Miracle-dz/UniFAR-FTUs huggingface dataset:
UNIVERSIDADE FEDERAL DO RIO DE JANEIRO DAVI PEDRONI BARRETO BIOGEOGRAFIA E PERFIL DE DIVERSIDADE DE ARCHAEAS METANOGÊNICAS EM LAGOS TROPICAIS

BARRETO, Davi Pedroni. Biogeography and diversity of methanogenic Archaea in tropical lakes. Rio de Janeiro, 2013. Dissertação (Mestrado em Ciências Biológicas Microbiologia), Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 2013. The study of the distribution of microorganisms through space (and time) allows the evaluation of biogeographic patterns, such as the species-area index (z). Owing to their high dispersal ability, high reproduction rates, and low rates of extinction, microorganisms tend to be widely distributed, and are thought to be virtually cosmopolitan and selected primarily by environmental factors. Recent studies have shown that, despite these characteristics, microorganisms may behave like larger organisms and exhibit geographical distribution. In this study, we investigated patterns of spatial diversity distribution of bacteria and archaea in a relatively homogeneous environment. We collected 26 sediment samples from two different tropical lakes, distributed in a nested grid, with distances between samples ranging from 0.01 m to 1400 m. The samples were analyzed using terminal restriction fragment length polymorphism (T-RFLP) targeting the mcrA gene (coding for a subunit of methyl-coenzyme M reductase) and the genes of archaeal and bacterial 16S rRNA. From the qualitative and quantitative results (relative abundance of operational taxonomic units), we calculated the similarity index for each pair to evaluate the taxa-area and distance-decay relationship slopes by linear regression. In both the studied environments, it was possible to confirm, by analyses of taxa-area relationships and the z-value, the existence of a biogeography pattern. The mcrA gene presented higher z-values in both the Pantanal and Amazonia lakes (0.014 and 0.009, respectively). Bacteria and Archaea also showed significant results in the Pantanal lake (0.009 and 0.015). This suggests that geographic distance-related factors direct, at some level, the composition of the microbial communities in these lakes. Sediment incubations were also carried out with samples from the Grande do Curuai Lake, to measure the hydrogenotrophic and acetotrophic methanogenesis rates of the same points that were analyzed for diversity. Despite similar methane production rates in different quadrants, we were able to observe that distant sampling points presented larger rate variations, and consequently, a larger coefficient of variation. In this study, we showed that the microorganisms of a methanogenic community that is active in a continuous and relatively homogeneous environment display spatial distribution and a taxa-area relationship, and that this might influence the rate of methanogenic transformation of organic matter in these environments.
Multiple samples dictionary learning and locality constrained coding for face recognition

: This paper proposes a new dictionary learning algorithm, Multiple Samples Dictionary Learning and Locality Constrained Coding algorithm (MSDL-LCC), to solve the problems that the insufficient number of training samples when learning a dictionary and the lack of discriminative power of the test coefficient. The proposed algorithm first generates virtual training samples for the origin training data, and then uses all the training samples to learn a dictionary. Finally the learned dictionary is used to encode the test samples under local constraint to obtain a coefficient matrix with discriminative power. Experimental results show that the proposed MSDL-LCC algorithm framework outperforms some previous state-of-the-art dictionary learning algorithms on the LFW and AR face databases.
UNIVERSIDADE FEDERAL DO RIO GRANDE-FURG PROGRAMA DE PÓS-GRADUAÇÃO EM AQUICULTURA

