Miracle-dz/UniFAR-FTUs huggingface dataset:
THE DEVELOPMENT OF PROBLEM-BASED PSYCHOTROPIC AND ADDICTIVE SUBSTANCE LEARNING KITS 2 1 TO IMPROVE THE STUDENTS’ METACOGNITION

Abstract: This study was aimed to develop problem-based psychotropic and addictive substance learning kits to improve the students’ metacognition and concept mastery. The kits were developed in three stages (define, design, develop). The “develop” stage used the non-equivalent control group design. The subjects were grade VIII students of SMP Nasima, Semarang. The findings of the study showed that the developed learning kits were valid in the very good category. The mastery of concepts and metacognition improved with an average percentage of N-gain for the experimental group with the high category. The result of the questionnaires on metacognition showed the N-gain percentage of the experimental group belonging to the fair category. The students’ ability to solve problems and the implementation in the small class and in the experimental group in the first and fourth meetings were in the good category. The students and teacher gave positive responses to the developed learning kits and its implementation in the class.The findings showed that the problem-based learning can be used for improving students’ metacognition. Keywords: Problem-based learning, metacognition, addictive and psychotropicsubstances PENGEMBANGAN PERANGKAT PEMBELAJARAN ZAT ADIKTIF DAN PSIKOTROPIKA BERBASIS MASALAH UNTUK MENINGKATKAN METAKOGNISI SISWA Abstrak: Tujuan penelitian ini adalah menghasilkan perangkat pembelajaran zat adiktif dan psikotropika berbasis masalah yang teruji mampu meningkatkan metakognisi dan penguasaan konsep siswa. Pengembangan perangkat pembelajaran menggunakan tiga langkah pengembangan (define, design, develop). Tahap develop menggunakan desain Non equivalent Control Group Design. Subjek penelitian adalah siswa kelas VIII SMP Nasima Semarang. Hasil penelitian menunjukkan bahwa perangkat pembelajaran yang dikembangkan valid dengan kategori sangat baik. Penguasaan konsep dan metakognisi mengalami peningkatan rata-rata persentase N-gain kelas eksperimen dengan kategori tinggi. Hasil kuesioner metakognisi menunjukkan persentase N-gain pada kelas eksperimen dengan kategori sedang. Kemampuan pemecahan masalah dan keterlaksanaan pembelajaran pada siswa kelas kecil dan kelas eksperimen pada pertemuan ke-1 sampai ke-4 dengan kategori baik. Siswa dan guru memberikan respon positif terhadap perangkat pembelajaran yang dikembangkan dan penerapannya di dalam kelas. Hasil penelitian menunjukkan bahwa pembelajaran berbasis masalah dapat digunakan untuk meningkatkan metakognisi siswa. Kata Kunci: pembelajaran berbasis masalah, metakognisi, zat adiktif dan psikotropika
Multi-Scale Vector Quantization with Reconstruction Trees

We propose and study a multi-scale approach to vector quantization (VQ). We develop an algorithm, dubbed reconstruction trees, inspired by decision trees. Here the objective is parsimonious reconstruction of unsupervised data, rather than classification. Contrasted to more standard VQ methods, such as $k$-means, the proposed approach leverages a family of given partitions, to quickly explore the data in a coarse-to-fine multi-scale fashion. Our main technical contribution is an analysis of the expected distortion achieved by the proposed algorithm, when the data are assumed to be sampled from a fixed unknown distribution. In this context, we derive both asymptotic and finite sample results under suitable regularity assumptions on the distribution. As a special case, we consider the setting where the data generating distribution is supported on a compact Riemannian submanifold. Tools from differential geometry and concentration of measure are useful in our analysis.
Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10, 000 Robot Swarm

