Miracle-dz/UniFAR-FTUs huggingface dataset:
Is there a proximate antiferromagnetic insulating phase in infinite-layer nickelates?

We provide a set of computational experiments based on \textit{ab initio} calculations to elucidate whether a cuprate-like antiferromagnetic insulating state can be present in the phase diagram of the infinite-layer nickelate family (RNiO$_2$, R= rare-earth). We show that metallicity in the parent phase is produced by an R-d band that requires hybridization with the Ni-d bands to become largely dispersive. If this off-plane R-Ni coupling is suppressed, the system is an antiferromagnetic insulator since that largely dispersive band is no longer able to cross the Fermi level. As such, the reduction of the strong out-of-plane Ni-d hopping leads to an electronic structure closer to the nominal Ni-d$^9$ occupation as the self-doping effect -- understood as charge transfer from the Ni-d to the R-d orbitals -- disappears. This can be achieved if a structural element that suppresses the c-axis dispersion is introduced (i.e. vacuum in a monolayer of NdNiO$_2$, or a blocking layer in multilayers formed by (NdNiO$_2$)$_1$/(NdNaO$_2$)$_1$). We also show how the reduced Ruddlesden-Popper counterparts (R$_4$Ni$_3$O$_8$) are able to produce the same effect due to the presence of fluorite RO$_2$ blocking slabs.
A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings

We propose a new unsupervised model for mapping a variable-duration speech segment to a fixed-dimensional representation. The resulting acoustic word embeddings can form the basis of search, discovery, and indexing systems for low- and zero-resource languages. Our model, which we refer to as a maximal sampling correspondence variational autoencoder (MCVAE), is a recurrent neural network (RNN) trained with a novel self-supervised correspondence loss that encourages consistency between embeddings of different instances of the same word. Our training scheme improves on previous correspondence training approaches through the use and comparison of multiple samples from the approximate posterior distribution. In the zero-resource setting, the MCVAE can be trained in an unsupervised way, without any ground-truth word pairs, by using the word-like segments discovered via an unsupervised term discovery system. In both this setting and a semi-supervised low-resource setting (with a limited set of ground-truth word pairs), the MCVAE outperforms previous state-of-the-art models, such as Siamese-, CAE- and VAE-based RNNs.
Graph Convolutional Neural Networks for Body Force Prediction

Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order of features for each sample. They thus cannot be easily constructed for an unstructured dataset. Therefore, a graph based data-driven model to perform inference on fields defined on an unstructured mesh, using a Graph Convolutional Neural Network (GCNN) is presented. The ability of the method to predict global properties from spatially irregular measurements with high accuracy is demonstrated by predicting the drag force associated with laminar flow around airfoils from scattered velocity measurements. The network can infer from field samples at different resolutions, and is invariant to the order in which the measurements within each sample are presented. The GCNN method, using inductive convolutional layers and adaptive pooling, is able to predict this quantity with a validation $R^{2}$ above 0.98, and a Normalized Mean Squared Error below 0.01, without relying on spatial structure.
Rethinking supervised learning: insights from biological learning and from calling it by its name

The renaissance of artificial neural networks was catalysed by the success of classification models, tagged by the community with the broader term supervised learning. The extraordinary results gave rise to a hype loaded with ambitious promises and overstatements. Soon the community realised that the success owed much to the availability of thousands of labelled examples. And supervised learning went, for many, from glory to shame. Some criticised deep learning as a whole and others proclaimed that the way forward had to be "alternatives" to supervised learning: predictive, unsupervised, semi-supervised and, more recently, self-supervised learning. However, these seem all brand names, rather than actual categories of a theoretically grounded taxonomy. Moreover, the call to banish supervised learning was motivated by the questionable claim that humans learn with little or no supervision. Here, we review insights about learning and supervision in nature, revisit the notion that learning is not possible without supervision and argue that we will make better progress if we just call it by its name.
Automating Seccomp Filter Generation for Linux Applications

