Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Rift Valley Fever Virus RNA in Formalin-Fixed Mosquitoes by In Situ Hybridization (RNAscope®)

Rift Valley fever virus (RVFV) causes a zoonotic mosquito-borne haemorrhagic disease that emerges to produce rapid large-scale outbreaks in livestock within sub-Saharan Africa. A range of mosquito species in Africa have been shown to transmit RVFV, and recent studies have assessed whether temperate mosquito species are also capable of transmission. In order to support vector competence studies, the ability to visualize virus localization in mosquito cells and tissue would enhance the understanding of the infection process within the mosquito body. Here, the application of in situ hybridization utilizing RNAscope® to detect RVFV infection within the mosquito species, Culex pipiens, derived from the United Kingdom was demonstrated. Extensive RVFV replication was detected in many tissues of the mosquito with the notable exception of the interior of ovarian follicles.
Acute Kidney Injury Related to Muscle Breakdown-Not All Due to Rhabdomyolysis: A Case of Acute Urate Nephropathy

A young man with a history of pseudo-seizures was admitted after an episode of severe agitation and rigorous physical activity. The patient was febrile on admission with notable leukocytosis and lactic acidosis but a negative infectious workup. During the hospitalization, the patient developed severe Acute Kidney Injury (AKI) despite fluid hydration. Creatinine Phosphoryl Kinase (CPK) was minimally elevated at 693 U/l. Urine microscopy was significant for many uric acid crystals. Subsequent lab studies revealed an elevated uric acid level of 13.4 mg/dl with fractional excretion of urate (Fe-Urate) of 13%. The patient was treated with rasburicase and allopurinol with the improvement of AKI that paralleled the improvement in hyperuricemia. AKI was likely due to acute urate nephropathy from a pseudo-seizure. AKI following an icteric activity or rigorous physical activity can occur via a mechanism that is independent of rhabdomyolysis. Acute Urate Nephropathy should be considered in cases with no significant elevations of CPK, with prompt initiation of rasburicase and potentially rapid resolution of AKI.
The c 2 Gravitational Potential Limit and its implications

The relationship between gravitational potential, black holes and squared light speed c is examined as well as the implications of the presented findings for the gravitational constant G and general relativity theory. It is common knowledge that the velocity limit in our universe is defined by light speed c and as shown in this work c plays a similar role for the gravitational potential since c/G is linked to the mass density of black holes, our local Hubble sphere and the overall universe. Furthermore, it is demonstrated that the rift between cosmology and quantum physics can be reconciled by acknowledging the physical meaning of the Planck units which proposedly define the characteristics of quantized space-time. This notion is also supported by the presented logarithmic relationships between the cosmological scale and the quantum scale. Finally, the presented findings allow uncovering a physical relationship between the constituents of Dirac’s contested large number hypothesis.
Bayesian hypothesis testing as a mixture estimation modelT1

We consider a novel paradigm for Bayesian testing of hypotheses and Bayesian model comparison. Instead of the traditional comparison of posterior probabilities of the competing hypotheses, given the data, we consider the hypotheses as components of a mixture model. We therefore replace the original testing problem with an estimation one that focus on the probability or weight of a given hypothesis within a mixture model as the parameter of interest and the posterior distribution of this weight as the outcome of the test. A major differentiating feature of this approach is that that generic improper priors are acceptable. For example, a reference Beta B(a0, a0) prior on the mixture weight parameter can be used for the common problem of testing two contrasting hypotheses. In this case, the sensitivity of the posterior estimates of the weights to the choice of a0 vanishes as the sample size increases, leading to a consistent procedure and a suggested default choice of a0 = 0.5. Another feature of this easily implemented alternative to the classical Bayesian solution is that the speeds of convergence of the posterior mean of the weight and of the corresponding posterior probability are quite similar.
Job Salary Prediction

Kaggle is an online platform for machine learning and analytics competitions where companies post data and individuals post solutions to compete for monetary prizes and to hone their skills [1]. Recently Adzuna, a UK based classifieds company, hosted a Kaggle competition with the goal of improving job salary predictions for job postings in which the salary does not appear. Using various linear models, random forests and neural network implementations we were able to achieve an mean error of £ 4933 corresponding to the top 6% of the of the competitors. This paper describes the details of our feature selection and modeling methods.
A Study on Spiritual Tourist Site Selection under Multi-criteria