The experiment considered the inclusion of 10% of açaí Euterpe olaracea in the diet of shrimp Litopenaeus vannamei, besides the control diet, without açaí. The feed was given twice a day for 30 days and, afterward, the shrimps were subdivided into another 3 treatments (Control, 0.25 and 1 μg/L of hepatotoxin nodularin), both in the group that received inclusion with açaí and those fed with the control diet. At the end of the experiment, it was evaluated the concentration of reduced glutathione (GSH), sulfhydryl groups associated with proteins (P-SH), lipid peroxidation (TBARS method), the activity of glutathione-S-transferase (GST) in muscle, hepatopâncreas and gills, and nodularin accumulation in muscle. The inclusion of açaí in the diet was able to increase the levels of GSH in the hepatopancreas and gills and to decrease the levels of lipid peroxidation in the muscle of nodularin-exposed shrimps in respect to the control group. Nodularin exposure did not affect P-SH levels in the analyzed organs, although açaí treatment reduced P-SH levels in muscle. TBARS muscle concentration was reduced in shrimps fed with açaí and exposed to nodularin. Finally, nodularin exposure was not followed by toxin accumulation in muscle but, notably, it was detected measurable levels in control groups (fed or not with açai). In general, the results showed that açaí was able to infuse the antioxidant effects of shrimp, and it was also able to reduce the levels of TBARS in the muscle when exposed to nodularina, suggesting the use of açaí
A Review on DNS Rebinding

: As an indispensable infrastructure of the Internet, the DNS system carries the task of mapping domain names and IP addresses to each other, and is closely related to various Internet activities. On the other hand, DNS has become an important springboard for malicious attacks and an important tool as a threat to the Internet security. As an attack method using the DNS system, DNS Rebinding appeared as early as the late 1990s. It can use the victim's browser as a springboard to enter the internal network, steal sensitive data, and obtain the control right of devices. With the popularity of IoT devices, DNS Rebinding technology has new uses. This paper studies the development and changes of DNS Rebinding technology in the past 20 years, and has a general grasp of the development trend of DNS Rebinding. It better understands this attack method and puts forward corresponding defense opinions.
SECONDARY SCHOOL STUDENTS’ TEST ANXIETY AND ACHIEVEMENT IN ENGLISH

This study was conducted to explore secondary school students’ relationship of test anxiety with their achievement in the subject of English. Gender differences were also measured in terms of test anxiety. Data was collected from 2,270 tenth grade students (1,126 males and 1,146 females) selected from 80 schools in eight districts of Punjab province. Relationships were measured by using simple correlations (r), multiple correlation (R) and standard regression coefficient (β). ANOVA was used to find out the differences, the Eta square values were also included. The results of the study indicated that there was negative correlation of test anxiety with students’ achievement in the subject of English. It was also concluded that female students had higher test anxiety than male students.
European and international standards for statements of uncertainty

To make measurements comparable it is necessary to state the quality of a measurement in terms of the uncertainty of measurement. The ISO-BIPM 'Guide to the expression of uncertainty in measurement', usually referred to as the GUIDE or the GUM, was published in 1993 and proposed a unifying method for evaluating and stating measurement uncertainties. This method is accepted by nearly all calibration services in the world and has become a quasi-standard in the field of metrology. This paper describes the fundamental aspects of the method, which also has entered the EAL-R2 requirement document edited by the European co-operation for Accreditation of Laboratories.
Matrix-free approaches for GPU acceleration of a high-order finite element hydrodynamics application using MFEM, Umpire, and RAJA

With the introduction of advanced heterogeneous computing architectures based on GPU accelerators, large-scale production codes have had to rethink their numerical algorithms and incorporate new programming models and memory management strategies in order to run efficiently on the latest supercomputers. In this work we discuss our co-design strategy to address these challenges and achieve performance and portability with MARBL, a next-generation multi-physics code in development at Lawrence Livermore National Laboratory. We present a two-fold approach, wherein new hardware is used to motivate both new algorithms and new abstraction layers, resulting in a single source application code suitable for a variety of platforms. Focusing on MARBL’s ALE hydrodynamics package, we demonstrate scalability on different platforms and highlight that many of our innovations have been contributed back to open-source software libraries, such as MFEM (finite element algorithms) and RAJA (kernel abstractions).
Massive-scale Decoding for Text Generation using Lattices

