Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational modeling of protein-RNA binding based on high-throughput in vitro data

Understanding post-transcriptional gene regulation is a key challenge in today’s biology. RNA-binding proteins (RBPs) play major roles in post-transcriptional regulation. Hence, RBP binding become a primary ﬁeld of research in the past decade. In addition, RBPs are instrumental in synthetic biology experiments, especially for in vivo RNA-tracking experiments. The emergence of high-throughput experimental techniques combined with computational methods and machine learning (ML) algorithms, provide the ingredients for a potential “big-data” analysis that can provide insights and better understanding of RBP binding. In this thesis, we utilize these innovations to achieve two main goals: (a) Improving the design process of RNA cassettes for RNA applications using deep learning models; (b) Comparing two major in vitro high-throughput datasets by k -mer binding models. For the ﬁrst goal, we trained a convolutional neural network on oligo-library-based experimental results containing thousands of binding sites for the phage coat proteins (CP) of bacteriophages MS2, PP7, and Q β (Pearson correlation of 0.45, 0.42 and 0.47, respectively) to vastly expand the space of binding sites. This network also enabled us to identify the structural and sequence features important for the binding of each CP. Using the network, our collaborators designed RNA cassettes that are easy to synthesize and validate in an in vivo experiment. For the second goal, we assessed the similarities and diﬀerences between binding models, represented as a list of k -mer scores, inferred from RNAcompete and RNA Bind-n-Seq. We tested how well these models predict in vitro and in vivo binding. Our results show that RNA Bind-n-Seq-and RNAcompete-derived models agree for most RBPs. RNA Bind-n-Seq-derived k -mer scores predict RNAcompete binding measurements quite well (Pearson correlation of 0.27), and both technologies produce k -mer scores that achieve comparable results in predicting in vivo binding (AUC of 0.7). When inspecting RNA structural preferences inferred from the data of RNA Bind-n-Seq and RNAcompete, we observed high concordance in binding preferences. Through our study, we developed a new k -mer score for RNA Bind-n-Seq, and extended it to include RNA structural preferences.
Centralized Control System for Smart Street Lights Based on STM32 and LoRa

In order to solve the problem of waste of power and human resources in the traditional street lamp control system, this paper designs a smart street lamp centralized control system based on STM32 and LoRa technology. The centralized control system consists of a centralized control device and multiple single lamp control devices. The STM32F103 is the MCU of the centralized control device. The system uses the characteristics of low communication cost and long transmission distance of LoRa technology to realize the communication between the centralized control device and the single lamp control device. The centralized control device uses the Modbus RTU protocol to communicate with the sensor module, and adjusts according to environmental factors. The brightness of the street lamp realizes the "on-demand lighting" of the street lamp, and an adaptive rate algorithm is proposed to optimize the communication of the LoRa network and improve the working efficiency of the system. This centralized control system combines embedded and wireless communication technology and has advantages in cost, reliability, energy saving and intelligent control. It can be applied to street lamp lighting control in various environments. It has a high degree of intelligence and has a strong market potential.
Improve the spot-like coding detection of U-net auto-encoder

The dot-like spray code on the product packaging has always been a difficult problem in industrial inspection due to its complicated background in the printing area, diverse characters in the code, and changeable fonts. With the powerful capabilities of deep learning in the field of computer vision, related algorithms have become popular solutions in the field of computer vision in recent years. The method based on convolutional autoencoders to achieve inkjet detection on food packaging boxes has become a feasible solution. This method takes the mask of the coding character area on the food packaging box as the final goal of network learning. The network inputs the picture with the coding area, and reconstructs the original background of the coding area. At the same time the network uses the residual channel attention mechanism to restore the details of the image, and at the same time introduces the gaussian operator to calculate the loss of network reconstruction. Since the convolutional auto-encoder is an unsupervised learning method, the training data does not require a large amount of manual labeling, and a good solution is proposed for scenarios such as small data sets in industrial production and difficulty in labeling. Through real-time testing of the coding data of the pipeline, it is verified that the method can effectively detect the coding area.
Modeling of Sandy Soil Compaction and Water Holding Capacity Due to Biogas Slurry Ameliorant

