Miracle-dz/UniFAR-FTUs huggingface dataset:
Breastfeeding pattern and nutritional status of children under two years in Oshogbo Local Government Area Osun State Nigeria

Background : This study aims to assess breastfeeding pattern among mothers and nutritional status of children under two years. Methodology : The cross sectional study was carried out in Oshogbo Local Government Area, Osun State. A total of 350 nursing mothers and their children undertwo years were randomly selected systematically. A structured questionnaire was used to obtain information on the bio-data and socioeconomic characteristics of the mothers. Breast feeding knowledge and pattern was assessed and anthropometric measurements taken. Data was subjected to descriptive and inferential statistics using SPSS version 20. Results : One-third(32.6%) of the mothers were between 26-30years of age and 40% of the children were between 7-12 months old. Only 2.6% of the mothers earned >$400 per month. Most mothers (85.7%) had adequate knowledge about optimal breastfeeding, 72% initiated breastfeeding more than 1hr after birth and about 72% gave prelacteal meal. About 16.4% breastfed exclusively. Only 16.6% express breast-milk and the median duration for continued breastfeeding in this study was 18months. Complementary foods given to the children are mostly home prepared cereals, solids and semi-solid foods. About 30.4% of the children were stunted, 25.4% were underweight and 15.3% were wasted. However 4.3% mothers were underweight, 29.1% were overweight and 2.6% were obese. Maternal age, family structure, parity and monthly income were significantly related to their knowledge of and the pattern of breastfeeding. Conclusion : The practice of exclusive breastfeeding and use of expressed breast milk is poor among the mothers. Stunting was also high among the children. Key words : Breastfeeding, Undertwo children, Nutritional Status, Prelacteal foods, Mothers
Deep Image Spatial Transformation for Person Image Generation

Pose-guided person image generation is to transform a source person image to a target pose. This task requires spatial manipulations of source data. However, Convolutional Neural Networks are limited by the lack of ability to spatially transform the inputs. In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. Specifically, our model first calculates the global correlations between sources and targets to predict flow fields. Then, the flowed local patch pairs are extracted from the feature maps to calculate the local attention coefficients. Finally, we warp the source features using a content-aware sampling method with the obtained local attention coefficients. The results of both subjective and objective experiments demonstrate the superiority of our model. Besides, additional results in video animation and view synthesis show that our model is applicable to other tasks requiring spatial transformation. Our source code is available at https://github.com/RenYurui/Global-Flow-Local-Attention.
Fine-Grained Video-Text Retrieval With Hierarchical Graph Reasoning

Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. The current dominant approach is to learn a joint embedding space to measure cross-modal similarities. However, simple embeddings are insufficient to represent complicated visual and textual details, such as scenes, objects, actions and their compositions. To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels. The model disentangles text into a hierarchical semantic graph including three levels of events, actions, entities, and generates hierarchical textual embeddings via attention-based graph reasoning. Different levels of texts can guide the learning of diverse and hierarchical video representations for cross-modal matching to capture both global and local details. Experimental results on three video-text datasets demonstrate the advantages of our model. Such hierarchical decomposition also enables better generalization across datasets and improves the ability to distinguish fine-grained semantic differences. Code will be released at \url{https://github.com/cshizhe/hgr_v2t}.
Image Hashing by Minimizing Independent Relaxed Wasserstein Distance

Image hashing is a fundamental problem in the computer vision domain with various challenges, primarily, in terms of efficiency and effectiveness. Existing hashing methods lack a principled characterization of the goodness of the hash codes and a principled approach to learn the discrete hash functions that are being optimized in the continuous space. Adversarial autoencoders are shown to be able to implicitly learn a robust hash function that generates hash codes which are balanced and have low-quantization error. However, the existing adversarial autoencoders for hashing are too inefficient to be employed for large-scale image retrieval applications because of the minmax optimization procedure. In this paper, we propose an Independent Relaxed Wasserstein Autoencoder, which presents a novel, efficient hashing method that can implicitly learn the optimal hash function by directly training the adversarial autoencoder without any discriminator/critic. Our method is an order-of-magnitude more efficient and has a much lower sample complexity than the Optimal Transport formulation of the Wasserstein distance. The proposed method outperforms the current state-of-the-art image hashing methods for the retrieval task on several prominent image collections.
AccQOC: Accelerating Quantum Optimal Control Based Pulse Generation

