Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF MATERNAL DIET ON SELECT FECAL BACTERIA IN MARES AND THEIR FOALS

OF THESIS EFFECT OF MATERNAL DIET ON SELECT FECAL BACTERIA IN MARES AND THEIR FOALS The process of microbial colonization in the gastrointestinal tract of foals and the factors influencing this process are not well understood. The objectives were to determine the effect of starch source in pelleted concentrates in the maternal diet on select fecal bacterial groups in mares and their foals and to evaluate changes in these bacterial groups over time. Thoroughbred foals (n=19) were from dams fed one of two concentrates: an oat-based (OB) or corn and wheat middling-based (CWB) pelleted concentrate. Fecal samples were collected from mares at 324 d of gestation, pre-foaling, 1 d, 14 d, and 28 d after parturition and from foals at 1 d, 4 d, 14 d, and 28 d after birth. Total starch-utilizing bacteria (TSU) and lactobacilli increased with age and were more numerous in the feces of OB foals compared to CWB foals at 1 d and 4 d of age (P<0.05). Foal cellulolytic bacteria were not influenced by maternal diet (P>0.05) and increased with age (P<0.05). There was no influence of starch source on fecal bacteria in mares (P>0.05). Lactobacilli and cellulolytic bacteria in mares decreased 1 d postpartum (P<0.05) whereas TSU remained stable over time (P>0.05). These results indicated that microbial colonization of the foal’s gastrointestinal tract begins soon after birth and some bacteria may be influenced by maternal diet. Furthermore, the hindgut microbiota of broodmares may be altered by parturition.
Underwater Acoustic Target Classification Based on LOFAR Spectrum and Convolutional Neural Network

The underwater acoustic target classification task has always been an important research direction of acoustic recognition and classification. The acoustic classification models include traditional models such as Gaussian Mixture Model (GMM), and deep learning models such as Convolutional Neural Network (CNN) and Long and Short Time Memory Network (LSTM). This paper proposes a deep sound feature extraction network based on VGGNet. An underwater acoustic target classification framework based on LOFAR spectrum and CNN is proposed. Although ordinary CNN can also extract underwater acoustic features, too few or too many network layers will cause problems such as insufficient features or increased calculations. Therefore, we draw on the excellent structure of VGGNet in feature extraction and delete several layers for feature extraction and classification of underwater acoustic targets. The accuracy are 94%, 98% and 96% respectively in three real data sets of civil ships, and the accuracy were improved com-pared with the traditional methods.
Effect Evaluation of Iraqi Date Palm Pollen on Sex Hormones Level of Male Local Rabbits

This study investigates the biochemical and histological effects of Iraqi date palm pollen (IDPP) ( Phoenix dactylifera L . variety El-Ghannmi Ahmar) on the levels of sex hormones in the sera of adult male rabbits. Twenty eight adult male rabbits were used, divided into 4 groups (C, G 1 , G 2 and G 3 ). C is a control group , whileG 1 , G 2 and G 3 were orally treated with 30, 60 and 90 mg/kg/day of oily IDPP for 4 weeks, respectively. The results indicated that the level of Luteinizing hormone (LH) was significantly (p≤0.05) increased in G 1 and G 2 , while there was no significant change in G 3 as compared with C. The level of Follicular Stimulating hormone (FSH) was significantly (p≤0.05) increased in G 2 , as compared with C and not significant in G 1 and G 3 . In addition, no significant change in prolactin levels, while the level of testosterone significantly (p≤0.05) improved in all groups . The effect of IDPP on testicular of treated male rabbits was studied histologically. The histological study indicated that IDPP increased the active spermatogenesis with significant rise in the number of mature sperms in G 1 , G 2 and G 3 as compared with C. It could be concluded that IDPP improved fertility by boost production of testosterone and increase the spermatogenesis at seminal tubules by increasing the primary and secondary spermatocytes.
Deep Sequential Learning For Cervical Spine Fracture Detection On Computed Tomography Imaging

Fractures of the cervical spine are a medical emergency and may lead to permanent paralysis and even death. Accurate diagnosis in patients with suspected fractures by computed tomography (CT) is critical to patient management. In this paper, we propose a deep convolutional neural network (DCNN) with a bidirectional long-short term memory (BLSTM) layer for the automated detection of cervical spine fractures in CT axial images. We used an annotated dataset of 3,666 CT scans (729 positive and 2,937 negative cases) to train and validate the model. The validation results show a classification accuracy of 70.92% and 79. 18% on the balanced (104 positive and 104 negative cases) and imbalanced (104 positive and 419 negative cases) test datasets, respectively.
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows

We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows framework to learn stable Stochastic Differential Equations. We prove the Lyapunov stability for a class of Stochastic Differential Equations and we propose a learning algorithm to learn them from a set of demonstrated trajectories. Our model extends the set of stable dynamical systems that can be represented by state-of-the-art approaches, eliminates the Gaussian assumption on the demonstrations, and outperforms the previous algorithms in terms of representation accuracy. We show the effectiveness of our method with both standard datasets and a real robot experiment.
Quality of life of older adults in nursing homes in Vietnam

This study evaluated the quality of life of older adults in Vietnam. A survey was conducted on 291 old people at eight public and private nursing homes using cluster sampling. Quality of life was predicted by gender, duration of stay, center activities and community tangible support, and connection with family. Results were consistent across the four subscales of the Quality of Life Index. Quality of life of Vietnamese older adults depends not only on the quality of services provided by the nursing homes but also on family and community connections.
Stability of topological edge states under strong nonlinear effects

We examine the role of strong nonlinearity on the topologically robust edge state in a one-dimensional system. We consider a chain inspired from the Su-Schrieffer-Heeger model but with a finite-frequency edge state and the dynamics governed by second-order differential equations. We introduce a cubic onsite nonlinearity and study this nonlinear effect on the edge state's frequency and linear stability. Nonlinear continuation reveals that the edge state loses its typical shape enforced by the chiral symmetry and becomes generally unstable due to various types of instabilities that we analyze using a combination of spectral stability and Krein signature analysis. This results in an initially excited nonlinear-edge state shedding its energy into the bulk over a long time. However, the stability trends differ both qualitatively and quantitatively when softening and stiffening types of nonlinearity are considered. In the latter, we find a frequency regime where nonlinear edge states can be linearly stable. This enables high-amplitude edge states to remain spatially localized without shedding their energy, a feature that we have confirmed via long-time dynamical simulations. Finally, we examine the robustness of frequency and stability of nonlinear edge states against disorder, and find that those are more robust under a chiral disorder compared to a nonchiral disorder. Moreover, the frequency-regime where high-amplitude edge states were found to be linearly stable remains intact in the presence of a small amount of disorder of both types.
Sharing & Cooperativism: Designing For Economies

Recent work on sharing and cooperativism has helped widen our understanding of the emerging systems for exchanges, interactions, and relationships beyond mainstream economic models, in particular through studying local cooperatives and their sharing practices across various domains. These efforts also indicate that design has the potential to shape our engagements with the global political economy. However, so far, there are few design resources tailored for exploring and further developing design insights from empirical and conceptual research on sharing and cooperativism. Therefore, we invite the community to discuss the role of design in relation to economies of sharing and cooperativism. In this workshop, we will gather a diverse group of scholars, designers, and activists to think together how designs for sustainable economies can be created and circulated across cooperatives and platforms, with the aim to springboard social and economic aspects of sharing cultures.
Black-box density function estimation using recursive partitioning

We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a recursive partitioning of the sample space. It does not rely on gradients, nor require any problem-specific tuning, and is asymptotically exact for any density function with a bounded domain. The output is an approximation to the whole density function including the normalization constant, via partitions organized in efficient data structures. This allows for evidence estimation, as well as approximate posteriors that allow for fast sampling and fast evaluations of the density. It shows competitive performance to recent state-of-the-art methods on synthetic and real-world problem examples including parameter inference for gravitational-wave physics.
SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion

Real-time scene reconstruction from depth data inevitably suffers from occlusion, thus leading to incomplete 3D models. Partial reconstructions, in turn, limit the performance of algorithms that leverage them for applications in the context of, e.g., augmented reality, robotic navigation, and 3D mapping. Most methods address this issue by predicting the missing geometry as an offline optimization, thus being incompatible with real-time applications. We propose a framework that ameliorates this issue by performing scene reconstruction and semantic scene completion jointly in an incremental and real-time manner, based on an input sequence of depth maps. Our framework relies on a novel neural architecture designed to process occupancy maps and leverages voxel states to accurately and efficiently fuse semantic completion with the 3D global model. We evaluate the proposed approach quantitatively and qualitatively, demonstrating that our method can obtain accurate 3D semantic scene completion in real-time.
Learning Fine-Grained Multimodal Alignment for Speech Emotion Recognition