Model could be used to study the behavior of real systems such as hydrosphere (soil system) for improving their performance as basis of decision-making that promote the sustainability. Here, the study focused on SDGs point 15, how the agriculture particularly soil management should keep the life on land. Biogas slurry (BS) is a liquid-solid mix waste from livestock that useful for soil improving soil quality. Sandy soil has some limitations when used as media of plant cultivation, mainly poor in water holding capacity and less of organic content. This study aimed to measure and explore the sandy soil quality (soil compaction, SC and water holding capacity, WHC) due to BS application. A completely randomized design (CRD) in laboratory was adopted in this study where soil and BS put in a pot of diameter and height of 8.2 cm and 12 cm, respectively. The experiment was carried out with treatment of sandy soil as control (K), sandy soil and BS dose15 ton/ha (D1), 30 ton/ha (D2), and 45 ton/ha (D3). The soil was incubated 84 days that every 14 days (incubation time, IT) the SC and WHC were measured. Mathematical model and Two Ways ANOVA Test were applied to analyze the data. Logistic equation was acceptable (R2 > 80%) to describe soil compaction behavior in sandy soil with BS ameliorant. The SC rate of K, D1, D2, and D3 treatments were - 0.0251, -0.0524, -0.0280, and -0.0360, respectively. This study revealed BS and IT significantly affect SC and WHC of sandy soil with BS ameliorant.
Workspace Analysis of Cable-driven Continuum Manipulator with Cable-constrained Synchronous Rotating Mechanism

In this paper, a cable-driven continuum manipulator with cable-constrained synchronous rotating mechanism (CCSRM) is studied. The purpose of this paper is to give the workspace for the cable-driven continuum manipulator, where the friction effects and the allowable range of cable tension are considered. The problem of solving workspace is transformed into a linear programming problem, which is solved by dual-simplex algorithm. On this basis, the influence of the pre-tightening force of the connecting cable on the workspace is analyzed. In addition, the tension of the motor on each driving cable is optimized to minimize the control error. Finally, the load range that the manipulator can withstand within the workspace is analyzed.
Polyethylene Terephthalate Waste as a Mobilizer for Heavy Metals in Gold Mine Tailings

The accumulation of gold mine tailings (GMT) and polyethylene terephthalate plastic waste (PW) continue to impact the ecosystem negatively, and strategies are required to counteract these issues to maintain a sustainable environment. This study investigated the use of PW as a binder to mobilize heavy metals in GMT. Plastic was used as a binder to develop specimens. were developed using plastic as a binder, the geotechnical and geochemical properties of the specimens were studied. The leachability of heavy metals from developed specimens was also studied. GMT and PW ratios of 80:20; 70:30, and 60:40 were investigated. The mineralogy and elemental composition of GMT and developed specimens showed that was quartz and silicon oxide were the predominant constituent in the materials. The specimen with the highest unconfined compressive strength yielded the lowest water absorption. The concentration of heavy metals in the PW-based specimens was lower than in the raw GMT. Waste plastic can be used as a sustainable binder to mobilize contaminants in GMT. The proposed solution can solve and prevent environmental pollution caused by GMT and WP.
Local to global Tracker: A Siamese Network for Long-term Tracking

Visual object tracking is a basic task in computer vision, which can be applied to different applications. Former methods of single object tracking problem demands to perform target state estimation according to object position of previous frame, but these methods are poorly capable of handling the fast movement of the target. In this paper, we introduce a Siamese network for single object tracking task. The network consists of two branches, one of which is the classification branch used to predict positive or negative samples, and the other is the regression branch used to predict the specific location of the object in sequence. In addition, we propose a local to global algorithm for long-term tracking to expand the search region when the target is out-of-view or full occlusion. Experiments show that our tracker achieves very promising results on VOT2018LT datasets while running at over 89 FPS.
Designing a Mobile Application for Tourism in the Architectural Heritage of Tehran

Tehran has many valuable historical buildings as historical and populous capital. However, the increasing growth of the city and the uncontrolled prevalence of construction have endangered the city's architectural heritage in recent years. Introducing architecturally valuable buildings to people, and planning to visit them can reduce the risk of demolition. Recently, mobile applications and social networks have provided new facilities to boost tourism in Tehran following the spread of the Internet and the use of smartphones. The aim of this research is to provide a solution for tourism prosperity in Tehran's architectural heritage. This solution is defined in the form of a smartphone- based application. Users in this app can search the list of Tehran's architectural heritage and start tours to them. The application tries to complete its database of historical buildings and tours in interaction with users and under the supervision of experts. This research includes ideation, preliminary studies, design of the initial model, and proposal of the mechanism and user interface of the above application.
Informatica &Economie

Reddit has made its mark online as one of the largest platforms for social interaction. The platform works through user appreciation, in the sense that more popular posts and comments get shown to more visitors. Users can grant “Reddit gildings” to posts and comments they feel deserve extra recognition. This gives the comment a golden hue and badge, while the author receives “Reddit Premium”. This thesis aims to predict whether or not a comment receives Reddit gildings through machine learning using the comment’s properties. Our data contains all publicly available comments on all posts from November to December 2018. For each comment, a set of 24 features is gathered. The results show that there is some predictability with an AUC of 0.73. The features that have a lot of inﬂuence on our machine learning model are the comment length based features. The number of comments on average per post on the subreddit is also of high importance. The most important feature however, is the average karma of the author. The results are helpful to scholars to further explore the inﬂuence of variables on the likelihood of gaining Reddit gildings and for Reddit users seeking to gain a high social status on the Reddit platform.
Neuropharmacological effects of Aconiti Lateralis Radix Praeparata