The emerging trend towards the search of spirituality can be tapped by creating niche tourism segment known as spiritual tourism. India has been the hub of spirituality for thousands of years and since it is the oldest living civilization, India can be the most sought for place for the spiritual tourism. However to attract more and more tourists to India, a good spiritual tourism destination image should be created by providing standard and satisfying service to the spiritual tourists. Spiritual tourists’ satisfaction depends upon their expectations and perceptions of services provided by tourism providers. This study aims to provide an Analytical Hierarchy Process Model to the tourists as well as to the tourist operators. Primary data is collected through questionnaire from 148 foreign tourists who visited Orissa during the month of December 2008. The model presents a comparative view of the different tourist zones of India as perceived by the tourists themselves. The findings of the study can be a guideline to the future tourists as well as to the tourist operators residing in foreign countries. The advantage of the Analytical Hierarchy Process is that one can choose one’s destination on the basis of one’s own requirements.
Genetic Algorithm based Predictive model for COVID 19 – Theoretical Model based on an evolutionary approach

In this manuscript we propose a novel theoretical method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. The constraint with respect to interaction has been implemented by a machine learning type algorithm and becomes the unique part of our study . Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.
Implementations of R-NET and Character-level Embeddings on SQUAD

Question answering (QA) has become a popular discipline within the field of natural language processing in recent years. The Stanford Question Answering Dataset (SQuAD), which consists of crowd-sourced questions on Wikipedia articles, has inspired many advancements in QA systems to correctly identify answers by selecting relevant spans of texts. In this paper, I explored two ways to improve the performance of the baseline Bidirectional Attention Flow (BiDAF) model on SQuAD: incorporate character-level embeddings with the existing word-level embeddings, and implement R-NET in place of BiDAF. Experiments have shown that character-level emebeddings enriches the understanding of components of words and provides improvement on key evaluation metrics. The implementation of R-NET also provides additional lift in model performance on SQUAD
Tzitzeica solitons vs. relativistic Calogero-Moser 3-body clusters

We establish a connection between the hyperbolic relativistic Calogero-Moser systems and a class of soliton solutions to the Tzitzeica equation (aka the DoddBullough-Zhiber-Shabat-Mikhailov equation). In the 6N -dimensional phase space Ω of the relativistic systems with 2N particles and N antiparticles, there exists a 2N dimensional Poincaré-invariant submanifold ΩP corresponding to N free particles and N bound particle-antiparticle pairs in their ground state. The Tzitzeica N soliton tau-functions under consideration are real-valued, and obtained via the dual Lax matrix evaluated in points of ΩP . This correspondence leads to a picture of the soliton as a cluster of two particles and one antiparticle in their lowest internal energy state.
Robust QA with Task-Adaptive Pretraining

It is often hard to find a lot of labeled data to train a QA (question answering) model. One possible approach to overcome this challenge is to use TAPT (task-adaptive pretraining) in which the model is pretrained further using the unlabeled data from the task itself [1]. We implement the TAPT technique to make a QA model perform robustly on a task with low-resource training data by first pertaining on the larger unlabeled data set. We then fine tune the model with a smaller labeled dataset. The results are mixed. Although a preliminary model that is pretrained on just the out-of-domain train oodomain_train data performed better than the baseline, additional pretraining using more unlabeled out-of-domain data performed worse than expected.
PopNet: Evaluating the Use of LSTMs and GPT-2 for Generating Pop Lyrics From Song Titles

Many artists now use lyricists to write the lyrics for their songs. We thought that it would be interesting to implement models which are able to take the place of a pop lyricist and generate song lyrics. Currently, LSTM models have been used to generate lyrics and verses, but not songs, and GPT-2 models have been shown to be effective in creative text generation problems, but have not yet been used to generate song lyrics. We implemented LSTM models and fine-tuned GPT-2 models to take in a song title then generate either 1) a line of lyrics, 2) the lyrics for a song verse, or 3) the lyrics to an entire song because we thought it would be interesting to characterize the behavior of LSTMS at generating longer pieces of text, and employ GPT-2 on a new task. Through perplexity scores, BERT scores, and human evaluation results, as well as qualitative evaluation, we are able to see that our finetuned GPT-2 and LSTM models are able to greatly outperform our baseline, the out-of-the-box pre-trained GPT-2 model in generating pop lyrics, verses, and songs. Through our human evaluation, we find that a fine-tuned GPT-2 is able to generate realistic pop lyrics and verses, and decent pop songs. The fine-tuned GPT-2 model outperformed the LSTM model in all three generation tasks, most likely due to difficulty that the LSTM cell state has in preserving upstream information and the difficulty that LSTM attention has in identifying different relevant parts of the input for different parts of the output.
Adversarial Training Methods for Cross-Domain Question Answering