In the last decades, we have witnessed the rapid growth of Quantum Computing. In the current Noisy Intermediate-Scale Quantum (NISQ) era, the capability of a quantum machine is limited by the decoherence time, gate fidelity and the number of Qubits. Current quantum computing applications are far from the real “quantum supremacy” due to the fragile physical Qubits, which can only be entangled for a few microseconds. Recent works use quantum optimal control to reduce the latency of quantum circuits, thereby effectively increasing quantum volume. However, the key challenge of this technique is the large overhead due to long compilation time. In this paper, we propose AccQOC, a comprehensive static/dynamic hybrid workflow to transform gate groups (equivalent to matrices) to pulses using QOC (Quantum Optimal Control) with a reasonable compilation time budget. AccQOC is composed of static pre-compilation and accelerated dynamic compilation. After the quantum program is mapped to the quantum circuit with our heuristic mapping algorithm considering crosstalk, we leverage static pre-compilation to generate pulses for the frequently used groups to eliminate the dynamic compilation time for them. The pulse is generated using QOC with binary search to determine the latency. For a new program, we use the same policy to generate groups, thus avoid incurring overhead for the “covered” groups. The dynamic compilation deals with “un-covered” groups with accelerated pulse generation. The key insight is that the pulse of a group can be generated faster based on the generated pulse of a similar group. We propose to reduce the compilation time by generating an ordered sequence of groups in which the sum of similarity among consecutive groups in the sequence is minimized. We can find the sequence by constructing a similarity graph – a complete graph in which each vertex is a gate group and the weight of an edge is the similarity between the two groups it connects, then construct a Minimum Spanning Tree (MST) for SG. With the methodology of AccQOC, we reached a balanced point of compilation time and overall latency. The results show that accelerated compilation based on MST achieves $9.88\times$ compilation speedup compared to the standard compilation of each group while maintaining an average $2.43\times$ latency reduction compared with gate-based compilation.
Distributed Leader-Follower Formation Tracking Control of Multiple Quad-rotors

The leader-follower formation control analysis for multiple quad-rotor systems is investigated in this paper. To achieve predefined formation in the three-dimensional air space ($x,y$ and $z$), a novel local tracking control law and a distributed observer are obtained. The local tracking control law starts with finding a bounded continuous yet greater-than-zero control in $z$, based on which following a feedback linearization controls derived for errors associated with $x$ and $y$. The distributed observer achieves position coordination among followers, though there are only partial followers can know the leader's states and only neighboring communication is available. Simulation results validate the proposed formation scheme.
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells

Unsupervised text encoding models have recently fueled substantial progress in Natural Language Processing (NLP). The key idea is to use neural networks to convert words in texts to vector space representations (embeddings) based on word positions in a sentence and their contexts. We see a strikingly similar situation in spatial analysis, which focuses on incorporating both absolute positions and spatial contexts of geographic objects such as Points of Interest (POIs) into models. A general space encoding method is valuable for a multitude of tasks such asPOI search, land use classification, point-based spatial interpolation and locationaware image classification. However, no such general model exists to date beyond simply applying discretizing or feed forward nets to coordinates, and little effort has been put into jointly modeling distributions with vastly different characteristics, which commonly emerges from GIS data. Meanwhile, Nobel Prize-winning Neuroscience research shows that grid cells in mammals provide a multi-scale periodic representation that functions as a metric for encoding space and are critical for recognizing places and for path-integration. Inspired by this research, wepropose a representation learning model called Space2vec to encode the absolutepositions and spatial relationships of places. We conduct experiments on realworld geographic data and predict types of POIs at given positions based on their1) locations and 2) nearby POIs. Results show that because of its multi-scale representations Space2vec outperforms well-established ML approaches such as RBF kernels, multi-layer feed forward nets, and tile embedding approaches.
Permutohedral-GCN: Graph Convolutional Networks with Global Attention

Graph convolutional networks (GCNs) update a node's feature vector by aggregating features from its neighbors in the graph. This ignores potentially useful contributions from distant nodes. Identifying such useful distant contributions is challenging due to scalability issues (too many nodes can potentially contribute) and oversmoothing (aggregating features from too many nodes risks swamping out relevant information and may result in nodes having different labels but indistinguishable features). We introduce a global attention mechanism where a node can selectively attend to, and aggregate features from, any other node in the graph. The attention coefficients depend on the Euclidean distance between learnable node embeddings, and we show that the resulting attention-based global aggregation scheme is analogous to high-dimensional Gaussian filtering. This makes it possible to use efficient approximate Gaussian filtering techniques to implement our attention-based global aggregation scheme. By employing an approximate filtering method based on the permutohedral lattice, the time complexity of our proposed global aggregation scheme only grows linearly with the number of nodes. The resulting GCNs, which we term permutohedral-GCNs, are differentiable and trained end-to-end, and they achieve state of the art performance on several node classification benchmarks.
The white dwarf planet WD J0914+1914 b: barricading potential rocky pollutants?

