Miracle-dz/UniFAR-FTUs huggingface dataset:
Skateboard Tricks and Topological Flips

We study the motion of skateboard flip tricks by modeling them as continuous curves in the group $SO(3)$ of special orthogonal matrices. We show that up to continuous deformation there are only four flip tricks. The proof relies on an analysis of the lift of such curves to the unit 3-sphere. We also derive explicit formulas for a number of tricks and continuous deformations between them.
Asymmetry in Earthquake Interevent Time Intervals

Here we focus on a basic statistical measure of earthquake catalogs that has not been studied before, the asymmetry of interevent time series (e.g., reflecting the tendency to have more aftershocks than spontaneous earthquakes). We define the asymmetry metric as the ratio between the number of positive interevent time increments minus negative increments and the total (positive plus negative) number of increments. Such asymmetry commonly exists in time series data for nonlinear geophysical systems like river flow which decays slowly and increases rapidly. We find that earthquake interevent time series are significantly asymmetric, where the asymmetry function exhibits a significant crossover to weak asymmetry at large lag index. We suggest that the Omori law can be associated with the large asymmetry at short time intervals below the crossover whereas overlapping aftershock sequences and the spontaneous events can be associated with a fast decay of asymmetry above the crossover. We show that the asymmetry is better reproduced by a recently modified Epidemic‐Type Aftershock Sequence (ETAS) model with two triggering processes in comparison to the standard ETAS model which only has one.
Pre-training of Temporal Convolutional Neural Networks for Popularity Prediction

Predicting the popularity of online content is a fundamental problem in various application areas. One practical challenge for popularity prediction takes roots in the different settings of popularity prediction tasks in different situations, e.g., the varying lengths of the observation time window or prediction horizon. In other words, a good model for popularity prediction is desired to handle various tasks with different settings. However, the conventional paradigm for popularity prediction is training a separate prediction model for each prediction task, and thus the obtained model for one task is difficult to be generalized to other tasks, causing a great waste of training time and computational resources. To solve this issue, in this paper, we propose a novel pre-training framework for popularity prediction , aiming to pre-train a general deep representation model by learning intrinsic knowledge about popularity dynamics from the readily available diffusion cascades. We design a novel pretext task for pre-training, i.e., temporal context prediction for two randomly sampled time slices of popularity dynamics, impelling the deep prediction model to effectively capture the characteristics of popularity dynamics. Taking the state-of-the-art deep model, i.e., temporal convolutional neural network, as an instantiation of our proposed framework, experimental results conducted on both Sina Weibo and Twitter datasets demonstrate both the effectiveness and efficiency of the proposed pre-training framework for multiple popularity prediction tasks.
On the uniqueness of Gibbs distributions with a non-negative and subcritical pair potential

We prove that the distribution of a Gibbs process with non-negative pair potential is uniquely determined as soon as an associated Poisson-driven random connection model (RCM) does not percolate. Our proof combines disagreement coupling in continuum with a coupling of a Gibbs process and a RCM. The improvement over previous uniqueness results is illustrated both in theory and simulations.
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning

Most previous methods for text data augmentation are limited to simple tasks and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot natural language understanding) and strong baselines (i.e., pretrained models with over one billion parameters). Under this setting, we reproduced a large number of previous augmentation methods and found that these methods bring marginal gains at best and sometimes degrade the performance much. To address this challenge, we propose a novel data augmentation method FlipDA that jointly uses a generative model and a classifier to generate label-flipped data. Central to the idea of FlipDA is the discovery that generating label-flipped data is more crucial to the performance than generating label-preserved data. Experiments show that FlipDA achieves a good tradeoff between effectiveness and robustness—it substantially improves many tasks while not negatively affecting the others.
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

The last half decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision-making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research. It includes learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforcement learning approaches that encourage safety or robustness, and methods that can formally certify the safety of a learned control policy. As data- and learning-based robot control methods continue to gain traction, researchers must understand when and how to best leverage them in real-world scenarios where safety is imperative, such as when operating in close proximity to humans. We highlight some of the open challenges that will drive the field of robot learning in the coming years, and emphasize the need for realistic physics-based benchmarks to facilitate fair comparisons between control and reinforcement learning approaches. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Zero-shot Task Transfer for Invoice Extraction via Class-aware QA Ensemble

