischer Water Testing for Solid Oral-Dosage Forms

For solid oraldosage forms, water testing usually is performed to control the chemical, physical, or microbiological properties of the drug product. Measurements of total water as made with Karl Fischer (KF) techniques is not needed and water-activity often will provide a better correlation with changes in chemical, physical, or microbiological properties than KF techniques. In these cases, water activity testing can easily replace KF testing.Wateractivity measurements are nondestructive, require little labor, and the equipment required is generally inexpensive. Only a few simple checks are needed to ensure the validity of measurements. Strategies for implementing water activity testing are described. ost pharmacopeial monographs that have procedures for the measurement of water are based on the measurement of total water either by the Karl Fischer titration or by loss on drying. Karl Fischer titrations have dominated the measurement of water in pharmaceutical products for many years. Although the technique is reliable under carefully controlled conditions, it is subject to a variety of problems such as sample handling and side-reactions that cause erroneous results. Results commonly vary with changes in room relative humidity (RH). In addition, general models for describing the effect of water on physical, chemical, and microbiological characteristics recognize that different types of water may be present (1–3) and that the measurement of total water may not be the best approach for understanding the effects of water. According to most common definitions, water can be present in at least three forms: free water, adsorbed water, and bound water. Free water is present in the void volume or in the pores. Free water can serve as a dispersing agent, as a solvent for crystalline compounds, or for microbiological growth. Adsorbed water is located on the surface of the material. Bound water is defined as the water of hydration bound to the product by strong H-bonds. Bound water relates to the monolayer of water molecules, whereas adsorbed water is present in the form of multilayers in the matrices. Water also may be present as a crystal hydrate that effectively is the same as bound water. An example of another type of water is structural water associated with hydrogen bonding between helices of polymer in a gel network (4). This explanation follows the theory of the BET isotherm describing vapor desorption or adsorption isotherms (3). When water interacts with solutes and surfaces, it is unavailable for other hydration interactions. The term water activity (aw) describes the (equilibrium) amount of water available for the hydration of materials. Water activity is unitless and a value of unity indicates pure water, whereas zero M
Brain Image Parcellation Using Multi-Atlas Guided Adversarial Fully Convolutional Network

Brain image parcellation is an essential procedure in neural image analysis. Recently, deep learning methods, such as fully convolutional network (FCN), have shown high computational efficiency and good performance in brain image parcellation. In this paper, a new multi-atlas guided adversarial FCN is proposed to enhance the parcellation quality. The generative model in our method is an improved FCN which is integrated with brain atlases information and multi-level feature skip connection. The discriminative model is a convolutional neural network (CNN) with multi-scale l1 loss function. Comparing to most existing deep learning based brain image parcellation methods, which use voxel-wise loss function only (e.g., cross entropy), the discriminative model in our method considers multi-scale deep features to guide the parcellation. In the experiment, two public MR brain image datasets LONI LPBA40 and NIREP-NAO are used to evaluate our method. Evaluation results demonstrate that our method outperforms the state-of-the-art methods in both datasets.
Evaluation of a Joint Detector Demodulator Decoder (JDDD) Performance With Modulation Schemes Specified in the DVB-S2 Standard

With the rapid increase in the growth of digital broadcast technologies, there has been a demand for high data rate, power, and bandwidth efficient transmission, which, today, is aided by the high-performing low-density parity check (LDPC) codes. In this paper, we explore a novel alternative scheme to the LDPC decoder that performs detection, demodulation, and decoding jointly, coined the joint detector demodulator decoder (JDDD). We test the JDDD through simulation using the modulation schemes defined in the DVB-S2 satellite broadcasting standard. The JDDD is a more recently developed algorithm to the sum–product algorithm (SPA) that is used in decoding the LDPC codes. The JDDD is optimal over a modulated additive white Gaussian noise/intersymbol interference (ISI) channel when resources are sufficient, with the main constraint limiting the algorithm being the availability of computing resources. In this paper, we compare the performance of the system using the JDDD against that of the LDPC decoder. The main result is that the JDDD is able to outperform the iterative detector decoder (IDD) at shorter codeword lengths, when resource requirement is smaller, while the increase in computational requirements tends to favor the IDD at longer CWLs.
Building Information Modelling for Cultural Heritage: A review

