Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating carotid pulse and breathing rate from near-infrared video of the neck

Objective: Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtrusively with remote devices. However, most of the approaches work only in bright environments in which subtle photoplethysmographic and ballistocardiographic signals can be easily analyzed and/or require expensive and custom hardware to perform the measurements. Approach: This work introduces a low-cost method to measure subtle motions associated with the carotid pulse and breathing movement from the neck using near-infrared (NIR) video imaging. A skin reflection model of the neck was established to provide a theoretical foundation for the method. In particular, the method relies on template matching for neck detection, principal component analysis for feature extraction, and hidden Markov models for data smoothing. Main results: We compared the estimated HR and BR measures with ones provided by an FDA-cleared device in a 12-participant laboratory study: the estimates achieved a mean absolute error of 0.36 beats per minute and 0.24 breaths per minute under both bright and dark lighting. Significance: This work advances the possibilities of non-contact physiological measurement in real-life conditions in which environmental illumination is limited and in which the face of the person is not readily available or needs to be protected. Due to the increasing availability of NIR imaging devices, the described methods are readily scalable.
What drives the observed variability of HCN in the troposphere and lower stratosphere

Abstract. We use the GEOS-Chem global 3-D chemistry transport model to investigate the relative importance of chemical and physical processes that determine observed variability of hydrogen cyanide (HCN) in the troposphere and lower stratosphere. Consequently, we reconcile ground-based FTIR column measurements of HCN, which show annual and semi-annual variations, with recent space-borne measurements of HCN mixing ratio in the tropical lower stratosphere, which show a large two-year variation. We find that the observed column variability over the ground-based stations is determined by a superposition of HCN from several regional burning sources, with GEOS-Chem reproducing these column data with a positive bias of 5%. GEOS-Chem reproduces the observed HCN mixing ratio from the Microwave Limb Sounder and the Atmospheric Chemistry Experiment satellite instruments with a mean negative bias of 20%, and the observed HCN variability with a mean negative bias of 7%. We show that tropical biomass burning emissions explain most of the observed HCN variations in the upper troposphere and lower stratosphere (UTLS), with the remainder due to atmospheric transport and HCN chemistry. In the mid and upper stratosphere, atmospheric dynamics progressively exerts more influence on HCN variations. The extent of temporal overlap between African and other continental burning seasons is key in establishing the apparent bienniel cycle in the UTLS. Similar analysis of other, shorter-lived trace gases have not observed the transition between annual and bienniel cycles in the UTLS probably because the signal of inter-annual variations from surface emission has been diluted before arriving at the lower stratosphere (LS), due to shorter atmospheric lifetimes.
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers

We present a scalable approach to performing approximate fully Bayesian inference in generic state space models. The proposed method is an alternative to particle MCMC that provides fully Bayesian inference of both the dynamic latent states and the static parameters of the model. We build up on recent advances in computational statistics that combine variational methods with sequential Monte Carlo sampling and we demonstrate the advantages of performing full Bayesian inference over the static parameters rather than just performing variational EM approximations. We illustrate how our approach enables scalable inference in multivariate stochastic volatility models and self-exciting point process models that allow for flexible dynamics in the latent intensity function.
Putting the Coach in the Loop: Improving Practice in Outdoor Activities Through Technology

Outdoor activities provide many beneﬁts, including physical and mental health, skill acquisition and social develop-ment. Wearable devices that measure activity data, such as GPS position, speed or heart rate, can potentially help users improve their performance. For this purpose, current systems either present data to the user, or have an expert system evaluate it and provide recommendations. But both these approaches leave out a key player: the expert coach. Expert mentoring has an important and irreplaceable role when it comes to making decisions to improve performance. We argue that in order to effectively improve performance, systems that use wearables to capture data during outdoor activities should provide useful visualizations to experts when these are available.
IMPACT OF SOIL MOISTURE INITIALIZATION ON NUMERICAL WEATHER FORECASTING OVER THE MISSISSIPPI DELTA REGION

Soil moisture estimates are considered as a valuable input for various environment models including weather forecasting, water management, agriculture, and forestry applications. Generally, the network of soil moisture observations is not dense (typically only few observation points are available within each state) enough to meet the spatial resolution requirements of these applications. Therefore, several approaches were developed to generate soil moisture fields. One approach utilizes Land Surface Model (LSM) offline simulations with a prescribed atmospheric forcing to produce highresolution surface fields. The problem of adequate soil moisture initialization/specification in short-term numerical forecasting models was recognized about 10 years ago (Smith et al. 1994, Dirmeyer 1995).
Variability and interactions of phytoplankton and bacterioplankton in the Antarctic neritic area