When designing swarm-robotic systems, system- atic comparison of algorithms from different do- mains is necessary to determine which is capa- ble of scaling up to handle the target problem size and target operating conditions. We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process. We demonstrate the applicability of our proposed met- rics as a design tool by solving a large object gath- ering problem in temporally varying operating con- ditions using iterative hypothesis evaluation. We provide experimental results obtained in simulation for swarms of over 10,000 robots.
The Application of Computer Music Technology to College Students

Contemporary music education started late in China on the basis of western teaching theories formed its own unique system, which has a great influence on present computer music technology. This paper explores that contemporary music education is analyzed advantages and disadvantages of the influence on the development of Chinese class music, and the solutions are found out to the existing problems, summed up the reality enlightenment of that the contemporary music on the impact of education.
Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain Multi-Task Frame Work to Solve Face Related Tasksks, Multi Task Learning, Semi-Supervised Learning

We propose a new semi-supervised learning method on face-related tasks based on Multi-Task Learning (MTL) and data distillation. The proposed method exploits multiple datasets with different labels for different-but-related tasks such as simultaneous age, gender, race, facial expression estimation. Specifically, when there are only a few well-labeled data for a specific task among the multiple related ones, we exploit the labels of other related tasks in different domains. Our approach is composed of (1) a new MTL method which can deal with weakly labeled datasets and perform several tasks simultaneously, and (2) an MTL-based data distillation framework which enables network generalization for the training and test data from different domains. Experiments show that the proposed multi-task system performs each task better than the baseline single task. It is also demonstrated that using different domain datasets along with the main dataset can enhance network generalization and overcome the domain differences between datasets. Also, comparing data distillation both on the baseline and MTL framework, the latter shows more accurate predictions on unlabeled data from different domains. Furthermore, by proposing a new learning-rate optimization method, our proposed network is able to dynamically tune its learning rate.
Sparse-to-Dense Hypercolumn Matching for Long-Term Visual Localization

We propose a novel approach to feature point matching, suitable for robust and accurate outdoor visual localization in long-term scenarios. Given a query image, we first match it against a database of registered reference images, using recent retrieval techniques. This gives us a first estimate of the camera pose. To refine this estimate, like previous approaches, we match 2D points across the query image and the retrieved reference image. This step, however, is prone to fail as it is still very difficult to detect and match sparse feature points across images captured in potentially very different conditions. Our key contribution is to show that we need to extract sparse feature points only in the retrieved reference image: We then search for the corresponding 2D locations in the query image exhaustively. This search can be performed efficiently using convolutional operations, and robustly by using hypercolumn descriptors, i.e. image features computed for retrieval. We refer to this method as 'Sparse-to-Dense Hypercolumn Matching'. Because we know the 3D locations of the sparse feature points in the reference images thanks to an offline reconstruction stage, it is then possible to accurately estimate the camera pose from these matches. Our experiments show that this method allows us to outperform the state-of-the-art on several challenging outdoor datasets.
Toward a characterization of X-ray galaxy clusters for cosmology

Context. In the framework of the hierarchical model the intra-cluster medium properties of galaxy clusters are tightly linked to structure formation, which makes X-ray surveys well suited for cosmological studies. To constrain cosmological parameters accurately by use of galaxy clusters in current and future X-ray surveys, a better understanding of selection effects related to the detection method of clusters is needed. Aims. We aim at a better understanding of the morphology of galaxy clusters to include corrections between the different core types and covariances with X-ray luminosities in selection functions. In particular, we stress the morphological deviations between a newly described surface brightness profile characterization and a commonly used single β-model. Methods. We investigated a novel approach to describe surface brightness profiles, where the excess cool-core emission in the centers of the galaxy clusters is modeled using wavelet decomposition. Morphological parameters and the residuals were compared to classical single β-models, fitted to the overall surface brightness profiles. Results. Using single β-models to describe the ensemble of overall surface brightness profiles leads on average to a non-zero bias (0.032 ± 0.003) in the outer part of the clusters, that is an approximate 3% systematic difference in the surface brightness at large radii. Furthermore, β-models show a general trend toward underestimating the flux in the outskirts for smaller core radii. Fixing the β parameter to 2/3 doubles the bias and increases the residuals from a single β-model up to more than 40%. Modeling the core region in the fitting procedure reduces the impact of these two effects significantly. In addition, we find a positive scaling between shape parameters and temperature, as well as a negative correlation of approximately −0.4 between extent and luminosity. Conclusion. We demonstrate the caveats in modeling galaxy clusters with single β-models and recommend using them with caution, especially when the systematics are not taken into account. Our non-parametric analysis of the self-similar scaled emission measure profiles indicates no systematic core-type differences of median profiles in the galaxy cluster outskirts.
Generalized bornological coarse spaces and coarse motivic spectra