Conditional neural text generation models generate high-quality outputs, but often concentrate around a mode when what we really want is a diverse set of options. We present a search algorithm to construct lattices encoding a massive number of generation options. First, we restructure decoding as a best-first search, which explores the space differently than beam search and improves efficiency by avoiding pruning paths. Second, we revisit the idea of hypothesis recombination: we can identify pairs of similar generation candidates during search and merge them as an approximation. On both summarization and machine translation, we show that our algorithm encodes thousands of diverse options that remain grammatical and high-quality into one lattice. This algorithm provides a foundation for building downstream generation applications on top of massive-scale diverse outputs.
Imaginary Zeroth-Order Optimization

Zeroth-order optimization methods are developed to overcome the practical hurdle of having knowledge of explicit derivatives. Instead, these schemes work with merely access to noisy functions evaluations. One of the predominant approaches is to mimic ﬁrst-order methods by means of some gradient estimator. The theoretical limitations are well-understood, yet, as most of these methods rely on ﬁnite-diﬀerencing for shrinking diﬀerences, numerical cancellation can be catastrophic. The numerical community developed an eﬃcient method to overcome this by passing to the complex domain. This approach has been recently adopted by the optimization community and in this work we analyze the practically relevant setting of dealing with computational noise. To exemplify the possibilities we focus on the strongly-convex optimization setting and provide a variety of non-asymptotic results, corroborated by numerical experiments, and end with local non-convex optimization.
Relative Kinematics Estimation Using Accelerometer Measurements

Given a network of N static nodes in D-dimensional space and the pairwise distances between them, the challenge of estimating the coordinates of the nodes is a well-studied problem. However, for numerous application domains, the nodes are mobile and the estimation of relative kinematics (e.g., position, velocity and acceleration) is a challenge, which has received limited attention in literature. In this paper, we introduce a time-varying Grammian-based data model for estimating the relative kinematics of mobile nodes with polynomial trajectories, given the time-varying pairwise distance measurements between the nodes. Furthermore, we consider a scenario where the nodes have on-board accelerometers, and extend the proposed data model to include these accelerometer measurements. We propose closed-form solutions to estimate the relative kinematics, based on the proposed data models. We conduct simulations to showcase the performance of the proposed estimators, which show improvement against state-of-the-art methods.
Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California

Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P‐ and S‐wave arrivals, auto‐identification of later seismic phases such as the Moho‐reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine‐identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced. In this work, by utilizing a high‐quality PmP data set (10,192 manual picks) in southern California, we develop PmPNet, a deep‐neural‐network‐based algorithm to automatically identify PmP waves efficiently; by doing so, we accelerate the process of identifying the PmP waves. PmPNet applies similar techniques in the machine learning community to address the unbalancement of PmP datasets. The architecture of PmPNet is a residual neural network (ResNet)‐autoencoder with additional predictor block, where encoder, decoder, and predictor are equipped with ResNet connection. We conduct systematic research with field data, concluding that PmPNet can efficiently achieve high precision and high recall simultaneously to automatically identify PmP waves from a massive seismic database. Applying the pre‐trained PmPNet to the seismic database from January 1990 to December 1999 in southern California, we obtain nearly twice more PmP picks than the original PmP data set, providing valuable data for other studies such as mapping the topography of the Moho discontinuity and imaging the lower crust structures of southern California.
Tree-based Focused Web Crawling with Reinforcement Learning

