Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Match-LSTM for SQuAD v2.0

In 2016, Wang and Jiang built a novel Question Answering machine comprehension model[1], based on LSTM and Pointer Net using the SQuAD 1.0 datastet. This model was considered state-of-the-art at the time and capable of far outperforming previous logistic regression models. However, since its publication, SQuADv2.0 has been released, leaving the model somewhat antiquated. In this project, we aim to implement the model described in the original paper, adapt the model to work with the SQuAD 2.0 Data set and perform updates to the model architecture to improve its performance against the benchmark BiDAF model. Our implementation of the Match-LSTM model trained on SQuAD v2.0 with an F1 score of 51.88 and EM score of 54.51 performs a bit lower compared to the Baseline BiDAF Model. Throughout the project, there were mutliple struggles when attempting to adapt it to the SQuAD v2.0 dataset that we were unable to resolve such as preventing the model from marking too many answerable questions as unanswerable. Our initial thoughts were that the prevalence of unanswerable questions in the dataset skewed our model towards predicting all other questions as unanswerable. However, though it does seem to demonstrate some improvement, upsampling answerable questions still does not seem to completely fix this issue. Overall, this model produces reasonable performance with modest modifications and shows promise if given further time to adapt the model to the SQuAD v2.0 dataset. However, we realized that the Match-LSTM model has an immensely long training time of 15 hours when compared the baseline BiDAF model which completed training in about 3 hours. And this continued to hamstring our progress due to resource limitations as well as inability to rapidly iterate upon past attempts.
Study of Lactobacillus species MTCC 10093 as Probiotic Bacteria

Due to inhibitory effects of arsenic the selected lactobacilli bacteria species, MTCC 10093 was studied as biological preservative probiotic. For the purpose of innovative information regarding referential lactic acid producing bacteria, MTCC 10093 species were isolated from different agricultural soil fields were identified by their biochemical tests through comparing their sugar differential fermentation patterns. Antibacterial activities were confirmed by agar spots, diffusion illustrations and through the blank disk method. Enzymatic sensitivities of supernatant fluids and concentrated cells free culture after treatment with α-amylase, lysozyme and trypsin were determined. It indicated that the isolated bacteria had strong activity against indicator strains and their antibacterial activity was stable at 100ºC for 10 mints whereas at 56ºC for 30 mints their activities were lost after autoclaving. On this basis, maximum production of plantaricin was obtained at 25 - 30ºC at pH 6.5 and thus the lactobacilli MTCC 10097 species were produced antimicrobial activity with heat stability bacteriocin, hence, these specified species of lactobacilli may be considered as healthy and effective probiotic diet. ,
Lecture 2: Linear Regression

We will begin this course with a review of linear regression, i.e. finding a line of best fit to training data. Although studied for hundreds of years, linear regression remarkably continues to serve as a simple model that provides invaluable intuition into modern machine learning phenomena. For example, one can provably establish the generalization benefits of over-parameterization given by the double descent curve in linear models [1, 2, 3, 5]. Importantly, our analysis of linear regression will provide us tools for developing nonlinear regression methods, namely kernel regression.
Optimization study of dynamic vibration absorbers parameters and distribution for its application on railway tunnels for the reduction of railway-induced vibration

The objective of this project is to develop an algorithm able to obtain the optimal distribution of Dynamic Vibration Absorbers (DVAs) in underground railway infrastructures for reduction of railway-induced vibrations. The scope of the project is limited to the application of DVAs on the tunnel's walls. The Green's functions of the superstructure/tunnel/ground/building system and the dynamic response of the train will be input parameters of the algorithm and they will be provided by the research group.
Modelling and Simulation of District Heating Networks

In the present paper a detailed mathematical model is derived for district heating networks. After semidiscretization of the convective heat equation and introducing coupling conditions at the nodes of the network one gets a high-dimensional system of differential-algebraic equations (DAEs). Neglecting temporal changes of the water velocity in the pipes, the numerical solutions do not change significantly and the DAEs have index one. Numerical experiments illustrate that the model describes the real situation very well.
Clinical Impact of IDH1 Mutations and MGMT Methylation in Adult Glioblastoma Multiforme