Aconiti Lateralis Radix Praeparata (Fuzi in Chinese), which are the lateral roots of Aconitum Carmichaelii Debx, is widely used in China to treat many neurological diseases. Fuzi, in its various forms, has many neuropharmacological effects. It can act as an analgesic and help with depression, epilepsy, and dementia. However, the neuropharmacological effects of Aconiti Lateralis Radix Praeparata are seldom comprehensively reviewed. In this review, the neuropharmacological activities of some components contained in Aconiti Lateralis Radix Praeparata are considered. These include aconitine, mesaconitine, hypaconitine, total alkaloid, polysaccharide‐1, benzoylmesaconine, fuziline, songorine, and napelline. We also specifically discuss the antidepressant effects of total alkaloids and polysaccharide‐1. This review may provide a theoretical basis for further utilization of Aconiti Lateralis Radix Praeparata for diseases that affect the central nervous system.
Increasing Robustness of DistilBERT QA System with Few Sample Finetuning and Data Augmentation

NLP systems often cannot accurately generalize information beyond their training domain because they learn superficial correlations between words rather than understanding their meaning. In order to build a model that demonstrates language understanding with minimal exposure to the domain, we must build a robust model that can quickly adapt to new domains. Our project attempts to build upon the existing DistilBERT model, a distilled, more manageable, scalable, and environmentally-friendly version of BERT, to increase language understanding by training and testing the model on a reading comprehension task. We leveraged few sample finetuning and data augmentation via back-translation and synonym replacement in our attempt to improve reading comprehension performance on out of domain data . While finetuning yielded promising results, we found that data augmentation produced negligible results compared to the baseline
The relationship between high-presentation asthma days in Melbourne, Australia and modelled thunderstorm environments

Epidemic asthma events represent a significant risk to emergency services as well as the wider community. In south-eastern Australia, these events occur in conjunction with relatively high amounts of grass pollen during the late-spring and early-summer, which may become concentrated in populated areas through atmospheric convergence caused by a number of physical mechanisms including thunderstorm outflow. Thunderstorm forecasts are therefore important for identifying epidemic asthma risk factors. However, the representation of thunderstorm environments using regional numerical weather prediction models, which are a key aspect of the construction of these forecasts, have not yet been systematically evaluated in the context of epidemic asthma events. Here, we evaluate diagnostics of thunderstorm environments from historical simulations of weather conditions in the vicinity of Melbourne, Australia, in relation to the identification of epidemic asthma cases based on hospital data from a set of controls. Skillful identification of epidemic asthma cases is achieved using a thunderstorm diagnostic which describes near-surface water vapor mixing ratio. This diagnostic is then used to gain insights on the variability of meteorological environments related to epidemic asthma in this region, including diurnal variations, long-term trends, and the relationship with large-scale climate drivers. Results suggest that there has been a long-term increase in days with high water vapor mixing ratio during the grass pollen season, with large-scale climate drivers having a limited influence on these conditions.
Direct democracy is no computer game

After years of trials in various cantons and communes, the result is on the table: e-voting has proved unsuitable in every respect. Nevertheless, the Federal Council has been insisting for two years on the widespread introduction of e-voting. In June 2018, it stated that, in the opinion of its expert group, electronic voting “could be developed as a secure and trustworthy voting channel”. Indeed, it spoke of “more than 200 successful attempts” in the country and did not say a single word about the numerous serious breakdowns due to which various software had to be taken out of circulation and many cantons have abolished e-voting again or not introduced it at all. Even the many critical responses received during the consultation process in the autumn of 2018 did not succeed in making the Federal Council see reason. In order to put a stop to these proceedings in Federal Berne, the collection of signatures for the popular initiative for an e-voting moratorium was launched on 16 March 2019. In the most glorious spring weather, the initiative committee gathered at the archway on the Bahnhofplatz in Lucerne for the “kick-off event”: a cheerful and highly motivated group of politicians across all political parties, and other citizens, most of them from the IT sector, almost all of them under the age of 30. It is now up to us citizens to make the initiative known: collecting 100,000 signatures means hundreds of thousands of face to face discussions. In the following you will read the initiative text and the main arguments for the initiative, as well as the recording of my discussions with the president of the initiative committee, National Councillor Franz Grüter (SVP LU – Swiss People’s Party, Lucerne), and with Hernani Marques of the Chaos Computer Club Switzerland, and also statements by committee members Jonas Ineichen, vice-president Juso LU (Young Social Democrats, Lucerne), and Simon Schlauri, Cantonal Councillor Grünliberale (GLP ZH – Green Liberal Party Zurich).
Is Feature Engineering Futile?