A focused crawler aims at discovering as many web pages relevant to a target topic as possible, while avoiding irrelevant ones. Reinforcement Learning (RL) has been utilized to optimize focused crawling. In this paper, we propose TRES, an RL-empowered framework for focused crawling. We model the crawling environment as a Markov Decision Process, which the RL agent aims at solving by determining a good crawling strategy. Starting from a few human provided keywords and a small text corpus, that are expected to be relevant to the target topic, TRES follows a keyword set expansion procedure, which guides crawling, and trains a classifier that constitutes the reward function. To avoid a computationally infeasible brute force method for selecting a best action, we propose Tree-Frontier, a decision-tree-based algorithm that adaptively discretizes the large state and action spaces and finds only a few representative actions. Tree-Frontier allows the agent to be likely to select near-optimal actions by being greedy over selecting the best representative action. Experimentally, we show that TRES significantly outperforms state-of-the-art methods in terms of harvest rate (ratio of relevant pages crawled), while Tree-Frontier reduces by orders of magnitude the number of actions needed to be evaluated at each timestep.
Motorcycle-Automobile Collision Prevention through Increased Motorcyclist Frontal Conspicuity

The majority of motorcycle-automobile collisions are the result of a violation of the motorcyclist's right-of-way by the automobile driver. The predominating cause of the multiple vehicle collision is a failure of the automobile driver to detect the presence of the motorcyclist in traffic when a traffic conflict develops. Such detection failures are associated with low conspicuity for the motorcyclist, and effective countermeasures have been investigated and applied to reduce accident involvement of motorcycles. However, comparable investigations of culpable automobile drivers have not been done to distinguish the exact nature of these detection failures.
The multirank likelihood and cyclically monotone Monte Carlo: a semiparametric approach to CCA

Many analyses of multivariate data are focused on evaluating the dependence between two sets of variables, rather than the dependence among individual variables within each set. Canonical correlation analysis (CCA) is a classical data analysis technique that estimates parameters describing the dependence between such sets. However, inference procedures based on traditional CCA rely on the assumption that all variables are jointly normally distributed. We present a semiparametric approach to CCA in which the multivariate margins of each variable set may be arbitrary, but the dependence between variable sets is described by a parametric model that provides a low-dimensional summary of dependence. While maximum likelihood estimation in the proposed model is intractable, we develop a novel MCMC algorithm called cyclically monotone Monte Carlo (CMMC) that provides estimates and confidence regions for the between-set dependence parameters. This algorithm is based on a multirank likelihood function, which uses only part of the information in the observed data in exchange for being free of assumptions about the multivariate margins. We illustrate the proposed inference procedure on nutrient data from the USDA.
Defects at the Intersection: the Supergroup Side

We consider two seemingly different theories in the $\Omega$-background: one arises upon the most generic Higgsing of a 5d $\mathcal{N}=1$ $\text{U}(N)$ gauge theory coupled to matter, yielding a 3d-1d intersecting defect; the other one arises upon simple Higgsing of a 5d $\mathcal{N}=1$ $\text{U}(N|M)$ supergroup gauge theory coupled to super-matter, yielding another defect. The cases $N=M=1$ are discussed in detail via equivariant localization to matrix-like models. The first theory exhibits itself a supergroup-like structure, which can be motivated via non-perturbative string dualities, and in a matter decoupling limit it is argued to be dual to a supergroup version of refined Chern-Simons theory. Furthermore, it is observed that the partition functions of the two defect theories are related by analytic continuation in one of the equivariant parameters. We find a common origin in the algebraic engineering through $q$-Virasoro screening currents. Another simple Higgsing of the 5d $\mathcal{N}=1$ $\text{U}(1|1)$ yields a single component defect whose partition function is reminiscent of ordinary refined Chern-Simons on a lens space.
Astrophysical Distance Scale. IV. Preliminary Zero-point Calibration of the JAGB Method in the HST/WFC3-IR Broad J-band (F110W) Filter