Software vulnerabilities undermine the security of applications. By blocking unused functionality, the impact of potential exploits can be reduced. While seccomp provides a solution for filtering syscalls, it requires manual implementation of filter rules for each individual application. Recent work has investigated approaches to automate this task. However, as we show, these approaches make assumptions that are not necessary or require overly time-consuming analysis. In this paper, we propose Chestnut, an automated approach for generating strict syscall filters with lower requirements and limitations. Chestnut comprises two phases, with the first phase consisting of two static components, i.e., a compiler and a binary analyzer, that statically extract the used syscalls. The compiler-based approach of Chestnut is up to factor 73 faster than previous approaches with the same accuracy. On the binary level, our approach extends over previous ones by also applying to non-PIC binaries. An optional second phase of Chestnut is dynamic refinement to restrict the set of allowed syscalls further. We demonstrate that Chestnut on average blocks 302 syscalls (86.5%) via the compiler and 288 (82.5%) using the binary analysis on a set of 18 applications. Chestnut blocks the dangerous exec syscall in 50% and 77.7% of the tested applications using the compiler- and binary-based approach, respectively. For the tested applications, Chestnut blocks exploitation of more than 61% of the 175 CVEs that target the kernel via syscalls.
Quantum Langevin approach for superradiant nanolasers

A new approach for analytically solving quantum nonlinear Langevin equations is proposed and applied to calculations of spectra of superradiant lasers where collective effects play an important role. We calculate lasing spectra for arbitrary pump rates and recover well-known results such as the pump dependence of the laser linewidth across the threshold region. We predict new sideband peaks in the spectrum of superradiant lasers with large relaxation oscillations as well as new nonlinear structures in the lasing spectra for weak pump rates. Our approach sheds new light on the importance of population fluctuations in the narrowing of the laser linewidth, in the structure of the lasing spectrum, and in the transition to coherent operation.
Pose-Based Servo Control with Soft Tactile Sensing

This paper describes a new way of controlling robots using soft tactile sensors: pose-based tactile servo (PBTS) control. The basic idea is to embed a tactile perception model for estimating the sensor pose within a servo control loop that is applied to local object features such as edges and surfaces. PBTS control is implemented with a soft curved optical tactile sensor (the BRL TacTip) using a convolutional neural network trained to be insensitive to shear. In consequence, robust and accurate controlled motion over various complex 3D objects is attained. First, we review tactile servoing and its relation to visual servoing, before formalising PBTS control. Then, we assess PBTS over a range of regular and irregular objects. Finally, we reflect on the relation to visual servo control and discuss how controlled soft touch gives a route towards human-like dexterity in robots. A summary video is available here https://youtu.be/12-DJeRcfn0
Boosting Offline Handwritten Text Recognition in Historical Documents With Few Labeled Lines

In this paper we address the problem of offline handwritten text recognition (HTR) in historical documents when few labeled samples are available and some of them contain errors in the train set. Our three main contributions are: first, we analyze how to perform transfer learning (TL) from a massive database to a smaller historical database, analyzing which layers of the model need fine-tuning. Second, we analyze methods to efficiently combine TL and data augmentation (DA). Finally, we propose an algorithm to mitigate the effects of incorrect labeling in the training set. The methods are analyzed over the ICFHR 2018 competition database, Washington and Parzival. Combining all these techniques, we demonstrate a remarkable reduction of CER (up to 6 percentage points in some cases) in the test set with little complexity overhead.
Automated, Cost-effective, and Update-driven App Testing

Apps’ pervasive role in our society led to the definition of test automation approaches to ensure their dependability. However, state-of-the-art approaches tend to generate large numbers of test inputs and are unlikely to achieve more than 50% method coverage. In this article, we propose a strategy to achieve significantly higher coverage of the code affected by updates with a much smaller number of test inputs, thus alleviating the test oracle problem. More specifically, we present ATUA, a model-based approach that synthesizes App models with static analysis, integrates a dynamically refined state abstraction function and combines complementary testing strategies, including (1) coverage of the model structure, (2) coverage of the App code, (3) random exploration, and (4) coverage of dependencies identified through information retrieval. Its model-based strategy enables ATUA to generate a small set of inputs that exercise only the code affected by the updates. In turn, this makes common test oracle solutions more cost-effective, as they tend to involve human effort. A large empirical evaluation, conducted with 72 App versions belonging to nine popular Android Apps, has shown that ATUA is more effective and less effort-intensive than state-of-the-art approaches when testing App updates.
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation

Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to learn a robust recommendation policy. The challenge becomes more critical when recommending to new users who have a limited number of interactions. To that end, in this paper, we address the cold-start challenge in the RL-based recommender systems by proposing a meta-level model-based reinforcement learning approach for fast user adaptation. In our approach, we learn to infer each user's preference with a user context variable that enables recommendation systems to better adapt to new users with few interactions. To improve adaptation efficiency, we learn to recover the user policy and reward from only a few interactions via an inverse reinforcement learning method to assist a meta-level recommendation agent. Moreover, we model the interaction relationship between the user model and recommendation agent from an information-theoretic perspective. Empirical results show the effectiveness of the proposed method when adapting to new users with only a single interaction sequence. We further provide a theoretical analysis of the recommendation performance bound.
A procura da generalização da 'Lei de Engel’

