Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of Performance of Health Systems Concerning Maternal and Child Health: A Global Approach

Aims To assess the association of social determinants on the performance of health systems around the world. Methods A transnational ecological study was conducted with an observation level focused on the country. In order to research on the strength of the association between the annual maternal and child mortality in 154 countries and social determinants: corruption, democratization, income inequality and cultural fragmentation, we used a mixed linear regression model for repeated measures with random intercepts and a conglomerate-based geographical analysis, between 2000 and 2010. Results Health determinants with a significant association on child mortality(<1year): higher access to water (βa Quartile 4(Q4) vs Quartile 1(Q1) = -6,14; 95%CI: -11,63 to -0,73), sanitation systems, (Q4 vs Q1 = -25,58; 95%CI: -31,91 to -19,25), % measles vaccination coverage (Q4 vs Q1 = -7.35; 95%CI: -10,18 to -4,52), % of births attended by a healthcare professional (Q4 vs Q1 = -7,91; 95%CI: -11,36 to -4,52) and a % of the total health expenditure (Q3 vs Q1 = -2,85; 95%CI: -4,93 to -0,7). Ethnic fragmentation (Q4 vs Q1 = 9,93; 95%CI: -0.03 to 19.89) had a marginal effect. For child mortality<5 years, an association was found for these variables and democratization (not free vs free = 11,23; 95%CI: -0,82 to 23,29), out-of-pocket expenditure (Q1 vs Q4 = 17,71; 95%CI: 5,86 to 29,56). For MMR (Maternal mortality ratio), % of access to water for all the quartiles, % of access to sanitation systems, (Q3 vs Q1 = -171,15; 95%CI: -281,29 to -61), birth attention by a healthcare professional (Q4 vs Q1 = -231,23; 95%CI: -349,32 to -113,15), and having corrupt government (Q3 vs Q1 = 83,05; 95%CI: 33,10 to 133). Conclusions Improving access to water and sanitation systems, decreasing corruption in the health sector must become priorities in health systems. The ethno-linguistic cultural fragmentation and the detriment of democracy turn out to be two factors related to health results.
Presenting The Accessory Approach: A Start-up's Journey Towards Designing An Engaging Fall Detection Device

This paper explores a design experiment concerning the development of a personalised and engaging wearable fall detection device customised for care home residents. The design experiment focuses on a start-up company's design process, which utilises a new design approach, which I name the accessory approach, to accommodate given cultural fit purposes of a wearer. Influenced by accessory design, that belong neither to fashion nor jewellery, the accessory approach is a way of designing wearables that involve both functional and expressive qualities including the wearer's physical, psychological and social needs. The accessory approach is proven to enable first hand insight of the wearer's preferences, leading to in-depth knowledge and enhanced iterative processes, which support the design of a customised device. This type of knowledge is important for the HCI community as it brings accessory design disciplines into play when wanting to understand and design for individual needs, creating engaging wearables design.
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time

We give a polynomial-time algorithm for learning neural networks with one layer of sigmoids feeding into any Lipschitz, monotone activation function (e.g., sigmoid or ReLU). We make no assumptions on the structure of the network, and the algorithm succeeds with respect to {\em any} distribution on the unit ball in $n$ dimensions (hidden weight vectors also have unit norm). This is the first assumption-free, provably efficient algorithm for learning neural networks with two nonlinear layers. Our algorithm-- {\em Alphatron}-- is a simple, iterative update rule that combines isotonic regression with kernel methods. It outputs a hypothesis that yields efficient oracle access to interpretable features. It also suggests a new approach to Boolean learning problems via real-valued conditional-mean functions, sidestepping traditional hardness results from computational learning theory. Along these lines, we subsume and improve many longstanding results for PAC learning Boolean functions to the more general, real-valued setting of {\em probabilistic concepts}, a model that (unlike PAC learning) requires non-i.i. d. noise-tolerance.
Estimating the number of multiple jamming attackers in Vehicular Ad Hoc Network

In Vehicular Ad Hoc Network (VANET), due to the openness of wireless medium and the city-wide applied region, wireless communications are vulnerable to malicious multiple jamming attackers. Such jamming attack can seriously disable the preset functions of VANET. Jammer localization method is proposed to eliminate the attacker recently. However, it becomes invalid when there are multiple attackers. To address this issue, we propose a novel method to determine the attacker number and classify the corresponding data set. Our proposed method first uses the moving features of vehicles and spatial features of jammers as basis to divide the data set into several point sets. Then, the method uses the distribution of no-jammed points to group the point sets. And the result is accordingly obtained through this process. The simulation results show that our proposed method is effective and has many advantages compared to the traditional method.
Weighted Activity and Costing of Surveillance and Control in Animal Epidemiology

