Miracle-dz/UniFAR-FTUs huggingface dataset:
Urban rural differnetials of infant mortality in Kenya using a shared frailty model

Kenya targets to reduce infant mortality to 26/1000 live births by 2015.The Kenya Demographic Health Survey (KDHS) 2008/09 shows that compared to the 2003 KDHS, the Infant Mortality Rate (IMR) improved to 52 from 77 per 1000 live births. However this reduction was achieved with an inverse in the trend whereby infant mortality rate for the first time in Kenya was higher in urban areas than rural areas. Although studies have focused on urban rural differentials in Kenya, most have utilised logistic regression and standard survival models. This study therefore set to examine the implications of unobserved heterogeneity on parameter estimates of urban rural differentials in infant mortality in Kenya. The study is conceptualised using the Mosley and Chen framework on child mortality. It utilises data from the KDHS 2008/09, the variables analysed include socio economic variables (Mother's education, Wealth quintile and region of residence), demographic variables (Mother's age at birth and birth order) and biological variables (Preceding birth interval and birth size). Standard Log normal Accelerated Failure Time model was used for analysis, shared frailty model was further fitted to account for unobserved heterogeneity; frailty was considered at household level. Results indicate that although infant mortality was seen to be higher in urban areas compared to rural areas, this difference was not significant. In urban areas birth size was the only significant factor that influenced infant mortality whereas in rural areas region of residence, preceding birth interval, birth order and birth size were significant factors. Consistent with other studies AFT model was seen to underestimate the negative parameters while overestimating the positive parameters. Recommendations emanating from this study are that, frailty provides a more precise estimate of parameters compared to standard AFT models. In addition, programs or interventions targeted at reducing infant mortality should seek to address factors by urban and rural differentials so as to address specific needs of the population in those regions. It would also be paramount to study other factors that act to influence infant mortality especially in urban areas.
Inverse Problems, Deep Learning, and Symmetry Breaking

In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output. When inverting such systems, i.e., solving the associated inverse problems, there is no unique solution. This causes fundamental difficulties for deploying the emerging end-to-end deep learning approach. Using the generalized phase retrieval problem as an illustrative example, we show that careful symmetry breaking on the training data can help get rid of the difficulties and significantly improve the learning performance. We also extract and highlight the underlying mathematical principle of the proposed solution, which is directly applicable to other inverse problems.
Finite temperature and quench dynamics in the Transverse Field Ising Model from form factor expansions

We consider the problems of calculating the dynamical order parameter two-point function at finite temperatures and the one-point function after a quantum quench in the transverse field Ising chain. Both of these can be expressed in terms of form factor sums in the basis of physical excitations of the model. We develop a general framework for carrying out these sums based on a decomposition of form factors into partial fractions, which leads to a factorization of the multiple sums and permits them to be evaluated asymptotically. This naturally leads to systematic low density expansions. At late times these expansions can be summed to all orders by means of a determinant representation. Our method has a natural generalization to semi-local operators in interacting integrable models.
TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction.

Human motion prediction is an increasingly interesting topic in computer vision and robotics. In this paper, we propose a new 2D CNN based network, TrajectoryNet, to predict future poses in the trajectory space. Compared with most existing methods, our model focuses on modeling the motion dynamics with coupled spatio-temporal features, local-global spatial features and global temporal co-occurrence features of the previous pose sequence. Specifically, the coupled spatio-temporal features describe the spatial and temporal structure information hidden in the natural human motion sequence, which can be mined by covering the space and time dimensions of the input pose sequence with the convolutional filters. The local-global spatial features that encode different correlations of different joints of the human body (e.g. strong correlations between joints of one limb, weak correlations between joints of different limbs) are captured hierarchically by enlarging the receptive field layer by layer and residual connections from the lower layers to the deeper layers in our proposed convolutional network. And the global temporal co-occurrence features represent the co-occurrence relationship that different subsequences in a complex motion sequence are appeared simultaneously, which can be obtained automatically with our proposed TrajectoryNet by reorganizing the temporal information as the depth dimension of the input tensor. Finally, future poses are approximated based on the captured motion dynamics features. Extensive experiments show that our method achieves state-of-the-art performance on three challenging benchmarks (e.g. Human3.6M, G3D, and FNTU), which demonstrates the effectiveness of our proposed method. The code will be available if the paper is accepted.
An Inexact Manifold Augmented Lagrangian Method for Adaptive Sparse Canonical Correlation Analysis with Trace Lasso Regularization