O presente artigo visa constatar a aplicabilidade da "Lei de Engel" no sistema economico e cultural do Brasil. O autor conclui que os principios de estruturas de consumo proposta por Ernest Engel em 1857 e complementados por Carrol D. Wright em 1875, sao validos em cQndi96es normais. Observa que o questionamento dessa validade se deu somente em situates na qual as economia's em observa^ao estavam em momento de exce9ao. Ocorre tambem a constata9ao de que, para a aplicabilidade da "Lei de Engel", deve haver liberdade para decisdes dos consumidores.
Leaf Essential Oil Composition of Kyllinga brevifolia Rottb. from Nepal

Abstract The essential oil from the leaf of Kyllinga brevifolia Rottb. , collected from Biratnagar, Nepal, was obtained by hydrodistillation and analyzed by GC-MS. A total of 22 compounds were identified in the oil accounting for 96.1 % of the oil composition. The majority of the essential oil was identified to be occadinol (40.3 %),’L-τ-muurolol (19.5 %), and germacrene D-4-ol (12.5 %). The oil also contained smaller amounts of δ-cadinene (2.9 %), and germacrene D (4.0 %). Reports on the essential oil composition of K. brevifolia from Nepal were compared with a sample from Brazil, which showed that 13-epi-manoyl oxide (26.1 %) and manoyl oxide derivatives constituted the majority of composition with a-cadinol comprising only 0.4 %. However germacrene D was found in similar percentages between Nepal and Brazil samples (4.0 %and 4.2 %, respectively).
Isolation and identification of stem cells derived of infrapatelar fat pad

Introduction: The infrapatellar fat pad (sometimes know Hoffa s pad) is a soft tissue that lies beneath the patella (kneecap) separating it from the femoral condyle. There is evidence suggesting the presence of stem cells on adipose tissue in the adult human. Objective: The purpose of this work is to isolate and identify grown stem cells from Hoffa s fat samples obtained from patients undergoing surgeries exposing this tissue. Materials and Methods: After obtaining informed consent, the biopsy of Hoffa s fat pad was obtained. The samples were incubated with collagenase and PBS a 37 oC and agitated, then it was inactivated with fetal bovine serum and centrifuged, washed twice with PBS, the pellet was resuspended and one part was cultivated on Mesencult medium the other part was used for flow cytometry and stained with methylene blue for morphologic analysis. Also before and after the enzyme digestion, the samples were added to 10% formaldehyde to evaluate the collagenase treatment. Results and analysis: The results show the effectiveness of the enzymatic treatment, the architecture of the adipose tissue was lost. The star shape stem cells morphology was appreciated with methylene blue in the cultures, it was corroborated by flow cytometry with CD13, CD29, CD59 and CD 105 markers. Conclusions: Primary cultures are consolidating, the next aim will be to obtain differentiated specific cell types that can be use in the study of obesity, diabetes and articular illnesses.
Diagnóstico ambiental de metais e organoclorados em córregos adjacentes a áreas de cultivo de cana-de-açúcar (Estado de São Paulo, Brasil)

Sugar cane cultivation in the State of Sao Paulo has expanded in the last years, with an annual production of 200106 t in an area of 2.5 106 hectares. The use of herbicides, pesticides and fertilizers in sugar cane plantations, together with deforestation of riparian vegetation, have caused impacts on the hydric resources of the adjacent areas. The aim of this work is to evaluate the influence of sugar cane plantations on streams in the central region of the State of Sao Paulo, studying 16 organochlorinated compounds and 7 metals (Cu, Fe, Cd, Zn, Mn, Cr and Ni) found in the sediments of 11 streams. The results show that there is a higher concentration of metals and organochlorinated compounds in streams without riparian vegetation when compared to forested areas.
Feedback between dust particles and atmospheric processes over West Africa in March 2006 and June 2007

Feedback between dust particles and atmospheric processes over West Africa in March 2006 and June 2007 T. Stanelle, B. Vogel, H. Vogel, D. Bäumer, and Ch. Kottmeier Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Germany now at: Institute for Atmospheric and Climate Science, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland now at: BASF, Ludwigshafen, Germany Received: 22 January 2010 – Accepted: 26 February 2010 – Published: 23 March 2010 Correspondence to: B. Vogel (bernhard.vogel@kit.edu) Published by Copernicus Publications on behalf of the European Geosciences Union.
Cobaloximes with dimesitylglyoxime : Synthesis, characterization, and spectral correlations with the related cobaloximes