Abstract. We discuss the evolution and state-of-the-art of the use of Building Information Modelling (BIM) in the field of culture heritage documentation. BIM is a hot theme involving different characteristics including principles, technology, even privacy rights for the cultural heritage objects. Modern documentation needs identified the potential of BIM in the recent years. Many architects, archaeologists, conservationists, engineers regard BIM as a disruptive force, changing the way professionals can document and manage a cultural heritage structure. The latest years, there are many developments in the BIM field while the developed technology and methods challenged the cultural heritage community in the documentation framework. In this review article, following a brief historic background for the BIM, we review the recent developments focusing in the cultural heritage documentation perspective.
A Predictive Decision Approach for Firefighters Using Variable Selection Technique

Firefighters are using empirical approaches to manage their human resources for covering the daily interventions. With data digitization, many data are incremental available and could be treated for prognostic purposes. Prognostics and health management could be an efficient way to provide a help in the decision-making for firefighter interventions. We hence have an access to a data set of the number of interventions of firefighters in the Bourgogne Franche-Comt'e region of eastern France. The aim of this paper is to propose a variable selection technique for firefighters interventions prediction. For that purpose, we estimate the contribution of each variable on the number of interventions for selecting major variables and show their impact on intervention prediction. The proposed model is applied on a case study, where a efficient variables selection is obtained for prediction.
Replica Management Services On The Grid:Evolving From A Centralized Design To A FullyDistributed, Scalable And Fault-tolerantPeer-to-peer Infrastructure

The Grid infrastructure enables global-scale collaboration of large scientific communities (Virtual Organizations – VOs) usually relying on a common set of data. As information is not necessarily generated and processed centrally, data resources need to be scattered among various data storage facilities. Data objects may also need to be copied from one facility to the other – “replicated” at a site – in order to be evaluated by local VO participants or unique to the site instruments and computing devices. While the main objective when replicating data to remote processes is to reduce aggregate network usage and boost end-application performance, replicas can also be exploited in favor of resiliency to network and site failures and increased data transfer throughput. Nevertheless, managing replicas in highly distributed data storage environments can be very complex. The processes involved in creating, locating, retrieving and updating replicas must guarantee both scalability and fault-tolerance. In this paper, we investigate the requirements of a Grid-centric, replica-based data storage and retrieval infrastructure, present the current trends and implementations and elaborate on how future architectures can capitalize on peer-to-peer technologies in order to achieve even higher levels of scale, without the need of deploying special data management servers.
Random Forest based Traffic Classification Method In SDN

With the rapid development of machine learning, it has been widely used in many industries and fields to solve problems. The application of machine learning to the classification of network traffic is also a new research direction. However, the machine learning methods used in the past cannot classify traffic data according to application types. In this paper, a method of random forest is proposed for traffic classification and identification. As a new idea of network construction, SDN separates the data plane from the control plane. The data layer collects the whole network state information, and the control layer realizes the centralized control of the network. The internal structure of random forest is actually composed of many decision trees. While the decision tree itself has been well represented in the field of traffic classification, the random forest model further improve the efficiency of classification on the basis of the decision tree. By effectively capturing the relevant characteristics of the original data set and training the random forest classification model, we can find through experiments that the classification of network traffic using random forest has obvious advantages in the accuracy of classification as well as the efficiency and stability of processing large-scale data sets.
Iteratively-Reweighted Beamforming For High-Resolution Ultrasound Imaging

Ultrasound imaging typically employs delay-and-sum (DAS) beamformers for image reconstruction. An apodization window is typically used to suppress the beam-pattern’s sidelobes. This approach introduces a trade-off between the mainlobe width versus the sidelobe attenuation and therefore offers limited performance. We consider a statistical framework for beamforming and present two variants. In the first one, the signal of interest is modeled as a Laplacian distributed random variable and the interference is modeled as additive and Gaussian distributed. A closed-form solution is obtained to this optimization problem. In the second variant, we propose an iteratively-reweighted (IR) beamforming algorithm, which solves a constrained optimization problem to determine the optimal apodization weights. This beamformer results in a sharper mainlobe that translates to a finer lateral resolution. The proposed method is compared with the standard DAS beamformer and a recently proposed statistically modeled beamformer, namely iMAP for different number of plane-wave (PW) insonifications. This algorithm is independent of the imaging modality employed and exhibits a superior performance in terms of lateral resolution.
Multi-Modal Fusion Learning For Cervical Dysplasia Diagnosis

