Miracle-dz/UniFAR-FTUs huggingface dataset:
Who gives birth in private facilities in Asia? A look at six countries

Over the past two decades, multilateral organizations have encouraged increased engagement with private healthcare providers in developing countries. As these efforts progress, there are concerns regarding how private delivery care may effect maternal health outcomes. Currently available data do not allow for an in-depth study of the direct effect of increasing private sector use on maternal health across countries. As a first step, however, we use demographic and health surveys (DHS) data to (1) examine trends in growth of delivery care provided by private facilities and (2) describe who is using the private sector within the healthcare system. As Asia has shown strong increases in institutional coverage of delivery care in the last decade, we will examine trends in six Asian countries. We hypothesize that if the private sector competes for clients based on perceived quality, their clientele will be wealthier, more educated and live in an area where there are enough health facilities to allow for competition. We test this hypothesis by examining factors of socio-demographic, economic and physical access and actual/perceived need related to a mother’s choice to deliver in a health facility and then, among women delivering in a facility, their use of a private provider. Results show a significant trend towards greater use of private sector delivery care over the last decade. Wealth and education are related to private sector delivery care in about half of our countries, but are not as universally related to use as we would expect. A previous private facility birth predicted repeat private facility use across nearly all countries. In two countries (Cambodia and India), primiparity also predicted private facility use. More in-depth work is needed to truly understand the behaviour of the private sector in these countries; these results warn against making generalizations about private sector delivery care.
Recognizing signs of malignancy — The quest for computer assisted cancer screening and diagnosis systems

Almost all cancers are diagnosed through visual examination of microscopic tissue samples. Visual screening of cell samples, so called PAP-smears, has drastically reduced the incidence of cervical cancers in countries that have implemented population wide screening programs. But the visual examination is tedious, subjective and expensive. There has therefore been much research aiming for computer assisted or automated cell image analysis systems for cancer detection and diagnosis. Progress has been made but still most of cytology and pathology is done visually. In this presentation I will discuss some of the major issues involved, examine some of the proposed solutions and give some comments about the state of the art.
"Whom-to-interact": does conference networking boost your citation count?

Recently, conference publications have gained a wide popularity, specially in the domain of computer science. In conferences, the opportunity of personal interactions between the fellow researchers opens up a new dimension for the citation network evolution. In this work, we propose a generic multiplex network framework to uncover the influence of the interactions in a conference on the appearance of the new citation links in future. We crawl the DBLP citation dataset and perform a case study on the leading conferences in the "Artificial Intelligence", "Hardware & Architecture", "Human-Computer Interaction" and "Networking & Distributed Systems" domains. Our empirical study is able to identify significant number of "successful" conference interactions which eventually results in "induced" citations. Interestingly, it is found that in most of the cases, it takes just 3 to 4 years to receive a citation from a participant interacted in a conference. It is also observed that the faster an interaction between two researchers can induce a citation between them, the longer this series of induced citations go on. Finally, we propose a machine learning based recommendation system 'Whom-to-Interact', for the researchers attending a conference, to suggest them 'with whom they should interact' for gaining incoming citations. The experimental results exhibit a decent performance of the system along with the impact of different regulating factors.
Avionic Software Verification by Abstract Interpretation

An flight control surface actuation system in avionics is safety critical and complex since it is placed between the pilot's controls (sidesticks, rudder pedals) and the control surfaces of the aircraft, whose movement it controls and monitors. For reliability and dependability, several redundant software and computers are used but each one must be proved to be correct. With the exponential increase of the power of computers, the flight control software has become much more powerful hence complex. Since the cost of tests increasing more rapidly that the size of programs, formal methods become an attractive complement for program verification. The difficulties with formal methods are that they need a formal specification, a formal semantics of the programming language and, because of undecidability, have serious limits in the automatic verification that the program semantics satisfies the specification. A theorem prover needs human assistance while model-checking requires finite models which, but for hardware, are generally incomplete. Static analysis offers an interesting completely automatic alternative in that the specification can be chosen to be implicit. For example the absence of runtime error with not require the user to define a complex specification. Moreover static analysis consider infinite models of computations that can be directly computed from the program text so that the end-user does not need to provide a (finite) model of the program computations and environment. Finally, the reachable states during any program computation are computed approximately through an overapproximation that omits no possible case. So the delicate questions about the program semantics can be solved by considering all possible alternatives. If this overapproximation copes with the undecidability problem, its inconvenience is that it considers spurious executions, which does not exist in any actual execution, but may be at the origin of false alarms. This approach is sound in that no runtime error will ever be omitted in the diagnostic. It is incomplete, because of the potential false alarms. The whole problem is therefore to choose abstractions of the program semantics that yield few or no alarm. To do so, one can restrict the family of programs to be analyzed so as to adapt the abstraction exactly the domain-specific primitives and algorithmic schema found in this family of programs. We will present elements of the theory of abstract interpretation on which the soundness of the notion of overapproximation does rely. Then we will introduce the ASTRÉE static an-alyzer (www.astree.ens.fr), which is specialized …
Identifying an Honest EXP^NP Oracle Among Many