Background. Genetic aberrations and epigenetic alterations have been reported in different types of cancer. Impact of Isocitrate dehydrogenase1 (IDH1) and O6-methylguanine-DNAmethyltransferase (MGMT) in glioblastoma multiforme (GBM) have been of great interest due to their implications in prediction of prognosis of several types of cancer. Authors aimed to investigate the clinical role of IDH1 mutation and MGMT methylation pattern among GBM patients versus non-neurooncological diseases (NND) patients and their impact on survival criteria. Methods. Formalin-Fixed Paraffin-Embedded (FFPE) tissue sections of 58 GBM and 20 non-onconeurological diseases patients were recruited and IDH1 mutation were detected using Cast-PCR technology and MGMT methylation was detected using Methyl II quantitative PCR approach. Their results were assessed with other clinicopathological criteria and assess its correlation with survival patterns. Results. IDH1 mutation was detected among 15 GBM cases (15/58) and it was not reported among NND (P=0.011). Receiver operating characteristic (ROC) curve were plotted to discriminate between MGMT methylation among studied groups. Patients with MGMT methylation [≥] 66% was reported as high methylation, which was recorded significantly in 51.7% and 100% of GBM cases and NND, respectively. Both showed significant difference with performance status, while MGMT methylation was significantly related with tumor size and tumor location. IDH1 mutation and MGMT methylation reported significant increase with GBM patients revealed complete response to treatment. Survival pattern was better for IDH1 mutation and MGMT high methylation as compared to IDH1 wild type or MGMT low-moderate methylation, respectively and favorable survival was detected when both were combined than using either of them alone. Conclusion. Detection of IDH1 mutation and MGMT methylation among GBM patients could aid in prediction of their response to treatment and their survival patterns, and their combination is better than using any of them alone.
Analysis of the Effect of Ordering Policies for a Manufacturing Cell Transitioning to Lean Production

Over the past two decades, Lean Production has begun to replace traditional manufacturing techniques around the world, mainly due to the success of the Toyota Motor Company. One key to Toyota’s success that many American companies have not been able to emulate is the transformation of their suppliers to the lean philosophy. This lack of supplier transformation in America is due to a variety of reasons including differences in supplier proximity, supplier relationships, supplier performance levels, and the ordering policies used for supplied parts. The focus of this research is analyzing the impact of ordering policies for supplied parts of a manufacturing cell utilizing Lean Production techniques. This thesis presents a simulation analysis of a multi-stage, lean manufacturing cell that produces a family of products. The analysis investigates how the ordering policy for supplied parts affects the performance of the cell under conditions of demand variability and imperfect supplier performance. The ordering policies evaluated are a periodic-review inventory control policy (s, S) and two kanban policies. The performance of the cell is measured by the flowtime of the product through the cell, the on-time-delivery to their customer, the number of products shipped each week, the amount of work-in-process inventory in the cell, the approximate percentage of time the cell was stocked out, and the average supplied part inventory levels for the cell. Using this simulation model, an experimental analysis is conducted using an augmented central composite design. Then, a multivariate analysis is performed on the results of the experiments. The results obtained from this study suggest that the preferred ordering policy for supplied parts is the (s, S) inventory policy for most levels of the other three factors and most of the performance measures. This policy, however, results in increased levels of supplied part inventory, which is the primary reason for the high performance for most response variables. This increased inventory is in direct conflict with the emphasis on inventory and waste reduction, one of the key principles of Lean Production. Furthermore, the inflated kanban policy tends to perform well at high levels of supplier on-time delivery and low levels of customer demand variability. These results are consistent with the proper conditions under which to implement Lean Production: good supplier performance and level customer demand. Thus, while the (s, S) inventory policy may be advantageous as a company begins transitioning to Lean Production, the inflated kanban policy may be preferable once the company has established good supplier performance and level customer demand.
Question-Answering Using Modified QANet