Fusion of multi-modal information from a patient’s screening tests can help improve the diagnostic accuracy of cervical dysplasia. In this paper, we present a novel multi-modal deep learning fusion network, called MultiFuseNet, for cervical dysplasia diagnosis, utilizing multi-modal data from cervical screening results. To exploit the relations among different image modalities, we propose an Attention Mutual-Enhance (AME) module to fuse features of each modality at the feature extraction stage. Specifically, we first develop the Fused Faster R-CNN with AME modules for automatic cervix region detection and fused image feature learning, and then incorporate non-image information into the learning model to jointly learn non-linear correlations among all the modalities. To effectively train the Fused Faster R-CNN, we employ an alternating training scheme. Experimental results show the effectiveness of our method, which achieves an average accuracy of 87.4% (88.6% sensitivity and 86.1% specificity) on a large dataset, outperforming the methods using any single modality alone and the known multi-modal methods.
Resource Scheduling for Delay Minimization in Multi-Server Cellular Edge Computing Systems

This paper studies resource scheduling for delay minimization in multi-server cellular edge computing systems. The traditional method defines queue length-based Lyapunov functions and designs scheduling algorithms which solve the corresponding queue length control problem. Different from the traditional method, this paper defines the delay-based Lyapunov function. Specifically, the formulas of the communication delay and computing delay in cellular edge computing systems are derived without needing the assumptions about traffic’s statistics. Then, a resource scheduling algorithm which directly minimizes the weighted sum of the communication delay and computing delay is proposed. The simulation results show that the total delay of the proposed scheduling algorithm is decreased as compared to that of the traditional method. The impact of parameters on the delay performance is also evaluated.
Using Heuristics to Evaluate a Computer Assisted Assessment Environment

Focusing on the usability of the software, this paper reports the findings from an expert evaluation of a single, off the shelf, computer assisted assessment environment with an emphasis on the context in which it is being utilised. The aim of the study was to establish whether the problems differed according to the context of use, and to also establish if the provision of additional information about the assessment aided the evaluators in identifying and rating problems. The method that was used was a heuristic evaluation using eight evaluators, divided into two groups, with one group receiving additional information about the context of use. The study revealed several usability problems and conclusions highlight the fact that a number of problems were only identified in one context but could equally have applied to both. The provision of additional information about assessment did not assist the evaluators in either identifying or rating the severity of the problems.
Small-signal averaged model and simple control of a high-power-factor three-phase/switch/level fixed-frequency PWM rectifier for high-power telecommunications

This paper presents a new small-signal model of a three-phase three-switch three-level boost-type rectifier operating with a fixed switching frequency. The proposed model is obtained by linearizing the (d,q)-averaged representation of the converter in the neighborhood of a desired unity-power-factor operating point. The linearized model is expressed both in the time domain, using the state representation, and the frequency domain by computing the corresponding transfer functions. These latter are then used into the design of linear-control-based regulators in order to ensure the convergence and stability of the system around the desired set point. The control system is implemented numerically using Matlab/Simulink, and simulation experiments are carried out in order to test and verify its tracking and regulation performance, as well as its robustness towards load or mains source disturbance.
Securing mobile appliances: new challenges for the system designer

As intelligent electronic systems pervade all aspects of our lives, capturing, storing, and communicating a wide range of sensitive and personal data, security is emerging as a critical concern that must be addressed in order to enable several current and future applications. Mobile appliances, which will play a critical role in enabling the visions of ubiquitous computing and communications, and ambient intelligence, are perhaps the most challenging to secure - they often rely on a public medium for (wireless) communications, are easily lost or stolen due to their small form factors and mobility, and are highly constrained in cost and size, as well as computing and battery resources. Thispaper presents an introduction to security concerns in mobile appliances, and translates them into challenges that confront system architects, HW engineers, and SW developers, including how to bridge the processing and battery gaps, efﬁcient tamper-prooﬁng of devices, content protection, etc . Recent innovations and emerging commercial technologies that address these issues are also highlighted. We envision that, for a large class of embedded systems, security considerations will pervade all aspects of system design, driving innovations in system architecture, software, circuits, and design methodologies.
Driver Gaze Zone Dataset With Depth Data

Drivers’ inattention and distraction detection are crucial features for driver monitoring and driver assistance systems (DAS) and a key point to avoid car crashes. Currently available datasets mainly track head pose and only a few were collected inside a car. Therefore, we present a novel public dataset designed to train algorithms that estimate driver gaze zone in real driving conditions. We provide labeled frame-by-frame images from 19 car points. Depth 3D images were also captured and they are aligned with infrared images. This new dataset is the largest one available for this purpose and the only one that provides 2D and 3D data aligned at pixel using a Intel® RealSense™ camera. Finally, in order to establish a baseline, we present a gaze zone estimator built using a transfer learning method.
Trading Algorithms Built with Directional Changes

