Miracle-dz/UniFAR-FTUs huggingface dataset:
SimVODIS: Simultaneous Visual Odometry, Object Detection, and Instance Segmentation

Intelligent agents need to understand the surrounding environment to provide meaningful services to or interact intelligently with humans. The agents should perceive geometric features as well as semantic entities inherent in the environment. Contemporary methods in general provide one type of information regarding the environment at a time, making it difficult to conduct high-level tasks. Moreover, running two types of methods and associating two resultant information requires a lot of computation and complicates the software architecture. To overcome these limitations, we propose a neural architecture that simultaneously performs both geometric and semantic tasks in a single thread: simultaneous visual odometry, object detection, and instance segmentation (SimVODIS). SimVODIS is built on top of Mask-RCNN which is trained in a supervised manner. Training the pose and depth branches of SimVODIS requires unlabeled video sequences and the photometric consistency between input image frames generates self-supervision signals. The performance of SimVODIS outperforms or matches the state-of-the-art performance in pose estimation, depth map prediction, object detection, and instance segmentation tasks while completing all the tasks in a single thread. We expect SimVODIS would enhance the autonomy of intelligent agents and let the agents provide effective services to humans.
Efficacy of Alpha-lipoic Acid in The Management of Diabetes Mellitus: A Systematic Review and Meta-analysis

Alpha-lipoic acid (ALA) is a naturally-occurring compound that has shown promising antioxidant and anti-inflammatory effects in experimental and human studies. The aim of this study was to assess the efficacy of ALA in the management of patients with diabetes mellitus (DM). We searched Medline (via PubMed), EBSCO, Scopus, and Web of Science for relevant randomized controlled trials. Data on glycated hemoglobin (HbA1c), blood glucose levels, lipid profile components, HOMA, and glutathione peroxidase (GPx) were extracted and pooled as the standardized mean difference (SMD) in a random effect model meta-analysis using RevMan version 5.3. Ten studies (n = 553 patients) were included. In the term of HBA1C, the overall SMD did not favor either of the two groups (SMD = 0.01, 95% CI [-0.32,0.35]; p = 0.94) in uncomplicated T2DM patients. Moreover, there was no statistically significant difference between the two groups in terms of FBG (SMD = -0.06, 95% CI [-0.44,0.33]; p = 0.78), PPBG (SMD = 0.04, 95% CI [-0.27,0.34]; p = 0.82), HDL (SMD = -0.05, 95% CI [-0.35,0.25]; p = 0.75), LDL (SMD = -0.05, 95% CI [-0.33,0.23]; p = 0.75). In terms of GPx, ALA was superior to placebo (SMD = 0.43, 95% CI [0.07,0.8]; p = 0.02). Our analysis showed that ALA was not superior to placebo in terms of HBA1C, LDL, HDL, TC, TG reduction in uncomplicated T2DM. However, in terms of GPx, ALA was significantly superior to the placebo. Further studies with larger sample sizes should investigate different doses of ALA in DM patients.
Programmable view update strategies on relations

View update is an important mechanism that allows updates on a view by translating them into the corresponding updates on the base relations. The existing literature has shown the ambiguity of translating view updates. To address this ambiguity, we propose a robust language-based approach for making view update strategies programmable and validatable. Specifically, we introduce a novel approach to use Datalog to describe these update strategies. We propose a validation algorithm to check the well-behavedness of the written Datalog programs. We present a fragment of the Datalog language for which our validation is both sound and complete. This fragment not only has good properties in theory but is also useful for solving practical view updates. Furthermore, we develop an algorithm for optimizing user-written programs to efficiently implement updatable views in relational database management systems. We have implemented our proposed approach. The experimental results show that our framework is feasible and efficient in practice.
The MUSE Atlas of Discs (MAD): Ionized gas kinematic maps and an application to diffuse ionized gas

