Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Time Variability of X-Ray Absorption in Swift GRBs

The existence of excess absorption in the X-ray spectra of GRBs is well known, but the primary location of the absorbing material is still uncertain. To gain more knowledge about this, we have performed a time-resolved analysis of the X-ray spectra of 199 GRBs observed by the Swift X-ray telescope, searching for evidence of a decreasing column density ( NH,intr ) that would indicate that the GRBs are ionizing matter in their surroundings. We structured the analysis as Bayesian inference and used an absorbed power law as our baseline model. We also explored alternative spectral models in cases where decreasing absorption was inferred. The analysis reveals seven GRBs that show signs of a decrease in NH,intr , but we note that alternative models for the spectral evolution cannot be ruled out. We conclude that the excess absorption in the vast majority of GRBs must originate on large scales of the host galaxies and/or in the intergalactic medium. Our results also imply that an evolving column density is unlikely to affect the spectral analysis of the early X-ray spectra of GRBs. In line with this, we show that estimating the total NH,intr from early Swift data in Window Timing mode reveals the same increasing trend with redshift as previous results based on data taken at later times, but with tighter constraints.
Robot Skill Learning Via Classical Robotics-Based Generated Datasets: Advantages, Disadvantages, and Future Improvement

Why do we not profit from our long-existing classical robotics knowledge and look for some alternative way for data collection? The situation ignoring all existing methods might be such a waste. This article argues that a dataset created using a classical robotics algorithm is a crucial part of future development. This developed classic algorithm has a perfect domain adaptation and generalization property, and most importantly, collecting datasets based on them is quite easy. It is well known that current robot skill-learning approaches perform exceptionally badly in the unseen domain, and their performance against adversarial attacks is quite limited as long as they do not have a very exclusive big dataset. Our experiment is the initial steps of using a dataset created by classical robotics codes. Our experiment investigated possible trajectory collection based on classical robotics. It addressed some advantages and disadvantages and pointed out other future development ideas.
Testing Many Zero Restrictions in a High Dimensional Linear Regression Setting

We propose a test of many zero parameter restrictions in a high dimensional linear iid regression model with $k$ $>>$ $n$ regressors. The test statistic is formed by estimating key parameters one at a time based on many low dimension regression models with nuisance terms. The parsimoniously parametrized models identify whether the original parameter of interest is or is not zero. Estimating fixed low dimension sub-parameters ensures greater estimator accuracy, it does not require a sparsity assumption nor therefore a regularized estimator, it is computationally fast compared to, e.g., de-biased Lasso, and using only the largest in a sequence of weighted estimators reduces test statistic complexity and therefore estimation error. We provide a parametric wild bootstrap for p-value computation, and prove the test is consistent and has non-trivial $\sqrt{n/\{\ln (n)\mathcal{M}% _{n}\}}$-local-to-null power where $\mathcal{M}_{n}$ is the $l_{\infty }$ covariate fourth moment.
Ddml: Double/Debiased Machine Learning in Stata

In this article, we introduce a package, ddml, for double/debiased machine learning in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using double/debiased machine learning in combination with stacking estimation, which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.
The umbilical cord of finite model theory

Model theory was born and developed as a part of mathematical logic. It has various application domains but is not beholden to any of them. A priori, the research area known as finite model theory would be just a part of model theory but didn't turn out that way. There is one application domain -- relational database management -- that finite model theory had been beholden to during a substantial early period when databases provided the motivation and were the main application target for finite model theory. Arguably, finite model theory was motivated even more by complexity theory. But the subject of this paper is how relational database theory influenced finite model theory. This is NOT a scholarly history of the subject with proper credits to all participants. My original intent was to cover just the developments that I witnessed or participated in. The need to make the story coherent forced me to cover some additional developments.
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference

The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private inference (PI). Existing techniques to reduce ReLU operations often involve manual effort and sacrifice significant accuracy. In this paper, we first present a novel measure of non-linearity layers' ReLU sensitivity, enabling mitigation of the time-consuming manual efforts in identifying the same. Based on this sensitivity, we then present SENet, a three-stage training method that for a given ReLU budget, automatically assigns per-layer ReLU counts, decides the ReLU locations for each layer's activation map, and trains a model with significantly fewer ReLUs to potentially yield latency and communication efficient PI. Experimental evaluations with multiple models on various datasets show SENet's superior performance both in terms of reduced ReLUs and improved classification accuracy compared to existing alternatives. In particular, SENet can yield models that require up to ~2x fewer ReLUs while yielding similar accuracy. For a similar ReLU budget SENet can yield models with ~2.32% improved classification accuracy, evaluated on CIFAR-100.
Ordinal Regression for Difficulty Estimation of StepMania Levels