Activity based modeling and simulation is a very promising field. It has re- cently demonstrated its potential from modeling software development methods (9) to the design of low energy sensors. In this position paper, I would like to initiate a reflexion on the use of the weighted activity in the context of financial costing using the formu- lation recently proposed by X. Hu and B.P. Zeigler (5). I propose here to formalize a recent approach of costing, the Time-Driven Activity Based Costing (3), using the the- oretical modeling framework initiated by B.P. Zeigler (6). I argue that this specification can potentially improve the traditional model of Time-Driven Activity Based Costing. I illustrate the approach in the context of surveillance and control in animal epidemiology. The demonstration of its effectiveness is not done in this position paper. Nevertheless, as the existing costing systems are designed to support decision making in business man- agement, the scenario based approach proposed in this paper should be very useful to support decisions in complex management situations.
α7 Nicotinic Acetylcholine Receptor-Specific Antibody Induces Inflammation and Amyloid β42 Accumulation in the Mouse Brain to Impair Memory

Nicotinic acetylcholine receptors (nAChRs) expressed in the brain are involved in regulating cognitive functions, as well as inflammatory reactions. Their density is decreased upon Alzheimer disease accompanied by accumulation of β-amyloid (Aβ42), memory deficit and neuroinflammation. Previously we found that α7 nAChR-specific antibody induced pro-inflammatory interleukin-6 production in U373 glioblastoma cells and that such antibodies were present in the blood of humans. We raised a hypothesis that α7 nAChR-specific antibody can cause neuroinflammation when penetrating the brain. To test this, C57Bl/6 mice were either immunized with extracellular domain of α7 nAChR subunit α7(1-208) or injected with bacterial lipopolysaccharide (LPS) for 5 months. We studied their behavior and the presence of α3, α4, α7, β2 and β4 nAChR subunits, Aβ40 and Aβ42 and activated astrocytes in the brain by sandwich ELISA and confocal microscopy. It was found that either LPS injections or immunizations with α7(1-208) resulted in region-specific decrease of α7 and α4β2 and increase of α3β4 nAChRs, accumulation of Aβ42 and activated astrocytes in the brain of mice and worsening of their episodic memory. Intravenously transferred α7 nAChR-specific-antibodies penetrated the brain parenchyma of mice pre-injected with LPS. Our data demonstrate that (1) neuroinflammation is sufficient to provoke the decrease of α7 and α4β2 nAChRs, Aβ42 accumulation and memory impairment in mice and (2) α7(1-208) nAChR-specific antibodies can cause inflammation within the brain resulting in the symptoms typical for Alzheimer disease.
Equivalence of Additive-Combinatorial Linear Inequalities for Shannon Entropy and Differential Entropy

This paper addresses the correspondence between linear inequalities for Shannon entropy and differential entropy for sums of independent group-valued random variables. We show that any balanced (with the sum of coefficients being zero) linear inequality for Shannon entropy holds if and only if its differential entropy counterpart also holds; moreover, any linear inequality for differential entropy must be balanced. In particular, our result shows that recently proved differential entropy inequalities by Kontoyiannis and Madiman can be deduced from their discrete counterparts due to Tao in a unified manner. Generalizations to certain abelian groups are also obtained. Our proof of extending inequalities for Shannon entropy to differential entropy relies on a result of Rényi which relates the Shannon entropy of a finely discretized random variable to its differential entropy and also helps in establishing that the entropy of the sum of quantized random variables is asymptotically equal to that of the quantized sum; the converse uses the asymptotics of the differential entropy of convolutions with weak additive noise.
Integrative analysis of competing endogenous RNA network focusing on long noncoding RNA associated with progression of cutaneous melanoma

Cutaneous melanoma (CM) is the most malignant tumor of skin cancers because of its rapid development and high mortality rate. Long noncoding RNAs (lncRNAs), which play essential roles in the tumorigenesis and metastasis of CM and interplay with microRNAs (miRNAs) and mRNAs, are hopefully considered to be efficient biomarkers to detect deterioration during the progression of CM to improve the prognosis. Bioinformatics analysis was fully applied to predict the vital lncRNAs and the associated miRNAs and mRNAs, which eventually constructed the competing endogenous RNA (ceRNA) network to explain the RNA expression patterns in the progression of CM. Further statistical analysis emphasized the importance of these key genes, which were statistically significantly related to one or few clinical features from the ceRNA network. The results showed the lncRNAs MGC12926 and LINC00937 were verified to be strongly connected with the prognosis of CM patients.
Adapting Blockchain Technology for Scientific Computing

