Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mathematics of Phylogenomics

The grand challenges in biology today are being shaped by powerful high-throughput technologies that have revealed the genomes of many organisms, global expression patterns of genes, and detailed information about variation within populations. We are therefore able to ask, for the ﬁrst time, fundamental questions about the evolution of genomes, the structure of genes and their regulation, and the connections between genotypes and phenotypes of individuals. The answers to these questions are all predicated on progress in a variety of computational, statistical, and mathematical ﬁelds. The rapid growth in the characterization of genomes has led to the advancement of a new discipline called phylogenomics. This discipline results from the combination of two major ﬁelds in the life sciences: genomics, i.e., the study of the function and structure of genes and genomes; and molecular phylogenetics, i.e., the study of the hierarchical evolutionary relationships among organisms and their genomes. The objective of this article is to oﬀer mathematicians a ﬁrst introduction to this emerging ﬁeld, and to discuss speciﬁc mathematical problems and developments arising from phylogenomics.
Hand Written Digit Prediction Using Neural Network

Handwritten grants a skill away from micro to predict the calligraphy digits picked escape starting points such as profile, test, meet screens, and others. It is an arduous duty as a gadget model as typed in numerals is not at all ideal to must live made with a lot of distinct flavours. In this paper, we will use various ML algorithms like CNN, SVM, Decision Tree, Gaussian Naive Bayes, etc.We import information from the image net dataset for both testing and training the machine design. MNIST includes 70000,28*28 images of handwritten digits ranges 0 to 9.
Model-Based Design of a Buck ZVS Quasi-Resonant DC-DC Converter

Power electronic devices and electrical energy conversion systems are widely used in the realization of the concept of green and circular economy. In view of the ever-increasing share of the energy that is converted, the questions related to the energy indicators of electronic converters are extremely important. One of the concepts for increasing the efficiency of power circuits is the use of soft switching based on the use of resonant switches. These circuits are known as quasi-resonant. The work examines the principle of operation, the main ratios describing electromagnetic processes and a mathematical model of a quasi-resonant Zero Voltage Switching Buck DC - DC converter. Then, on the basis of numerical experiments conducted with the model, the optimal values of the resonant elements were determined with a view to minimal voltage loading of the semiconductor switches. The proposed design approach is suitable for use in power circuits for which no suitable design methodologies have been established.
“Ancona?” Aha! that’s her name! Tip-of-the-tongue experiences

Someone asks me her mother’s surname. I try to retrieve it from my memory but fail to do so. I am, however, sure that in a few moments it will come back to me. As we often say, ‘‘It’s on the tip of my tongue. ’’ At some point, I shout: ‘‘Ancona! ’’ The name is back, as if the obstacles blocking it had disappeared completely, although I have done nothing to remove them. The word clicks! We often have words on the tips of our tongues: the beginning of a poem, the title of a novel, or simply a name. Users of sign language have signs on the tips of their fingers (Thompson et al. 2005). To keep things simple, I shall focus on tip-of-the-tongue phenomena (TOTs) concerning names. Let me first say that TOTs are experiences that occur after the failed retrieval of a particular name. The strange thing about these experiences is that when someone asks you ‘‘Do you know X’s surname? ’’ you respond ‘‘Yes. ’’ And if you are then asked what the name is, you are unable to answer right away, although you are certain that retrieval is imminent. Hence, there is a failure, albeit one accompanied by an anticipation of success. Finally, if retrieval does occur and the word is recognized, the click experience you enjoy (i.e., the recognition) is not simply fulfillment of the anticipation, but also an experience of familiarity—you recognize the name as a name with which you were previously acquainted. This article focuses on TOT experiences rather than the mechanisms underpinning them. Theorists disagree about the nature of such experiences, while acknowledging their intriguing and insidious character. For example, Bennett Schwartz comments:
The Study of Option Pricing Problems based on Transformer Model

Option pricing is an important topic in the field of quantitative finance. The traditional Black-Scholes model formulation requires a large number of assumptions, which often does not exist in practice, and the statistically-based regression analysis and time series methods have poor fitting ability for non-stationary data. Deep learning has advantages over traditional econometric models in identifying the structure and patterns of data, and can effectively learn the nonlinear and non-stationary characteristics of time series, which is more suitable for the study of option pricing problems. The Transformer model has greater advantages over the traditional recurrent neural network model in the processing of time series data, mainly in terms of performance and speed. In this work, we will compare different models and get the deep learning model with the strongest prediction ability. Based on the collected data related to 50 ETF options and stocks in the Chinese market for empirical analysis, it is demonstrated that the Transformer model outperforms the traditional deep learning model in time series prediction.
Analog Tuning of Floating-Gate Cells with Sub-Elementary Charge Accuracy for In-Memory Computing Applications