We have obtained data for 41 star forming galaxies in the MUSE Atlas of Discs (MAD) survey with VLT/MUSE. These data allow us, at high resolution of a few 100 pc, to extract ionized gas kinematics (V, σ) of the centres of nearby star forming galaxies spanning 3 dex in stellar mass. This paper outlines the methodology for measuring the ionized gas kinematics, which we will use in subsequent papers of this survey. We also show how the maps can be used to study the kinematics of diffuse ionized gas for galaxies of various inclinations and masses. Using two different methods to identify the diffuse ionized gas, we measure rotation velocities of this gas for a subsample of six galaxies. We find that the diffuse ionized gas rotates on average slower than the star forming gas with lags of 0–10 km s−1 while also having higher velocity dispersion. The magnitude of these lags is on average 5 km s−1 lower than observed velocity lags between ionized and molecular gas. Using Jeans models to interpret the lags in rotation velocity and the increase in velocity dispersion we show that most of the diffuse ionized gas kinematics are consistent with its emission originating from a somewhat thicker layer than the star forming gas, with a scale height that is lower than that of the stellar disc.
Structured Mean-field Variational Inference and Learning in Winner-take-all Spiking Neural Networks

The Bayesian view of the brain hypothesizes that the brain constructs a generative model of the world, and uses it to make inferences via Bayes' rule. Although many types of approximate inference schemes have been proposed for hierarchical Bayesian models of the brain, the questions of how these distinct inference procedures can be realized by hierarchical networks of spiking neurons remains largely unresolved. Based on a previously proposed multi-compartment neuron model in which dendrites perform logarithmic compression, and stochastic spiking winner-take-all (WTA) circuits in which firing probability of each neuron is normalized by activities of other neurons, here we construct Spiking Neural Networks that perform \emph{structured} mean-field variational inference and learning, on hierarchical directed probabilistic graphical models with discrete random variables. In these models, we do away with symmetric synaptic weights previously assumed for \emph{unstructured} mean-field variational inference by learning both the feedback and feedforward weights separately. The resulting online learning rules take the form of an error-modulated local Spike-Timing-Dependent Plasticity rule. Importantly, we consider two types of WTA circuits in which only one neuron is allowed to fire at a time (hard WTA) or neurons can fire independently (soft WTA), which makes neurons in these circuits operate in regimes of temporal and rate coding respectively. We show how the hard WTA circuits can be used to perform Gibbs sampling whereas the soft WTA circuits can be used to implement a message passing algorithm that computes the marginals approximately. Notably, a simple change in the amount of lateral inhibition realizes switching between the hard and soft WTA spiking regimes. Hence the proposed network provides a unified view of the two previously disparate modes of inference and coding by spiking neurons.
Scalable Exact Inference in Multi-Output Gaussian Processes

Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling. The key problem with MOGPs is their computational scaling $O(n^3 p^3)$, which is cubic in the number of both inputs $n$ (e.g., time points or locations) and outputs $p$. For this reason, a popular class of MOGPs assumes that the data live around a low-dimensional linear subspace, reducing the complexity to $O(n^3 m^3)$. However, this cost is still cubic in the dimensionality of the subspace $m$, which is still prohibitively expensive for many applications. We propose the use of a sufficient statistic of the data to accelerate inference and learning in MOGPs with orthogonal bases. The method achieves linear scaling in $m$ in practice, allowing these models to scale to large $m$ without sacrificing significant expressivity or requiring approximation. This advance opens up a wide range of real-world tasks and can be combined with existing GP approximations in a plug-and-play way. We demonstrate the efficacy of the method on various synthetic and real-world data sets.
Deep Encoder-Decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-View Data

X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dose has been an important topic in recent years. Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper, we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT image reconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in 3D instead of 2D geometry, a 3D deep network has a great potential for improving the image quality in a data driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing 3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publicly available abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other 2D deep learning methods, the proposed DEAR-3D network can utilize 3D information to produce promising reconstruction results.
Interactive Attention for Semantic Text Matching

