Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Biometry of Reproductive Organs between Indigenous and Cross Bred Cow in Bangladesh

A comparative biometrical study was conducted on reproductive organs of dairy cows (n = 86) of different genotypes available in Rajshahi city corporation of Bangladesh. Three types of genotype were considered for this experiment. The genotypes are Local, Local×Holstein Friesian and Local×Sahiwal. Reproductive tracts of cows were collected immediately after slaughter from different slaughter houses in Rajshahi, Bangladesh from July 2017 to June 2018. The comparison of measurements of most of the parameters of reproductive tract observed in different genotypic cows showed significant differences (p<0.05). Local×Holstein Friesian had significantly higher values (p<0.05) on the most of parameters of genitalia measured, followed by the Local×Sahiwal and Local. Local×Holstein Friesian cows had the longest uterine horn (28.16±1.64 cm for right and 29.83±1.71 cm for left), uterine body (3.45±0.22 cm length and 2.84±1.37 cm width), cervix (5.62±0.23 cm length and 4.95±0.23 cm width) and vagina (25.45±0.63 cm length and 6.12±0.36 cm width). The right ovary was wider in diameter, larger in length as compared to left one in all genotypes. This confirms the fact of right ovary being more active than the left one. Moreover, the biometry of ovary and tubular parts of genitalia in Local×Holstein Friesian and Local×Sahiwal cows are suggestive for selection of crossbred dairy cows to get maximum benefits from crossbreeding in respect of productive and reproductive performance and also for genetic improvement.
First-order de(cid:12)nability for distributive modal substructural logics: an indirect method

We investigate the relationship between Kripke semantics and bi-approximation semantics over distributive lattice-based logics such as distributive modal substructural logics. In particular, based on the prime skeletons introduced in [21], we provide a back-and-forth construction be-tween Kripke semantics and bi-approximation semantics. Throughout the procedure, we transfer the Sahlqvist (cid:12)rst-order de(cid:12)nability shown in [26] to a (cid:12)rst-order de(cid:12)nability result on Kripke semantics. Finally, we will give a positive answer for an open question raised in [8] as an application of our result.
A Supervised Machine Learning Approach for Distinguishing Between Additive and Replacing Horizontal Gene Transfers

Horizontal gene transfer is one of the most important drivers of microbial gene and genome evolution. Despite its central role in microbial evolution, several aspects of horizontal gene transfer remain poorly understood. In particular, transfers can be either additive or replacing depending on whether the transferred gene adds itself as a new gene in the recipient genome or replaces an existing homologous gene. However, despite recent efforts, there do not yet exist effective computational approaches for classifying inferred transfers as being additive or replacing. In this work, we address this gap by devising a novel supervised machine learning approach for classifying transfers as being either additive or replacing. Our approach is based on phylogenetic reconciliation, a standard computational technique for inferring transfers. Our classifier, named ARTra, uses as features the classifications provided by several simple reconciliation-based classification rules, along with topological information from the gene tree, and ensembles them to produce a more accurate classification. ARTra is efficient and robust and significantly improves upon the classification accuracy of the only existing computational approach for this problem. We demonstrate the accuracy of ARTra by applying it to a wide range of simulated datasets and to a large biological dataset. Our results show that ARTra performs well over a broad range of evolutionary conditions and on real data, and that it does so even when trained only on a narrow range of such conditions and only using simulated data. An open-source implementation of ARTra is freely available from https://compbio.engr. uconn.edu/software/ARTra/.
Cross-domain sound event detection: from synthesized audio to real audio Technical Report

This technical report describes some of the system information submitted to dcase2020 task4 - Sound Event Detection in Do-mestic Environments. We use the dataset of dcase2019 task4 to train our model, contains strongly labeled synthetic data, large unlabeled data, and weakly labeled data. There is a very large domain gaps in the statistical distribution between the synthesized audio and the real audio, and the performance of the SED model obtained on the synthesized audio applied to the real audio is greatly reduced. To perform this task, we propose a DA-CRNN network for joint learning of sound event detection(SED) and domain adaptation(DA). We consider the impact of the distribution within a single sound on the generalization performance of the model by mitigating the impact of complex background noise on event detection and the self-correlation consistency regularization of clip-level sound event classification, these make the intra-domain of a single sound smoother ; for cross-domain adaptation, adversarial learning through feature extraction network with frame-level domain discriminator and clip-level domain discriminator, forcing the feature extraction network to learn the invariant features of the domain, and further improve the generalization performance of the model. We did not use sound source separation, achieved an F1 score of 48.25% on the validation dataset and an F1 score of 49.43% on the public evaluation dataset.
Learning to Learn from Failures Using Replay