The process of ESF3 memory cell analog tuning for neuromorphic applications was studied with a single-electron accuracy. It was experimentally shown that the number of electrons injected to the floating gate during incremental programming pulses follows Poisson distribution, which sets a fundamental limit for the minimum width of the tuned cell current/threshold voltage distributions. The new method for analog tuning, based on capacitive coupling, allows one to override the electron charge granularity limitation, and to achieve the sub-elementary charge tuning accuracy.
Strain Engineering in Modern Si Trench Power MOSFETs — A Performance Booster for Future Generations

The ongoing trend towards electrification and digitalization requires the continuous improvement of high frequency, high power density switching solutions such as split-gate Si trench power MOSFETs. However, their pure figure of merit (FOM) related performance improvement through conventional scaling has reached a physical limit. Similar to the approaches for planar MOSFETs, the device performance can also be improved by strain engineering, leading to a modified charge carrier mobility in the electrically active monocrystalline Si. Therefore, a novel strain engineering approach is proposed for Si trench power MOSFETs, adding a strain functionality to the trenches on top of their charge compensation purpose. It is shown that the device performance is increased by up to 16.8%, which is achieved by not only altering the mobility in the channel but also influencing the drift region of the device. Since the correlation of electrical and mechanical device characteristics is key to effectively implement strain engineering concepts in Si trench power MOSFETs, high-precision nanoscale stress and strain characterization techniques as well as finite element mechanical simulations are established and presented.
Si IGBT and SiC MOSFET – Potentials and Limitations of Plasma Shaping versus Unipolar Switching in Medium Power Applications

For a trade-off between turn-off and on-state of power switches in medium and high power applications, it is crucial to consider the switching speed restrictions due to turn-off peak voltage. By varying the p-emitter efficiency and the front side plasma level of an IGBT, an $E_{\text{off}}-V_{\text{ce},\text{sat}}$ trade-off curve for a fixed turn-off peak voltage can be obtained. It is shown that a turn-off behavior similar to that of a SiC MOSFET can be achieved with an IGBT with low carrier confinement, but with the drawback of a higher on-state voltage drop. At low current operations, the losses dissipated by the SiC MOSET are lower, not only in the on-state, but also during turn-off. The charge carrier plasma in the IGBT reduces both $\mathrm{d}v/\mathrm{d}t$ and $\mathrm{d}i/\mathrm{d}t$ for lower current densities. Switching curves at the rim of the RBSOA and beyond show that there is no dynamic avalanche in the SiC MOSFET. However, a quasi-static clamping mode can be observed for both types of devices.
A Universal Low Complexity Compression Algorithm for Sparse Marked Graphs

Many modern applications involve accessing and processing graphical data, i.e., data that is naturally indexed by graphs. Examples come from Internet graphs, social networks, genomics and proteomics, and other sources. The typically large size of such data motivates seeking efficient ways for its compression and decompression. The current compression methods are usually tailored to specific models, or do not provide theoretical guarantees. In this paper, we introduce a low–complexity lossless compression algorithm for sparse marked graphs, i.e., graphical data indexed by sparse graphs, which is capable of universally achieving the optimal compression rate defined on a per–node basis. The time and memory complexities of our compression and decompression algorithms are optimal within logarithmic factors. In order to define universality we employ the framework of local weak convergence, which allows one to make sense of a notion of stochastic processes for sparse graphs. Moreover, we investigate the performance of our algorithm through some experimental results on both synthetic and real–world data.
Construction of Language Models for Uzbek Language

A language model is a set of restrictions on the sequence of words allowed in a given language, and these restrictions can be expressed, for example, by the rules of a generative grammar or by a statistic of each pair of words evaluated in a given language. simple educational building. Although there are words with similar-sounding phonemes, it is usually not difficult for people to recognize the word. It mostly has to do with knowing the context and being very good at what words or phrases might be in it. The purpose of the language model is to provide context to the speech recognition system. The language model determines what words are allowed in the system language and in what order they can occur.Language models are trained, i.e., n-gram probabilities are estimated by observing sequences of words in a text corpus. Confusion reduction is typically performed on training data containing millions of word tokens. But, as has been observed, reducing confusion does not improve speech recognition results. Therefore, algorithms should be used that improve language models in terms of their impact on speech recognition, especially language models that determine the probability distribution of the speaker’s next spoken words given the speech history.In recent years, many speech recognition systems have been developed that use language models created for specific languages. And the use of language models in speech recognition serves to increase the efficiency of speech recognition. Many researchers have developed a traditional language model for the Uzbek language [8] –[12], but this model does not give the expected results. This requires the construction of other models for the Uzbek language. This article provides information about natural language, building natural language models, and applying them to speech recognition. Discusses research related to the construction of natural language models, problems that arise in the construction of statistical models, and approaches that can be used to solve them.
Design & Development of Semi-Automated Multipurpose Electric Farming Bot