Semantic text matching, which matches a target text to a source text, is a general problem in many domains like information retrieval, question answering, and recommendation. There are several challenges for this problem, such as semantic gaps between words, implicit matching, and mismatch due to out-of-vocabulary or low-frequency words, etc. Most existing studies made great efforts to overcome these challenges by learning good representations for different text pieces or operating on global matching signals to get the matching score. However, they did not learn the local fine-grained interactive information for a specific source and target pair. In this paper, we propose a novel interactive attention model for semantic text matching, which learns new representations for source and target texts through interactive attention via global matching matrix and updates local fine-grained relevance between source and target. Our model could enrich the representations of source and target objects by adopting global relevance and learned local fine-grained relevance. The enriched representations of source and target encode global relevance and local relevance of each other, therefore, could empower the semantic match of texts. We conduct empirical evaluations of our model with three applications including biomedical literature retrieval, tweet and news linking, and factoid question answering. Experimental results on three data sets demonstrate that our model significantly outperforms competitive baseline methods.
Statistical double $\Lambda$ hypernuclear formation from $\Xi^-$ absorption at rest in light nuclei

We investigate double $\Lambda$ hyperfragment formation from the statistical decay of double $\Lambda$ compound nuclei produced in the $\Xi^-$ absorption at rest in light nuclei, $^{12}\mathrm{C}$, $^{14}\mathrm{N}$ and $^{16}\mathrm{O}$. We examine the target and the $\Lambda\Lambda$ bond energy dependence of the double $\Lambda$ hyperfragment formation probabilities, especially of those double hypernuclei observed in experiments. For the $^{12}\mathrm{C}$ ($^{14}\mathrm{N}$) target, the formation probabilities of $^{6}_{\Lambda\Lambda}\mathrm{He}$ and $^{10}_{\Lambda\Lambda}\mathrm{Be}$ ($^{13}_{\Lambda\Lambda}\mathrm{B}$) are found to be reasonably large as they are observed in the KEK-E373 (KEK-E176) experiment. By comparison, for $^{16}\mathrm{O}$ target, the formation probability of $^{11}_{\Lambda\Lambda}\mathrm{Be}$ is calculated to be small with $\Delta B_{\Lambda\Lambda}$ consistent with the Nagara event. We also evaluate the formation probability of ${}^{5}_{\Lambda\Lambda}\mathrm{H}$ from a $\Xi^-$-${}^{6}\mathrm{He}$ bound state, ${}^{7}_{\Xi}\mathrm{H}$.
Gene gun-delivered Human Basic Fibroblast Growth Factor Gene Facilitates the Healing of Deep Partial Thickness Burn Wounds

BACKGROUND: A large amount of in vivo and in vitro experiments have confirmed that, basic fibroblast growth factor (bFGF) has been widely utilized in various tissues and cells, it can facilitate the wound healing. OBJECTIVE: To observe the efficacy and feasibility of gene gun-mediate delivery of human bFGF on the healing of deep partial thickness burn wounds. DESIGN, TIME AND SETTING: Randomized design, an observational trial was performed at the Military Central Laboratory of Changhai Hospital in the Second Military Medical University of Chinese PLA between December 2007 and October 2008. MATERIALS: SD rats of clean grade, weighing 200-250 g, irrespective of genders, were involved in this study. METHODS: Natural human bFGF gene was recombined and optimized, then eukaryotic expression vector pCI-neo-bFGF was constructed taking pCI-neo as a vector, and transfected with human embryonic kidney cells 293 T cells. Dot blot and Western blot methods were utilized to determine the bFGF expression. Rat model of deep partial thickness burn wounds was processed into transgene process using gene gun technique, pCI-neo-bFGF-transfected ones served as experiment group while pCI-neo-transfected ones served as controls. MAIN OUTCOME MEASURES: Wound healing time was recorded and the efficacy was evaluated. The contents of hydroxyproline and collagenase Ⅰ in burn wound tissues were determined at 24 hours, 48 hours, 96 hours, 7 days, 10 days and 14 days following transgene process. RESULTS: the recombinant pCI-neo-bFGF was transfected with human embryonic kidney 293T cells. Dot blot and Western blot analysis have showed that, the constructed pCI-neo-bFGF expression vector could express human bFGF, and the expression of synthesized gene was remarkably higher than that of natural gene under fluorescence microscope; gene gun-mediated transgene experiment have showed that, the wound healing time was (13.00±1.31) days in the experiment group and (14.75±1.28) days in the control group, with significant differences (P＜0.05). The contents of hydroxyproline and collagenase Ⅰ reached a peak at 5 days after the injury, that is 48 hours after transfection, and then gradually decreased and maintained at a certain level. The experiment group had higher hydroxyproline levels compared with control group at different time points (P＜0.05, P＜0.01); the collagenase Ⅰ in the experiment group was notably higher than that in the control group at 48 hours and 96 hours after transfection (P＜0.01). CONCLUSION: Gene gun-mediated delivery of human bFGF can short the time of wound healing, increase the contents of hydroxyproline and collagenase Ⅰ during the healing period, accelerate the healing of deep partial thickness burn wounds.
An Application of Multiple-Instance Learning to Estimate Generalization Risk

