Miracle-dz/UniFAR-FTUs huggingface dataset:
A Self-Supervised Framework for Learning Biological Entities Representation by Fusing Class Information

Ontologies are widely utilized in the biological domain for data annotation, integration, and analysis. Some representation learning methods have been proposed to learn the representation of entities to assist intelligent applications, such as knowledge discovery. However, most of them neglect the class information of entities in the ontology. In this article, we propose a unified framework, named ERCI, which jointly optimizes the knowledge graph embedding model and self-supervised learning. In this way, we can generate embeddings of bio-entities by fusing the class information. Moreover, ERCI is a pluggable framework that can be easily incorporated with any knowledge graph embedding model. We validate ERCI in two different ways. In the first way, we utilize the protein embeddings learned by the ERCI to predict protein-protein interactions on two different datasets. In the second way, we leverage the gene and disease embeddings generated by the ERCI to predict gene-disease associations. In addition, we create three datasets to simulate the long-tail scenario and evaluate ERCI on these. Experimental results show that ERCI has superior performance on all metrics compared with the state-of-the-art methods.
The effects of voluntary running exercise on the astrocytes of the medial prefrontal cortex in APP/PS1 mice

Pathological changes in the medial prefrontal cortex (mPFC) and astrocytes are closely associated with Alzheimer's disease (AD). Voluntary running has been found to effectively delay AD. However, the effects of voluntary running on mPFC astrocytes in AD are unclear. A total of 40 10‐month‐old male amyloid precursor protein/presenilin 1 (APP/PS1) mice and 40 wild‐type (WT) mice were randomly divided into control and running groups, and the running groups underwent voluntary running for 3 months. Mouse cognition was assessed by the novel object recognition (NOR), Morris water maze (MWM), and Y maze tests. The effects of voluntary running on mPFC astrocytes were investigated using immunohistochemistry, immunofluorescence, western blotting, and stereology. APP/PS1 mice performed significantly worse than WT mice in the NOR, MWM, and Y maze tests, and voluntary running improved the performance of APP/PS1 mice in these tests. The total number of mPFC astrocytes was increased, cell bodies were enlarged, and protrusion number and length were increased in AD mice compared with WT mice, but there was no difference in component 3 (C3) levels in the mPFC (total mPFC level); however, C3 and S100B levels in astrocytes were increased in AD mice. Voluntary running reduced the total number of astrocytes and S100B levels in astrocytes and increased the density of PSD95+ puncta in direct contact with astrocyte protrusions in the APP/PS1 mouse mPFC. Three months of voluntary running inhibited astrocyte hyperplasia and S100B expression in astrocytes, increased the density of synapses in contact with astrocytes, and improved cognitive function in APP/PS1 mice.
Coping strategies among older adults living with HIV/AIDS with history of childhood sexual abuse

Background: Childhood sexual abuse (CSA) may be a risk factor for poor mental health in adulthood. Survivors may experience emotions detrimental to their social and mental wellbeing. Some of these emotions may include anger, fear, rage, helplessness, guilt, shame, which may impact their coping strategies. The aim of this study was to determine the association between CSA and coping among older adults living with HIV (OALH). Method: Data were obtained from 91 OALH via convenience sampling. The participants were recruited from an immunology clinic and were at least 50 years or older and living with HIV. CSA was operationalized using questions from the Adverse Childhood Experiences Questionnaire. Coping was assessed using the Brief COPE Inventory. Crude and adjusted linear regression models, controlling for age, sex, race, gender, and income were used to determine the association between CSA and each coping subscale. The analyses were conducted in SAS version 9.4. Results: Crude analyses showed statistically significant associations between CSA and specific coping strategies: humor (β = 1.244; p = 0.0018), religion (β = 1.122; p = 0.0291), Self-blame (β = 1.103; p = 0.0154), planning β = 1.197; p = 0.0196), venting (β = 1.218; p = 0.0063), substance use (β = 0.828; p = 0.0335) and instrumental support (β = 0.949; p = 0.0416) After adjusting for sociodemographic characteristics, there was a statistically significant association between CSA and humor (β = 1.321; p = 0.0048) and self-blame (β = 1.046; p = 0.0382). Conclusion: OALH with a history of CSA were more likely to use humor and self-blame as coping strategies. Trauma-informed interventions should be geared towards decreasing self-blame for OALH who are CSA survivors.
Drugs activating hypoxia-inducible factors correct erythropoiesis and hepcidin levels via renal EPO induction in mice