Speech emotion recognition is a challenging task because the emotion expression is complex, multimodal and fine-grained. In this paper, we propose a novel multimodal deep learning approach to perform fine-grained emotion recognition from real-life speeches. We design a temporal alignment pooling mechanism to capture the subtle and fine-grained emotions implied in every utterance. In addition, we propose a cross modality excitation module to conduct sample-specific activations on acoustic embedding dimensions and adaptively recalibrate the corresponding values by latent semantic features. The proposed model is evaluated on two well-known real-world speech emotion recognition datasets. The results demonstrate that our approach is superior on the prediction tasks for multimodal speech utterances, and it outperforms a wide range of baselines in terms of prediction accuracy. In order to encourage the research reproducibility, we make the code publicly available at https://github.com/hzlihang99/icassp2021_CME. git.
A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge

The objective of the first CARLA autonomous driving challenge was to deploy autonomous driving systems to lead with complex traffic scenarios where all participants faced the same challenging traffic situations. According to the organizers, this competition emerges as a way to democratize and to accelerate the research and development of autonomous vehicles around the world using the CARLA simulator contributing to the development of the autonomous vehicle area. Therefore, this paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment that attempts to commit the least number of traffic infractions, which used as the baseline the original architecture of the platform for autonomous navigation CaRINA 2. Our agent traveled in simulated scenarios for several hours, demonstrating his capabilities, winning three out of the four tracks of the challenge, and being ranked second in the remaining track. Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge and has components for obstacle detection using 3D point clouds, traffic signs detection and classification which employs Convolutional Neural Networks (CNN) and depth information, risk assessment with collision detection using short-term motion prediction, decision-making with Markov Decision Process (MDP), and control using Model Predictive Control (MPC).
An Adiabatic Superconductor Comparator With 46 nA Sensitivity

Adiabatic quantum-flux-parametron (AQFP) circuits can operate with extremely small energy dissipation (∼1 zJ per junction at 5 GHz) and high sensitivity (∼1 μA) owing to adiabatic switching. Thus, AQFP logic is suitable to use as readout interfaces for cryogenic detectors, such as superconducting nanowire single-photon detectors (SSPDs). In order to extend the application of AQFP logic to various detectors, it is crucial to achieve even better sensitivity because some detectors, such as WSi SSPDs and transition edge sensors (TESs), require sub-μA sensitivity. In the present study, we propose a high-sensitivity AQFP comparator with a current transformer (CT), which increases the equivalent input current and thereby improves the sensitivity. Numerical simulation shows that the proposed comparator achieves a sensitivity approximately six times better than that of a conventional AQFP comparator. Furthermore, we demonstrate a sensitivity of 46 nA at 4.2 K for a sampling frequency of 500 Hz in the experiment.
Multi-object tracking with self-supervised associating network

Multi-Object Tracking (MOT) is the task that has a lot of potential for development, and there are still many problems to be solved. In the traditional tracking by detection paradigm, There has been a lot of work on feature based object re-identification methods. However, this method has a lack of training data problem. For labeling multi-object tracking dataset, every detection in a video sequence need its location and IDs. Since assigning consecutive IDs to each detection in every sequence is a very labor-intensive task, current multi-object tracking dataset is not sufficient enough to train re-identification network. So in this paper, we propose a novel self-supervised learning method using a lot of short videos which has no human labeling, and improve the tracking performance through the re-identification network trained in the self-supervised manner to solve the lack of training data problem. Despite the re-identification network is trained in a self-supervised manner, it achieves the state-of-the-art performance of MOTA 62.0\% and IDF1 62.6\% on the MOT17 test benchmark. Furthermore, the performance is improved as much as learned with a large amount of data, it shows the potential of self-supervised method.
Pricing Adjustment System for Spatial-Temporal Load of Charging Station

Electric vehicles(EVs) have lately received great attention due to their low emission pollution and recyclability. However, the spatial-temporal charging load has made it challenging to popularize EVs. In the temporal aspect, the extra charging load causes power loss to the power grid, which even results in an overload of the power gird. In the spatial aspect, a long waiting queue during the peak period of charging in some charging stations is detrimental to the development of EVs. Although some encouraging progress has been made, the extra charging load during the peak period of domestic electricity consumption remains an ongoing challenge, and the pricing adjustment system without considering the income of charging station companies is hard to implement and still requires further research. In this paper, we proposed an innovative pricing adjustment system to solve these problems. By studying the charging behaviors and consumption behaviors of drivers, our system will provide each driver with the location, real-time queuing, and customized charging fee of charging stations with the consideration of the income of charging station companies. We use a real data set from Xi'an, e-charge, which contains 241,346 of vehicle charging and driving records for three months with data from 400 charging stations. The experiment shows that our system is effective in decreasing the number of charging vehicles during the peak period of domestic electricity consumption. Also, it reduces the average queue waiting time and the peak waiting time while protecting the profit of charging station operators.
Cross-Modal Transfer Learning for Multilingual Speech-to-Text Translation