We provide a general framework to remove short advice by formulating the following computational task for a function $f$: given two oracles at least one of which is honest (i.e. correctly computes $f$ on all inputs) as well as an input, the task is to compute $f$ on the input with the help of the oracles by a probabilistic polynomial-time machine, which we shall call a selector. We characterize the languages for which short advice can be removed by the notion of selector: a paddable language has a selector if and only if short advice of a probabilistic machine that accepts the language can be removed under any relativized world. Previously, instance checkers have served as a useful tool to remove short advice of probabilistic computation. We indicate that existence of instance checkers is a property stronger than that of removing short advice: although no instance checker for ${\rm EXP}^{\rm NP}$-complete languages exists unless ${\rm EXP}^{\rm NP} = {\rm NEXP}$, we prove that there exists a selector for any ${\rm EXP}^{\rm NP}$-complete language, by building on the proof of ${\rm MIP} = {\rm NEXP}$ by Babai, Fortnow, and Lund (1991).
Innate immunity defines the capacity of antiviral T cells to limit persistent infection

Effective immunity requires the coordinated activation of innate and adaptive immune responses. Natural killer (NK) cells are central innate immune effectors, but can also affect the generation of acquired immune responses to viruses and malignancies. How NK cells influence the efficacy of adaptive immunity, however, is poorly understood. Here, we show that NK cells negatively regulate the duration and effectiveness of virus-specific CD4+ and CD8+ T cell responses by limiting exposure of T cells to infected antigen-presenting cells. This impacts the quality of T cell responses and the ability to limit viral persistence. Our studies provide unexpected insights into novel interplays between innate and adaptive immune effectors, and define the critical requirements for efficient control of viral persistence.
IMPROVING VIRTUAL MACHINE SECURITY THROUGH INTELLIGENT INTRUSION DETECTION SYSTEM

Virtualization is the key feature of cloud computing which facilitates sharing of common resources among cloud users. As cloud computing is a shared facility and accessed remotely, it is vulnerable to various attacks. The shared resources may be exploited by the attackers through vulnerabilities. Virtualization technology is implemented by instantiating a virtual machine to each cloud user based on their requirements. Many virtual machines are instantiated on a single physical infrastructure. Although this virtualization technology is more beneficent for the users by means of low cost and for service providers by means of better utilization, it has several risks, in which security is the major one. Benefits of virtualization go beyond the cost savings. By better planning and management virtualization risk can be mitigated. The proposed work secures virtual machines by creating VM profiles, packet monitoring and by conducting periodic centralized vulnerability scans. It is consistent because both signature based and anomaly based IDS are combined. It is efficient because only the VMs affected by infected packets are scanned for vulnerability.
A new nonnegative matrix factorization for independent component analysis

Nonnegative matrix factorization (NMF) is known as a parts-based linear representation for nonnegative data. This method has been applied for blind source separation (BSS) when the sources are nonnegative. This paper presents a new NMF method for independent component analysis (ICA), which is useful for BSS without the nonnegativity constraint. Using this method, we transform the sources by their cumulative distribution functions (CDFs) and perform the nonparametric quantization to construct a nonnegative matrix where each entry represents the joint probability density of two transformed signals. The NMF procedure is accordingly realized to find the ICA demixing matrix. The independence between sources is maximized towards attaining the uniformity in the joint probability density. In the experiments on the separation of signal and music signals, we show the effectiveness of the proposed NMF-ICA compared to the infomax ICA and FastICA algorithms.
Software Assisted Digital RF Processor for Single-Chip GSM Radio in 90 nm CMOS

This paper proposes and describes a new software and application programming interface view of an RF transceiver as implemented in the first single-chip GSM radio in 90 nm CMOS. It demonstrates benefits of using programmable digital control logic in deep-submicron CMOS RF system. It also describes a micro-processor architecture design in digital RF processor (DRP) and how it controls compensation for process, temperature and voltage variations of the analog and RF circuits to meet the required RF performance
Technology for Restoring and Maintaining Sustainability of Populations:Practical and Theoretical Results of Genourbanology