Learning from past mistakes is a quintessential aspect of intelligence. In sequential decision-making, existing meta-learning methods that learn a learning algorithm utilize experience from only a few previous episodes to adapt their policy to new environments and tasks. Such methods must learn to correct their mistakes from highly-correlated sequences of states and actions generated by the same policy’s consequent roll-outs during training. Learning from correlated data is known to be problematic and can signiﬁcantly impact the quality of the learned correction mechanism. We show that this problem can be mitigated by augmenting current systems with an external memory bank that stores a larger and more diverse set of past experiences. Detailed experiments demonstrate that our method out-performs existing meta-learning algorithms on a suite of challenging tasks from raw visual observations. Code and videos are available at: https://sites.google. com/view/ learn-from-failures .
The Control and Treatment of Neurological Complicationsin COVID-19 Pandemic Era

The novel coronavirus (COVID-19) is a family of large enveloped non-segmented positive-sense RNA viruses which has been considered as a global health concern. COVID-19 itself has been a very high transmissibility potential, which declared as pandemic by WHO. It is postulated that the COVID-19 accumulates mainly in the nasal epithelia and lower respiratory airways. However, there is evidence suggesting the COVID-19 neurotropism which might contribute to respiratory failure. The magnitude of the COVID-19 pandemic will result in substantial neurological disease, whether through direct infection, para-infectious complications, or critical illness more generally. Thus, we aim to review the central nervous system complications of the COVID-19 and how to control and treat since the emergence of the virus in this study.
Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms

In many fields of research, labeled datasets are hard to acquire. This is where data augmentation promises to overcome the lack of training data in the context of neural network engineering and classification tasks. The idea here is to reduce model over-fitting to the feature distribution of a small under-descriptive training dataset. We try to evaluate such data augmentation techniques to gather insights in the performance boost they provide for several convolutional neural networks on mel-spectrogram representations of audio data. We show the impact of data augmentation on the binary classification task of surgical mask detection in samples of human voice (ComParE Challenge 2020). Also we consider four varying architectures to account for augmentation robustness. Results show that most of the baselines given by ComParE are outperformed.
H i imaging of dwarf star-forming galaxies: masses, morphologies, and gas deficiencies

The GMRT observations of the HI 21~cm-line emission from 13~nearby dwarf star-forming galaxies are presented. The ranges of star formation rates and stellar masses of the sample galaxies are 0.03 -- 1.7~$M_\odot~{\rm yr}^{-1}$ and 0.04 -- 22.3~$\times 10^8~M_\odot$, respectively. The HI channel images, moment images, global profiles and mass surface density profiles are presented here. The average value of the peak HI mass surface density is estimated to be $\sim$2.5~M$_{\odot}$~pc$^{-2}$, which is significantly less compared to that in massive spiral galaxies. The scaling relations of $(M_{stars} + M_{\rm H\,I} + M_{\rm He})$ vs $M_{dyn}$, gas fraction vs $M_B$, $M_{\rm H\,I}$ vs $M_{stars}$, \ion{H}{i}-to-stellar mass ratio vs $M_{stars}$, and $M_{\rm H\,I}$ vs $D_{\rm H\,I}$ for the sample galaxies are estimated. These scaling relations can be used to constraint the key parameters in the galaxy evolution models. These galaxies are residing in group environment with galaxy density up to 8~galaxy~Mpc$^{-3}$. A HI mass deficiency (with $DEF_{\rm HI} > 0.3$) is noticed in majority of galaxies for their optical diameters as compared to galaxies in field environments. Clear signatures of tidal interactions in these galaxies could be inferred using the HI images. Isolated HI clouds without known optical counterparts are seen in the vicinity of several galaxies. HI emission envelope is found to be having an offset from the optical envelope in several galaxies. Consistent with the previous studies on galaxy evolution in group environments, tidal interactions seem to play an important role in triggering recent star formation.
Measuring the polarization of boosted, hadronic $W$ bosons with jet substructure observables

In this work, we present a new technique to measure the longitudinal and transverse polarization fractions of hadronic decays of boosted $W$ bosons. We introduce a new jet substructure observable denoted as $p_\theta$, which is a proxy for the parton level decay polar angle of the $W$ boson in its rest-frame. We show that the distribution of this observable is sensitive to the polarization of $W$ bosons and can therefore be used to reconstruct the $W$ polarization in a model-independent way. As a test case, we study the efficacy of our technique on vector boson scattering processes at the high luminosity Large Hadron Collider and we find that our technique can determine the longitudinal polarization fraction to within $\pm 0.15$. We also show that our technique can be used to identify the parity of beyond Standard Model scalar or pseudo-scalar resonances decaying to $W$ bosons with just 20 events.
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings

Abstract We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global latent topics are shared across documents, a word is generated by a hidden semantic vector encoding its contextual semantic meaning, and its context words are generated conditional on both the hidden semantic vector and global latent topics. Topics are trained jointly with the word embeddings. The trained model maps words to topic-dependent embeddings, which naturally addresses the issue of word polysemy. Experimental results show that the proposed model outperforms the word-level embedding methods in both word similarity evaluation and word sense disambiguation. Furthermore, the model also extracts more coherent topics compared with existing neural topic models or other models for joint learning of topics and word embeddings. Finally, the model can be easily integrated with existing deep contextualized word embedding learning methods to further improve the performance of downstream tasks such as sentiment classification.
Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling

Visual Storytelling~(VIST) is a task to tell a narrative story about a certain topic according to the given photo stream. The existing studies focus on designing complex models, which rely on a huge amount of human-annotated data. However, the annotation of VIST is extremely costly and many topics cannot be covered in the training dataset due to the long-tail topic distribution. In this paper, we focus on enhancing the generalization ability of the VIST model by considering the few-shot setting. Inspired by the way humans tell a story, we propose a topic adaptive storyteller to model the ability of inter-topic generalization. In practice, we apply the gradient-based meta-learning algorithm on multi-modal seq2seq models to endow the model the ability to adapt quickly from topic to topic. Besides, We further propose a prototype encoding structure to model the ability of intra-topic derivation. Specifically, we encode and restore the few training story text to serve as a reference to guide the generation at inference time. Experimental results show that topic adaptation and prototype encoding structure mutually bring benefit to the few-shot model on BLEU and METEOR metric. The further case study shows that the stories generated after few-shot adaptation are more relative and expressive.
Efficient sampling for polynomial chaos‐based uncertainty quantification and sensitivity analysis using weighted approximate Fekete points

Performing uncertainty quantification (UQ) and sensitivity analysis (SA) is vital when developing a patient‐specific physiological model because it can quantify model output uncertainty and estimate the effect of each of the model's input parameters on the mathematical model. By providing this information, UQ and SA act as diagnostic tools to evaluate model fidelity and compare model characteristics with expert knowledge and real world observation. Computational efficiency is an important part of UQ and SA methods and thus optimization is an active area of research. In this work, we investigate a new efficient sampling method for least‐squares polynomial approximation, weighted approximate Fekete points (WAFP). We analyze the performance of this method by demonstrating its utility in stochastic analysis of a cardiovascular model that estimates changes in oxyhemoglobin saturation response. Polynomial chaos (PC) expansion using WAFP produced results similar to the more standard Monte Carlo in quantifying uncertainty and identifying the most influential model inputs (including input interactions) when modeling oxyhemoglobin saturation, PC expansion using WAFP was far more efficient. These findings show the usefulness of using WAFP based PC expansion to quantify uncertainty and analyze sensitivity of a oxyhemoglobin dissociation response model. Applying these techniques could help analyze the fidelity of other relevant models in preparation for clinical application.
Investigation of the Sense of Agency in Social Cognition, Based on Frameworks of Predictive Coding and Active Inference: A Simulation Study on Multimodal Imitative Interaction

When agents interact socially with different intentions (or wills), conflicts are difficult to avoid. Although the means by which social agents can resolve such problems autonomously has not been determined, dynamic characteristics of agency may shed light on underlying mechanisms. Therefore, the current study focused on the sense of agency, a specific aspect of agency referring to congruence between the agent's intention in acting and the outcome, especially in social interaction contexts. Employing predictive coding and active inference as theoretical frameworks of perception and action generation, we hypothesize that regulation of complexity in the evidence lower bound of an agent's model should affect the strength of the agent's sense of agency and should have a significant impact on social interactions. To evaluate this hypothesis, we built a computational model of imitative interaction between a robot and a human via visuo-proprioceptive sensation with a variational Bayes recurrent neural network, and simulated the model in the form of pseudo-imitative interaction using recorded human body movement data, which serve as the counterpart in the interactions. A key feature of the model is that the complexity of each modality can be regulated differently by changing the values of a hyperparameter assigned to each local module of the model. We first searched for an optimal setting of hyperparameters that endow the model with appropriate coordination of multimodal sensation. These searches revealed that complexity of the vision module should be more tightly regulated than that of the proprioception module because of greater uncertainty in visual information flow. Using this optimally trained model as a default model, we investigated how changing the tightness of complexity regulation in the entire network after training affects the strength of the sense of agency during imitative interactions. The results showed that with looser regulation of complexity, an agent tends to act more egocentrically, without adapting to the other. In contrast, with tighter regulation, the agent tends to follow the other by adjusting its intention. We conclude that the tightness of complexity regulation significantly affects the strength of the sense of agency and the dynamics of interactions between agents in social settings.
Cross-correlation between Subaru Hyper Suprime-Cam Galaxy Weak Lensing and Planck Cosmic Microwave Background Lensing