Canonical correlation analysis (CCA for short) describes the relationship between two sets of variables by finding some linear combinations of these variables that maximizing the correlation coefficient. However, in high-dimensional settings where the number of variables exceeds sample size, or in the case of that the variables are highly correlated, the traditional CCA is no longer appropriate. In this paper, an adaptive sparse version of CCA (ASCCA for short) is proposed by using the trace Lasso regularization. The proposed ASCCA reduces the instability of the estimator when the covariates are highly correlated, and thus improves its interpretation. The ASCCA is further reformulated to an optimization problem on Riemannian manifolds, and an manifold inexact augmented Lagrangian method is then proposed for the resulting optimization problem. The performance of the ASCCA is compared with the other sparse CCA techniques in different simulation settings, which illustrates that the ASCCA is feasible and efficient.
Posterior contraction rates for non-parametric state and drift estimation

We consider a combined state and drift estimation problem for the linear stochastic heat equation. The infinite-dimensional Bayesian inference problem is formulated in terms of the Kalman-Bucy filter over an extended state space, and its long-time asymptotic properties are studied. Asymptotic posterior contraction rates in the unknown drift function are the main contribution of this paper. Such rates have been studied before for stationary non-parametric Bayesian inverse problems, and here we demonstrate the consistency of our time-dependent formulation with these previous results building upon scale separation and a slow manifold approximation.
Deep Reinforcement Learning with Weighted Q-Learning

Overestimation of the maximum action-value is a well-known problem that hinders Q-Learning performance, leading to suboptimal policies and unstable learning. Among several Q-Learning variants proposed to address this issue, Weighted Q-Learning (WQL) effectively reduces the bias and shows remarkable results in stochastic environments. WQL uses a weighted sum of the estimated action-values, where the weights correspond to the probability of each action-value being the maximum; however, the computation of these probabilities is only practical in the tabular settings. In this work, we provide the methodological advances to benefit from the WQL properties in Deep Reinforcement Learning (DRL), by using neural networks with Dropout Variational Inference as an effective approximation of deep Gaussian processes. In particular, we adopt the Concrete Dropout variant to obtain calibrated estimates of epistemic uncertainty in DRL. We show that model uncertainty in DRL can be useful not only for action selection, but also action evaluation. We analyze how the novel Weighted Deep Q-Learning algorithm reduces the bias w.r. t. relevant baselines and provide empirical evidence of its advantages on several representative benchmarks.
Swept-source optical coherence tomography by off-axis Fresnel transform digital holography with an output throughput of 10 Giga voxels per second in real-time

We demonstrate swept-source optical coherence tomography in real-time by high throughput digital Fresnel hologram rendering from optically-acquired interferograms with a high-speed camera. The interferogram stream is spatially rescaled with respect to wavelength to compensate for field-of-view dilation inherent to discrete Fresnel transformation. Holograms are calculated from an input stream of 16-bit, 1024-by-1024-pixel interferograms recorded at up to 512 frames per second with a digital camera. All calculations are performed by a NVIDIA TITAN Xp graphics card on single-precision floating-point complex-valued arrays (32-bit per quadrature). It allows sustained computation of 1024-by-1024-by-256-voxel volumes at 10 billion voxel/s, from which three perpendicular cuts are displayed in real-time at user-selected locations, up to 38 frames per second.
Wave optics gravitational lensing by effect of preferred reference frames.

We use wave optics feature to study image formation in gravitational lensing in presence of the preferred reference frame. Recently we showed a time like dynamical vector field which is coupled non-minimally with scalar tensor Brans-Dicke gravity has a modified Schwarzschild de Sitter metric solution in weak field limit. In fact physical effects of the preferred reference frames make an effective cosmological parameter. AdS regime (negative cosmological constant) of this metric solution is used to obtain lensing potential because with this condition the metric solution reaches to Minkowski form at an important local position. To do so we apply Fresnel-Kirchhoff diffraction theory of a massless scalar field instead of the vector electromagnetic waves in absence of the polarization property of the EM waves. This is done to obtain corrections on location of the images formed by a point like source in wave optics. Our main goal in this work is study effects of the effective negative cosmological parameter on the interference fringes of the diffracted waves, position of the formed images, distance between the formed images and their intensity. We get methodology proposed by Nambu et al to write this paper.
TF-Coder: Program Synthesis for Tensor Manipulations

The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch, which make it easier to develop deep learning models. However, these libraries also come with steep learning curves, since programming in these frameworks is quite different from traditional imperative programming with explicit loops and conditionals. In this work, we present a tool called TF-Coder for programming by example in TensorFlow. TF-Coder uses a bottom-up weighted enumerative search, with value-based pruning of equivalent expressions and flexible type- and value-based filtering to ensure that expressions adhere to various requirements imposed by the TensorFlow library. We train models to predict TensorFlow operations from features of the input and output tensors and natural language descriptions of tasks to prioritize relevant operations during search. TF-Coder solves 63 of 70 real-world tasks within 5 minutes, sometimes finding simpler solutions in less time compared to experienced human programmers.
Excitation Schemes of Uncorrelated Channels