Tractors are extensively used in farming nowadays. But now the price of fuel is ever increasing and using a tractor for farming is becoming a challenge for small farmers financially. Modern agriculture techniques and equipment are not used by small land hold because this equipment’s are too expensive. Despite the large-scale mechanization of agriculture in some parts of the country, most of the agricultural operations in larger parts are carried on by human hand using simple tools and implements like wooden plough, rake etc. The present study aims to design and develop a compact and multifunctional electric robot for farming. This system is a multifunctional semiautomatic farming bot that can do multiple farming activities namely Ploughing, sowing, and weeding farmer friendly and efficiently. The scope is to design and fabricate a high torque, traction and power locomotion drive taking into consideration the forces/power required for Ploughing. Having a detachable configuration/mechanism of the different process tools for sowing and weeding.
Deep Learning in Crowd Analysis: A Survey of Current Research Trends

Deep learning, or artificial neural networks, has been used for years to solve a range of problems. The computation burden increased with the intricacy of challenges and models as well as with the volume of data. This research provides a quick overview of the process of moving beyond artificial neural networks to deep learning, theories, and applications. There's also information regarding used libraries, applications, or electronics. Specifically covered the canonical strategies for crowd analysis. Throughout the study on deep learning approaches for a crowd, analysis is included in the literature as well as an overview of datasets. Studies carried out more recently have also been reviewed and compared. As a result, crowd analysis is an area of inquiry that blends academic theory with real-world application. As a corollary, deep learning is utilized to achieve successful outcomes in the academic and practical realms of crowd analysis.
Electric Vehicle Charging Scheduling using Branch and Cut Algorithm

Electric vehicles (EVs) are powered entirely by the energy that is stored in rechargeable batteries, which get charged by either the grid or a renewable energy source (RES). Usage of EVs play a major role in the sustainable development approach of lifestyle. In the current scenario in developing countries, the percentage of EVs used is very low compared to petrol or diesel vehicles. Still, most of the countries are lagging behind in building the necessary infrastructure for EV charging. The objective of this paper is formulated as an integer linear programming (ILP) to identify the optimal EV charging station distance and wait time. A dashboard/mobile software application has been developed in this paper to reduce the overall time a user would need to charge their electric vehicle. The Branch and Cut Algorithm (BCA) is effectively applied in this software to select the optimal charging station in terms of reduced travel and charging time. The software is concerned with the constraints like the EV's charge status, the locations of the nearest charging stations, the number of cars waiting in line at the charging stations, and the EV model. The software is concerned with the constraints like the EV's charge status, the locations of the nearest charging stations, the number of cars waiting in line at the charging stations, and the EV model.
Deep Learning Classification based on Merge Ear and Single Finger Geometry Dataset

Since the development of technology is always going on, we needed more accurate and secure methods, so we needed to strengthen the fingerprints that a person must get rid of from continuous impersonations and because of the series of crimes, fingerprints and DNA have become one of the most common biometrics among people, but with Advances in internet technology and the interplay between computers and the things around us, other forms of biometrics must be sought for more security. For example, the distinctive physical characteristics of users in computer science are increasingly being used as forms of identification and access restriction. Mobile devices use fingerprints, eye scanning, and face recognition. Other biometrics use iris, veins, and palm prints. The ear is also a potential biometric method, and one-finger engineering is a new and powerful way to detect and identify people. 70% of the samples were trained and 30% of the samples were tested, and they were processed by using one of the primary processing methods, the Convolutional Neural Networks method, which eliminates the noise resulting from the imaging process because the data was built, and then the bitwise filter method was used, where eight layers were used. As a result of merging the images, the distinguishing characteristics were extracted, and then the person was classified. The study proved the success of the method, giving an accuracy rate of 99.8%.
Smart City Based on Open Data: A Survey

Open data are gold mines because they can be used to create services that develop a smart city while improving users’ living conditions. Several research works go in this direction, presenting open data impact in the smart city for some, while others have focused on data processing methods. We have therefore deemed it necessary to make a state of the art on these different issues. The particularity of our study is that it shows the link between open data and smart city in all its aspects, describing what kind of open data is suitable for the smart city, how it is important for its development, and how these open data are processed to create services. Thus, in this article, we first present a review of existing surveys since 2015. Then, we present different smart city dimensions based on open data as well as some applications, and we detail how to process these data. We end with a list of open data sources as well as some challenges and solutions related to smart city services.
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding

Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance reported from different embedding learning algorithms, few efforts were devoted to structurally interpreting how features are encoded in the learned embedding space. This work proposes EmbeddingTree, a hierarchical embedding exploration algorithm that relates the semantics of entity features with the less-interpretable embedding vectors. An interactive visualization tool is also developed based on EmbeddingTree to explore high-dimensional embeddings. The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities. We demonstrate the efficacy of EmbeddingTree and our visualization tool through embeddings generated for industry-scale merchant data and the public 30Music listening/playlists dataset.
A 15V operated Shallow Trench IGBT(ST-IGBT) fabricated by low temperature process and optimized for 12inch wafers

In this paper, we propose shallow trench IGBT (ST -IGBT) and its fabrication process. It is designed for 15V of gate operation, as is the same as conventional IGBTs. The cell is consist of shallow trench gate MOS structure and shallow doping layers, formed by ion implantation and RTA (Rapid Thermal Anneal). The edge termination structure is composed by many shallow FLRs. The optimized cell design reduces V ce(sat) by 0.2V, compared with conventional IGBTs.
7-Bit/2Cell (X3.5), 9-Bit/2Cell (X4.5) NAND Flash Memory: Half Bit technology

In this work, a 3. 5bit/cell (X3.5) flash memory is introduced to achieve a lower giga-byte cost with high reliability and performance. Accordingly, 7-bit data are stored in two memory cells with 12-level Vth distributions. The X3.5 Programming method does not require the complex approach used in QLC and can easily be handled by NAND controller. QLC needs a more powerful ECC than TLC; hence, increasing the size of the ECC parity for QLC enlarges the die size. However, the same ECC parity size as TLC can be applicable to X3.5. An X3.5 die size with a TLC ECC is only 6.3% bigger than QLC. By contrast, X3.5 Program performance is 2.3 times faster than QLC with the QLC reliability criteria, which is relaxed from the TLC reliability criteria. If the size of the ECC parity is reduced to that of 32nmMLC, X3.5 die size will be close to that of QLC, while X3.5 Program performance will be 1.6 times faster than that of QLC. Moreover, X3.5 will meet the TLC reliability criteria. In addition to expanding X3.5 technologies, 4.5 bit/cell (X4.5) with a 24-level Vth distributions is also possible, which is suitable for cold storage applications.
Learning Analytics to study the use of virtual tutorials for teaching university general educational subjects

The COVID-19 pandemic with social distancing converts most of the face-to-face (F2F) classes to online/hybrid mode of class delivery. To better facilitate online/hybrid teaching and learning, pre-class or after-class learning activities, online videos/pre-class or post-class recordings, self-learning modules in Learning Management System (LMS) as well as interactive technologies, can enhance students’ learning during the time.Our preliminary evaluation about the use of virtual tutorials to assist the teaching and learning for two of our General Educational Subjects ("ABCT1D09 Greenhouse Gases and Life" & "ABCT1D10 Food Color, Taste and Smell") was reported in this paper. To understand the learning behavior and students’ engagement, we applied learning analytics (LA) and the ICAP (Interactive/Constructive/Active/Passive) model in this study.Voluntary online survey about our students’ learning experience was conducted and system data of our students’ "click" number within the platform Blackboard Collaborate Ultra (BB) with uRewind (empowered by Panopto) was collected. In this study, we discovered that our students welcome the use of virtual tutorials with rating average >3.5 for the voluntary online survey. The number of "click" for recorded videos and online teaching materials within LMS reached highest values near the assessment due date. In general, students performed better with more frequent usage of LMS teaching materials and videos. This observation revealed by a better performance of ABCT1D10 students with respect to ABCT1D09 students. The online materials may help students’ learning and maintain their performance although they encountered a sudden change of study mode.
Two Wilkinson-Power-Combiners designed with Transmission Lines and Lumped Elements in 22 nm FD-SOI CMOS

This paper analyses and compares two sub- 60GHz broadband and low-loss Wilkinson power combiners/dividers (WPCDs) based on transmission lines and lumped elements. Both WPCDs were implemented in a 22nm FDSOI CMOS technology. A two-stage WPCD based on lumped elements was fabricated and compared against the conventional transmission line based WPCD. The lumped element based WPCD enables an area reduction of 33%, while providing similar performance. Both WPCDs show a measured insertion loss (IL) of less than 2.2 dB, a return loss (RL) of better than 10dB and an amplitude and phase error of less than 0.12 dB and 2. 2o from DC up to 65 GHz. The port isolation (ISO) is better than 15 dB from 35 GHz to 60 GHz.