StepMania is a popular open-source clone of a rhythm-based video game. As is common in popular games, there is a large number of community-designed levels. It is often difficult for players and level authors to determine the difficulty level of such community contributions. In this work, we formalize and analyze the difficulty prediction task on StepMania levels as an ordinal regression (OR) task. We standardize a more extensive and diverse selection of this data resulting in five data sets, two of which are extensions of previous work. We evaluate many competitive OR and non-OR models, demonstrating that neural network-based models significantly outperform the state of the art and that StepMania-level data makes for an excellent test bed for deep OR models. We conclude with a user experiment showing our trained models' superiority over human labeling.
Representations of the $p$-Adic $GSpin_4$ and $GSpin_6$ and the Adjoint L-Function

We prove a conjecture of B. Gross and D. Prasad about determination of generic $L$-packets in terms of the analytic properties of the adjoint $L$-function for $p$-adic general even spin groups of semi-simple ranks 2 and 3. We also explicitly write the adjoint $L$-function for each $L$-packet in terms of the local Langlands $L$-functions for the general linear groups.
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with $1\le\alpha\le2$ which holds in a wide range of applications in machine learning and signal processing. This condition ensures that any first-order stationary point is a global optimum. We prove that the total sample complexity of SCRN in achieving $\epsilon$-global optimum is $\mathcal{O}(\epsilon^{-7/(2\alpha)+1})$ for $1\le\alpha<3/2$ and $\mathcal{\tilde{O}}(\epsilon^{-2/(\alpha)})$ for $3/2\le\alpha\le 2$. SCRN improves the best-known sample complexity of stochastic gradient descent. Even under a weak version of gradient dominance property, which is applicable to policy-based reinforcement learning (RL), SCRN achieves the same improvement over stochastic policy gradient methods. Additionally, we show that the average sample complexity of SCRN can be reduced to ${\mathcal{O}}(\epsilon^{-2})$ for $\alpha=1$ using a variance reduction method with time-varying batch sizes. Experimental results in various RL settings showcase the remarkable performance of SCRN compared to first-order methods.
Formation of large-scale structures in ablative Kelvin-Helmholtz instability

In this research, we studied numerically nonlinear evolutions of the Kelvin–Helmholtz instability (KHI) with and without thermal conduction, aka, the ablative KHI (AKHI) and the classical KHI (CKHI). The second order thermal conduction term with a variable thermal conductivity coefficient is added to the energy equation in the Euler equations in the AKHI to investigate the effect of thermal conduction on the evolution of large and small scale structures within the shear layer which separate the fluids with different velocities. The inviscid hyperbolic flux of Euler equation is computed via the classical fifth order weighted essentially nonoscillatory finite difference scheme and the temperature is solved by an implicit fourth order finite difference scheme with variable coefficients in the second order parabolic term to avoid severe time step restriction imposed by the stability of the numerical scheme. As opposed to the CKHI, fine scale structures such as the vortical structures are suppressed from formi.
Development of Nanoscale Thermal Properties Measurement Technique by Using Near-Field Optics

The nanoscale thermal properties are becoming increasingly important for the thermal design of electronic devises as the MEMS technology makes progress. The thermal conductivity of nanoscale thin film is remarkably lower than that of bulk materials because of its various size effects. In addition, the nano-materials will have pointing defects or lattice imperfections during the production process. Therefore, nanoscale thermal properties measurement technique, which can be applied in-situ or in-process, is required. We have developed a new thermal properties measurement technique by using near-field optics, which targets spatial resolution better than 100nm (up to 10nm) and is applicable to measure the thermal properties of nanoscale materials in-situ. In this article, in order to check the validity of the control system, the topographic image of diffraction grating is monitored. Moreover, the temperature change of Al thin film is detected as a thermoreflectance signal. Finally, the capability of our present work, to measure the thermal properties of nanostructures, is discussed.
Generation of high-order rotational lines in hydrogen by four-wave Raman mixing in the femtosecond regime

More than 40 rotational Raman lines are generated using an 800-fs Ti:sapphire laser. The spectral region extends from the near-infrared to the far-ultraviolet with a considerably flat intensity distribution. The effects of laser polarization, pulsewidth, hydrogen pressure, and focusing conditions on the efficiencies of stimulated Raman scattering, four-wave Raman mixing, self-phase modulation, self-focusing, and harmonic generation are investigated. A white light continuum, generated by self-phase modulation, acts as a seed beam for the generation of high-order rotational lines through four-wave Raman mixing. Strong self-phase modulation, however, suppresses the generation of the Raman emission, due to a line broadening of the pump beam. Thus, optimization of experimental conditions is necessary for the efficient generation of high-order rotational lines.
Dietary diversity and niche partitioning of carnivores across the Qinghai–Tibetan Plateau of China using DNA metabarcoding