We focus on several learning approaches that employ max-operator to evaluate the margin. For example, such approaches are commonly used in multi-class learning task and top-rank learning task. In general, in order to estimate the theoretical generalization risk, we need to individually evaluate the complexity of each hypothesis class used in the learning approaches. In this paper, we provide a technique to estimate a theoretical generalization risk for such learning approaches in a same fashion. The key idea is to "redundantly" reformulate the learning problem as one-class multiple-instance learning by redefining the specific input space based on the original input space. Surprisingly, we succeed to improve the generalization risk bounds for some multi-class learning and top-rank learning algorithms.
Flat Field Forensics

We present two subtle charge transport problems revealed by the statistics of flat fields. Mark Downing has presented photon transfer curves showing variance dips of order 25% at signal levels around 80% of blooming. These dips appear when substrate voltage is raised above zero, for - 0V to 8V parallel clock swing. We present a modified parallel transfer sequence that eliminates the dip, based on the hypothesis that it is caused by charge spillage from last line to the 2nd last line. We discuss an experiment to test whether the electrode map is incorrectly reported in the data sheet. A more subtle dip in the variance occurs at signals around 6000 e-. This is eliminated by increasing serial clock high by a few volts, suggesting the existence of a small structural trap at the parallel-serial interface. Tails above blooming stars are suppressed using an inverted clocking during readout and a positive clocking during exposure to maintain sharpness of the PTC. We show that integrating under three parallel phases, instead of the two recommended, reduces pixel area variations from 0.39% to 0.28%, while also eliminating striations observed along central columns in pixel area maps. We show that systematic line and column width errors at stitching boundaries (~15 nm) are now an order of magnitude less than the random pixel area variations.
A Massive Collection of Cross-Lingual Web-Document Pairs

Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. Small-scale efforts have been made to collect aligned document level data on a limited set of language-pairs such as English-German or on limited comparable collections such as Wikipedia. In this paper, we mine twelve snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of 54 million URL pairs from Common Crawl covering documents in 92 languages paired with English. We evaluate the quality of the dataset by measuring the quality of machine translations from models that have been trained on mined parallel sentence pairs from this aligned corpora and introduce a simple yet effective baseline for identifying these aligned documents. The objective of this dataset and paper is to foster new research in cross-lingual NLP across a variety of low, mid, and high-resource languages.
A distributive law for compact symmetric multicategories