An ice giant planet was recently reported orbiting white dwarf WD J0914+1914 at an approximate distance of 0.07 au. The striking non-detection of rocky pollutants in this white dwarf’s photosphere contrasts with the observations of nearly every other known white dwarf planetary system. Here, I analyse the prospects for exterior extant rocky asteroids, boulders, cobbles, and pebbles to radiatively drift inward past the planet due to the relatively high luminosity ($0.1 \, \mathrm{L}_{\odot }$) of this particularly young (13 Myr) white dwarf. Pebbles and cobbles drift too slowly from Poynting–Robertson drag to bypass the planet, but boulders and asteroids are subject to the much stronger Yarkovsky effect. In this paper, I (i) place lower limits on the time-scales for these objects to reach the planet’s orbit, (ii) establish 3 m as the approximate limiting radius above which a boulder drifts too slowly to avoid colliding with the planet, and (iii) compute bounds on the fraction of boulders that succeed in traversing mean motion resonances and the planet’s Hill sphere to eventually pollute the star. Overall, I find that the planet acts as a barrier against rather than a facilitator for radiatively driven rocky pollution, suggesting that future rocky pollutants would most likely originate from distant scattering events.
Observation and analysis of multiple dark-antidark solitons in two-component Bose-Einstein condensates

We report on the static and dynamical properties of multiple dark-antidark solitons (DADs) in two-component, repulsively interacting Bose-Einstein condensates. Motivated by experimental observations involving multiple DADs, we present a theoretical study which showcases that bound states consisting of dark (antidark) solitons in the first (second) component of the mixture exist for different values of interspecies interactions. It is found that ensembles of few DADs may exist as stable configurations, while for larger DAD arrays, the relevant windows of stability with respect to the interspecies interaction strength become progressively narrower. Moreover, the dynamical formation of states consisting of alternating DADs in the two components of the mixture is monitored. A complex dynamical evolution of these states is observed, leading either to sorted DADs or to beating dark-dark solitons depending on the strength of the interspecies coupling. This study demonstrates clear avenues for future investigations of DAD configurations.
Cops-Ref: A New Dataset and Task on Compositional Referring Expression Comprehension

Referring expression comprehension (REF) aims at identifying a particular object in a scene by a natural language expression. It requires joint reasoning over the textual and visual domains to solve the problem. Some popular referring expression datasets, however, fail to provide an ideal test bed for evaluating the reasoning ability of the models, mainly because 1) their expressions typically describe only some simple distinctive properties of the object and 2) their images contain limited distracting information. To bridge the gap, we propose a new dataset for visual reasoning in context of referring expression comprehension with two main features. First, we design a novel expression engine rendering various reasoning logics that can be flexibly combined with rich visual properties to generate expressions with varying compositionality. Second, to better exploit the full reasoning chain embodied in an expression, we propose a new test setting by adding additional distracting images containing objects sharing similar properties with the referent, thus minimising the success rate of reasoning-free cross-domain alignment. We evaluate several state-of-the-art REF models, but find none of them can achieve promising performance. A proposed modular hard mining strategy performs the best but still leaves substantial room for improvement.
A Multi-view Perspective of Self-supervised Learning

As a newly emerging unsupervised learning paradigm, self-supervised learning (SSL) recently gained widespread attention, which usually introduces a pretext task without manual annotation of data. With its help, SSL effectively learns the feature representation beneficial for downstream tasks. Thus the pretext task plays a key role. However, the study of its design, especially its essence currently is still open. In this paper, we borrow a multi-view perspective to decouple a class of popular pretext tasks into a combination of view data augmentation (VDA) and view label classification (VLC), where we attempt to explore the essence of such pretext task while providing some insights into its design. Specifically, a simple multi-view learning framework is specially designed (SSL-MV), which assists the feature learning of downstream tasks (original view) through the same tasks on the augmented views. SSL-MV focuses on VDA while abandons VLC, empirically uncovering that it is VDA rather than generally considered VLC that dominates the performance of such SSL. Additionally, thanks to replacing VLC with VDA tasks, SSL-MV also enables an integrated inference combining the predictions from the augmented views, further improving the performance. Experiments on several benchmark datasets demonstrate its advantages.
Signatures of the Higgs mode in transport through a normal-metal–superconductor junction