The distributions of phytoplankton biomass and bacterial populations were investigated at 37 statlons on the continental shelf of Terre Adelie (Antarctica) during Austral summer 1989. Despite a potentially favourable environment, phytoplankton biomass was relatively low. Surface chlorophyll a values ranged from 1.2 mg m-3 in the coastal area, with a maximum of 2.5 mg m-3 in the vicinity of the penguin rookeries, to only 0.2 mg m-3 70 km offshore. In the Pointe Geologie Archipelago, diatoms larger than 10 pm were predominant and represented more than 80 'h, of the total biomass. In offshore waters, these large cells represented only 38 % of the total biomass with 59 % of the biomass in the 1 to 10 fraction. Total bacterial abundance ranged from 9 4 X 10' to 1.0 x 10" cells m-3 and heterotrophic bacteria ranged from 1.4 x 106 to 7.7 X 10' colony forming units m-' Frequency of dividing cells ranged from 0.8 to 6.6 %. The highest numbers of heterotrophic bacteria were recorded in the immediate vicinity of penguin rookeries and the lowest in offshore waters. Bacterial biomass represents between 2 to 15 % of total microbial biomass in offshore waters and up to 30 O/ O in the coastal area. There was no direct correlation between bacterial and phytoplanktonic standing stocks. This lack of relationship can be explained by considering the differences in scale. It is suggested that in the coastal area where large diatoms are dominant, the bacteria are mainly dependant on organic matter introduced by bird manuring. On the other hand, in offshore waters where the dominant phytoplankton fraction under 10 pm represents a large part of the microbial loop, an indirect relationship between algae and bacteria may be expected.
A REVIEW OF FACE SKETCH RECOGNITION SYSTEMS

Police sketching techniques are nevertheless a routine part of law enforcement investigation and often used to identify suspects from an eye witness memory. This classic technique of identifying is generally slow and fastidious and may not conduct to arrest the right offender. Therefore an automatic face sketch recognition systems that determine efficiently the perpetrator’s appearance from gallery of face images is required. Such technology design is open challenging research because faces and sketches are generated from distinct sources and have different gaps to overcome in low and high levels. Although many methods have been proposed, we are unaware of any surveys on this particular topic. For this reason we wrote this paper for reviewing the different researches on recognizing face from forensic sketch by analyzing their approaches and identifying their limitations. We also discuss relevant issues such as benchmarking datasets and evaluation protocols. After, we conclude with several promising directions for future research. +
Throwhammer: Rowhammer Attacks over the Network and Defenses

Increasingly sophisticated Rowhammer exploits allow an attacker that can execute code on a vulnerable system to escalate privileges and compromise browsers, clouds, and mobile systems. In all these attacks, the common assumption is that attackers first need to obtain code execution on the victim machine to be able to exploit Rowhammer either by having (unprivileged) code execution on the victim machine or by luring the victim to a website that employs a malicious JavaScript application. In this paper, we revisit this assumption and show that an attacker can trigger and exploit Rowhammer bit flips directly from a remote machine by only sending network packets. This is made possible by increasingly fast, RDMA-enabled networks, which are in wide use in clouds and data centers. To demonstrate the new threat, we show how a malicious client can exploit Rowhammer bit flips to gain code execution on a remote key-value server application. To counter this threat, we propose protecting unmodified applications with a new buffer allocator that is capable of fine-grained memory isolation in the DRAM address space. Using two real-world applications, we show that this defense is practical, self-contained, and can efficiently stop remote Rowhammer attacks by surgically isolating memory buffers that are exposed to untrusted network input.
THE INTERNAL-COLLISION-INDUCED MAGNETIC RECONNECTION AND TURBULENCE (ICMART) MODEL OF GAMMA-RAY BURSTS