Cross-correlations between galaxy weak lensing (WL) and cosmic microwave background (CMB) lensing are powerful tools to probe matter fluctuations at intermediate redshifts and to detect residual systematics in either probe. In this paper, we study the cross-correlation of galaxy WL from the Hyper Suprime-Cam Subaru Strategic Program (HSC) first data release and CMB lensing from the final Planck data release, for HSC source galaxies at 0.3 ≤ z ≤ 1.5. HSC is the deepest Stage-III galaxy WL survey, and provides a great opportunity to study the high-redshift universe. It also presents new challenges related to its exceptionally high source density, such as source blending. The cross-correlation signal is measured at a significance level of 3. 1σ. The amplitude of our best-fit model with respect to the best-fit 2018 Planck cosmology is A = 0.81 ± 0.25, consistent with A = 1. Our result is also consistent with previous CMB lensing and galaxy WL cross-correlation studies using different surveys. We perform tests with respect to the WL B-modes, the point-spread-function, photometric redshift errors, and thermal Sunyaev–Zel’dovich leakage, and find no significant evidence of residual systematics.
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations

Accelerating deep model training and inference is crucial in practice. Existing deep learning frameworks usually concentrate on optimizing training speed and pay fewer attentions to inference-specific optimizations. Actually, model inference differs from training in terms of computation, e.g. parameters are refreshed each gradient update step during training, but kept invariant during inference. These special characteristics of model inference open new opportunities for its optimization. In this paper, we propose a hardware-aware optimization framework, namely Woodpecker-DL (WPK), to accelerate inference by taking advantage of multiple joint optimizations from the perspectives of graph optimization, automated searches, domain-specific language (DSL) compiler techniques and system-level exploration. In WPK, we investigated two new automated search approaches based on genetic algorithm and reinforcement learning, respectively, to hunt the best operator code configurations targeting specific hardware. A customized DSL compiler is further attached to these search algorithms to generate efficient codes. To create an optimized inference plan, WPK systematically explores high-speed operator implementations from third-party libraries besides our automatically generated codes and singles out the best implementation per operator for use. Extensive experiments demonstrated that on a Tesla P100 GPU, we can achieve the maximum speedup of 5.40 over cuDNN and 1.63 over TVM on individual convolution operators, and run up to 1.18 times faster than TensorRT for end-to-end model inference.
Ultraviolet Line Identifications and Spectral Formation Near Max Light in Type Ia Supernova 2011fe

We present ultraviolet line identifications of near maximum-light Hubble Space Telescope observations of SN 2011fe using synthetic spectra generated from both SYNOW and PHOENIX. We find the spectrum to be dominated by blends of iron group elements Fe, Co, and Ni (as expected due to heavy line blanketing by these elements in the UV) and for the first time identify lines from C iv and Si iv in a supernova spectrum. We also find that classical delayed detonation models of SNe Ia are able to accurately reproduce the flux levels of SN 2011fe in the UV. Further analysis reveals that photionization edges play an important role in feature formation in the far-UV, and that temperature variations in the outer layers of the ejecta significantly alter the Fe iii/Fe ii ratio producing large flux changes in the far-UV and velocity shifts in mid-UV features. SN 2011fe is the best observed core-normal SNe Ia; therefore analysis of its UV spectra shows the power of UV spectra in discriminating between different metallicities and progenitor scenarios of SNe Ia, due to the fact that the UV probes the outermost layers of the SN Ia, which are most sensitive to metallicity and progenitor variations.
IGANI: Iterative Generative Adversarial Networks for Imputation Applied to Prediction of Traffic Data

Generative adversarial networks (GANs) are implicit generative models that can be used for data imputation as an unsupervised learning problem. This works introduces an iterative GAN architecture for data imputation based on the invertibility of the generative imputer. This property is a sufficient condition for the convergence of the proposed GAN architecture. The performance of imputation is demonstrated by applying different imputation algorithms on the traffic speed data from Guangzhou (China). It is shown that our proposed algorithm produces more accurate results compared to those of previous GAN-based imputation architectures.
Connected Incomplete Preferences

The standard model of choice in economics is the maximization of a complete and transitive preference relation over a fixed set of alternatives. While completeness of preferences is usually regarded as a strong assumption, weakening it requires care to ensure that the resulting model still has enough structure to yield interesting results. This paper takes a step in this direction by studying the class of "connected preferences", that is, preferences that may fail to be complete but have connected maximal domains of comparability. We offer four new results. Theorem 1 identifies a basic necessary condition for a continuous preference to be connected in the sense above, while Theorem 2 provides sufficient conditions. Building on the latter, Theorem 3 characterizes the maximal domains of comparability. Finally, Theorem 4 presents conditions that ensure that maximal domains are arc-connected.