Question-answering (QA) systems aim to understand a natural language passage and answer questions about the passage. In this project, we build a QA system for the Stanford Question Answering (SQuAD) 2.0 dataset. Our goal is to hand-implement and tune deep learning language models to achieve strong performance on this task. Our primary contribution is our exploration of various modifications to the QANet architecture. Most notably, we show that the coattention mechanism can not be a substitute for the context-query layer. Using our implementation of QANet, we obtained a performance of F1/EM of 69.104/65.754 for the dev set and F1/EM of 65.992/62.637 on the test set
Question Answering on SQuAD 2.0 using BiDAF and QANet

In this project, we explored the BiDAF model [1] and QANet [2] to tackle SQuAD 2.0. First we improved the BIDAF model by implementing character level embeddings. Next, we implemented our own version of QANet, a non-recurrent model whose encoder consists exclusively of convolution layers (which model local interactions), self-attention layers (which model global interactions), and a multi-layer perceptron feed-forward net. We experimented with QANet’s architecture by varying the number of encoder blocks in the model encoder block stack. We found that our best model (F1 score of 66.05 and an EM score of 62.87 on the test set) came from following the original authors’ QANet architecture specifications (namely, 7 encoder blocks in the model encoder block stack and the use of layer dropout). Finally, we performed qualitative analyses which showed that our models performed worse on questions that asked for descriptions and and better on questions that had more distinctive expected answers such as date questions.
Risk management regulation and corporate demand for reinsurance in the Spanish autarky (1940-1952) Risk management regulation and corporate demand for reinsurance in the Spanish Autarky (1940-1952)

This paper examines the changes to reinsurance regulation implemented during the Spanish autarky to analyze how the restriction of imports of financial services affects the performance of the insurance and risk management industry. We build a novel dataset on domestic insurers and reinsurers to identify the effects of the barriers to purchasing foreign reinsurance on corporate strategies and ultimately, on the determinants of reinsurance demand. We find that, in spite of a chronic dependence on foreign risk exchange networks, Spanish insurers faced this period of isolation by means of an intensive use of captive reinsurance. This strategy helped to alleviate financial distress and resulted in the substitution of foreign reinsurers by domestic companies, as far as the restrictive regulation was in force.
The natural history of treated Parkinson’s disease in an incident, community based cohort

Background Our understanding of the natural history of idiopathic Parkinson’s disease (PD) remains limited. In the era of potential disease modifying therapies, there is an urgent need for studies assessing the natural evolution of treated PD from onset so that relevant outcome measures can be identiﬁed for clinical trials. No previous studies have charted progression in unselected patients followed from the point of diagnosis. Methods A representative cohort of 132 PD patients was followed from diagnosis for up to 7.9 years (mean 5.2 years). Comprehensive clinical and neuropsychological evaluations were performed every 18 months. Disease progression was evaluated using well validated clinical measures (motor progression and development of dyskinesia on the Uniﬁed PD Rating Scale and Hoehn e Yahr scale, dementia onset according to DSM-IV criteria). Multi-level linear modelling was used to chart the nature and rate of progression in parkinsonian symptoms and signs over time. The prognostic importance of baseline demogr‘aphic, clinical and genetic variables was evaluated using survival analysis. Results Axial (gait and postural) symptoms evolve more rapidly than other motor features of PD and appear to be the best index of disease progression. Conversely, conventional outcome measures are relatively insensitive to change over time. Earlier onset of postural instability (Hoehn e Yahr stage 3) is strongly associated with increased age at disease onset and has a signiﬁcant impact on quality of life. Conclusions Dementia risk is associated with increased age, impaired baseline semantic ﬂuency and the MAPT H1/H1 genotype. The efﬁcacy of disease modifying therapies may be more meaningfully assessed in terms of their effects in delaying the major milestones of PD, such as postural instability and dementia, since it is these that have the greatest impact on patients.
Attending in BiDAF and QANet