We generalize the notion of a bornology by omitting the condition that a one-point-subset is bounded and obtain a complete and co-complete generalization of the category of bornological coarse spaces. Then we imitate the construction of motivic coarse spectra in this new setting and show that the inclusion functor from the category of bornological coarse spaces to its generalization induces an equivalence of motivic coarse spectra. In particular, for any stable co-complete $\infty$-category $C$, it induces an equivalence between the category of $C$-valued coarse homology theories on bornological coarse spaces and the category of $C$-valued coarse homology theories on generalized bornological coarse spaces.
The Impact of Cosmic Rays on the Sensitivity of JWST/NIRSpec

The focal plane of the Near Infrared Spectrograph (NIRSpec) instrument on board the James Webb Space Telescope (JWST) is equipped with two Teledyne H2RG near-IR detectors, state-of-the-art HgCdTe sensors with excellent noise performance. Once JWST is in space, however, the noise level in NIRSpec exposures will be affected by the cosmic ray (CR) fluence at the JWST orbit and our ability to detect CR hits and to mitigate their effect. We have simulated the effect of CRs on NIRSpec detectors by injecting realistic CR events onto dark exposures that were recently acquired during the JWST cryo-vacuum test campaign undertaken at Johnson Space Flight Center. Here we present the method we have implemented to detect the hits in the exposure integration cubes, to reject the affected data points within our ramp-to-slope processing pipeline (the prototype of the NIRSpec official pipeline), and assess the performance of this method for different choices of the algorithm parameters. Using the optimal parameter set to reject CR hits from the data, we estimate that, for an exposure length of 1000 s, the presence of CRs in space will lead to an increase of typically ∼7% in the detector noise level with respect to the on-ground performance, and the corresponding decrease in the limiting sensitivity of the instrument, for the medium and high-spectral resolution modes.
Asymptotic Bayes Risk for Gaussian Mixture in a Semi-Supervised Setting

Semi-supervised learning (SSL) uses unlabeled data for training and has been shown to greatly improve performances when compared to a supervised approach on the labeled data available. This claim depends both on the amount of labeled data available and on the algorithm used. In this paper, we compute analytically the gap between the best fully-supervised approach on labeled data and the best semi-supervised approach using both labeled and unlabeled data, in a simple high-dimensional Gaussian mixture model. We quantify the best possible increase in performance obtained thanks to the unlabeled data, i.e. we compute the accuracy increase due to the information contained in the unlabeled data.
The Kelvin-Helmholtz instability and smoothed particle hydrodynamics

There has been interest in recent years to assess the ability of astrophysical hydrodynamics codes to correctly model the Kelvin-Helmholtz instability. Smoothed particle hydrodynamics (SPH), in particular, has received significant attention, though there has yet to be a clear demonstration that SPH yields converged solutions that are in agreement with other methods. We have performed SPH simulations of the Kelvin-Helmholtz instability using the test problem put forward by Lecoanet et al (2016). We demonstrate that the SPH solutions converge to the reference solution in both the linear and non-linear regimes. Quantitative convergence in the strongly non-linear regime is achieved by using a physical Navier-Stokes viscosity and thermal conductivity. We conclude that standard SPH with an artificial viscosity can correctly capture the Kelvin-Helmholtz instability.
Belief places and spaces: Mapping cognitive environments