A technique of designing antenna excitation realizing orthogonal free-space channels is presented. It is shown that a symmetric antenna geometry is required in order to achieve orthogonality with respect to all physical quantities. A maximal number of reachable uncorrelated channels and a minimal number of feeders required to excite them are rigorously determined from the knowledge of an antenna's symmetries. The number of channels and number of feeders are summarized for the commonly used point groups (a rectangle, a square, etc.). The theory is applied to an example of a rectangular rim where the positions of feeders providing the best total active reflection coefficient, an important metric in multi-port systems, are determined. The described technique can easily be implemented in existing integral solvers.
An Attribute-Aware Attentive GCN Model for Attribute Missing in Recommendation

As important side information, attributes have been widely exploited in the existing recommender system for better performance. However, in the real-world scenarios, it is common that some attributes of items/users are missing (e.g., some movies miss the genre data). Prior studies usually use a default value (i.e., “other”) to represent the missing attribute, resulting in sub-optimal performance. To address this problem, in this paper, we present an attribute-aware attentive graph convolution network (A<inline-formula><tex-math notation="LaTeX">${^2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="cheng-ieq1-3040772.gif"/></alternatives></inline-formula>-GCN). In particular, we first construct a graph, where users, items, and attributes are three types of nodes and their associations are edges. Thereafter, we leverage the graph convolution network to characterize the complicated interactions among <inline-formula><tex-math notation="LaTeX">$<$</tex-math><alternatives><mml:math><mml:mo><</mml:mo></mml:math><inline-graphic xlink:href="cheng-ieq2-3040772.gif"/></alternatives></inline-formula> users, items, attributes<inline-formula><tex-math notation="LaTeX">$>$</tex-math><alternatives><mml:math><mml:mo>></mml:mo></mml:math><inline-graphic xlink:href="cheng-ieq3-3040772.gif"/></alternatives></inline-formula>. Furthermore, to learn the node representation, we adopt the message-passing strategy to aggregate the messages passed from the other directly linked types of nodes (e.g., a user or an attribute). Towards this end, we are capable of incorporating associate attributes to strengthen the user and item representation learning, and thus naturally solve the attribute missing problem. Given that for different users, the attributes of an item have different influence on their preference to this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information. Extensive experiments have been conducted on several publicly accessible datasets to justify our model, demonstrating that our model outperforms several state-of-the-art methods and demonstrate the effectiveness of our attention method.
On the performance of different excitation-residual blocks for Acoustic Scene Classification

Acoustic Scene Classification (ASC) is a problem related to the field of machine listening whose objective is to classify/tag an audio clip in a predefined label describing a scene location. Interest in this topic has grown so much over the years that an annual international challenge (Dectection and Classification of Acoustic Scenes and Events) is held to propose novel solutions. Solutions to these problems often incorporate different methods such as data augmentation or with an ensemble of various models. Although the main line of research in the state-of-the-art usually implements these methods, considerable improvements and state-of-the-art results can also be achieved only by modifying the architecture of convolutional neural networks (CNNs). In this work we propose two novel squeeze-excitation blocks to improve the accuracy of an ASC framework by modifying the architecture of the residual block in a CNN together with an analysis of several state-of-the-art blocks. The main idea of squeeze-excitation blocks is to learn spatial and channel-wise feature maps independently instead of jointly as standard CNNs do. This is done by some global grouping operators, linear operators and a final calibration between the input of the block and the relationships obtained by that block. The behavior of the block that implements these operators and, therefore, the entire neural network can be modified depending on the input to the block, the residual configurations and the non-linear activations, that is, at what point of the block they are performed. The analysis has been carried out using TAU Urban Acoustic Scenes 2019 dataset presented in DCASE 2019 edition. All configurations discussed in this document exceed baseline proposed by DCASE organization by 13% percentage points. In turn, the novel configurations proposed in this paper exceed the residual configuration proposed in previous works.
On the value of the Immirzi parameter and the horizon entropy