In this paper, we build a question answering (QA) system and apply it on the Stanford Question Answering Dataset (SQuAD) version 2.0. Our goal is to achieve strong performance on this task without using pre-trained language models by exploring two of the most widely used architectures for this task: RNN-based and Transformer-based models. Our primary contribution are two high-performing models: a BiDAF model with character-embeddding, self-and co-attention and a QANet model. We analyze their performance and also provide some qualitative analysis of our different model’s prdicted answers. Using our BiDAF model with self-and co-attention, we achieve an F1 score of 63.044 and an EM score of 59.476 on the unseen SQUAD 2.0 test set.
Reconstructing solar magnetic ﬁelds from historical observations V. Sunspot magnetic ﬁeld measurements at Mount Wilson Observatory

Context. Systematic observations of magnetic ﬁeld strength and polarity in sunspots began at Mount Wilson Observatory (MWO), USA in early 1917. Except for a few brief interruptions, this historical dataset has continued until the present. Aims. Sunspot ﬁeld strength and polarity observations are critical in our project of reconstructing the solar magnetic ﬁeld over the last hundred years. We provide a detailed description of the newly digitized dataset of drawings of sunspot magnetic ﬁeld observations. Methods. The digitization of MWO drawings is based on a software package that we developed. It includes a semiautomatic selection of solar limbs and other features of the drawing, and a manual entry of the time of observations, measured ﬁeld strength, and other notes handwritten on each drawing. The data are preserved in an MySQL database. Results. We provide a brief history of the project and describe the results from digitizing this historical dataset. We also provide a summary of the ﬁnal dataset and describe its known limitations. Finally, we compare the sunspot magnetic ﬁeld measurements with those from other instruments, and demonstrate that, if needed, the dataset could be continued using modern observations such as, for example, the Vector Stokes Magnetograph on the Synoptic Optical Long-term Investigations of the Sun platform. see- ing in the time of observations correspond to a shading of the coelo-stat mirror tracking the Sun by the support pillar and secondary mir- ror (which reﬂects light to the main telescope mirror) in early morning hours (about 15:00 UT) and the break for the lunch hour at the MWO (about 20:00 UT). The lunch break was a formal event at MWO until about 1984, when the operations of solar telescopes were transferred from Carnegie observatories to UCLA. Early morning horizontal gap (15:00 UT) seems to disappear after 1970, which suggests that at that time, the morning observations may have been taken with the ﬁrst ﬂat mirror moved to the east position, and thus having a strongly oblique angle between the two mirrors.
Link Recommendations for PageRank Fairness

Network algorithms play a critical role in various applications, such as recommendations, diffusion maximization, and web search. In this paper, we focus on the fairness of such algorithms and in particular of PageRank. PageRank fairness refers to a fair allocation of the PageRank weights among the nodes. We consider the effect of the network structure on PageRank fairness. Concretely, we provide analytical formulas for computing the effect of edge additions on fairness and for the conditions that an edge must satisfy so that its addition improves fairness. We also provide analytical formulas for evaluating the role of existing edges in fairness. We use our findings to propose efficient linear time link recommendation algorithms for maximizing fairness, and we evaluate them on real datasets. Our approach can be seen as an effort towards making the network itself fairer as opposed to making fairer the network algorithms, or their outputs.
a Case study of Mobile Learning Pilot Project in K-12 schools

This case study reports findings from a four-month pilot project of one-to-one learning with iPads in four 9 grade Geography classrooms in a large K-12 school district in the United States. The findings of the study revealed many promising opportunities and technical challenges for both teachers and students. The positive impact of iPad integration on student learning includes active engagement, increased time for projects, improved digital literacy, and digital citizenship. The challenge for student learning is mainly distraction by the multitude of irrelevant apps and Websites. With regard to instructional activities, the positive impact includes the implementation of student-centered activities and enhanced teaching practices with updated information. The challenges include a lack of teacher-selected apps and the need for more time to prep and conduct training. Faculty professional development has also played an important role on teaching practices. This study recommends continuous faculty development and student learning support through innovative approaches to transform one-to-one learning with iPads in the classroom.
CS 234 : Reinforcement Learning – Problem Session # 3 Winter 2021-2022 Problem 1