Key Points • EPO induction in the kidney, but not the liver, is essential for the therapeutic effects of HIF activators (HIF-PHIs) in renal anemia. • HIF-PHIs indirectly, but not directly, suppress hepcidin production through EPO induction in the kidney.
Adding density-independent breeding success reduce process error in a state space model of the population dynamics of Willow grouse (Lagopus lagopus)

Decisions on management policies require insights in how populations respond to different actions. The form and strength of negative density feedback is central in understanding population response, but identifying the proper function can be challenging when the relative magnitude of stochastic variation is high. Observation error in addition to natural process error will bias population variability and estimate of density dependence. Hierarchical state-space models can be used to separate process and observation errors, and recent advances in Bayesian framework and MCMC methods have increased their popularity. Here we develop a hierarchical state-space model, where the process equation is a Gompertz model with per capita variation in breeding success added as a stochastic process. We use data from a 56 year line transect monitoring program of a willow grouse (Lagopus lagopus) population in south central Sweden. The model fit resulted in a carrying capacity of 8.608 adults per km2 and a λmaxof 3.20, which is close to the 4.5 based on maximum survival and fecundity values. Accounting for a stochastic density-independent per captita breeding success resulted in a substantially reduced processes error (standard deviation), 0.2410 compared to 0.1638. Maximum growth is expected to occur at 2.208 adults per km2. Combining both process and observation error resulted in a CV-value of 0.163 at carrying capacity, which is similar to previously reported range of CV-values of many bird populations. However, only using the reduced process error result in a CV of 0.076. A bootstrap test for monotonic trend was statistically insignificant, probably due to a steady increase in density the last six years of the time series. We conclude that state-space models to separate observation and process error can provide important information on population dynamics, but that effort should be made to estimate measurement error independently. There is a lack of data on population dynamics at low densities, and we suggest that additional experimental harvest should be considered to improve the understanding of negative density feedback in relation to stochastic processes at low densities.
Parallax handling of image stitching using dominant-plane homography

In this paper, we present a novel image stitching method to handle parallax in practical application. For images with significant amount of parallax, the more effective approach is to align roughly and globally the overlapping regions and then apply a seam-cutting method to composite naturally stitched images. It is well known that images can be modeled by various planes result from the projective parallax under non-ideal imaging condition. The dominant-plane homography has important advantages of warping an image globally and avoiding some local distortions. The proposed method primarily addresses large parallax problem through two steps: (1) selecting matching point pairs located on the dominant plane, by clustering matching correspondences and then measuring the cost of each cluster; and (2) in order to obtain a plausible seam, edge maps of overlapped area incorporation arithmetic is adopted to modify the standard seam-cutting method. Furthermore, our approach is demonstrated to achieve reliable performance of handling parallax through a mass of experimental comparisons with state-of-the-art methods.
A New Measure of Semantic Similarity between Unknown Chinese Words Based on HowNet

A new measure based on HowNet is put forward to compute the semantic similarity between unknown Chinese words.Firstly,the semantic matching function is constructed according the YiHeNet;secondly,nodes in the concept graphs of unknown Chinese words are classified according to their different effects in expressing the semantic information;then,the three notions of arcs,node pairs and node pair sets are classified according to matching functions;finally,similarity measures are designed to compute the similarities of unknown Chinese words,similarities of different node pairs and similarities of different node pair sets.This new measure helps to classify the semantic information of those unknown words and to apply it into the computing course,and experiments prove its effectiveness.
Committee Decisions with Complementary Valuation.

The following monograph was first presented for publication in November, 1949. When, after eighteen months, it was found impossible to publish it in a journal, the authors sought to have it published as a separate monograph;2 and they have been enabled to do so by the generous financial assistance of the Carnegie Trust for Scottish Universities.
Universal Optimality of Net and Lattice Designs

This article considers universal optimality of net and lattice designs for a regression model. Based on the equivalence theorem for matrix means and majorization theory, the necessary and sufficient conditions for lattice designs being φpand universally optimal for trigonometric function and Chebyshev polynomial regression models are obtained. It is shown that digital net designs are universally optimal for both complete and incomplete Walsh function regression models under some specified conditions. It is further pointed out that digital net designs are universally optimal for complete Haar wavelet regression models but may not for incomplete Haar wavelet regression models.
Inference with Distributional Semantic Models

Distributional Semantic Models have emerged as a strong theoretical and practical approach to model the meaning of words. Indeed, an increasing body of work has proved their value in accounting for a wide range of semantic phenomena. Yet, it is still unclear how we can use the semantic information contained in these representations to support the natural inferences that we produce in our every day usage of natural language. In this thesis, I explore a selection of challenging relations that exemplify these inferential processes. To this end, on one hand, I present new publicly available datasets to allow for their empirical treatment. On the other, I introduce computational models that can account for these relations using distributional representations as their conceptual knowledge repository. The performance of these models demonstrate the feasibility of this approach while leaving room for improvement in future work.
The Pierre Auger Observatory scaler mode for the study of solar activity modulation of galactic cosmic rays