The recent Fermi observation of GRB 080916C shows that the bright photosphere emission associated with a putative fireball is missing, which suggests that the central engine likely launches a Poynting-flux-dominated (PFD) outflow. We propose a model of gamma-ray burst (GRB) prompt emission in the PFD regime, namely, the Internal-Collision-induced MAgnetic Reconnection and Turbulence (ICMART) model. It is envisaged that the GRB central engine launches an intermittent, magnetically dominated wind, and that in the GRB emission region, the ejecta is still moderately magnetized (e.g., 1 ≲ σ ≲ 100). Similar to the internal shock (IS) model, the mini-shells interact internally at the radius RIS ∼ Γ2cΔt. Most of these early collisions, however, have little energy dissipation, but serve to distort the ordered magnetic field lines entrained in the ejecta. At a certain point, the distortion of magnetic field configuration reaches the critical condition to allow fast reconnection seeds to occur, which induce relativistic MHD turbulence in the interaction regions. The turbulence further distorts field lines easing additional magnetic reconnections, resulting in a runway release of the stored magnetic field energy (an ICMART event). Particles are accelerated either directly in the reconnection zone, or stochastically in the turbulent regions, which radiate synchrotron photons that power the observed gamma rays. Each ICMART event corresponds to a broad pulse in the GRB light curve, and a GRB is composed of multiple ICMART events. This model retains the merits of IS and other models, but may overcome several difficulties/issues faced by the IS model (e.g., low efficiency, fast cooling, electron number excess, Amati/Yonetoku relation inconsistency, and missing bright photosphere). Within this model, the observed GRB variability timescales could have two components, one slow component associated with the central engine time history, and another fast component associated with relativistic magnetic turbulence in the emission region. The model predicts a decrease of gamma-ray polarization degree and Ep in each ICMART event (broad pulse) during the prompt GRB phase, as well as a moderately magnetized external reverse shock. The model may be applied to the GRBs that have time-resolved, featureless Band-function spectra, such as GRB 080916C and most GRBs detected by Fermi LAT.
Applying convolutional neural networks for pre-detection of alzheimer's disease from structural MRI data

During the recent years, there have been many studies implemented on the automatic diagnosis of Alzheimer's Disease (AD) using different methods. The focus of most of these studies has relied upon the detection of AD from neu-roimaging data. However, recognizing symptoms early as much as possible(Pre-detection) is crucial as disease modifying drugs will be most effective if administered early in the course of disease, before the occurrence of irreversible brain damages. Therefore, there is a high importance of utilizing automated techniques for the purpose of pre-detection of AD symptoms from such data. We report an experimental approach to evaluate the best pre-detection method of AD. Our study consists of two main experiments. Those two experiments were implemented using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Prior to our first experiment we have stated an assumption which is, if there is a successful AD detection method that will be successful in AD pre-detection also. Different studies have used different data sets and different diagnosing methods. Therefore we have verified the existing and the most successful detection method which is Support Vector Machine (SVMs) as the first experiment. According to the results obtained from the initial experiment (detection study) the sensitivity is 95.3%, the specificity is 71.4% and the accuracy is 84.4% with use of a SVM. Since those results were not successful, a deep learning based technique (Convolutional Neural Network) was proposed as the second experiment. The proposed Convolutional Neural Network (CNN) model was being tested using different image segmentation methods and different datasets. Finally the best image segmentation method obtained a high accuracy around 96% (sensitivity — 96%, specificity — 98%). And the CNN model remains unbiased to the dataset. Results of those experiments suggest an important role for early diagnosis of Alzheimer's disease using image processing and deep learning techniques.
From Arabic user-generated content to machinetranslation: integrating automatic errorcorrection

With the wide spread of the social media and online forums, individual users have been able to actively participate in the generation of online content in different languages and dialects. Arabic is one of the fastest growing languages used on Internet, but dialects (like Egyptian and Saudi Arabian) have a big share of the Arabic online content. There are many differences between Dialectal Arabic and Modern Standard Arabic which cause many challenges for Machine Translation of informal Arabic language. In this paper, we investigate the use of Automatic Error Correction method to improve the quality of Arabic User-Generated texts and its automatic translation. Our experiments show that the new system with automatic correction module outperforms the baseline system by nearly 22.59% of relative improvement.
Virtualized Reality using Depth Camera Point Clouds

We explored various ways to achieve Virtualized Reality, the technique of scanning a user’s real-world surroundings and reconstructing it as a virtual scene. Using the Kinect and Intel RealSense depth cameras, we attempted both real-time and ofﬂine techniques to construct virtual scenes from real-world scenes, such as KinectFusion, point cloud stitching, and raycasted point clouds. In particular, the Kinect live depth feed and RealSense live depth feed were both rather effective as prototypes. Given the opportunity to pursue this topic further, we would ideally like to create a system that can capture parts of a scene in real-time, automatically stitch their point clouds together, and pipe this reconstruction into a game engine like Unity for instant visualization and the opportunity for interaction.
Inside help: An integrative review of champions in healthcare-related implementation