Understanding predator guild resource use is vital for carnivore species preservation and ecosystem function in high-altitude landscapes. We describe the dietary composition, similarity, and niche overlap for seven carnivore species across three regions of the Qinghai–Tibetan Plateau (Qilian Shan, East Burhanbuda Mountain, Yushu) using DNA metabarcoding of 760 scat samples. Analyses found 33 unique prey items representative of the classes Mammalia, Aves, and Actinopterygii. Blue sheep (Pseudois nayaur) and pika (Ochotona sp.) were most frequently detected. Livestock in carnivore diets ranged from 4% to 7% depending on site, but accounted for 21.7% of Tibetan wolf (Canis lupus) diet. Eurasian lynx (Lynx lynx), Tibetan fox (Vulpes ferrilata), Pallas's cat (Otocolobus manul), and snow leopard (Panthera uncia) had highly uneven diets. Intraguild predation of mesocarnivores by apex carnivores was found. Analysis of dietary niche overlap using Pianka's index showed that overlap was significantly greater than expected among all carnivores examined and between apex and mesocarnivores, though significant correlations between carnivore body mass and prey body mass suggested broad separation in consumed prey. Snow leopard and Tibetan wolf diets were consistently the most similar of any two given species pairs. This study provides evidence of high dietary overlap. Protection for species consumed by a wide variety of carnivores will benefit predator guilds in the area and assist in maintaining the relatively low frequency of livestock predation. This work provides insight into the dynamics of carnivore guilds living in an area that has worldwide impacts, and methods herein could be applied on global scales for conservation efforts.
The Social Critic as Liar: Wilde, Adorno, and the Crisis of Post-truth Politics

Abstract:How does one speak the truth in a “post-truth” polity? This article turns to Oscar Wilde and Theodor Adorno to develop an account of critique as mendacious truth-telling that mobilizes the aesthetically pleasing, world-reconstructing power of lying embraced by contemporary neofascists on behalf of democratic social transformation. Rejecting demystification and empirical truth-telling as incapable of responding to the post-truth predicament, the critic as liar combats the “false lies” of neofascism by devising “fine lies” that imagine new, more egalitarian modes of social organization.
A COMPUTATIONAL DESIGN OF Si-BASED DIRECT BAND-GAP MATERIALS

A bulk silicon is an indirect band gap semiconductor, the radiative transitions in silicon involves a electron-hole-phonon three-body processes and is therefore making it an inability of light emission. In order to integrate Si-based microelectronics with optical components, a best solution is to design a Si-based material with direct band-gap. Here we suggest a new Si-based superlattice consists of a nano-silicon layer and a VI-atom monolayer with dimer-structures in the interfaces between silicon and VI-atom layers. The structural formula is VI/Sim/VI/Sim/VI, (m = 2n or/and 2n+1,n ≥ 3). A simple form of (2×1) surface reconstruction has been considered in the ab initio calculation of electronic structures for this Si-based superlattices system. It is found from our computational design that a Se/Si10/Se/Si10/Se superlattice has a direct band-gap at Γ point. As a kind of new Si-based materials, the important potential and advantages in optoelectronic applications will be discussed.
An efficient brute-force solution to the network reconfiguration problem

The authors suggest a method for determining a minimal-loss radial configuration for a power distribution network, using an exhaustive search algorithm. While exhaustive, the method is highly efficient, deriving its efficiency from the use of graph-theoretic techniques involving semi-sparse transformations of a current sensitivity matrix. The algorithm can he applied to networks of moderate size and has advantages over existing algorithms for network reconfiguration in that it guarantees a globally optimal solution (under appropriate modeling assumptions), and is easily extended to take account of phase imbalance and network operation constraints. A 33-bus example is used to demonstrate the operation of the algorithm.
Fast Spirulina detection with fixed-focus microscope and deep learning

As a kind of microalgae, Spirulina plays an important role in fish culture, food processing industry, medical treatment and bioenergetic development due to its reasonable nutritional composition and high hydrogenase activity. However, the purity of Spirulina , which could be significantly affected by virus infection and miscellaneous algal issues, has great impact on the quality of the product. Thus, periodic Spirulina detection is necessary for quality control of Spirulina culture. Currently, there are two main methods of Spirulina detection: the optical microscopic method and the fluorescence detection method. The former has higher accuracy and a lower speed while the latter has a higher speed in a sample destructing mode. Deep learning-based method has the ability to accelerate data processing. Meanwhile, it can achieve high accuracy by model training and validation. In this work, we have applied deep learning to Spirulina detection to achieve a higher accuracy rate. The process was divided into four main steps: Spirulina culture, image acquisition, image preprocessing and YOLO-v3 model training. The hyperparametric modulation was carried out to determine the appropriate training parameters, providing a trained model with mAP of 0.839 at a detection speed of 20.53 fps. It has great application potential in quantity detection and size detection of cultured Spirulina .
VOC gas leakage detection using infrared image and convolutional neural networks

