Miracle-dz/UniFAR-FTUs huggingface dataset:
Sistem Ujian Online Berbasis Website

Ujian berdasarkan kamus besar Bahasa Indonesia dapat berarti sesuatu yg dipakai untuk menguji mutu sesuatu (kepandaian, kemampuan, hasil belajar, dsb). Ujian selain dilaksanakan secara tertulis maupun lisan dapat pula dilaksanakan menggunakan alat bantu komputer. Jika pelaksanaan ujian menggunakan komputer dilaksanakan dalam sebuah jaringan komputer baik itu jaringan intranet maupun internet maka bisa dikategorikan sebagai ujian online. Metode penelitian yang digunakan adalah Pengkajian ilmiah dalam bentuk Penelitian Pengembangan, atau lebih tepat disebut Penelitian Rekayasa yang berupa Rencana (Plan), Rancangan (Design), Bangunan / kontruksi (Construct), Terapan (Applied), atau Hasil pengembangan (Development) dari suatu Model, Sistem, atau Produk. Dalam penelitian ini dilakukan proses rekayasa perangkat lunak sistem ujian online berbasis website. Luaran penelitian adalah analisis kebutuhan, rancangan serta produk perangkat lunak sistem ujian online itu sendiri.
Learning Customer Behaviors for Effective Load Forecasting

Load forecasting has been deeply studied because of its critical role in Smart Grid. In current Smart Grid, there are various types of customers with different energy consumption patterns. Customer’s energy consumption patterns are referred to as customer behaviors. It would significantly benefit load forecasting in a grid if customer behaviors could be taken into account. This paper proposes an innovative method that aggregates different types of customers by their identified behaviors, and then predicts the load of each customer cluster, so as to improve load forecasting accuracy of the whole grid. Sparse Continuous Conditional Random Fields (sCCRF) is proposed to effectively identify different customer behaviors through learning. A hierarchical clustering process is then introduced to aggregate customers according to the identified behaviors. Within each customer cluster, a representative sCCRF is fine-tuned to predict the load of its cluster. The final load of the whole grid is obtained by summing the loads of each cluster. The proposed method for load forecasting in Smart Grid has two major advantages. 1) Learning customer behaviors not only improves the prediction accuracy but also has a low computational cost. 2) sCCRF can effectively model the load forecasting problem of one customer, and simultaneously select key features to identify its energy consumption pattern. Experiments conducted from different perspectives demonstrate the advantages of the proposed load forecasting method. Further discussion is provided, indicating that the approach of learning customer behaviors can be extended as a general framework to facilitate decision making in other market domains.
Circular RNA Expression Profiling and Selection of Key Circular RNAs in the Hypothalamus of Heat-Acclimated Rats

Circular RNAs (circRNAs) have vital roles in great variety of biological processes. However, expression levels and functions of circRNAs related to heat acclimation (HA) are poorly understood. This study is the first time an in-depth circRNA expression profiling were used to investigate circRNA–miRNA interactions in HA rats in order to further comprehend the mechanisms underlying HA. CircRNA expression profile was performed in rats’ hypothalamus of HA and control group with microarray assays and their functions were predicted by using Bioinformatics analysis. Differential circRNAs and their regulated downstream miRNAs and mRNAs were quantitatively validated by means of quantitative polymerase chain reaction in real-time (RT-qPCR). Enzyme-linked immunosorbent assay (ELISA) was then applied to predict the expression of proteins. In total, 53 circRNAs were expressed distinctively between the HA and Control; up- and down-regulation of circRNAs were 28 and 25, respectively, in HA (fold change > 1.5, P < 0.05). Three circRNAs and two miRNAs and three predicted mRNAs were obviously regulated after validated by RT-qPCR in HA rats. Two proteins expression were proportional to their mRNA changes. Further analysis demonstrates that circRNAs closest to HA can be categorized into three signal pathways: including rno_circRNA_014301-vs-rno-miR-3575-vs-Hif-1α, rno_circRNA_014301-vs-rno-miR-3575-vs-Lppr4, and rno_circRNA_010393-vs-rno-miR-20b-3p-vs-Mfap4 in hypoxia response pathways, substance/energy metabolism, and inflammatory response pathways. Our findings implicate that many circRNAs regulate expressions of genes that interact with each other to exert their functions during HA.
An Introduction to Probability Theory and Its Applications

The classic text for understanding complex statistical probability An Introduction to Probability Theory and Its Applications offers comprehensive explanations to complex statistical problems. Delving deep into densities and distributions while relating critical formulas, processes and approaches, this rigorous text provides a solid grounding in probability with practice problems throughout. Heavy on application without sacrificing theory, the discussion takes the time to explain difficult topics and how to use them. This new second edition includes new material related to the substitution of probabilistic arguments for combinatorial artifices as well as new sections on branching processes, Markov chains, and the DeMoivreLaplace theorem.
Reionization optical depth determination from Planck HFI data with ten percent accuracy