We propose an effective approach to utilize pretrained speech and text models to perform speech-to-text translation (ST). Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task. With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder (wav2vec) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark [1] for English into 15 languages as well as 6 Romance languages into English with on average +2.8 BLEU and +3.9 BLEU, respectively. On low-resource languages (with less than 10 hours training data), our approach significantly improves the quality of speech-to-text translation with +9.0 BLEU on Portuguese-English and +5.2 BLEU on Dutch-English.
The Manufacture of Political Echo Chambers by Follow Train Abuse on Twitter

A growing body of evidence points to critical vulnerabilities of social media, such as the emergence of partisan echo chambers and the viral spread of misinformation. We show that these vulnerabilities are amplified by abusive behaviors associated with so-called ''follow trains'' on Twitter, in which long lists of like-minded accounts are mentioned for others to follow. This leads to the formation of highly dense and hierarchical echo chambers. We present the first systematic analysis of U.S. political train networks, which involve many thousands of hyper-partisan accounts. These accounts engage in various suspicious behaviors, including some that violate platform policies: we find evidence of inauthentic automated accounts, artificial inflation of friends and followers, and abnormal content deletion. The networks are also responsible for amplifying toxic content from low-credibility and conspiratorial sources. Platforms may be reluctant to curb this kind of abuse for fear of being accused of political bias. As a result, the political echo chambers manufactured by follow trains grow denser and train accounts accumulate influence; even political leaders occasionally engage with them.
Forecasting Quarterly Brazilian GDP: Univariate Models Approach

Gross domestic product (GDP) is an important economic indicator that aggregates useful information to assist economic agents and policymakers in their decision-making process. In this context, GDP forecasting becomes a powerful decision optimization tool in several areas. In order to contribute in this direction, we investigated the efficiency of classical time series models and the class of state-space models, applied to Brazilian gross domestic product. The models used were: a Seasonal Autoregressive Integrated Moving Average (SARIMA) and a Holt-Winters method, which are classical time series models; and the dynamic linear model, a state-space model. Based on statistical metrics of model comparison, the dynamic linear model presented the best forecasting model and fit performance for the analyzed period, also incorporating the growth rate structure significantly.
Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors

We propose a variational Bayesian (VB) procedure for high-dimensional linear model inferences with heavy tail shrinkage priors, such as student-t prior. Theoretically, we establish the consistency of the proposed VB method and prove that under the proper choice of prior specifications, the contraction rate of the VB posterior is nearly optimal. It justifies the validity of VB inference as an alternative of Markov Chain Monte Carlo (MCMC) sampling. Meanwhile, comparing to conventional MCMC methods, the VB procedure achieves much higher computational efficiency, which greatly alleviates the computing burden for modern machine learning applications such as massive data analysis. Through numerical studies, we demonstrate that the proposed VB method leads to shorter computing time, higher estimation accuracy, and lower variable selection error than competitive sparse Bayesian methods.
Visuomotor Influence of Attached Robotic Neck Augmentation

The combination of eye and head movements plays a major part in our visual process. The neck provides mobility for the head motion and also limits the range of visual motion in space. In this paper, a robotic neck augmentation system is designed to surmount the physical limitations of the neck. It applies in essential a visuomotor modification to the vision-neck relationship. We conducted two experiments to measure and verify the performance of the neck alternation. The multiple test results indicate the system has a positive effect to augment the motions of vision. Specifically, the robotic neck can enlarge the range of neck motion to 200%, and influence the response motion, by overall 22% less in time and 28% faster in speed.
Nudging the User with Privacy Indicator: A Study on the App Selection Behavior of the User

This paper presents an empirical study on user behavior, decision making, and perception about privacy concern while selecting apps. An app store demo was presented to the user with a minor modification—a privacy indicator for each app. After carrying out several tasks using this modified mobile interface, participants were interviewed to document reasons behind their decisions, thought process, and perception regarding individual privacy. A total of 82 adults volunteered under the pretext of a usability study. A significant influence of the privacy indicator on their app selection behavior was observed, although this influence decreased in case of familiar apps. Furthermore, responses from questionnaires, data from eye-tracking device and documented interviews, with video confrontation showed coherence with respect to the corresponding app selection behavior.