Algorithm trading has become more and more important to financial markets. Most existing algorithms use time series as input. Instead of relying on physical time, Directional Changes (DC) focus on the price reversion events where the reversion reaches a certain magnitude, which is referred to as the threshold. In this paper, we propose two trading algorithms based on DC - TA1 and TA2. TA1 is also based on the Average Overshoot Length scaling law (AOL). An Overshoot refers to the event of price continuing to change in the current direction before the next reversion takes place. The AOL states that on average the Overshoot length is approximately equal to the threshold of DC. We have designed two DC based trading algorithms: TA1 takes advantage of the AOL and T2 takes profit with a more conservative criteria. By testing the algorithms with five stock market indices, the results suggest that in most scenarios, the algorithms are able to generate a positive outcome. The input arguments can be changed in order to change the performance of the algorithms, so TA1 and TA2 could be tailored to trade in different markets.
Feature Aggregation in Perceptual Loss for Ultra Low-Dose (ULD) CT Denoising

Lung cancer CT screening programs are continuously reducing patient exposure to radiation at the expense of image quality. State-of-the-art denoising algorithms are instrumental in preserving the diagnostic value of these images. In this work, a novel neural denoising scheme is proposed for ULD chest CT. The proposed method aggregates multi-scale features that provide rich information for the computation of a perceptive loss. The loss is further optimized for chest CT data by using denoising auto-encoders on real CT images to build the feature extracting network instead of using an existing network trained on natural images. The proposed method was validated on co-registered pairs of real ULD and normal dose scans and compared favorably with published state-of-the-art denoising$ networks both qualitatively and quantitatively.
Generative Aging Of Brain MRI For Early Prediction Of MCI-AD Conversion

Automatic diagnosis of Alzheimer’s disease (AD) from MR images of the brain promises to yield important information of a patient’s disease status or even early prediction of disease onset. This work investigates deep learning based methods to predict conversion of Mild Cognitive Impairment (MCI) to AD based on widely available T1-weighted MR brain images. We present a novel approach breaking up the conversion prediction into a generative and a discriminative step. Using the recently proposed Wasserstein-GAN model, we generate a synthetically aged brain image given a baseline image. The aged image is passed to an MCI/AD discriminator deciding the future disease status. Using only one coronal slice of a patient’s baseline T1image, our approach achieves 73% accuracy, 68% precision, and 75% recall on MCI-to-AD conversion prediction at a 48months follow-up.
Automatic Segmentation of the Scoliotic Spine from Mr Images

Segmenting vertebral bodies (VBs) and intervertebral discs (IVDs) in magnetic resonance imaging (MRI) data is an important step towards the creation of 3D spine models for image-guided surgical treatement of adolescent idiopathic scoliosis (AIS). Recent advances in deep learning have established state-of-the-art results in medical image segmentation. Thus, in this paper, we present a method based on convolutional neural networks to simultaneously segment VBs and IVDs in MRI data sets of AIS patients, a difficult problem which has not yet been adressed in the literature. Our architecure is inspired by the U-net architecture, combined with the recently proposed and promising squeeze-and-excitation (SE) block and an objective function incorporating Cohen’s kappa to deal with the imbalamce class problem. Our model is first trained using a public dataset of non-AIS patients, then fine tuned using a few images of AIS patients. Results in 8 test AIS patient MRI volumes show that the fine tuning and SE strategies both improve segmentation considerably while remaining highly complementary to each other.
Determining Ischemic Stroke From CT-Angiography Imaging Using Symmetry-Sensitive Convolutional Networks

Acute Ischemic Stroke (AIS) is the second leading cause of death worldwide in 2015, and 5th in the United States. Neuro-imaging is routinely used in the diagnosis and management of these patients. To create a decision support method for AIS, we propose a convolutional neural network for automated detection of ischemic stroke from CT Angiography (CTA), an imaging technique that is widely available and used routinely in stroke evaluations. The network has a novel design that makes it sensitive to changes in symmetry of vascular and brain tissue texture which allows it to detect ischemic stroke from CTA brain images. The proposed model is inspired from the paradigm of Siamese networks and applied to the two brain hemispheres in parallel. We tested the model on a clinical dataset of 217 subjects, 123 controls and 94 subjects imaged less than 24 hours after stroke onset. First, we tested the ability of the network in recognizing strokes with the original images, which contain asymmetries in both vascular structures and brain tissues. Then, we digitally removed the vasculature in order to evaluate the ability of the network to recognize strokes by analyzing brain tissue only. We achieved AUC 0.914 (CI 0.88-0.95) and AUC 0.899 (CI 0.86-0.94) on the two experiments respectively. The qualitative analysis of the network activation confirms that the model efficiently learns the vasculature and brain tissue structures in one hemisphere that does not appear in the opposite hemisphere.