We present VESPA, an intentionally simple yet novel zero-shot system for a layout, locale and domain agnostic document extraction. In spite of the availability of large corpora of documents, the lack of labeled and validated datasets makes it a challenge to discriminatively train document extraction models for enterprises. We show that this problem can be addressed by simply transferring the information extraction (IE) task to a natural language QuestionAnswering (QA) task without engineering task-specific architectures. We demonstrate the effectiveness of our system by evaluating on a closed corpus of real-world retail and tax invoices with multiple complex layouts, domains and geographies. The empirical evaluation shows that our system outperforms 4 prominent commercial invoice solutions 1 that use discriminatively trained models with architectures specifically crafted for invoice extraction. We extracted 6 fields with zero upfront human annotation or training with an Avg. F1 of 87.50.
Pembelajaran Thinking Actively in Social Context untuk Meningkatkan Kemampuan Higher Order Thinking Siswa SMA

<p>Penelitian ini bertujuan untuk mengembangkan model pembelajaran <em>Thinking Actively in Social Contexts</em> (<em>TASC</em>) pada pembelajaran Biologi SMA disertai dengan panduan model yang layak untuk meningkatkan kemampuan <em>higher order thinking</em>. Penelitian ini menggunakan model 4D: <em>define, design, develop, disseminate</em> yang dikombinasikan dengan model Borg and Gall. Tahap <em>define </em>meliputi analisis kurikulum, karakteristik siswa, kegiatan siswa, materi dan tujuan pembelajaran. Tahap <em>design </em>meliputi pemilihan media, perancangan modifikasi sintaks, perancangan format panduan model, serta pembuatan produk awal. Tahap <em>develop </em>meliputi pengembangan model TASC disertai panduan model dengan penilaian oleh ahli dan uji pengembangan (uji coba terbatas dan uji coba lapangan). Tahap <em>disseminate </em>merupakan upaya penyebaran model <em>TASC </em>dilengkapi panduan model yang telah direvisi kepada beberapa pihak terkait. Hasil penelitian menunjukkan bahwa model pembelajaran <em>TASC </em>layak dan<em> </em>efektif untuk meningkatkan kemampuan <em>higher order thinking</em> siswa. Pencapaian nilai kemampuan <em>higher order thinking</em> mengalami peningkatan yang signifikan berdasarkan hasil pretest dan postes yang telah diuji dengan uji <em>gain score</em> dan uji <em>t</em><em>. </em></p><p> </p><p><strong>Kata kunci</strong>: Model pembelajaran <em>TASC</em>, <em>higher order thinking</em>. </p><p> </p>
Erratum: Narrow-beam antennas using an artificial dielectric medium with permittivity less than unity

Radiation from a line source of electric current placed inside a slab of artificial dielectric medium with a permittivity less than unity has been studied. It is found that radiation patterns with single or multiple narrow beams may be obtained by the proper choice of parameters. In a typical case for er=0.1378, µr=1.322, a beamwidth of about 1° is obtained when the thickness of artificial dielectric slab is about 2.33 times the wavelength.
Application of Low-Cost Cu–Sn Bimetal Alloy as Oxygen Reduction Reaction Catalyst for Improving Performance of the Microbial Fuel Cell

In this experiment, a new bimetal low–cost Cu–Sn alloy was synthesized and it was tested as catalyst for oxygen reduction reaction (ORR) in MFC and the results were compared with the commercially available Pt-C catalyst. Cyclic voltammetry for evaluating ORR of the test cathode containing Cu-Sn catalysts under oxygen saturated environment displayed large ORR current peak, showing less overpotential demand for ORR. Maximum power density of 457 mW/m^2 obtained from MFC using Cu–Sn catalyst, was found to be slightly higher than the power density of 446 mW/m^2 demonstrated by MFC using Pt based cathode. Biochemical conversion of organic matter to direct electric current in Cu–Sn based MFC occurred at a coulombic efficiency of 55.8%, while demonstrating 92% of chemical oxygen demand removal. This study demonstrated application of low cost Cu–Sn bimetal alloy as excellent ORR catalyst in MFC and would be very helpful to commission larger MFCs for field applications to harvest energy in the form of direct electricity from wastewaters while offering wastewater treatment.
In Vitro Evaluation of the Effect of Different Luting Cements and Tooth Preparation Angle on the Microleakage of Zirconia Crowns