We present an estimation of the reionization optical depth τ from an improved analysis of data from the High Frequency Instrument (HFI) on board the Planck satellite. By using an improved version of the HFI map-making code, we greatly reduce the residual large-scale contamination affecting the data, characterised in, but not fully removed from, the Planck 2018 legacy release. This brings the dipole distortion systematic effect, contaminating the very low multipoles, below the noise level. On large-scale polarization-only data, we measure τ = 0.0566−0.0062+0.0053 at 68% C.L. , reducing the Planck 2018 legacy release uncertainty by ∼40%. Within the ΛCDM model, in combination with the Planck large-scale temperature likelihood, and the high-ℓ temperature and polarization likelihood, we measure τ = 0.059 ± 0.006 at 68% C.L. , which corresponds to a mid-point reionization redshift of zre = 8.14 ± 0.61 at 68% C.L. This estimation of the reionization optical depth with 10% accuracy is the strongest constraint to date.
Exposure-background duality in the searches of neutrinoless double beta decay

Neutrinoless double-β decay (0υββ) is an experimentally sensitive avenue to probe the nature (Majorana versus Dirac) and exact mass of neutrinos. This work quantitatively explores the interplay between exposure and background levels in 0υββ experiments at their design stage. Although the primary goal of current running and projected experiments is to probe the inverted mass hierarchy, current neutrino oscillation experiments reveal a preference for non-degenerate (ND) normal mass hierarchy. Therefore the strategy of scaling the summit of 0υββ should also take this genuine possibility into account. Present work would explore the role of background suppression in alleviating the necessity of large exposure for future 0υββ experiments with sensitivity goals of approaching and covering ND-NH. In particular, the roles of energy resolution in suppressing the irreducible background of 2υββ are investigated.
Resolution of the skew Brownian motion equations with stochastic calculus for signed measures

Contributions of the present paper consist of two parts. In the first one, we contribute to the theory of stochastic calculus for signed measures. For instance, we provide some results permitting to characterize martingales and Brownian motion both defined under a signed measure. We also prove that the uniformly integrable martingales (defined with respect to a signed measure) can be expressed as relative martingales and we provide some new results to the study of the class $\Sigma(H)$ which appeared for the first time in \cite{f} and studied in \cite{f,e,o}. The second part is devoted to the construction of solutions for the \textbf{homogeneous skew Brownian motion equation} and for the \textbf{inhomogeneous skew Brownian motion equation}. To do this, our ingredients are the techniques and results developed in the first part that we apply on some stochastic processes borrowed from the theory of stochastic calculus for signed measures. Our methods are inspired by those used by Bouhadou and Ouknine in \cite{siam}. Moreover, their solution of the inhomogeneous skew Brownian motion equation is a particular case of those we propose in this paper.
On the Minimax Optimality of Estimating the Wasserstein Metric

We study the minimax optimal rate for estimating the Wasserstein-$1$ metric between two unknown probability measures based on $n$ i.i. d. empirical samples from them. We show that estimating the Wasserstein metric itself between probability measures, is not significantly easier than estimating the probability measures under the Wasserstein metric. We prove that the minimax optimal rates for these two problems are multiplicatively equivalent, up to a $\log \log (n)/\log (n)$ factor.
Neutral pion mass in the linear sigma model coupled to quarks at arbitrary magnetic field

We calculate the neutral pion mass in the presence of an external magnetic field of arbitrary strength in the framework of the linear sigma model coupled to quarks at zero temperature. We find nonmonotonic behavior of the pion mass as a function of magnetic field. We are also able to reproduce existing results for the weak-field approximation.
On NMT Search Errors and Model Errors: Cat Got Your Tongue?

We report on search errors and model errors in neural machine translation (NMT). We present an exact inference procedure for neural sequence models based on a combination of beam search and depth-first search. We use our exact search to find the global best model scores under a Transformer base model for the entire WMT15 English-German test set. Surprisingly, beam search fails to find these global best model scores in most cases, even with a very large beam size of 100. For more than 50% of the sentences, the model in fact assigns its global best score to the empty translation, revealing a massive failure of neural models in properly accounting for adequacy. We show by constraining search with a minimum translation length that at the root of the problem of empty translations lies an inherent bias towards shorter translations. We conclude that vanilla NMT in its current form requires just the right amount of beam search errors, which, from a modelling perspective, is a highly unsatisfactory conclusion indeed, as the model often prefers an empty translation.
Deep-Learning Based Linear Average Consensus for Faster Convergence over Temporal Network