Blockchain stores information into a chain of blocks, whose integrity is usually guaranteed by Proof of Work (PoW). In many blockchain applications (including cryptocurrencies), users compete with each other to win the ownership of the blocks, a process commonly referred as mining. Mining activities consume huge amount of power, while the outcome appears to be useless besides validating a block. Here we discuss the requirements of designing a new PoW algorithm. We also propose a PoW scheme to help solve high-dimension, non-linear optimization problems. The revised scheme enables us to address difficult scientific questions as a byproduct of mining.
TopoText: Context-Preserving Text Data Exploration Across Multiple Spatial Scales

TopoText is a context-preserving technique for visualizing text data for multi-scale spatial aggregates to gain insight into spatial phenomena. Conventional exploration requires users to navigate across multiple scales but only presents the information related to the current scale. This limitation potentially adds more steps of interaction and cognitive overload to the users. TopoText renders multi-scale aggregates into a single visual display combining novel text-based encoding and layout methods that draw labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each individual scale, but also indicates the spatial coverage of the aggregates and their underlying hierarchical relationships. We validate TopoText with both a user study as well as several application examples.
KeyTime: Super-Accurate Prediction of Stroke Gesture Production Times

We introduce KeyTime, a new technique and accompanying software for predicting the production times of users' stroke gestures articulated on touchscreens. KeyTime employs the principles and concepts of the Kinematic Theory, such as lognormal modeling of stroke gestures' velocity profiles, to estimate gesture production times significantly more accurately than existing approaches. Our experimental results obtained on several public datasets show that KeyTime predicts user-independent production times that correlate r=. 99 with groundtruth from just one example of a gesture articulation, while delivering an average error in the predicted time magnitude that is 3 to 6 times smaller than that delivered by CLC, the best prediction technique up to date. Moreover, KeyTime reports a wide range of useful statistics, such as the trimmed mean, median, standard deviation, and confidence intervals, providing practitioners with unprecedented levels of accuracy and sophistication to characterize their users' a priori time performance with stroke gesture input.
Applying Computational Analysis to Textual Data from the Wild: A Feminist Perspective

With technologies that afford much larger-scale data collection than previously imagined, new ways of processing and interpreting qualitative textual data are required. HCI researchers use a range of methods for interpreting the 'full range of human experience' from qualitative data, however, such approaches are not always scalable. Feminist geography seeks to explore how diverse and varied accounts of place can be understood and represented, whilst avoiding reductive classification systems. In this paper, we assess the extent to which unsupervised topic models can support such a research agenda. Drawing on literature from Feminist and Critical GIS, we present a case study analysis of a Volunteered Geographic Information dataset of reviews about breastfeeding in public spaces. We demonstrate that topic modelling can offer novel insights and nuanced interpretations of complex concepts such as privacy and be integrated into a critically reflexive feminist data analysis approach that captures and represents diverse experiences of place.
Dopamine and the Development of Executive Dysfunction in Autism Spectrum Disorders

Persons with autism regularly exhibit executive dysfunction (ED), including problems with deliberate goal-directed behavior, planning, and flexible responding in changing environments. Indeed, this array of deficits is sufficiently prominent to have prompted a theory that executive dysfunction is at the heart of these disorders. A more detailed examination of these behaviors reveals, however, that some aspects of executive function remain developmentaly appropriate. In particular, while people with autism often have difficulty with tasks requiring cognitive flexibility, their fundamental cognitive control capabilities, such as those involved in inhibiting an inappropriate but relatively automatic response, show no significant impairment on many tasks. In this article, an existing computational model of the prefrontal cortex and its role in executive control is shown to explain this dichotomous pattern of behavior by positing abnormalities in the dopamine-based modulation of frontal systems in individuals with autism. This model offers excellent qualitative and quantitative fits to performance on standard tests of cognitive control and cognitive flexibility in this clinical population. By simulating the development of the prefrontal cortex, the computational model also offers a potential explanation for an observed lack of executive dysfunction early in life.
Multi-bit non-volatile spintronic flip-flop

As leakage increases proportionally with the technology downscaling, it becomes extremely challenging to manage to meet the total power budget. This is because, CMOS-based logic blocks can not be completely power-gated as their flip-flops always require a retention supply to hold the system states. Alternatively, their data can be stored in a separate memory during the standby mode, however, that results in a huge area and energy overhead. Spin Transfer Torque (STT) based nonvolatile flip-flops can offer normally-off/instant-on computing features to reduce leakage by complete power shut-down without the need to transfer and restore system states separately. The non-volatile component of such flip-flops can be easily shared for the overall design optimizations. In this paper, we design a unique multi-bit non-volatile flip-flop architecture using STT devices to reduce the area and energy costs associated with nonvolatile components. This architecture is developed based on the resource sharing principle using a custom design that enables the optimization for the area and energy consumption. Moreover, we have developed a framework in which we have replaced the conventional neighbor flipflops in the layout with our proposed multi-bit non-volatile designs. Results show that using our multi-bit flip-flop architecture, we improve the system-level area and energy by 26% and 14% in average, respectively, compared to the standard single-bit non-volatile flip-flop design.
Coordinated Multicast Beamforming in Multicell Networks