Compact symmetric multicategories (CSMs) generalise a number of operad-like devices, such as wheeled properads and modular operads, that admit a contraction operation as well as an operadic multiplication. They were introduced in 2011 by Joyal and Kock, who constructed them as the algebras for an endofunctor on a certain presheaf category. CSMs are known to exhibit strange behaviour, related to the contraction of units, that presents some difficulty for describing their combinatorics, and hence for proving a nerve theorem. Here, non-unital CSMs are constructed as algebras for a monad defined in terms of connected graphs, and this provides the foundation for a new construction of CSMs based on a distributive law. This enables a proof, in the style of Joyal and Kock, of a corresponding nerve theorem characterising CSMs in terms of a Segal condition.
Tight Lower Bound on the Tensor Rank based on the Maximally Square Unfolding

—Tensors decompositions are a class of tools for analysing datasets of high dimensionality and variety in a natural manner, with the Canonical Polyadic Decomposition (CPD) being a main pillar. While the notion of CPD is closely intertwined with that of the tensor rank, R , unlike the matrix rank, the computation of the tensor rank is an NP-hard problem, owing to the associated computational burden of evaluating the CPD. To address this issue, we investigate tight lower bounds on R with the aim to provide a reduced search space, and hence to lessen the computational costs of the CPD evaluation. This is achieved by establishing a link between the maximum attainable lower bound on R and the dimensions of the matrix unfolding of the tensor with aspect ratio closest to unity (maximally square). Moreover, we demonstrate that, for a generic tensor, such lower bound can be attained under very mild conditions, whereby the tensor rank becomes detectable. Numerical examples demonstrate the beneﬁts of this result.
Quantum percolation of monopole paths and the response of quantum spin ice

We consider quantum spin ice in a temperature regime in which its response is dominated by the coherent motion of a dilute gas of monopoles. The hopping amplitude of a monopole is sensitive to the configuration of its surrounding spins, taken to be quasi-static on the relevant timescales. This leads to well-known blocked directions in the monopole motion; we find that these are sufficient to reduce the coherent propagation of monopoles to quantum diffusion. This result is robust against disorder, as a direct consequence of the ground-state degeneracy, which disrupts the quantum interference processes needed for weak localization. Moreover, recent work [Tomasello et al., Phys. Rev. Lett. 123, 067204 (2019)] has shown that the monopole hopping amplitudes are roughly bimodal: for $\approx 1/3$ of the flippable spins surrounding a monopole, these amplitudes are extremely small. We exploit this structure to construct a theory of quantum monopole motion in spin ice. In the limit where the slow hopping terms are set to zero, the monopole wavefunctions appear to be fractal; we explain this observation via a mapping to quantum percolation on trees. The fractal, non-ergodic nature of monopole wavefunctions manifests itself in the low-frequency behavior of monopole spectral functions, and is consistent with experimental observations.
Gestion du risque infectieux associé aux soins et services au Centre National Hospitalier et Universitaire Hubert Koutoukou Maga de Cotonou (Bénin)

La qualite des soins est une priorite nationale et les infections associees aux soins un defi pour les acteurs hospitaliers. Toute intervention visant la reduction d’un probleme passe par un etat des lieux. L’objectif de cette etude etait d’evaluer la qualite de la gestion du risque infectieux associe aux soins au Centre National Hospitalier et Universitaire de Cotonou en vue de proposer des mesures pour son amelioration. Une etude transversale retrospective et evaluative sur 193 patients a ete realisee dans cinq (5) services dudit centre. Le choix etait raisonne base sur les services a risque eleve des infections associees aux soins. La frequence des patients infectes etait de 9,84%. Les sites de localisation observes etaient dans l’ordre decroissant, les tissus mous, le site urinaire, le sang et le site operatoire. Quinze (15) bacteries ont ete denombrees comme etant responsables des infections dont huit (8) multiresistantes aux antibiotiques usuels. Les principaux germes identifies etaient Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, Enterobacter cloacae et Acinetobacter spp . Le risque d’infections associees aux soins reste une preoccupation majeure avec une frequence importante de bacteries multi resistantes aux antibiotiques usuels. Mots cles : Infections associees aux soins, frequence, bacteries multi resistantes, antibiotique, Cotonou.