Genourbanology (the synthesis of system ecology and population genetics) is a new scientific and practical school of thought, founded and developed by the authors. The task of genourbanology consists in studying the genetic parameters and laws of stability conservation of ecosystems and ecosystem restoration in anthropogenic and, in particular, urbanized landscapes. An ecological genetic technology for restoring and maintaining sustainability of populations viability, including the method for assessing the state and duration of existence of populations as well as the enrichment and control of the population gene pool in urban ecosystems was developed in the framework of genourbanology. The results of quantitative assessment of the state of the gene pool of 40 populations are described and the recommendations for restoring its viability are given using the following model species of animals and plants inhabiting the fragmented landscape of Moscow and Moscow region: 20 populations of the terrestrial bush snail (Bradybaena fruticum (Mull.), 16 populations of brown frogs Rana temporaria L. and Rana arvalis Nills. , and 4 populations of Norway spruce Picea abies (L.) Karst. ). A sharp decline in the genetic diversity (up to 70%) in the urban isolated compared to the natural populations was found. Comparative analysis of the gene pool of natural populations and planted spruce stands revealed a correlation between the state of the gene pool of spruce populations and the degree of their lesion with the bark beetle (Ips typographus (L.).
Cointegration Analysis of Climate Change: An Exposition

This paper describes in a non-technical manner the concept of cointegration and its implications in the modelling of climate data. It illustrates a number of inference procedures appropriate in integrated-cointegrated vector autoregressive processes (VARs). Particular attention is paid to the properties of VARs, to the modelling of deterministic terms, to the determination of the number of cointegration vectors, and to testing hypotheses on long-run relation and short-run adjustment. The analysis is illustrated by an empirical analysis of sea and land temperatures in the period 1500-2000 and how the long-run movements in these temperatures have been in uenced by solar radiation, greenhouse gasses and volcanic erruptions.
Simulation of Exit Choosing in Pedestrian Evacuation Using a Cellular Automaton Model Based on Surrounding Pedestrian Density

A cellular automata model is proposed to simulate the pedestrian evacuation behavior in a room with multiple exits by considering the surrounding pedestrian density related to current cell based on the direction. The numerical results show that the pedestrian density and the radius of visual area are two important factors in an evacuation which can affect the evacuation times apparently. It is found that blindly constant choosing the direction with smaller pedestrian density may lead to an increase of evacuation time.
Energy Storage and the Balance of Producers and Decomposers in Ecological Systems

Birch, L. C. 1955. Selection in Drosophlila pseudoobscura in relation to crowding. Evolution 9:389399. 1960. The genetic factor in population ecology. Amer. Nat. 94:5-24. Boggild, O. , and J. Keiding. 1958. Competition in house fly larvae. Experiments involving a DDT-resistant and susceptible strain. Oikos 9:1-25. Hiroyoshi, T. 1961. The linkage map of the house fly, Musca domestic L. Genetics 46:1373-1380. Kollros, C. L. 1944. A study of the gene, pearl, in populations of Tribolium castaneumn, Herbst. Unpublished Ph.D. dissertation, University of Chicago.
An enhanced initialization method for non-negative matrix factorization

Non-negative matrix factorization (NMF) is a dimensionality reduction tool, and has been applied to many areas such as bioinformatics, face image classification, etc. However, it often converges to some local optima because of its random initial NMF factors (W and H matrices). To solve this problem, some researchers have paid much attention to the NMF initialization problem. In this paper, we first apply the k-means clustering to initialize the factor W, and then we calculate the initial factor H using four different initialization methods (three standard and one new). The experiments were carried out on the eight real datasets and the results showed that the proposed method (EIn-NMF) achieved less error and faster convergence compared with both random initialization based NMF and the three standard methods for k-means based NMF.
Independent Factor Analysis 1 Statistical Modeling and Blind Source Separation

