Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Transfer Switch (Suatu Tinjauan)

— Sumber tegangan listrik utama yaitu PLN (Pembangkit Listrik Negara) tidak selamanya kontinyu dalam penyaluranya, suatu saat pasti terjadi pemadaman yang kemungkinan dapat disebabkan oleh gangguan pada sistem transmisi atau sistem distribusi. Untuk mengantisipasi dari pemadaman tersebut, perlu didesain sebuah kontrol otomatis yang disebut ATS (Autamatic Transfer Switch). Automatic Transfer Switch adalah sebuah rangkaian kontrol sakelar Power Inverter dengan PLN yang sudah Full Automatic. Alat ini berguna untuk Menghidupkan, dan Menghubungkan Power Inverter ke Beban secara otomatis pada saat PLN padam. Pada saat PLN hidup kembali, alat ini akan Memindahkan sumber daya ke Beban dari Power Inverter ke PLN. Dimasa sekarang kemajuan teknologi berkembang pesat, teknologi menjadi inspirasi agar kehidupan di bumi menjadi lebih ringan, mudah dan praktis. Pada artikel ini akan di bahas tentang kendali sakelar otomatis yang bisa dikendalikan sesuai program yang diinginkan. Keywords— ATS, power inverter, PLN, kontrol
Classification of Breast Cancer Cell Images using Multiple Convolution Neural Network Architectures

Breast cancer is a malignant tumor that affects women. It is the most prevalent cancer in women, affecting about 10% of all women at any point in their lives. The development of breast cancer begins in the lobules or ducts of the cells. Early detection and prevention are the best ways to stop this cancer from spreading. In this study, five Convolution Neural Network (CNN) models are used to process image data of breast cells. AlexNet, InceptionV3, GoogLeNet, VGG19 and Xception models are used for the classification of Invasive Ductal Carcinoma, IDC and Non-Invasive Ductal Carcinoma (Non-IDC) cells. The models are trained and tested at different epochs to record the learning rate. It is observed from the study that with higher epochs, the data loss decreases and accuracy increases. The accuracy of InceptionV3 and Xception is 92.48% and 90.72% respectively. Likewise, VGG19 and AlexNet have fairly close accuracy of 94.83% and 96.74%. However, GoogLeNet dominates over the other implemented models with the highest accuracy of 97.80%. The GoogLeNet model performs with high accuracy and precision in detecting IDC cells responsible for breast cancer. Keywords—Breast cancer; IDC; non-IDC; AlexNet; VGG19; Inception sV3; GoogLeNet; Xecption; accuracy
Paper Title (use style: paper title)

A web application is comprised of a social event of contents, which live on a web server and interface with databases or distinctive sources of dynamic substance. The security of a web application is mainly depends on the integrity and privacy of its assets inside the web program. For instance, session identifiers in cookies should be secured against access by untrusted principals. Web applications are very presented to attacks from anyplace in the environment, which can be led by utilizing broadly accessible and basic devices like a web program. It is normal to discover web application designers, power engineers and managers without the required learning and involvement in the security space. In this paper, the comparative analysis performed on recall and precision value for Fuzzy-ELM, SVM, Random Forest and Naïve bayes. Different vulnerabilities used for the illustration of recall and precision to show the efficiency. Keywords—Software Vulnerability, Fuzzy-ELM, SVM, Random Forest, Naïve bayes, Recall, Precision.
Limited impact of contact tracing in a University setting for COVID-19 due to asymptomatic transmission and social distancing

Contact tracing is an important tool for controlling the spread of infectious diseases, including COVID-19. Here, we investigate the spread of COVID-19 and the effectiveness of contact tracing in a university population, using a data-driven ego-centric network model constructed with social contact data collected during 2020 and similar data collected in 2010. We find that during 2020, university staff and students consistently reported fewer social contacts than in 2010, however those contacts occurred more frequently and were of longer duration. We find that contact tracing in the presence of social distancing is less impactful than without social distancing. By combining multiple data sources, we show that University-aged populations are likely to develop asymptomatic COVID-19 infections. We find that asymptomatic index cases cannot be reliably back-traced through contact tracing and consequently transmission in their social network is not significantly reduced through contact tracing. In summary, social distancing restrictions had a large impact on limiting COVID-19 outbreaks in universities; to reduce transmission further contact tracing should be used in conjunction with alternative interventions.
Conditional CRISPR-Cas Genome Editing in Drosophila to Generate Intestinal Tumors