Beliefs are not facts, but they are factive - they feel like facts. This property is what can make misinformation dangerous. Being able to deliberately navigate through a landscape of often conflicting factive statements is difficult when there is no way to show the relationships between them without incorporating the information in linear, narrative forms. In this paper, we present a mechanism to produce maps of belief places, where populations agree on salient features of fictional environments, and belief spaces, where subgroups have related but distinct perspectives. Using a model developed using agent-based simulation, we show that by observing the repeated behaviors of human participants in the same social context, it is possible to build maps that show the shared narrative environment overlaid with traces that show unique, individual or subgroup perspectives. Our contribution is a proof-of-concept system, based on the affordances of fantasy tabletop role-playing games, which support multiple groups interacting with the same dungeon in a controlled, online environment. The techniques used in this process are mathematically straightforward, and should be generalizable to auto-generating larger-scale maps of belief spaces from other corpora, such as discussions on social media.
The multiphase gas structure and kinematics in the circumnuclear region of NGC 5728

We report on our combined analysis of HST, VLT/MUSE, VLT/SINFONI, and ALMA observations of the local Seyfert 2 galaxy, NGC 5728 to investigate in detail the feeding and feedback of the active galactic nucleus (AGN). The data sets simultaneously probe the morphology, excitation, and kinematics of the stars, ionized gas, and molecular gas over a large range of spatial scales (10 pc to 10 kpc). NGC 5728 contains a large stellar bar that is driving gas along prominent dust lanes to the inner 1 kpc where the gas settles into a circumnuclear ring. The ring is strongly star forming and contains a substantial population of young stars as indicated by the lowered stellar velocity dispersion and gas excitation consistent with H ii regions. We model the kinematics of the ring using the velocity field of the CO (2–1) emission and stars and find it is consistent with a rotating disc. The outer regions of the disc, where the dust lanes meet the ring, show signatures of inflow at a rate of 1 M$\odot$ yr−1. Inside the ring, we observe three molecular gas components corresponding to the circular rotation of the outer ring, a warped disc, and the nuclear stellar bar. The AGN is driving an ionized gas outflow that reaches a radius of 250 pc with a mass outflow rate of 0.08 M$\odot$ yr−1 consistent with its luminosity and scaling relations from previous studies. While we observe distinct holes in CO emission which could be signs of molecular gas removal, we find that largely the AGN is not disrupting the structure of the circumnuclear region.
Positional Normalization

A popular method to reduce the training time of deep neural networks is to normalize activations at each layer. Although various normalization schemes have been proposed, they all follow a common theme: normalize across spatial dimensions and discard the extracted statistics. In this paper, we propose an alternative normalization method that noticeably departs from this convention and normalizes exclusively across channels. We argue that the channel dimension is naturally appealing as it allows us to extract the first and second moments of features extracted at a particular image position. These moments capture structural information about the input image and extracted features, which opens a new avenue along which a network can benefit from feature normalization: Instead of disregarding the normalization constants, we propose to re-inject them into later layers to preserve or transfer structural information in generative networks. Codes are available at https://github.com/Boyiliee/PONO.
Unified Optimal Analysis of the (Stochastic) Gradient Method

In this note we give a simple proof for the convergence of stochastic gradient (SGD) methods on $\mu$-convex functions under a (milder than standard) $L$-smoothness assumption. We show that for carefully chosen stepsizes SGD converges after $T$ iterations as $O\left( LR^2 \exp \bigl[-\frac{\mu}{4L}T\bigr] + \frac{\sigma^2}{\mu T} \right)$ where $\sigma^2$ measures the variance in the stochastic noise. For deterministic gradient descent (GD) and SGD in the interpolation setting we have $\sigma^2 =0$ and we recover the exponential convergence rate. The bound matches with the best known iteration complexity of GD and SGD, up to constants.
Interactive Verifiable Polynomial Evaluation