Introduction Discrepancy between the crown border and prepared tooth margin leads to a microleakage that eases the penetration of microorganisms and causes the dissolution of luting cement consequently. Several factors should be considered to achieve optimal fitness, including tooth preparation taper and type of cementing agent. The study aimed to determine the relation of tooth preparation taper and cement type on the microleakage of zirconia crowns. Materials and Methods Fifty-six freshly extracted premolars without caries and restorations were selected as the study sample and divided into two groups of different tapering degrees (6 and 12 degrees). Zirconia copings were designed and fabricated by the CAD/CAM system. The samples were divided into four subgroups for cementation, and each subgroup was cemented with a different luting cement (n = 7). After 5000 thermocycles at 5°C–55°C and dye penetration, the specimens were sectioned in the mid-buccolingual direction, and a digital photograph of each section was taken under a stereomicroscope. Data were analyzed by the Kruskal–Wallis and Mann–Whitney tests (α = 0.05). Results The results showed significant differences among the four types of luting cement in marginal permeability (PV < 0.001). Regardless of the type of cement, the 12-degree tapering resulted in a lower microleakage (46.4% without microleakage) with statistically significant differences from the 6-degree tapering (PV = 0.042). Conclusion Within the limitations of this study, increasing the tapering degree of the prepared tooth for CAD/CAM zirconia copings improved the marginal fit and decreased the microleakage score. In addition, total-etch resin cement indicated the least microleakage.
A CFD approach to assess the effects of different opening combinations in poultry houses

Uma abordagem em DFC para avaliar os efeitos das diferentes combinações de abertura em aviários R E S U M O O objetivo deste trabalho foi utilizar a dinâmica de fluidos computacional (DFC) para avaliar as condições de higiene e conforto, em função dos níveis de temperatura e concentração de amônia (NH3) durante a noite, em galpões avícolas comerciais no Brasil e em países com climas tropicais e subtropicais. Para isto, foram utilizados quatro modelos com diferentes posições de altura, cortina com entrada e saída de ar adotando a tipologia de instalação aberta, sem isolamento térmico. O modelo em DFC validado não apresentou diferenças estatísticas com os dados experimentais, o que torna possível usá-lo para prever o comportamento dos quatro casos propostos. Este modelo pode ser usado para a melhoria da condição interna das instalações de aves durante a noite podendo, também, ser uma ferramenta importante para reduzir o consumo de energia e criar melhores condições para a produção de aves com idade superior a 21 dias de idade.
Mechanical and electrochemical prelithiation of silicon nanoparticles in lithium-ion battery anodes

Silicon (Si) is one of the most promising anode materials for the next generation lithium ion batteries, as it can store up to ten times the capacity compared to the conventional graphite anode. However, with its alloying capacity follows great volume expansions resulting in capacity fade during the early cycles. In this study, prelithiation was studied as a possible solution to reduce the irreversible capacity loss seen during the first cycle, and to potentially increase cycle life. Two methods for prelithiation of silicon nanoparticles (SiNPs) were developed: electrochemical prelithiation (EP) and mechanical prelithiation (MP). In the EP process the half-cells were electrochemically cycled one cycle with a rate of C/20, before disassembled, washed, and reassembled into new, now prelithiated, half-cells. In the MP process a Si electrode with electrolyte was sandwiched with a piece of Li foil between two glass dishes. Both methods were shown to successfully prelithiate the Si electrodes, and a degree of prelithiation close to 100 % was obtained with both EP and MP. Through a time-dependent study it was seen that the degree of prelithiation increases with prelithiation time, and that prelithiation potentially can be regulated by time and mass measurements. The results showed that a degree of prelithiation > 90 % could be reached in just 60 min of MP. Both reference and prelithiated Si electrodes were assembled into coin cells with lithium metal as the counter electrode, and electrochemically cycled. The results showed no significant change in electrochemical cycling signature, and scanning electron microscopy (SEM) measurements showed that the structure of the SiNPs was maintained during prelithiation. Even though the discharge capacity decay was seen reduced for the prelithiated cells during the first cycles, they retained as poor cycle life as the reference cells of ∼ 50 cycles. Two of the MP cells with the highest degree of prelithiation showed however slightly increased cycle lives up to ∼ 60 cycles. Additionally, prelithiated cells were cycled on two different limited capacity cycling programs, one limiting the lithiation process, the other limiting the delithiation process. Both were limited by the capacity of 372 mAh/gSi, similar to the theoretical value of the graphite anode. All cells cycled 500 cycles with no significant decrease in discharge capacity.
Negative-supply-driven Uncertainty Shocks