Background/aims: The idea that champions are crucial to effective healthcare-related implementation has gained broad acceptance; yet the champion construct has been hampered by inconsistent use across the published literature. This integrative review sought to establish the current state of the literature on champions in healthcare settings and bring greater clarity to this important construct. Methods: This integrative review was limited to research articles in peer-reviewed, English-language journals published from 1980 to 2016. Searches were conducted on the online MEDLINE database via OVID and PubMed using the keyword “champion. ” Several additional terms often describe champions and were also included as keywords: implementation leader, opinion leader, facilitator, and change agent. Bibliographies of full-text articles that met inclusion criteria were reviewed for additional references not yet identified via the main strategy of conducting keyword searches in MEDLINE. A five-member team abstracted all full-text articles meeting inclusion criteria. Results: The final dataset for the integrative review consisted of 199 unique articles. Use of the term champion varied widely across the articles with respect to topic, specific job positions, or broader organizational roles. The most common method for operationalizing champion for purposes of analysis was the use of a dichotomous variable designating champion presence or absence. Four studies randomly allocated of the presence or absence of champions. Conclusions: The number of published champion-related articles has markedly increased: more articles were published during the last two years of this review (i.e. 2015–2016) than during its first 30 years (i.e. 1980–2009). The number of champion-related articles has continued to increase sharply since the year 2000. Individual studies consistently found that champions were important positive influences on implementation effectiveness. Although few in number, the randomized trials of champions that have been conducted demonstrate the feasibility of using experimental design to study the effects of champions in healthcare.
On the Factor Structure of a Reading Comprehension Test.

To investigate the construct validly of a section of a high stakes test, an exploratory factor analysis using principal components analysis was employed. The rotation used was varimax with the suppression level of . 30. Eleven factors were extracted out of 35 reading comprehension items. The fact that these factors emerged speak to the construct validity of the test. However the problem of over-factoring was obvious. This may be attributable to different paradigms of testing on which the items were based. In other words, the test constructer opted for passages from TOEFL, FCE and IELTS books with much alteration.
Video Steganography By Using Discrete Wavelet Packet Transform : A Survey

Data security is most important requirement for the protection of secret data from the hackers in secure communication. Now as the world becomes more digitized it has become very important to secure our secret data from eavesdroppers. Now a day a lot of applications are internet based and in some cases it is desired to made secure communication. For the data security there are two techniques: one is cryptography and second is Steganography. In cryptography technique the secret data is changed its character such that it can’t be understood by unauthorized user. While in Steganography technique the secret data is hiding inside cover medium. Message, image, audio, video etc can be used as cover medium. The word Steganography comes from Greek word Steganos which means “covered writing” or “concealed writing”. Cryptography and Steganography are different technique. In cryptography scrambling of messages is applied where as in steganography message is unobservable and undetectable. Steganography is the art of insensible communication. The aim of Steganography is to hide a secret message by embedding a message in such a way that a third person cannot see the presence of hidden message, on other hand cryptography is the technique to change the original message into cipher text by encrypting original secret message. Steganography provides on more security by hiding cipher text into another cover message [2]. Both technique can be used together for better security.
Ensemble Policy Optimization for Learning Robust Policies

—Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich function approximators like deep neural networks. Model-based methods where the real-world target domain is approximated using a simulated source domain provide an avenue to tackle the above challenges by augmenting real data with simulated data. However, discrepancies between the simulated source domain and the target domain pose a challenge for simulated training. We introduce the EPOpt algorithm, which uses an ensemble of simulated source domains and a form of adversarial training to learn policies that are robust and generalize to a broad range of possible target domains, including unmodeled effects. Further, the probability distribution over source domains in the ensemble can be adapted using data from target domain and approximate Bayesian methods, to progressively make it a better approximation. Thus, learning on a model ensemble, along with source domain adaptation, provides the beneﬁt of both robustness and learning/adaptation.
Supplementation or in situ conservation? Evidence of local adaptation in the Italian agile frog Rana latastei and consequences for the management of populations

Relocation of endangered species can be an effective conservation tool if it does not mix populations that represent significant intraspecific variation. The threatened Italian agile frog, Rana latastei, has small populations with low genetic diversity: translocation has been proposed to improve the likelihood of survival of populations. Using a common environment experiment and field surveys, we investigated whether there were differences in larval growth and developmental rate between foothill and lowland R. latastei populations, to evaluate if they are evolutionarily significant units. In nature, the colder climate of the foothills causes delayed metamorphosis. Conversely, in a common environment, larvae from foothill populations show faster growth and development. We did not find a significant egg‐size related maternal effect or any differences in size at metamorphosis. We hypothesise that counter‐gradient selection promoted fast growing phenotypes in a cold environment, where low temperatures slow down larval development. Foothill populations, despite being only a small geographical distance away from lowland populations, seem to be adapted to a colder climate and represent an evolutionarily significant unit. Different populations should, therefore, be managed independently, avoiding translocation. We suggest that evolutionary divergence between populations should be verified prior to planning relocation programmes, to prevent the risk of genetic homogenisation.