Despite recent work demonstrated considerable advantages on many NLP tasks by pre-training on a large corpus of text and then fine-tuning on a specific task, this approach can hardly scale as it requires task-specific fine-tuning data for any new task and, unfortunately, this is also the case when it comes to adapt one model trained for a specific task and dataset to a new dataset. To address this problem, domain adaptation techniques aim to adapt models trained for a task on in-domain datasets to a target domain by using efficiently samples from the latter. On the contrary, domain generalization techniques aim to incentivate the model to learn domain-invariant features directly from in-domain data to generalize the model for any out-of-domain dataset, pushing to learn task-relevant features and preventing overfitting on in-domain data. We like to compare this approach the way humans learn a task, as they can generally perform the same task on different domains from only a few examples. However, domain generalization is often performed by augmenting in-domain data by applying semantic-preserving transformations to challenge the model during training, leveraging some kind of rules or domain knowledge. Contrarily, in this project our goal is to explore domain generalization techniques applied to question answering based on adversarial training without leveraging any set of rules or domain knowledge, but using adversarial terms to make more robust the regular loss with or without adopting task-agnostic critic networks. Such extremely general methodology does not suffer from the limitations of synonym replacement approaches and can be applied to other NLP tasks. Our best variant combines two different and complementary approaches of adversarial training on a DistilBERT baseline, achieving >3% F1-score improvement over the regular fine-tuning process, outperforming several other adversarial and energy-based approaches.
F eb 2 01 2 Non-equilibrium and information : the role of cross-correlations

We discuss the relevance of information contained in cross-correlations among different degrees of freedom, which is crucial in non-equilibrium systems. In particular we consider a stochastic system where two degrees of freedom X1 and X2 in contact with two different thermostats are coupled together. The production of entropy and the violation of equilibrium fluctuation-dissipation theorem (FDT) are both related to the cross-correlation betweenX1 andX2. Information about such crosscorrelation may be lost when single-variable reduced models, for X1, are considered. Two different procedures are typically applied: (a) one totally ignores the coupling with X2; (b) one models the effect of X2 as an average memory effect, obtaining a generalized Langevin equation. In case (a) discrepancies between the system and the model appear both in entropy production and linear response; the latter can be exploited to define effective temperatures, but those are meaningful only when timescales are well separated. In case (b) linear response of the model well reproduces that of the system; however the loss of information is reflected in a loss of entropy production. When only linear forces are present, such a reduction is dramatic and makes the average entropy production vanish, posing problems in interpreting FDT violations.
Improving Question Answering on SQuAD 2.0: Exploring the QANet Architecture

In this project, we investigated QANet [1] - an end-to-end, non-recurrent model that is based on the use of convolutions and self-attention. Our first goal was to reimplement the QANet model from scratch and compare its performance to that of our baseline BiDAF [2] - a model that relies on recurrent neural networks with attention. Both of the QA answering systems were tested on SQUAD 2.0 (the Stanford Question Answering Dataset) which includes both questions that are answerable given a context and questions that are not answerable given the context. Finally, after evaluation of our "vanilla" QANet and investigation of related work, we implemented an extended model called EQuANT [3]. The model adds an additional output to explicitly predict the answerability of a question given the context. Our best model (QANet with tuned hyperparameters) achieves F1 = 57.56 and EM = 54.66 on the developmental set, and F'l = 56.76 and EM = 53.34 on the test set.
J un 2 02 1 UNIRULEDNESS OF UNITARY SHIMURA VARIETIES ASSOCIATED WITH HERMITIAN FORMS OF SIGNATURES

Gritsenko-Hulek showed that some orthogonal Shimura varieties are uniruled. In particular, they have the Kodaira dimension ́8. In this paper, we apply the method of Gritsenko-Hulek to unitary Shimura varieties associated with Hermitian forms of signatures p1, 3q, p1, 4q and p1, 5q, and show that some of them are uniruled. We use the restrictions of strongly reflective modular forms constructed by Yoshikawa and Gritsenko-Hulek. We give examples of Hermitian lattices over the rings of integers of imaginary quadratic fields Qp ? ́1q and Qp ? ́2q for which the associated unitary Shimura varieties are uniruled. Moreover, we prove they are rationally chain connected and some of them are Fano.
Delegation with Technology Migration: An Empirical Analysis of Mobile Virtual Network Operators