Suppose we have an infinite-horizon, discounted MDP M = 〈S,A,R, T , γ〉 with a finite state-action space, |S×A| <∞ and 0 ≤ γ < 1. For any two arbitrary sets X and Y, we denote the class of all functions mapping from X to Y as {X → Y} , {f | f : X → Y}. In the questions that follow, let Q,Q′ ∈ {S ×A → R} be any two arbitrary action-value functions and consider any fixed state s ∈ S. Without loss of generality, you may assume that Q(s, a) ≥ Q′(s, a), ∀(s, a) ∈ S ×A.
All SQuAD Needs is Self-Attention: Char-QANet

Question answering (QA) is a key problem within natural language processing (NLP), and initial solutions to this problem, such as using LSTMs, perform decently well but still not nearly at the level of human benchmarks. (Rajpurkar & Jia et al. ’18)[1] Therefore, state of the art models have investigated the use of self-attention, a key feature of the Transformer model. In this project, we will use self-attention building off QANet and apply it to the SQuAD 2.0 dataset. (Wei Yu1 & Dohan et al. ’18)[2] In the process of building a robust model to improve F1 and EM scores, we have explored the addition of custom character embeddings as well as introducing coattention for the encoder layer on top of a Bidirectional Attention Flow (BiDAF) architecture. Each customization of the baseline BiDAF system provided us provided certain improvements, but in the end the introduction of QANet [2] with its version of character embeddings performed the best in terms of F1 and EM scores.
Robust Cross-domain Question Answering via Adversarial Training and Document Embedding Clustering

State-of-the-art question answering models often perform well on in-domain questions, but fail to generalize to out-of-domain questions. In this project, we leverage domain adversarial training [LKP19] to encourage the model to learn domain-agnostic representations, thereby improving generalization performance. We further re-examine the definition of a domain, where we explore generating domain labels using document embeddings and k-means clustering rather than simply equating domains with dataset names. We show that the embedding-based labeling approach results in improvement over the dataset-based labeling approach
ZnO-Based Inverters for Flexible Electronics

In this paper, we report the development of low power and low temperature zinc oxide (ZnO)-based inverters. Logic inverters were evaluated as function of gate dielectric thicknesses and several operation bias voltages. Hafnium oxide (HfO2) was used as gate dielectric. The HfO2 dielectric thickness was evaluated from 15 to 90 nm. TFTs carrier mobility decreases as the dielectric thickness increases for a maximum bias fixed to 5 V, while threshold voltage (VTH) and threshold voltage shift (∆VTH) increases. Mobility, VTH and ∆VTH also have a direct correlation with applied gate voltage. Inverters fabricated show high gains (up to 150) when biased at 20 V. A low-voltage, low temperature ZnO inverter operating at VDD= 2.5 V with gains ~20 is also demonstrated. Devices operating at VDD= 1 V have gains ~10. This technology is a potential candidate for low-power consumption circuits for portable and low-cost applications.
Evolution of the Efficiency of a Photovoltaic/Supercapcitor System with Aging

The evolution of the optimization of photovoltaic/supercapacitor system according to its age is inquired. The system is modeled by an accurate equivalent circuit. The durations and energies of charge/discharge cycles are calculated for a 10 year use. The temporal and energy efficiencies are then deduced according to time, and optimal values are hence extracted. Results show that the optimal efficiencies can be kept constant over time at a value around 90% for the energy. However, the amount of energy stored and released by the supercapacitors is reduced over the years and the response time becomes shorter. So, the behavior of the system is modified with aging regarding its initial nominal functioning. This has to be considered to know the real profitability of the system.