In loop quantum gravity (LQG) the quantisation of general relativity leads to precise predictions for the eigenvalues of geometrical observables like volume and area, up to the value of the only free parameter of the theory, the Barbero–Immirzi (BI) parameter. With the help of the eigenvalues equation for the area operator, LQG successfully derives the Bekenstein–Hawking entropy of large black holes with isolated horizons, fixing at this limit the BI parameter as γ ≈ 0.274. In the present paper we show some evidence that a black hole with angular momentum ℏ and Planck mass is an eigenstate of the area operator provided that γ=3/6≈1.05×0.274 . As the black hole is extremal, there is no Hawking radiation and the horizon is isolated. We also suggest that such a black hole can be formed in the head-on scattering of two parallel standard model neutrinos in the mass state m 2 (assuming m 1 = 0). Furthermore, we use the obtained BI parameter to numerically compute the entropy of isolated horizons with areas ranging up to 250lP2 , by counting the number of micro-states associated to a given area. The resulting entropy has a leading term S≈0.25A , in agreement to the Bekenstein–Hawking entropy. As the identification of the above eigenstate rests on the matching between classical areas and quantum area eigenvalues, we also present, on the basis of an effective quantum model for the Schwarzschild black hole recently proposed by Ashtekar, Olmedo and Singh, an expression for the quantum corrected area of isolated horizons, valid for any black hole mass. Quantum corrections are shown to be negligible for a Planck mass black hole, of order 10−3 relative to the classical area.
SwarmSGD: Scalable Decentralized SGD with Local Updates.

The ability to scale distributed optimization to large node counts has been one of the main enablers of recent progress in machine learning. To this end, several techniques have been explored, such as asynchronous and decentralized execution--which significantly reduce the impact of communication and synchronization, and the ability for nodes to perform several local model updates before communicating--which reduces the frequency of communication. In this paper, we show that these techniques, which have so far been considered independently, can be jointly leveraged to obtain near-perfect scalability for training neural network models via stochastic gradient descent (SGD). We consider a setting with minimal coordination: we have a large number of nodes on a communication graph, each with a local subset of data, performing independent SGD updates onto their local models. After some number of local updates, each node chooses an interaction partner uniformly at random from its neighbors, and averages its local model with the neighbor's model. Our first contribution is in proving that, even under such a relaxed setting, SGD can still be guaranteed to converge to local minima under standard assumptions. The proof improves existing techniques by jointly handling decentralization, asynchrony, and local updates, and by bounding their impact. On the practical side, we instantiate this algorithm onto a supercomputing environment, and show that it can successfully converge and scale for large-scale image classification models, matching or even slightly improving the accuracy of the baseline parallel variants.
Action graphs for proactive robot assistance in smart environments

Smart environments can already observe the actions of a human through pervasive sensors. Based on these observations, our work aims to predict the actions a human is likely to perform next. Predictions can enable a robot to proactively assist humans by autonomously executing an action on their behalf. In this paper, Action Graphs are introduced to model the order constraints between actions. Action Graphs are derived from a problem defined in Planning Domain Definition Language (PDDL). When an action is observed, the node values are updated and next actions predicted. Subsequently, a robot executes one of the predicted actions if it does not impact the flow of the human by obstructing or delaying them. Our Action Graph approach is applied to a kitchen domain.
Practice of child spacing and its associated factors among women of child bearing age (15 to 49 years) in Illubabor zone, South West Ethiopia

Optimal birth spacing refers to resting period between pregnancies that allows the mother time to recuperate from pregnancy, labor and lactation. Long time period between births allows the next pregnancy and birth to be at full gestation and growth for years. This study aimed to assess practice of child spacing and its associated factors among women of child bearing age in Ilubabor Zone of Oromia National Regional State, South West Ethiopia, from January to June 2016. A community based cross sectional quantitative study design was used. A stratified multistage sampling procedure and face to face interview with Afan Oromo and Amharic version was done by administering questionnaire on 826 women of child bearing age. Women from rural areas were about 3 times more likely to have short birth interval than their urban counterparts [AOR =3.39 (95% CI: 1.13, 4.10)]. Respondents with no formal education were 2.56 times more likely to practice short birth interval than those with higher education [AOR = 2.56, 95% CI (1.60, 3.42)]. As compared to mothers whose husbands were employee, women whose husbands were farmers were more likely to have short birth interval [AOR = 3.50, 95% CI (1.29, 4.42)]. Mothers who breast fed their child for less than 12 months were 5 times more likely to practice short birth interval than those who breast fed for 24 months or more [AOR = 5.36, 95% CI ((3.43,6.34)]. Women who were not using contraceptives were 4.42 times more likely to give birth within short period of time than contraceptive users [AOR = 4.12 (95% CI: 2.71, 5.82)]. Women with fourth wealth quartile were 3.18 times more likely to have short birth interval than those with the lowest wealth quartile [AOR = 3.18(95% CI: 1.75, 4.56)]. Birth interval was short in this study. Therefore, greater attention should be given to contraceptive use and paternal education in addition to infant and child mortality prevention. Key words: Birth spacing, knowledge, contraceptive, women.