This study examines the impact of mobile virtual network operators (MVNOs) on the performance of mobile network operators (MNOs) in the presence of overlapping generations of wireless mobile technologies (2G and 3G). MVNOs distribute MNOs’ mobile services to customers without owning any spectrum or network infrastructures. Some MVNOs are wholly owned by MNOs with a revenue-sharing mechanism (branded MVNOs), while others operate through wholesale agreements with MNOs (third-party MVNOs). By focusing on the impact of MVNOs on MNOs’ performance, we investigate governance issues, e.g., delegation vs. ownership, in supply chains with overlapping generations of technologies. Using proprietary data, we design a quasi-experiment to analyze branded and third-party MVNOs. First, we use a sliding window adjusted difference-in-differences model as an exploratory analysis. Next, in the main analysis, we employ dynamic propensity score matching, which allows us to explore the variation of the impact over time. We find that branded and third-party MVNO launches both increase an MNO's 2G market share, whereas its 3G market share increases only with a third-party MVNO launch. In addition, the positive effects diminish over time for branded MVNOs, but strengthen for third-party MVNOs. These results suggest that, when launching MVNOs, managers should decide on supply chain governance based on technologies and market maturity. If the focus is to quickly seize the market share with new mobile technologies, branded MVNOs are preferred. On the other hand, if the focus is to expand in a lower-end, mature market, delegating to third-party MVNOs may be more effective.
Learning Graph Models for Retrosynthesis Prediction

Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building on this viewpoint, this paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. The model first predicts the set of graph edits transforming the target into incomplete molecules called synthons. Next, the model learns to expand synthons into complete molecules by attaching relevant leaving groups. This decomposition simplifies the architecture, making its predictions more interpretable, and also amenable to manual correction. Our model achieves a top-1 accuracy of $53.7\%$, outperforming previous template-free and semi-template-based methods.
Low-Precision Reinforcement Learning

Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning. In contrast, this promising approach has not yet enjoyed similarly widespread adoption within the reinforcement learning (RL) community, partly because RL agents can be notoriously hard to train even in full precision. In this paper we consider continuous control with the state-of-the-art SAC agent and demonstrate that a na\"ive adaptation of low-precision methods from supervised learning fails. We propose a set of six modifications, all straightforward to implement, that leaves the underlying agent and its hyperparameters unchanged but improves the numerical stability dramatically. The resulting modified SAC agent has lower memory and compute requirements while matching full-precision rewards, demonstrating that low-precision training can substantially accelerate state-of-the-art RL without parameter tuning.
Analysis of the robustness of NMF algorithms

We examine three non-negative matrix factorization techniques; L2-norm, L1-norm, and L2,1-norm. Our aim is to establish the performance of these different approaches, and their robustness in real-world applications such as feature selection while managing computational complexity, sensitivity to noise and more. We thoroughly examine each approach from a theoretical perspective, and examine the performance of each using a series of experiments drawing on both the ORL and YaleB datasets. We examine the Relative Reconstruction Errors (RRE), Average Accuracy and Normalized Mutual Information (NMI) as criteria under a range of simulated noise scenarios.
Joint Scheduling and Throughput Maximization in Self-backhauled Millimeter Wave Cellular Networks

Integrated access and backhaul (IAB) networks have the potential to provide high data rate in both access and backhaul networks by sharing the same spectrum. Due to the dense deployment of small base stations (SBSs), IAB networks connect SBSs to the core network in a wireless manner without the deployment of high-cost optical fiber. As large spectrum is available in mmWave bands and high data rate is achieved by using directional beamforming, the access and backhaul links can be integrated in the same frequency band while satisfying quality-of-service constraints. In this work, we optimize the scheduling of access and backhaul links such that the minimum throughput of the access links is maximized based on the revised simplex method. By considering a probability based line-of-sight (LOS) and non-line-of-sight (NLOS) path loss model and the antenna array gains, we compare the achievable minimum access throughput of the IAB network with the network with only macro base stations, and study the effect of the network topology and antenna parameters on the achievable minimum throughput. Simulation results show that, for a broad range of parameter settings, the implementation of IAB networks improves the access minimum achievable throughput.
On the effect of scalar fields on Hawking radiation and quasinormal modes of black holes

The present thesis attempts to study the effect of scalar fields on Hawking radiation and quasinormal modes of black holes. We selected a static, spherically symmetric electrically charged black hole with an additional scalar 'hair' for our analysis. The scalar 'hair' is sourced by a scalar field, conformally coupled to the Einstein-Hilbert action. The scalar field can survive even in the absence of the black hole's electric charge and is characterized as a 'primary hair'. This scalar field changes the gravitational constant, and hence modifies the ADM (Arnowitt-Deser-Missner) mass of the black hole. The scalar field's strength is determined by a scalar 'charge' that manifests itself as an additive correction to the square of the electric charge in the standard Reissner-Nordstr\"{o}m metric. This seemingly simple modification leads to nontrivial physical implications.