Cloud computing platforms have created the possibility for computationally limited users to delegate demanding tasks to strong but untrusted servers. Verifiable computing algorithms help build trust in such interactions by enabling the server to provide a proof of correctness of his results which the user can check very efficiently. In this paper, we present a doubly-efficient interactive algorithm for verifiable polynomial evaluation. Unlike the mainstream literature on verifiable computing, the soundness of our algorithm is information-theoretic and cannot be broken by a computationally unbounded server. By relying on basic properties of error correcting codes, our algorithm enforces a dishonest server to provide false results to problems which become progressively easier to verify. After roughly logd rounds, the user can verify the response of the server against a look-up table that has been pre-computed during an initialization phase. For a polynomial of degree d, we achieve a user complexity of $O\left( {{d^\varepsilon }} \right)$, a server complexity of $O\left( {{d^{1 + \varepsilon }}} \right)$, a round complexity of O(logd) and an initialization complexity of $O\left( {{d^{1 + \varepsilon }}} \right)$.
Multilingual Universal Sentence Encoder for Semantic Retrieval

We present easy-to-use retrieval focused multilingual sentence embedding models, made available on TensorFlow Hub. The models embed text from 16 languages into a shared semantic space using a multi-task trained dual-encoder that learns tied cross-lingual representations via translation bridge tasks (Chidambaram et al., 2018). The models achieve a new state-of-the-art in performance on monolingual and cross-lingual semantic retrieval (SR). Competitive performance is obtained on the related tasks of translation pair bitext retrieval (BR) and retrieval question answering (ReQA). On transfer learning tasks, our multilingual embeddings approach, and in some cases exceed, the performance of English only sentence embeddings.
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model

Increasing the batch size is a popular way to speed up neural network training, but beyond some critical batch size, larger batch sizes yield diminishing returns. In this work, we study how the critical batch size changes based on properties of the optimization algorithm, including acceleration and preconditioning, through two different lenses: large scale experiments, and analysis of a simple noisy quadratic model (NQM). We experimentally demonstrate that optimization algorithms that employ preconditioning, specifically Adam and K-FAC, result in much larger critical batch sizes than stochastic gradient descent with momentum. We also demonstrate that the NQM captures many of the essential features of real neural network training, despite being drastically simpler to work with. The NQM predicts our results with preconditioned optimizers, previous results with accelerated gradient descent, and other results around optimal learning rates and large batch training, making it a useful tool to generate testable predictions about neural network optimization.
Probing the many-body localization phase transition with superconducting circuits

Chains of superconducting circuit devices provide a natural platform for studies of synthetic bosonic quantum matter. Motivated by the recent experimental progress in realizing disordered and interacting chains of superconducting transmon devices, we study the bosonic many-body localization phase transition using the methods of exact diagonalization as well as matrix product state dynamics. We estimate the location of transition separating the ergodic and the many-body localized phases as a function of the disorder strength and the many-body on-site interaction strength. The main difference between the bosonic model realized by superconducting circuits and similar fermionic model is that the effect of the on-site interaction is stronger due to the possibility of multiple excitations occupying the same site. The phase transition is found to be robust upon including longer-range hopping and interaction terms present in the experiments. Furthermore, we calculate experimentally relevant local observables and show that their temporal fluctuations can be used to distinguish between the dynamics of Anderson insulator, many-body localization, and delocalized phases. While we consider unitary dynamics, neglecting the effects of dissipation, decoherence, and measurement back action, the timescales on which the dynamics is unitary are sufficient for observation of characteristic dynamics in the many-body localized phase. Moreover, the experimentally available disorder strength and interactions allow for tuning the many-body localization phase transition, thus making the arrays of superconducting circuit devices a promising platform for exploring localization physics and phase transition.