A superconductor subject to electromagnetic irradiation in the terahertz range can show amplitude oscillations of its order parameter. However, coupling this so-called Higgs mode to the charge current is notoriously difficult. We propose to achieve such a coupling in a particle-hole-asymmetric configuration using a DC-voltage-biased normal-metal--superconductor tunnel junction. Using the quasiclassical Green's function formalism, we demonstrate three characteristic signatures of the Higgs mode: (i) The AC charge current exhibits a pronounced resonant behavior and is maximal when the radiation frequency coincides with the order parameter. (ii) The AC charge current amplitude exhibits a characteristic nonmonotonic behavior with increasing voltage bias. (iii) At resonance for large voltage bias, the AC current vanishes inversely proportional to the bias. These signatures provide an electric detection scheme for the Higgs mode.
Cross-Spectrum Dual-Subspace Pairing for RGB-infrared Cross-Modality Person Re-Identification

Due to its potential wide applications in video surveillance and other computer vision tasks like tracking, person re-identification (ReID) has become popular and been widely investigated. However, conventional person re-identification can only handle RGB color images, which will fail at dark conditions. Thus RGB-infrared ReID (also known as Infrared-Visible ReID or Visible-Thermal ReID) is proposed. Apart from appearance discrepancy in traditional ReID caused by illumination, pose variations and viewpoint changes, modality discrepancy produced by cameras of the different spectrum also exists, which makes RGB-infrared ReID more difficult. To address this problem, we focus on extracting the shared cross-spectrum features of different modalities. In this paper, a novel multi-spectrum image generation method is proposed and the generated samples are utilized to help the network to find discriminative information for re-identifying the same person across modalities. Another challenge of RGB-infrared ReID is that the intra-person (images from the same person) discrepancy is often larger than the inter-person (images from different persons) discrepancy, so a dual-subspace pairing strategy is proposed to alleviate this problem. Combining those two parts together, we also design a one-stream neural network combining the aforementioned methods to extract compact representations of person images, called Cross-spectrum Dual-subspace Pairing (CDP) model. Furthermore, during the training process, we also propose a Dynamic Hard Spectrum Mining method to automatically mine more hard samples from hard spectrum based on the current model state to further boost the performance. Extensive experimental results on two public datasets, SYSU-MM01 with RGB + near-infrared images and RegDB with RGB + far-infrared images, have demonstrated the efficiency and generality of our proposed method.
Riemann solitons and almost Riemann solitons on almost Kenmotsu manifolds

The aim of this paper, is to study the Riemann soliton and gradient almost Riemann soliton on certain class of almost Kenmotsu manifolds. Also, some suitable examples of Kenmotsu and [Formula: see text]-almost Kenmotsu manifolds are constructed to justify our results.
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise

The choice of batch-size in a stochastic optimization algorithm plays a substantial role for both optimization and generalization. Increasing the batch-size used typically improves optimization but degrades generalization. To address the problem of improving generalization while maintaining optimal convergence in large-batch training, we propose to add covariance noise to the gradients. We demonstrate that the learning performance of our method is more accurately captured by the structure of the covariance matrix of the noise rather than by the variance of gradients. Moreover, over the convex-quadratic, we prove in theory that it can be characterized by the Frobenius norm of the noise matrix. Our empirical studies with standard deep learning model-architectures and datasets shows that our method not only improves generalization performance in large-batch training, but furthermore, does so in a way where the optimization performance remains desirable and the training duration is not elongated.
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry

We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. In particular, it aligns the training image pairs into similar lighting condition with predictive brightness transformation parameters. Besides, we model the photometric uncertainties of pixels on the input images, which improves the depth estimation accuracy and provides a learned weighting function for the photometric residuals in direct (feature-less) visual odometry. Evaluation results show that the proposed network outperforms state-of-the-art self-supervised depth estimation networks. D3VO tightly incorporates the predicted depth, pose and uncertainty into a direct visual odometry method to boost both the front-end tracking as well as the back-end non-linear optimization. We evaluate D3VO in terms of monocular visual odometry on both the KITTI odometry benchmark and the EuRoC MAV dataset. The results show that D3VO outperforms state-of-the-art traditional monocular VO methods by a large margin. It also achieves comparable results to state-of-the-art stereo/LiDAR odometry on KITTI and to the state-of-the-art visual-inertial odometry on EuRoC MAV, while using only a single camera.
Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

As a biometric modality, palmprints have been largely under-utilized, but they offer some advantages over fingerprints and facial biometrics. Recent improvements in imaging capabilities on handheld and wearable consumer devices have re-awakened interest in the use of palmprints. The aim of this paper is to provide a comprehensive review of state-of-the-art methods for palmprint recognition including Region of Interest extraction methods, feature extraction approaches and matching algorithms along with an overview of available palmprint datasets in order to understand the latest trends and research dynamics in the palmprint recognition field.