What is the dominant driver of macroeconomic uncertainty? Is it the aggregate demand or the aggregate supply shock? In this paper, first, I investigate the two-way relationship between macroeconomic uncertainty and economic activity. I used a Vector Autoregressive identification scheme by imposing sign restrictions to disentangle supply shocks from demand shocks. According to our analysis of the US over the period 1960M07-2020M04, the supply side is the dominant driver of uncertainty. However, an identified uncertainty shock in the data causes a contraction in output and significant declines in consumption, inflation, and hours worked. The same results have been confirmed by the local projection method, revealing that the effects of uncertainty resemble the impact of a negative demand shock. These findings suggest that the long-lived supply-driven uncertainty shocks can strengthen demand shortages and may lead to a demand-deficient recession. Thus, whether negative-supply-driven uncertainty shock can cause a demand-deficient recession remains an open question. I think finding a new supply chains channel through which macro uncertainty can cause a demand-deficient recession would be possible for future research. Second, turning to policy implications, the results also reveal that the adverse supply-driven uncertainty shocks weaken the effectiveness of fiscal policy in easing its negative impact on demand. Therefore, I argue that encouraging consumption alone is not enough and stimulating production is a must.
High-Frequency Optimal Pricing Strategy for a Monopolist Seller Facing Patient Customers

We consider a market where most profit-relevant factors (e.g., storage costs and selling expenses) are negligible, and the trade-off between price and volume is the only concern. We investigate the optimal discrete-time pricing strategy of a monopolist seller who is able to change prices frequently when facing customers with different patience levels. We characterize the optimal strategy and unveil the asymptotic optimal cycle length and optimal revenue when the time step size goes to 0. It is found that a rational monopolist seller who wants to maximize revenue should not adjust prices frequently.
COMET: Convolutional Dimension Interaction for Collaborative Filtering

Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods assume both historical interactions and embedding dimensions are independent of each other, and thus regrettably ignore the high-order interaction information among historical interactions and embedding dimensions. In this article, we propose a novel representation learning-based model called COMET (COnvolutional diMEnsion inTeraction), which simultaneously models the high-order interaction patterns among historical interactions and embedding dimensions. To be specific, COMET stacks the embeddings of historical interactions horizontally at first, which results in two “embedding maps”. In this way, internal interactions and dimensional interactions can be exploited by convolutional neural networks (CNN) with kernels of different sizes simultaneously. A fully connected multi-layer perceptron (MLP) is then applied to obtain two interaction vectors. Lastly, the representations of users and items are enriched by the learnt interaction vectors, which can further be used to produce the final prediction. Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and rationality of our proposed method.
Uncertainty quantification and testing in a stochastic block model with two unequal communities

Abstract: We show posterior convergence for the community structure in the planted bi-section model, for several interesting priors. Examples include where the label on each vertex is iid Bernoulli distributed, with some parameter r ∈ (0, 1). The parameter r may be fixed, or equipped with a beta distribution. We do not have constraints on the class sizes, which might be as small as zero, or include all vertices, and everything in between. This enables us to test between a uniform (Erdős-Rényi) random graph with no distinguishable community or the planted bi-section model. The exact bounds for posterior convergence enable us to convert credible sets into confidence sets. Symmetric testing with posterior odds is shown to be consistent.
Indian Index Correlation and Integration with the International Indexes

In India, after 1991 liberalization and globalization policy, many things have been changed, like many foreign companies and investors came to India and invested in India, like those Indian companies and investors going to international markets and investing in foreign markets. So, International markets and investors are influencing the Indian markets vice versa. In this context, the researcher analyzes international indexes' returns and correlation with the Indian index. For this research, the researcher has selected BSE SENSEX and eleven popular international indexes from different continents.
Quantifying Photoinduced Charge Transfer in Graphene-Transition Metal Dichalcogenide van der Waals Heterostructures using Raman Spectroscopy

Van der Waals heterostuctures, made from stacks of two-dimensional materials, offer promising routes for novel optoelectronic devices. The performance of such devices is governed by near-field coupling through, e.g., interlayer charge and/or energy transfer. New concepts and experimental methodologies are needed to properly describe interlayer coupling in these atomically thin heterointerfaces. Here, we report an original study of interlayer charge transfer in single-layer graphene/single-layer molybdenum diselenide (MoSe$_2$) heterostructures by means of micro-Raman scattering spectroscopy. By analyzing the Raman features of graphene and MoSe$_2$, we demonstrate and quantify the transfer of photoexcited electrons from MoSe$_2$ to graphene. Remarkably, the amount of transferred charges saturates as the incident photon flux increases, suggesting that interlayer charge transfer is quenched as the Fermi energy of graphene rises towards the conduction band edge of MoSe$_2$, allowing an estimation of band alignments between MoSe$_2$ and graphene. Our work invites applications for reversible, spatially-resolved photogating and should help address the question of the relative efficiencies of charge and energy transfer in van der Waals heterostructures.