Since data-taking began in January 2004, the Pierre Auger Observatory has been recording the count rates of low energy secondary cosmic ray particles for the self-calibration of the ground detectors of its surface detector array. After correcting for atmospheric effects, modulations of galactic cosmic rays due to solar activity and transient events are observed. Temporal variations related with the activity of the heliosphere can be determined with high accuracy due to the high total count rates. In this study, the available data are presented together with an analysis focused on the observation of Forbush decreases, where a strong correlation with neutron monitor data is found.
Teachers’ Approaches to Proportional Relationship Problems in Multiple Measure Spaces

Ratio and proportion have many daily life applications and hence form an important part of the Mathematical Literacy (ML) curriculum in South African schools. The purpose of this study was to explore ML teachers’ application of ratio in an assessment task with multiple measure spaces set within the real-life context of the need to establish the ingredients to produce 84 biscuits based on a recipe for 24 biscuits. The participants were 101 ML teachers who were enrolled in an in-service teacher upgrading programme for practising ML teachers. Data for the study were generated from the written responses to the task. The study found that approximately 60% of the teachers were able to complete the task correctly. Many teachers applied the cross-multiplication strategy as a character distribution matrix where the procedures are dictated by the spatial arrangement of symbols instead of being underpinned by an understanding of proportional reasoning. It is recommended that ML teacher education programmes should provide opportunities for teachers to engage with applications of ratio across multiple measure spaces.
Study the Phase Transition and Dielectric Property of Sr-Doping Hexagonal Ba(Ti,Ga)O3 Ceramic

In this work, we study the phase transition and structure properties of hexagonal Ba1-xSrxTi0.8Ga0. 2O3 (x = 0.01, 0.03, 0.05, 0.07, and 0.09) systems, and also investigate the microwave dielectric property of this system. The all samples are prepared by the mixed oxide method, the structure of samples are transferred from hexagonal phase to mixed phases then tetragonal phase through the Sr-doping process, which is consistent with the thermodynamic prediction on literature. The samples are included the tetragonal phase, whose dielectric property is poorer than the hexagonal phase samples, so it also points out that the hexagonal BaTiO3 material has very good dielectric property and a large potential for application.
Semantically-Informed Deep Neural Networks For Sound Recognition

Deep neural networks (DNNs) for sound recognition learn to categorize a barking sound as a "dog" and a meowing sound as a "cat" but do not exploit information inherent to the semantic relations between classes (e.g., both are animal vocalisations). Cognitive neuroscience research, however, suggests that human listeners automatically exploit higher-level semantic information on the sources besides acoustic information. Inspired by this notion, we introduce here a DNN that learns to recognize sounds and simultaneously learns the semantic relation between the sources (semDNN). Comparison of semDNN with a homologous network trained with categorical labels (catDNN) revealed that semDNN produces semantically more accurate labelling than catDNN in sound recognition tasks and that semDNN-embeddings preserve higherlevel semantic relations between sound sources. Importantly, through a model-based analysis of human dissimilarity ratings of natural sounds, we show that semDNN approximates the behaviour of human listeners better than catDNN and several other DNN and NLP comparison models.
Continual Cell Instance Segmentation of Microscopy Images

A continual cell instance segmenter aims to continually learn to segment new objects while preserving the ability to localize and distinguish old objects without access to previous data. Besides catastrophic forgetting, background shift, where the background class could contain objects in the old and unseen future classes, could occur. In addition, as acquiring annotations is label-intensive, cell images can be partially labeled. In this paper, we present iMRCNN, which extends Mask R-CNN with knowledge distillation and pseudo labeling, to address these challenges. To preserve the learned skills, the current student distills knowledge from the former teacher at output and feature levels. Furthermore, we employ a pseudo labeling scheme, where the teacher is utilized to identify objects with no labels provided, to deal with background shift and partially labeled data. Experiments on two microscopy image sets demonstrate the effectiveness of iMRCNN over other alternatives in various incremental learning scenarios.
Improved Indoor Localization With NLOS Signal Propagations

This paper considers the indoor localization problem with possible non-line-of-sight (NLOS) signal propagations. Although centimeter-level accuracy can be achieved using ultrawideband (UWB) signal sources, NLOS propagation due to reflection and refraction of signals as well as multipath effect may lead to significant errors in UWB-based localization systems. To mitigate this issue, a novel and simple NLOS error compensation method is proposed in this paper, which introduces a correction factor that takes into account the influence of NLOS conditions on the UWB distance measurements. The inertial measurement unit (IMU) information are then used to correct the UWB distance measurements before passing them through a Kalman filter for data fusion. The effectiveness of the proposed approach over the state-of-the-art one is illustrated by both simulation and experiment.
Dialogue System with Missing Observation