CRISPR-Cas has revolutionized genetics and extensive efforts have been made to enhance its editing efficiency by developing increasingly more elaborate tools. Here, we evaluate the CRISPR-Cas9 system in Drosophila melanogaster to assess its ability to induce stem cell-derived tumors in the intestine. We generated conditional tissue-specific CRISPR knockouts using different Cas9 expression vectors with guide RNAs targeting the BMP, Notch, and JNK pathways in intestinal progenitors such as stem cells (ISCs) and enteroblasts (EBs). Perturbing Notch and BMP signaling increased the proliferation of ISCs/EBs and resulted in the formation of intestinal tumors, albeit with different efficiencies. By assessing both the anterior and posterior regions of the midgut, we observed regional differences in ISC/EB proliferation and tumor formation upon mutagenesis. Surprisingly, high continuous expression of Cas9 in ISCs/EBs blocked age-dependent increase in ISCs/EBs proliferation and when combined with gRNAs targeting tumor suppressors, it prevented tumorigenesis. However, no such effects were seen when temporal parameters of Cas9 were adjusted to regulate its expression levels or with a genetically modified version, which expresses Cas9 at lower levels, suggesting that fine-tuning Cas9 expression is essential to avoid deleterious effects. Our findings suggest that modifications to Cas9 expression results in differences in editing efficiency and careful considerations are required when choosing reagents for CRISPR-Cas9 mutagenesis studies. In summary, Drosophila can serve as a powerful model for context-dependent CRISPR-Cas based perturbations and to test genome-editing systems in vivo.
DeepGEM: Generalized Expectation-Maximization for Blind Inversion

Typically, inversion algorithms assume that a forward model, which relates a source to its resulting measurements, is known and ﬁxed. Using collected indirect measurements and the forward model, the goal becomes to recover the source. When the forward model is unknown, or imperfect, artifacts due to model mismatch occur in the recovery of the source. In this paper, we study the problem of blind inversion: solving an inverse problem with unknown or imperfect knowledge of the forward model parameters. We propose DeepGEM, a variational Expectation-Maximization (EM) framework that can be used to solve for the unknown parameters of the forward model in an unsupervised manner. DeepGEM makes use of a normalizing ﬂow generative network to efﬁciently capture complex posterior distributions, which leads to more accurate evaluation of the source’s posterior distribution used in EM. We showcase the effectiveness of our DeepGEM approach by achieving strong performance on the challenging problem of blind seismic tomography, where we signiﬁcantly outperform the standard method used in seismology. We also demonstrate the generality of DeepGEM by applying it to a simple case of blind deconvolution. achieves strong performance in the task of joint seismic tomography and earthquake source localization, substantially outperforming standard approaches currently being used in seismology on synthetic data. The proposed framework is ﬂexible and can be applied to different applications that require estimation or ﬁne tuning of forward model parameters. We demonstrate this ﬂexibility by also applying the approach to a simple, but challenging, blind deconvolution problem. Future work includes applying this method to real seismic data, extending to other applications, and incorporating data-driven priors. Our results highlight the beneﬁt of blending physically sound model-based techniques with learning machinery for blind inverse problems.
Error perception classiﬁcation in Brain-Computer interfaces using Convolutional Neural Networks

Motivation: Capturing the error perception of a human interacting with a Brain-Computer interface is a key element in improving the performance of these systems and making the interaction more seamless. Convolutional Neural Networks (CNN) have been recently applied for this task rendering the classiﬁcation model free of feature-selection. Despite the advances in the last years, there is still room for improving the accuracy so that it can be used in real-life applications. Objectives: The goals of the present work are to investigate, replicate and validate previous CNN models used for error perception classiﬁcation; to propose new CNN models based on the advances in Machine Learning and lastly, to make all the code and models developed publicly available. Methods: After a literature review, three recent CNN models for error-related potential (ErrP) classiﬁcation are replicated. Then, the author evaluates different new models that result from investigating CNN models used for classiﬁcation of ErrP and P300 signals. The Monitoring Error-Related Potential dataset is used to train and test all the models. Results: The best model from the literature achieves an accuracy, sensitivity, and speciﬁcity of 77 . 6% , 71 . 7% , and 83 . 1% , respectively. For the best proposed model, these metrics are of 80 . 4% , 75 . 9% , and 84 . 7% , respectively, which represents a statistically signiﬁcant increase on the literature models ( p = 0 . 0004 ). Furthermore, an EEG input with a shorter temporal size of 600 ms is successfully applied instead of the typical one-second-long input without signiﬁcant loss of performance ( p = 0 . 647 ). All models are made available online for easier future replication and peer review. Conclusions: The new proposed model outperforms the state-of-the-art. The 600 ms input allows faster processing times in real-time BCI applications without loss of performance.
Frequency domain processing techniques for continuous phase modulation