Question answering is a problem that we have approached from a naive perspective, using word embeddings as the extent of our feature engineering. While this has produced mediocre results, applying additional feature engineering can improve upon this baseline. Some well motivated changes produced improved results, such as an exact match feature, while others actually harmed the performance compared to the baseline, a combination of exact match and a lemma match feature. My findings indicate that enhanced feature engineering is useful for fine-tuning a model’s performance on a task, but it will not produce significantly improved results.
AlterNet: Improving Span Conditioning for Convolutional Neural Network Q&A Systems

Recent improvements in the field of Q&A have seen a progression towards using convolutional neural networks (CNNs) due to improved training and inference speeds. The QANet model, first introduced in Yu et al. (2018) [1], combines CNNs with self-attention, a concept first seen in the Transformer architecture introduced in Vaswani et al. (2017) [2]. We build upon the BiDAF model described in Seo et al. (2016) [3] to create our own implementation of the QANet model, achieving a single-model F1 score of 65.47, 4.48 points higher than the baseline BiDAF model. We then complement the QANet model with our own extension on the conditional output layer described in Kim and Wolff [4], achieving a dev F1 score of 64.51. Our ensembled model achieves a test F1 score of 63.998.
Using Weakly Supervised Learning to Enhance Text Classification

In today’s day and age, companies are making concerted efforts every day to be digitally advanced by automating their processes to increase accuracy, save time, and cut costs. They are trying to incorporate predictive algorithms that rely on machine learning modules to increase revenue and extract valuable insights that allow them to stay relevant and competitive in today’s market. As a senior data scientist working to help clients achieve these goals, a problem I see every day is how predictive models are limited in their performance by the amount of data provided. My goal is to leverage weakly supervised learning to generate data that can not only increase performance, but also prevent overfitting (common in model trained with very less data). To close this gap, I explore how data generation can amplify performance of classification models. I create a pipeline that aims to advance results shown in a researched approach by Meng, Yu, et al. I combine language generation through a combination of skip-gram and LSTM models which uses keywords and existing labeled documents to build a vocabulary and learn semantics to generate new data mimicking the data provided with a BERT classification model. The results show that my proposed hybrid approach outperform both the researched WeSHClass method and BERT alone.
Improving BiDAF Heuristically and with Self-Attention for SQuAD 2.0

Our primary goal in this project was to build a QA system that improves upon a provided baseline’s performance on the SQuAD 2.0 dataset, doing so through three major approaches. We first explore the performance of the provided baseline with and without a character-level embedding layer. Secondly, we explore the performance of the provdided baseline with the character-level embedding layer as well as an added simple context/type matching heuristic using a binary-like word-in-question feature (as discussed further later in this work). Thirdly, we add a self-attention layer on top of the existing BiDAF attention layer combined with character embeddings, and explore the performance of this model with and without the aforementioned binary-like word-in-question feature. We find that the model subsisting of the baseline with character embeddings yielded the best performance on the test set, yielding an EM score of 59.104 and an F1 score of 62.673.
Tri-bend TM_(01)-TE_(11) mode converter with input-output aligned on the same axis

A serpentine TM01-TE11 mode converter with input and output ports aligned on the same axis was designed by adopting phase rematch technique. The converter is formed with three constant-curvature bent waveguides and two straight waveguides. Numerical investigations on this mode converter were presented with a general optimizing code employing coupled mode theory. A mode converter of this kind centered at 7.0 GHz was designed. The efficiency of the conversion from TM01 to TE11 exceeds 99% at 7.0 GHz and 90% over the frequency range of 6.5 to 7.5 GHz. Simulations with FDTD and FEM methods verified the theoretical designing results respectively.
The Association between Body Mass Index and Cardiovascular-Related Mortality: A Systematic Review and Meta-Analysis

--- The relationship between body mass index (BMI) and cardiovascular (CVD) related mortality has been extensively investigated in the general population. However, the research on this matter is relatively limited, and the conclusions from these studies are were included in this study. Random and Fixed-effects summary of CVD-related mortality HRs were 1.72 (95% CI, 1.51-1.97) for underweight, 1.40 (95% CI, 1.15-1.69), and 1.33 (95% CI, 1.17-1.52) for obesity. Being underweight and obese might increase CVD related mortality in all populations, including males and females.