We present an absolute calibration of the J-region Asymptotic Giant Branch (JAGB) method using published photometry of resolved stars in 20 nearby galaxies observed with the Hubble Space Telescope using the WFC3-IR camera and the F110W (broad J-band) filter. True distance moduli for each of the galaxies are based on the Tip of the Red Giant Branch (TRGB) method as uniformly determined by Dalcanton et al. From a composite color–magnitude diagram composed of over 6 million stars, leading to a sample of 453 JAGB stars in these galaxies, we find MF110WJAGB=−5.77±0.02 mag (statistical error on the mean). The external scatter seen in a comparison of the individual TRGB and the JAGB moduli is ±0.081 mag (or 4% in distance). Some of this scatter can be attributed to small number statistics arising from the sparse JAGB populations found in the generally low-luminosity galaxies that comprise the particular sample studied here. However, if this intermethod scatter is shared equitably between the JAGB and TRGB methods, that implies that each is good to ±0.06 mag, or better than 3% in distance.
Structure-Exploiting Newton-Type Method for Optimal Control of Switched Systems

This study proposes an efficient Newton-type method for the optimal control of switched systems under a given mode sequence. A mesh-refinement-based approach is utilized to discretize continuous-time optimal control problems (OCPs) and formulate a nonlinear program (NLP), which guarantees the local convergence of a Newton-type method. A dedicated structure-exploiting algorithm (Riccati recursion) is proposed to perform a Newton-type method for the NLP efficiently because its sparsity structure is different from a standard OCP. The proposed method computes each Newton step with linear time-complexity for the total number of discretization grids as the standard Riccati recursion algorithm. Additionally, the computation is always successful if the solution is sufficiently close to a local minimum. Conversely, general quadratic programming (QP) solvers cannot accomplish this because the Hessian matrix is inherently indefinite. Moreover, a modification on the reduced Hessian matrix is proposed using the nature of the Riccati recursion algorithm as the dynamic programming for a QP subproblem to enhance the convergence. A numerical comparison is conducted with off-the-shelf NLP solvers, which demonstrates that the proposed method is up to two orders of magnitude faster. Whole-body optimal control of quadrupedal gaits is also demonstrated and shows that the proposed method can achieve the whole-body model predictive control (MPC) of robotic systems with rigid contacts.
Semiparametric Conditional Factor Models: Estimation and Inference

This paper introduces a simple and tractable sieve estimation of semiparametric conditional factor models with latent factors. We establish large-$N$-asymptotic properties of the estimators without requiring large $T$. We also develop a simple bootstrap procedure for conducting inference about the conditional pricing errors as well as the shapes of the factor loading functions. These results enable us to estimate conditional factor structure of a large set of individual assets by utilizing arbitrary nonlinear functions of a number of characteristics without the need to pre-specify the factors, while allowing us to disentangle the characteristics' role in capturing factor betas from alphas (i.e., undiversifiable risk from mispricing). We apply these methods to the cross-section of individual U.S. stock returns and find strong evidence of large nonzero pricing errors that combine to produce arbitrage portfolios with Sharpe ratios above 3. We also document a significant decline in apparent mispricing over time.
Exploring Latent Dimensions of Crowd-sourced Creativity

Recently, the discovery of interpretable directions in the latent spaces of pre-trained GANs has become a popular topic. While existing works mostly consider directions for semantic image manipulations, we focus on an abstract property: creativity. Can we manipulate an image to be more or less creative? We build our work on the largest AI-based creativity platform, Artbreeder, where users can generate images using pre-trained GAN models. We explore the latent dimensions of images generated on this platform and present a novel framework for manipulating images to make them more creative. Our code and dataset are available at http://github.com/catlab-team/latentcreative.
Nonlinear electrodynamics nonminimally coupled to gravity: Symmetric-hyperbolicity and causal structure

It is shown here that symmetric hyperbolicity, which guarantees well-posedness, leads to a set of two inequalities for matrices whose elements are determined by a given theory. As a part of the calculation, carried out in a mostly-covariant formalism, the general form for the symmetrizer, valid for a general Lagrangian theory, was obtained. When applied to nonlinear electromagnetism linearly coupled to curvature, the inequalities lead to strong constraints on the relevant quantities, which were illustrated with applications to particular cases. The examples show that non-linearity leads to constraints on the field intensities, and non-minimal coupling imposes restrictions on quantities associated to curvature.