In this paper, we study the problem of accelerating the linear average consensus algorithm over complex networks. We specifically present a data-driven methodology for tuning the weights of temporal (i.e., time-varying) networks by using deep learning techniques. We first unfold the linear average consensus protocol to obtain a feedforward signal flow graph, which we regard as a neural network. We then train the neural network by using standard deep learning technique to minimize the consensus error over a given finite time-horizon. As a result of the training, we obtain a set of optimized time-varying weights for faster consensus in the network. Numerical simulations are presented to show that our methodology can achieve a significantly smaller consensus error than the static optimal strategy.
Early Classification for Agricultural Monitoring from Satellite Time Series

In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring. It augments existing classification models by an additional stopping probability based on the previously seen information. This mechanism is end-to-end trainable and derives its stopping decision solely from the observed satellite data. We show results on field parcels in central Europe where sufficient ground truth data is available for an empiric evaluation of the results with local phenological information obtained from authorities. We observe that the recurrent neural network outfitted with this early classification mechanism was able to distinguish the many of the crop types before the end of the vegetative period. Further, we associated these stopping times with evaluated ground truth information and saw that the times of classification were related to characteristic events of the observed plants' phenology.
Private Stochastic Convex Optimization with Optimal Rates

We study differentially private (DP) algorithms for stochastic convex optimization (SCO). In this problem the goal is to approximately minimize the population loss given i.i. d. ~samples from a distribution over convex and Lipschitz loss functions. A long line of existing work on private convex optimization focuses on the empirical loss and derives asymptotically tight bounds on the excess empirical loss. However a significant gap exists in the known bounds for the population loss. We show that, up to logarithmic factors, the optimal excess population loss for DP algorithms is equal to the larger of the optimal non-private excess population loss, and the optimal excess empirical loss of DP algorithms. This implies that, contrary to intuition based on private ERM, private SCO has asymptotically the same rate of $1/\sqrt{n}$ as non-private SCO in the parameter regime most common in practice. The best previous result in this setting gives rate of $1/n^{1/4}$. Our approach builds on existing differentially private algorithms and relies on the analysis of algorithmic stability to ensure generalization.
Chondrule Formation by the Jovian Sweeping Secular Resonance

Chondrules are silicate spheroids found in meteorites, and they serve as important fossil records of the early solar system. In order to form chondrules, chondrule precursors must be heated to temperatures much higher than the typical conditions in the current asteroid belt. One proposed mechanism for chondrule heating is the passage through bow shocks of highly eccentric planetesimals in the protoplanetary disk in the early solar system. However, it is difficult for planetesimals to gain and maintain such high eccentricities. In this paper, we present a new scenario in which planetesimals in the asteroid belt region are excited to high eccentricities by the Jovian sweeping secular resonance in a depleting disk, leading to efficient formation of chondrules. We study the orbital evolution of planetesimals in the disk using semi-analytic models and numerical simulations. We investigate the dependence of eccentricity excitation on the planetesimal’s size, as well as the physical environment and the probability for chondrule formation. We find that 50–2000 km planetesimals can obtain eccentricities larger than 0.6 and cause effective chondrule heating. Most chondrules form in high-velocity shocks, in low-density gas, and in the inner disk. The fraction of chondrule precursors that become chondrules is about 4%–9% between 1.5 and 3 au. Our model implies that the disk depletion timescale is τdep ≈ 1 Myr, comparable to the age spread of chondrules, and that Jupiter formed before chondrules, no more than 0.7 Myr after the formation of calcium aluminum inclusions.
A simplified L-curve method as error estimator

The L-curve method is a well-known heuristic method for choosing the regularization parameter for ill-posed problems by selecting it according to the maximal curvature of the L-curve. In this article, we propose a simplified version that replaces the curvature essentially by the derivative of the parameterization on the $y$-axis. This method shows a similar behaviour to the original L-curve method, but unlike the latter, it may serve as an error estimator under typical conditions. Thus, we can accordingly prove convergence for the simplified L-curve method.
Imaging crystal stress in diamond using ensembles of nitrogen-vacancy centers

We present a micrometer-resolution millimeter-field-of-view stress imaging method for diamonds containing a thin surface layer of nitrogen vacancy (NV) centers. In this method, we reconstruct stress tensor elements over a two-dimensional field of view from NV optically-detected magnetic resonance (ODMR) spectra. We use this technique to study how stress inhomogeneity affects NV magnetometry performance, and show how NV stress imaging is a useful and direct way to assess these effects. This new tool for mapping stress in diamond will aid optimization of NV-diamond sensing, with wide-ranging applications in the physical and life sciences.
A new cryogenic apparatus to search for the neutron electric dipole moment

A cryogenic apparatus is described that enables a new experiment, nEDM@SNS, with a major improvement in sensitivity compared to the existing limit in the search for a neutron Electric Dipole Moment (EDM). This apparatus uses superfluid 4He to produce a high density of Ultra-Cold Neutrons (UCN) which are contained in a suitably coated pair of measurement cells. The experiment, to be operated at the Spallation Neutron Source at Oak Ridge National Laboratory, uses polarized 3He from an Atomic Beam Source injected into the superfluid 4He and transported to the measurement cells where it serves as a co-magnetometer. The superfluid 4He is also used as an insulating medium allowing significantly higher electric fields, compared to previous experiments, to be maintained across the measurement cells. These features provide an ultimate statistical uncertainty for the EDM of 2−3× 10−28 e-cm, with anticipated systematic uncertainties below this level.
Weakly-Supervised Unconstrained Action Unit Detection via Latent Feature Domain.