Leakage of volatile organic compounds (VOC) gas is one of the main sources of air pollution, and it poses a great threat to health and safety in many ways. Optical gas imaging (OGI) technique utilizes mid-wave infrared camera to visualize VOC gas and helps people observe the leakage of VOC gas. In this paper, we propose a novel method that utilizes deep learning technique and convolutional neural networks to detect the leakage of VOC gas from single-frame mid-wave infrared image. The proposed method consists of three components: color transformation pre-processing unit, feature extraction networks, and single-stage object detection sub-networks. Location-aware deformable convolution, which adjusts its sampling grid to fit the ever-changing shape of VOC gases, is employed for better feature extraction. Besides, a new loss function called leakage center loss is introduced to estimate where leakage comes from, and it forces the network to pay more attention to leakage center where the density of VOC gases is higher than dissipated parts. The proposed method is evaluated using a self-collected dataset where thousands of gas images are captured and annotated. Experimental results show that location-aware deformable convolution contributes to around 7%mAP improvement, while leakage center loss contributes to around 4% mAP improvement. Finally, our method achieves 81%mAP, which is better than existing general-purpose object detection methods. By simplifying the network architecture, our proposed method can also be implemented on embedded system for handheld VOC leakage detection devices.
PAPR mitigation based on superimposed training scheme in time-varying underwater acoustic OFDM communications

Peak-to-average power ratio (PAPR) is a hot issue in orthogonal frequency division multiplex (OFDM) technology. High PAPR of OFDM system limits its application in underwater acoustic communications. The DFT-s-OFDM technology has been widely used in long term evolution (LTE) and fifth-generation new radio (5G NR) uplink transmission schemes for wireless communications due to its good PAPR performance. However, it is difficult to adapt to time-varying underwater acoustic channels by using time-multiplexed training. We use the DFT-s-OFDM technology of the sideband tone reservation method to suppress the PAPR of the system. At the same time, for the DFT-s-OFDM system, it is impossible to insert data and pilots in a single symbol block at the same time to track the channels. A low-power training sequence is superimposed with symbols, and the training sequence is transmitted over all time. It is used to enhance the tracking channel capability with a small energy overhead and is used to process time-varying channels. Field experiments were carried in Qiandao Lake in 2021, and the results verify the effectiveness of the proposed scheme.
Monitoring the Potential of Lithuania's Civic Technologies Platforms to Co-create Collective Intelligence

ABSTRACT:This paper presents the findings of a systematic survey that evaluated the potential of online communities (or civic tech) in Lithuania to co-create collective intelligence. Civic intelligence is a form of collective intelligence exercised by a group's capacity to perceive societal problems and its ability to address them effectively. The research subject is "bottom-up" digital-enabled online platforms initiated by Lithuanian public organisations, civic movements, or business entities. Traditional approaches to public engagement remain relevant. Notwithstanding, our enquiry is more interested in the potential of digital-enabled citizens to increase efficient collective performance. This scientific project advances our understanding of the essential pre-conditions in online communities through which collective intelligence is being systematically co-created. By monitoring the performance of civic tech platforms, the scientific question was examined: What are the socio-technological conditions that led the communities to become more intelligent? The results of the web-based assessment were obtained by applying the collective intelligence monitoring technique.
The Pursuit of Efficiency versus Robustness: A Learning Experience from Analyzing a Semiparametric Nonignorable Propensity Score Model

Abstract:Rosenbaum and Rubin’s pioneering work on “The Central Role of the Propensity Score in Observational Studies for Causal Effects” has shaped the landscape of the literature in causal inference and missing data analysis. In the past decades, the concept of propensity score has been used not only under ignorability assumption, but also under nonignorability assumption. The nice properties of double robustness and semiparametric efficiency are well known under ignorability; however, the situation is a lot more sophisticated under nonignorability. In this paper, we summarize what we have learnt from analyzing a semi-parametric nonignorable propensity score model. It turns out that, under nonignorability, the efficient estimator for the quantity of interest might be too complicated to be practically implemented. On the other hand, by sacrificing the efficiency to some extent, one type of robust estimators is much easier to derive and implement; hence is recommended. This is a general tradeoff between efficiency and robustness in a typical semiparametric model.
Perturbação de spin zero no espaço-tempo de Kerr-Randall-Sundrum

This dissertation aims at studying the braneworld models in the context proposed by Randall and Sundrum. The focus is on the spin-0 perturbations in the Kerr space-time as a 4−dimensional braneworld. The work deals the main aspects of Einstein General Relativity as well as perturbations of black holes metrics. We also review the Randall-Sundrum models and their motivations and attempts to describe braneworld black holes. In the end the Kerr-Randall-Sundrum black string scalar perturbation and superradiance are obtained.