We introduce the independent factor analysis (IFA) method for recovering independent hidden sources from their observed mixtures. IFA generalizes and uniies ordinary factor analysis (FA), principal component analysis (PCA), and independent component analysis (ICA), and can handle not only square noiseless mixing, but also the general case where the number of mixtures diiers from the number of sources and the data are noisy. IFA is a two-step procedure. In the rst step, the source densities, mixing matrix and noise covariance are estimated from the observed data by maximum likelihood. For this purpose we present an expectation-maximization (EM) algorithm, which performs unsupervised learning of an associated probabilistic model of the mixing situation. Each source in our model is described by a mixture of Gaussians, thus all the probabilistic calculations can be performed analytically. In the second step, the sources are reconstructed from the observed data by an optimal non-linear estimator. A variational approximation of this algorithm is derived for cases with a large number of sources, where the exact algorithm becomes intractable. Our IFA algorithm reduces to the one for ordinary FA when the sources become Gaussian, and to an EM algorithm for PCA in the zero-noise limit. We derive an additional EM algorithm speciically for noiseless IFA. This algorithm is shown to be superior to ICA since it can learn arbitrary source densities from the data. Beyond blind separation, IFA can be used for modeling multi-dimensional data by a highly constrained mixture of Gaussians, and as a tool for non-linear signal encoding. In the blind source separation (BSS) problem one is presented with multi-variable data measured by L 0 sensors. It is known that these data arise from L source signals that are mixed together by some linear transformation corrupted by noise. It is further known that the sources are mutually statistically independent. The task is to obtain those source signals. However, the sources are not observable and nothing is known about their properties beyond their mutual statistical independence, nor about the properties of the mixing process and the noise. In the absence of this information, one has to proceed`blindly' to recover the source signals from their observed noisy mixtures. Despite its signal-processing appearance, BSS is a problem in statistical modeling of data. In this context, one wishes to describe the L 0 observed variables y i , which are generally correlated, in terms of a smaller set of L …
Difficult Conversations: Teaching Medical Oncology Trainees Communication Skills One Hour at a Time

Difficult conversations about prognosis, end of life, and goals of care arise commonly in medical oncology practice. These conversations are often highly emotional. Medical oncologists need outstanding, patient-centered communication skills to build trust and rapport with their patients and help them make well-informed decisions. Key skills include exploring patients’ perspectives, responding to emotion with empathy, and maintaining mindfulness during highly charged conversations. These skills can be taught and learned. Most previously described communication skills training curricula for oncology providers involve multiday retreats, which are costly and can disrupt busy clinical schedules. Many curricula involve a variety of oncology providers, such as physicians and nurses, at various stages of their careers. The authors developed a monthly, one-hour communication skills training seminar series exclusively for physicians in their first year of medical oncology subspecialty training. The curriculum involved a variety of interactive and engaging educational methods, including sociodramatic techniques, role-play, reflective writing, and Balint-type case discussion groups. Medical oncologists in their second and third years of training served as teaching assistants and peer mentors. Learners had the opportunity to practice skills during sessions and with patients between sessions. Learners acquired important skills and found the curriculum to be clinically relevant, judging by anonymous surveys and anonymous responses on reflective writing exercises. Results from the current curriculum are preliminary but lay the foundation for enhanced and expanded communication skills training programs in the future.
An approach for precise 2D/3D semantic annotation of spatially-oriented images for in situ visualization applications

Thanks to nowadays technologies, innovative tools afford to increase our knowledge of historic monuments, in the field of preservation and valuation of cultural heritage. These tools are aimed to help experts to create, enrich and share information on historical buildings. Among the various documentary sources, photographs contain a high level of details about shapes and colors. With the development of image analysis and image-based-modeling techniques, large sets of images can be spatially oriented towards a digital mock-up. For these reasons, digital photographs prove to be an easy to use, affordable and flexible support, for heritage documentation. This article presents, in a first step, an approach for 2D/3D semantic annotations in a set of spatially-oriented photographs (whose positions and orientations in space are automatically estimated). In a second step, we will focus on a method for displaying those annotations on new images acquired by mobile devices in situ. Firstly, an automated image-based reconstruction method produces 3D information (specifically 3D coordinates) by processing a large images set. Then, images are semantically annotated and a process uses the previously generated 3D information inherent to images for the annotations transfer. As a consequence, this protocol provides a simple way to finely annotate a large quantity of images at once instead of one by one. As those images annotations are directly inherent to 3D information, they can be stored as 3D files. To bring up on screen the information related to a building, the user takes a picture in situ. An image processing method allows estimating the orientation parameters of this new photograph inside the already oriented large images base. Then the annotations can be precisely projected on the oriented picture and send back to the user. In this way a continuity of information could be established from the initial acquisition to the in situ visualization.
In-band control, queuing, and failure recovery functionalities for openflow

In OpenFlow, a network as a whole can be controlled from one or more external entities (controllers) using in-band or out-of-band control networks. In this article, we propose in-band control, queuing, and failure recovery functionalities for OpenFlow. In addition, we report experimental studies and practical challenges for implementing these functionalities in existing software packages containing different versions of OpenFlow. The experimental results show that the in-band control functionality is suitable for all types of topologies. The results with the queuing functionality show that control traffic can be served with the highest priority in in-band networks and hence, data traffic cannot affect the communication between the controller and networking devices. The results with the failure recovery functionality show that traffic can be recovered from failures within 50 ms.