Alkyl and non-alkyl cobaloximes with dimesitylglyoxime have been synthesized and characterized with 1H and 13C NMR, UV−vis, and X-ray diffraction. The X-ray structures of MeCo(dmestgH)2Py, ClCo(dmestgH)2Py, and BrCo(dmestgH)2Py are reported. The cis−trans influence has been studied by 1H and 13C NMR, UV−vis, and X-ray diffraction and is correlated with the reported cobaloximes. The spectral correlations are much better understood when both cobalt anisotropy and ring current are considered operating together. The trans influence of R/X has been monitored by the coordination shift of the Pyγ proton/carbon, and its chemical shift is a net result of the interplay of cobalt anisotropy and the trans effect of the R/X group. Two factors have been considered to study cis influence: (a) the effect of axial ligands on the equatorial dioxime moiety and (b) the effect of dioxime on the axial ligands. It is found that CN and Pyα are the most sensitive to any change in the molecule. A change in the axial R/X and dioxi.
Alkali Rydberg States in Electromagnetic Fields

We study highly excited hydrogen and alkali atoms (’Rydberg states’) under the influence of a strong microwave field. As the external frequency is comparable to the highly excited electron’s classical Kepler frequency, the external field induces a strong coupling of many different quantum mechanical energy levels and finally leads to the ionization of the outer electron. While periodically driven atomic hydrogen can be seen as a paradigm of quantum chaotic motion in an open (decaying) quantum system, the presence of the non-hydrogenic atomic core – which unavoidably has to be treated quantum mechanically – entails some complications. Indeed, laboratory experiments show clear differences in the ionization dynamics of microwave driven hydrogen and non-hydrogenic Rydberg states. In the first part of this thesis, a machinery is developed that allows for numerical experiments on alkali and hydrogen atoms under precisely identical laboratory conditions. Due to the high density of states in the parameter regime typically explored in laboratory experiments, such simulations are only possible with the most advanced parallel computing facilities, in combination with an efficient parallel implementation of the numerical approach. The second part of the thesis is devoted to the results of the numerical experiment. We identify and describe significant differences and surprising similarities in the ionization dynamics of atomic hydrogen as compared to alkali atoms, and give account of the relevant frequency scales that distinguish hydrogenic from nonhydrogenic ionization behavior. Our results necessitate a reinterpretation of the experimental results so far available, and solve the puzzle of a distinct ionization behavior of periodically driven hydrogen and non-hydrogenic Rydberg atoms – an unresolved question for about one decade. Finally, microwave-driven Rydberg states will be considered as prototypes of open, complex quantum systems that exhibit a complicated temporal decay. However, we find considerable differences in the decay of such real and experimentally accessible atomic systems, as opposed to predictions based on the study of quantum maps or other toy models with mixed regular-chaotic classical counterparts.
The relationship between affect and cognition in maltreated infants: quality of attachment and the development of visual self-recognition

37 19-month-old infants, 18 maltreated and 19 matched lower-class comparisons, were seen in Ainsworth and Wittig 's Strange Situation in order to document the impact that early maltreatment by the mother has on the attachment relationship. In addition, these infants were observed in the standard mirror-and- rouge paradigm to investigate the hypothesis that individual differences in the emergence of the capacity for visual self-recognition could be related to qualitative differences in the attachment relationship. Consistent with predictions from attachment theory, maltreated infants were found to manifest a significantly greater proportion of insecure attachments than were nonmaltreated infants. When data for the entire sample of infants were analyzed, it was found that those infants who evidenced visual self-recognition were significantly more likely to be securely attached to their mothers. However, a separate analysis of the maltreated and comparison groups of infants revealed a different pattern of results. 90% of the nonmaltreated infants who recognized themselves were securely attached to their caregivers. In contrast, for those maltreated infants who recognized themselves, there was no significant relationship between this capacity and qualitative differences in the security of attachment. Furthermore, an analysis of the affective responses of the infants to their rough-marked noses revealed that nonmaltreated infants were more likely to show an increase in positive affect following the application of rouge , whereas maltreated infants manifested neutral or negative reactions. These results demonstrate that early maltreatment may have deleterious effects for the infant, independent of those risk factors commonly associated with lower-class membership. In addition, they underscore the impact of maltreatment upon the successful achievement of salient developmental tasks and upon the relationship between affective and cognitive development in the maltreated infant. Language: en