Within the domain of dialogue, the ability to orchestrate multiple independently trained dialogue agents to create a unified system is of particular importance. Where we define orchestration as the task of selecting a subset of skills which most appropriately answer a user input using features extracted from both the user input and the individual skills. In this work, we study the task of online dialogue orchestration where the user feedback associated with the dialogue agent may not always be observed. In order to address the missing feedback setting, we propose to combine the attentive contextual bandit approach with an unsupervised learning mechanism such as clustering. By leveraging clustering to estimate missing reward, we are able to learn from each incoming event, even those with missing rewards. Promising empirical results are obtained on proprietary conversational datasets.
Analysis of fractional stochastic evolution equations by using Hilfer derivative of finite approximate controllability

The approximate controllability of a class of fractional stochastic evolution equations (FSEEs) are discussed in this study utilizes the Hilbert space by using Hilfer derivative. For different approaches, we remove the Lipschitz or compactness conditions and merely have to assume a weak growth requirement. The fixed point theorem, the diagonal argument, and approximation methods serve as the foundation for the study. The abstract theory is demonstrated using an example. A conclusion is given at the end.
A Model-Based Hearing Compensation Method Using a Self-Supervised Framework

Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical hearing thresholds. This paper proposes a model-based hearing compensation method using a self-supervised framework with a given auditory model. The influence of outer/inner hair cells dysfunction was simulated in the auditory model. And then, a neural network was trained to compensate for the given hearing impairment. Both objective and subjective experiments were conducted to evaluate the present method, and the results showed that listeners are sensitive to the parameter controlling the contribution of outer hair cells dysfunction. Additionally, the result indicated that listeners significantly preferred the speech processed by the proposed method to the traditional perspective fitting.
KEPS-NET: Robust Parking slot Detection based Keypoint estimation for High Localization Accuracy

In this paper, we study parking slot detection problem for automated parking function. Main contribution is to propose a neural network to detect parking slot based on keypoint estimation, which is called KEPS-NET. The proposed network detects three kinds of parking slot (perpendicular, parallel, and slanted) with two entrance corner points. We first analyze how precise localization of the entrance points in pixel level is important for real automated parking scenario. Then we construct parking slot detector with four heads using a common feature extraction backbone network to estimate center point, entrance points, slot type, and slot angle. And separate key-point detector with two heads is added to find entrance points more accurately by leveraging richer local features. We also design a slope loss helping the network minimize the orientation errors of the entrance line, which allows the network to obtain greatly improved localization accuracy. Through the experimental results, we show that the proposed algorithm achieves state-of-the-art performance compared to the previous works.
Eigen-Decomposition-Free Directed Graph Sampling via Gershgorin Disc Alignment

Graph sampling is the problem of choosing a node subset via sampling matrix H ∈ {0, 1}K×N to collect samples y = Hx ∈ℝK, K < N, so that the target signal x ∈ ℝN can be reconstructed in high fidelity. While sampling on undirected graphs is well studied, we propose the first eigen-decomposition-free sampling scheme tailored specifically for directed graphs, leveraging a previous undirected graph sampling method based on Gershgorin disc alignment (GDAS). Concretely, given a directed positive graph ${{\mathcal{G}}^d}$ specified by random-walk graph Laplacian matrix Lrw, we first define reconstruction of a smooth signal x∗ from samples y using graph shift variation (GSV) $\left\| {{{\mathbf{L}}_{rw}}{\mathbf{x}}} \right\|_2^2$ as a signal prior. To minimize the worst-case reconstruction error of the linear system solution x∗ = C−1H⊤y with symmetric coefficient matrix${\mathbf{C}} = {{\mathbf{H}}^ \top }{\mathbf{H}} + \mu {\mathbf{L}}_{rw}^ \top {{\mathbf{L}}_{rw}}$, the E-optimality sampling objective is to choose H to maximize the smallest eigenvalue λmin(C) of C. To circumvent eigen-decomposition, we maximize instead a lower bound $\lambda _{\min }^ - \left( {{\mathbf{SC}}{{\mathbf{S}}^{ - 1}}} \right)$ of λmin(C)—smallest Gershgorin disc left-end of a similarity transform of C—via a variant of GDAS based on Gershgorin circle theorem (GCT). Experimental results show that our sampling method yielded smaller signal reconstruction errors at a faster speed compared to competing schemes.