prayer and care have made my family and me experience God's blessing. I would also like to express my gratitude and love to my parents, brother, and sisters for their love and encouragement. Last but most, I would like to express my sincerest thanks to my wife Youjin and our children Hay-oung, Hamin, and Hajin. Their unbounded sacrifice, belief, and love made me complete my Ph.D. work. In addition, thanks to them for the happiness which they bring into my life. The continuous phase modulation (CPM) has a constant envelope and compact output power spectrum that makes it a promising underlying technology for power and spectrum efficient broadband wireless communications. However, high implementation complexity (especially the complexity of the receiver) required to deal with the phase memory and inter-symbol interference has impeded its adoption for broadband wireless communications, and only a few simple CPM modulation schemes have mainly been used, e.g. binary MSK and GMSK. Thus, research on efficient CPM transceivers to reduce the computational and hardware complexity is important. The major contribution of this dissertation is the development of novel frequency domain processing techniques and transceiver strategies to improve power and spectral efficiency, and reduce the complexity of CPM modulation schemes. First, this dissertation presents simplified frequency domain receiver structures and decoding schemes in the frequency domain for binary and M-ary CPM block transmission. The frequency domain receivers utilize vi parallel and serial structures with frequency domain processing which considerably reduces hardware and computational complexity compared to conventional time-domain processing. In addition, the decoding schemes in the frequency domain eliminate the controlled phase memory through frequency domain phase equalization instead of maximum-likelihood sequential decoders, e.g. Viterbi decoders. Second, frequency domain channel estimation schemes for CPM block transmission are presented, which adopt superimposed training signals to achieve bandwidth and power efficiency while reducing the complexity. In these schemes, the proposed frequency domain channel estimation uses the superimposed training signals as a reference signal to reduce the throughput loss caused by conventionally multiplexed training signals. Superimposed training signal design is presented, and the trade-off between bandwidth efficiency and power efficiency is also analyzed. Third, block transmission schemes and frequency domain equalization methods for CPM are proposed, which consider linear processing instead of conventional decomposition-based processing. The schemes of frequency domain linear processing avoid the complexity overhead (both in computation and hardware) of conventional orthogonal-or Laurent decomposed-based equalizers. Finally, this dissertation extends …
Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods

This paper investigates the problem of online statistical inference of model parameters in stochastic optimization problems via the Kiefer-Wolfowitz algorithm with random search directions. We first present the asymptotic distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW) estimators, whose asymptotic covariance matrices depend on the distribution of search directions and the function-value query complexity. The distributional result reflects the trade-off between statistical efficiency and function query complexity. We further analyze the choice of random search directions to minimize certain summary statistics of the asymptotic covariance matrix. Based on the asymptotic distribution, we conduct online statistical inference by providing two construction procedures of valid confidence intervals.
Stick-slip Dynamics in Penetration Experiments on Simulated Regolith

The surfaces of many planetary bodies, including asteroids and small moons, are covered with dust to pebble-sized regolith held weakly to the surface by gravity and contact forces. Understanding the reaction of regolith to an external perturbation will allow for instruments, including sensors and anchoring mechanisms for use on such surfaces, to implement optimized design principles. We analyze the behavior of a flexible probe inserted into loose regolith simulant as a function of probe speed and ambient gravitational acceleration to explore the relevant dynamics. The EMPANADA experiment (Ejecta-Minimizing Protocols for Applications Needing Anchoring or Digging on Asteroids) flew on several parabolic flights. It employs a classic granular physics technique, photoelasticity, to quantify the dynamics of a flexible probe during its insertion into a system of bi-disperse, centimeter-sized model grains. We identify the force chain structure throughout the system during probe insertion at a variety of speeds and for four different levels of gravity: terrestrial, Martian, lunar, and microgravity. We identify discrete, stick-slip failure events that increase in frequency as a function of the gravitational acceleration. In microgravity environments, stick-slip behaviors are negligible, and we find that faster probe insertion can suppress stick-slip behaviors where they are present. We conclude that the mechanical response of regolith on rubble-pile asteroids is likely quite distinct from that found on larger planetary objects, and scaling terrestrial experiments to microgravity conditions may not capture the full physical dynamics.
Supersymmetric multi-charge solitons in AdS$_5$

We construct supersymmetric, asymptotically AdS 5 gravitational soliton solutions of ﬁve-dimensional gauged supergravity coupled to arbitrarily many vector multiplets. These gener-alize the supersymmetric solitons of U (1) 3 gauged supergravity previously obtained by Chong, Cvetic, and Pope as limits of local, non-supersymmetric metrics. We show that the soliton spacetimes contain evanescent ergosurfaces and argue that, as a result, these solitons should be nonlinearly unstable.
Improving Structured Text Recognition with Regular Expression Biasing