Facial action unit (AU) detection in the wild is a challenging problem, with current methods either depending on impractical labor-intensive labeling by experts, or inaccurate pseudo labels. In this paper, we propose an end-to-end weakly-supervised AU detection framework for unconstrained target-domain images by exploiting available and accurate AU labels from a well-constrained source domain. Instead of mapping directly from source to target domains in the image space, which could result in well-trained image synthesis results but losing transferred AU information, we generate a latent feature domain where we combine source-domain landmark-related features with target-domain landmark-free features. Since facial inner-landmarks are highly relevant to AUs, we can train target-domain AU detection with source-domain AU labels in the latent feature domain. The mapping from source and target domains to the latent domain is learned by maximizing the AU detection performance. Moreover, to disentangle the images into landmark-related features and landmark-free features, we introduce a novel landmark adversarial loss which can solve the multi-player minimax game in adversarial learning. Our framework can also be naturally extended for use with pseudo AU labels. Extensive experiments show that our method soundly outperforms the baselines, upper-bounds and state-of-the-art approaches on the challenging BP4D and EmotioNet benchmarks. The code for our method is available at https://github.com/ZhiwenShao/ADLD.
Intra-Camera Supervised Person Re-Identification: A New Benchmark

Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process therefore leading to poor scalability in practical re-id applications. To overcome this fundamental limitation, we consider person re-identification without inter-camera identity association but only with identity labels independently annotated within each individual camera-view. This eliminates the most time-consuming and tedious inter-camera identity labeling process in order to significantly reduce the amount of human efforts required during annotation. It hence gives rise to a more scalable and more feasible learning scenario, which we call Intra-Camera Supervised (ICS) person re-id. Under this ICS setting with weaker label supervision, we formulate a Multi-Task Multi-Label (MTML) deep learning method. Given no inter-camera association, MTML is specially designed for self-discovering the inter-camera identity correspondence. This is achieved by inter-camera multi-label learning under a joint multi-task inference framework. In addition, MTML can also efficiently learn the discriminative re-id feature representations by fully using the available identity labels within each camera-view. Extensive experiments demonstrate the performance superiority of our MTML model over the state-of-the-art alternative methods on three large-scale person re-id datasets in the proposed intra-camera supervised learning setting.
Angular-Asymmetric Transmitting Metasurface and Splitter for Acoustic Waves: Combining the Coherent Perfect Absorber and a Laser

Coherent Perfect Absorber (CPA) and Laser is a pair of inversely worked wave structure satisfying the time-reversal relation. We point out that, if the wave energy absorbed by CPA can be recovered by somewhat way and then be reemitted by its corresponded Laser, their combination can be a wave-controlling device that can "move" the wave energy from one place to another. By extending the concepts of CPA and Laser from the previously studied one-dimensional non-Hermitian systems to the two-dimensional ones, this understanding is used to simplify the designing of the metasurface in this letter. As examples, an angular-asymmetric transmitting metasurface and wave splitter, which acts as perfect transmitter/retro-reflector for waves coming from two oppositely tilted angles and split an incident wave into two directions with arbitrary amplitude ratio and phase difference, respectively, are constructed by combining two pieces of the separately designed CPAs. The idea can not only greatly simplify the designing of metasurface, but also bridge the researches between the metasurface and non-Hermitian system.
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics

We present an end-to-end statistical framework for personalized, accurate, and minimally invasive modeling of female reproductive hormonal patterns. Reconstructing and forecasting the evolution of hormonal dynamics is a challenging task, but a critical one to improve general understanding of the menstrual cycle and personalized detection of potential health issues. Our goal is to infer and forecast individual hormone daily levels over time, while accommodating pragmatic and minimally invasive measurement settings. To that end, our approach combines the power of probabilistic generative models (i.e., multi-task Gaussian processes) with the flexibility of neural networks (i.e., a dilated convolutional architecture) to learn complex temporal mappings. To attain accurate hormone level reconstruction with as little data as possible, we propose a sampling mechanism for optimal reconstruction accuracy with limited sampling budget. Our results show the validity of our proposed hormonal dynamic modeling framework, as it provides accurate predictive performance across different realistic sampling budgets and outperforms baselines methods.