We study physical layer multicasting in multicell networks where each base station, equipped with multiple antennas, transmits a common message using a single beamformer to multiple users in the same cell. We investigate two coordinated beamforming designs: the quality-of-service (QoS) beamforming and the max-min SINR (signal-to-interference-plus-noise ratio) beamforming. The goal of the QoS beamforming is to minimize the total power consumption while guaranteeing that received SINR at each user is above a predetermined threshold. We present a necessary condition for the optimization problem to be feasible. Then, based on the decomposition theory, we propose a novel decentralized algorithm to implement the coordinated beamforming with limited information sharing among different base stations. The algorithm is guaranteed to converge and in most cases it converges to the optimal solution. The max-min SINR (MMS) beamforming is to maximize the minimum received SINR among all users under per-base station power constraints. We show that the MMS problem and a weighted peak-power minimization (WPPM) problem are inverse problems. Based on this inversion relationship, we then propose an efficient algorithm to solve the MMS problem in an approximate manner. Simulation results demonstrate significant advantages of the proposed multicast beamforming algorithms over conventional multicasting schemes.
GEM-NI+: Leveraging Difference Visualization and Multiple Displays for Supporting Multiple Complex Generative Design Alternatives

In the conceptual design phase, designers routinely generate dozens of alternatives based on a single idea. This is especially relevant in generative design where an algorithm can generate a large number of viable design options. While solutions for creating and managing a small number of simple alternatives have been proposed, practical applications of these solutions are liMassachusetts Institute of Technologyed. As a result, we present GEM-NI+, an extension to the original GEM-NI system for creating and managing alternatives in generative design. GEM-NI+ is designed to enable editing, managing and comparing up to 24 alternatives simultaneously using a multi-monitor setup. GEM-NI+ also features a new "jamming spaces" technique for assigning individual monitors into different visualization states, which makes organization of a large workspace easier. Finally, GEM-NI+ enables comparison of complex alternatives using recursive group node difference visualization.
Simulating Fiction: Individual Differences in Literature Comprehension Revealed with fMRI

When we read literary fiction, we are transported to fictional places, and we feel and think along with the characters. Despite the importance of narrative in adult life and during development, the neurocognitive mechanisms underlying fiction comprehension are unclear. We used functional magnetic resonance imaging (fMRI) to investigate how individuals differently employ neural networks important for understanding others’ beliefs and intentions (mentalizing), and for sensori-motor simulation while listening to excerpts from literary novels. Localizer tasks were used to localize both the cortical motor network and the mentalizing network in participants after they listened to excerpts from literary novels. Results show that participants who had high activation in anterior medial prefrontal cortex (aMPFC; part of the mentalizing network) when listening to mentalizing content of literary fiction, had lower motor cortex activity when they listened to action-related content of the story, and vice versa. This qualifies how people differ in their engagement with fiction: some people are mostly drawn into a story by mentalizing about the thoughts and beliefs of others, whereas others engage in literature by simulating more concrete events such as actions. This study provides on-line neural evidence for the existence of qualitatively different styles of moving into literary worlds, and adds to a growing body of literature showing the potential to study narrative comprehension with neuroimaging methods.
Webcam Covering as Planned Behavior

Most of today's laptops come with an integrated webcam placed above the screen to enable video conferencing. Due to the risk of webcam spying attacks, some laptop users seem to be concerned about their privacy and seek protection by covering the webcam. This paper is the first to investigate personal characteristics and beliefs of users with and without webcam covers by applying the Theory of Planned Behavior. We record the privacy behavior of 180 users, develop a path model, and analyze it by applying Partial Least Squares. The analysis indicates that privacy concerns do not significantly influence users' decision to use a webcam cover. Rather, this behavior is influenced by users' attitudes, social environment, and perceived control over protecting privacy. Developers should take this as a lesson to design privacy enhancing technologies which are convenient, verifiably effective and endorsed by peers.
A fast algorithm for touring the disjoint convex polygons in the given order

Given a start point s, a target point t, and k disjoint convex polygons in the given order in the plane, finding the shortest path of visiting the convex polygons sequence from s to t is the goal. In this paper, we present a fast algorithm to compute the shortest path based on the last step shortest path maps. Firstly, we locate the dividing-points quickly in the two adjacent polygons, which can reduce amounts of iteratively computation. Then, we convert this problem to solving the shortest path of visiting the disjoint line segments sequence which makes the solution much simpler. Furthermore, we analyze the complexity of new algorithm and obtain that it is superior to the previous solution. Finally, we implement this algorithm and show that it is correct.