We study the problem of recognizing structured text, i.e. text that follows certain formats, and propose to improve the recognition accuracy of structured text by specifying regular expressions (regexes) for biasing. A biased recognizer recognizes text that matches the specified regexes with significantly improved accuracy, at the cost of a generally small degradation on other text. The biasing is realized by modeling regexes as a Weighted Finite-State Transducer (WFST) and injecting it into the decoder via dynamic replacement. A single hyperparameter controls the biasing strength. The method is useful for recognizing text lines with known formats or containing words from a domain vocabulary. Examples include driver license numbers, drug names in prescriptions, etc. We demonstrate the efficacy of regex biasing on datasets of printed and handwritten structured text and measures its side effects.
Frequency learning for structured CNN filters with Gaussian fractional derivatives

Frequency information lies at the base of discriminating between textures, and therefore between different objects. Classical CNN architectures limit the frequency learning through fixed filter sizes, and lack a way of explicitly controlling it. Here, we build on the structured receptive field filters with Gaussian derivative basis. Yet, rather than using predetermined derivative orders, which typically result in fixed frequency responses for the basis functions, we learn these. We show that by learning the order of the basis we can accurately learn the frequency of the filters, and hence adapt to the optimal frequencies for the underlying learning task. We investigate the well-founded mathematical formulation of fractional derivatives to adapt the filter frequencies during training. Our formulation leads to parameter savings and data efficiency when compared to the standard CNNs and the Gaussian derivative CNN filter networks that we build upon.
The Gelfand-Phillips property for locally convex spaces

We extend the well-known Gelfand–Phillips property for Banach spaces to locally convex spaces, defining a locally convex space E to be Gelfand–Phillips if every limited set in E is precompact in the topology on E defined by barrels. Several characterizations of Gelfand–Phillips spaces are given. The problem of preservation of the Gelfand-Phillips property by standard operations over locally convex spaces is considered. Also we explore the Gelfand–Phillips property in spaces C(X) of continuous functions on a Tychonoff space X. If τ and T are two locally convex topologies on C(X) such that Tp ⊆ τ ⊆ T ⊆ Tk, where Tp is the topology of pointwise convergence and Tk is the compact-open topology on C(X), then the Gelfand–Phillips property of the function space (C(X), τ ) implies the Gelfand–Phillips property of (C(X), T ). If additionally X is metrizable, then the function space (
Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-Based Approaches

In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task depending on many external factors. The topology of the scene and the interactions between the pedestrians are just some of them. Due to advancements in data-science and data collection technologies deep learning methods have recently become a research hotspot in numerous domains. Therefore, it is not surprising that more and more researchers apply these methods to predict trajectories of pedestrians. This paper compares these relatively new deep learning algorithms with classical knowledge-based models that are widely used to simulate pedestrian dynamics. It provides a comprehensive literature review of both approaches, explores technical and application oriented differences, and addresses open questions as well as future development directions. Our investigations point out that the pertinence of knowledge-based models to predict local trajectories is nowadays questionable because of the high accuracy of the deep learning algorithms. Nevertheless, the ability of deep-learning algorithms for large-scale simulation and the description of collective dynamics remains to be demonstrated. Furthermore, the comparison shows that the combination of both approaches (the hybrid approach) seems to be promising to overcome disadvantages like the missing explainability of the deep learning approach.
Electrostatic linear micro actuators with vibrating motion for pan-tilt drive of a micro CCD camera

Newly developed linear electrostatic micro actuators employing a vibrating motion and fabricated by a bulk silicon micromachining process are described. The moving part (slider) of these actuators is sandwiched between two glass plates whose surfaces have many electrodes (fixed electrodes), is attached and detached to these fixed electrodes by turns, and actuates linearly on a macroscopic level. A feature of these actuators is that the influence of friction is reduced significantly and a large stroke is achieved. However, several difficulties have been identified regarding attachment/detachment operation actuators. The interactive forces, including the accumulation of charge caused by contact between each stator and slider create one of the most difficult problems. Our developed actuators incorporate micro pyramid stoppers, which prevent the accumulation of charge and other interactive forces, on both surfaces of the moving parts. The casing size of the prototype is 8/spl times/8/spl times/1.2 mm and achieves a 2 mm range of movement. The minimum driving voltage is 60 V and maximum velocity is 2.0 mm/s. The behavior of the slider is not simple and further analysis is very important in order to improve positioning accuracy, increase output force, and decrease driving voltage. The characteristics of the actuator (Stepping Model) obtained by experiments is reported. The authors further apply these actuators to a micro pan-tilt mechanism for altering the vision angle of a micro CCD camera and experimentally confirm their practical utility.