Miracle-dz/UniFAR-FTUs huggingface dataset:
Shear performance of single-keyed dry joints between reactive power concrete and high strength concrete in push-off tests

Shear key joints are commonly used in constructions of precast concrete segmental bridges. Most researches focused on shear performance of the dry joints with the identical-strength concrete, leaving a research gap on that of composite joints between different concrete segments. This research aims to investigate shear behavior of shear key joint between reactive powder concrete and high strength concrete. Totally 12 specimens of single-keyed dry joint were tested, with the parameters of concrete compressive strength, steel fibers, and confining stress. The experimental results indicated that shear failure was observed, but crushing phenomenon occasionally occurred in composite joints in testing, which was confirmed by stress distribution from numerical simulation. In terms of shear capacity of composite joint, peak shear loads of reactive powder concrete specimens without steel fibers were enhanced by 10%–12% as increasing of concrete compressive strength, while those with steel fibers achieved 22%–25% enhancement. Nevertheless, a slight reduction of normalized shear strength was obtained because of its lower volume fraction of coarse aggregate. In numerical simulation, as increasing the concrete compressive strength of convex part, peak shear load was enhanced, but the increment rate of peak shear load decreased. Shear design formulae underestimated shear capacity of reactive powder concrete specimens with steel fibers, but the models proposed by Buyukozturk and Rombach gave accurate predictions on those without steel fibers.
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism

We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under drifting non-stationarity, i.e., both the reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets. We first develop the Sliding Window Upper-Confidence bound for Reinforcement Learning with Confidence Widening (SWUCRL2-CW) algorithm, and establish its dynamic regret bound when the variation budgets are known. In addition, we propose the Bandit-over-Reinforcement Learning (BORL) algorithm to adaptively tune the SWUCRL2-CW algorithm to achieve the same dynamic regret bound, but in a parameter-free manner, i.e., without knowing the variation budgets. Notably, learning non-stationary MDPs via the conventional optimistic exploration technique presents a unique challenge absent in existing (non-stationary) bandit learning settings. We overcome the challenge by a novel confidence widening technique that incorporates additional optimism.
Augmenting the Algebraic Connectivity of Graphs

For any undirected graph $G=(V,E)$ and a set $E_W$ of candidate edges with $E\cap E_W=\emptyset$, the $(k,\gamma)$-spectral augmentability problem is to find a set $F$ of $k$ edges from $E_W$ with appropriate weighting, such that the algebraic connectivity of the resulting graph $H=(V,E\cup F)$ is least $\gamma$. Because of a tight connection between the algebraic connectivity and many other graph parameters, including the graph's conductance and the mixing time of random walks in a graph, maximising the resulting graph's algebraic connectivity by adding a small number of edges has been studied over the past 15 years. In this work we present an approximate and efficient algorithm for the $(k,\gamma)$-spectral augmentability problem, and our algorithm runs in almost-linear time under a wide regime of parameters. Our main algorithm is based on the following two novel techniques developed in the paper, which might have applications beyond the $(k,\gamma)$-spectral augmentability problem. (1) We present a fast algorithm for solving a feasibility version of an SDP for the algebraic connectivity maximisation problem from [GB06]. Our algorithm is based on the classic primal-dual framework for solving SDP, which in turn uses the multiplicative weight update algorithm. We present a novel approach of unifying SDP constraints of different matrix and vector variables and give a good separation oracle accordingly. (2) We present an efficient algorithm for the subgraph sparsification problem, and for a wide range of parameters our algorithm runs in almost-linear time, in contrast to the previously best known algorithm running in at least $\Omega(n^2mk)$ time [KMST10]. Our analysis shows how the randomised BSS framework can be generalised in the setting of subgraph sparsification, and how the potential functions can be applied to approximately keep track of different subspaces.
Holographic baby universes: an observable story

We formulate the baby universe construction rigorously by giving a primordial role to the algebra of observables of quantum gravity rather than the Hilbert space. Utilizing diffeomorphism invariance, we study baby universe creation and annihilation via change in topology. We then construct the algebra of boundary observables for holographic theories and show that it enhances to contain an 'extra' Abelian tensor factor to describe the bulk in the quantum regime; via the gravitational path integral we realize this extra tensor factor, at the level of the Hilbert space, in the context of the Gelfand--Naimark--Segal (GNS) representation. We reformulate the necessary assumptions for the "baby universe hypothesis" using the GNS representation. When the baby universe hypothesis is satisfied, we demonstrate that the "miraculous cancellations" in the corresponding gravitational path integral have a natural explanation in terms of the character theory of Abelian groups. We find the necessary and sufficient mathematical condition for the baby universe hypothesis to hold, and transcribe it into sufficient physical conditions. We find that they are incompatible with a baby universe formation that is influenced by any bulk process from the AdS/CFT correspondence.
Graph Convolutional Neural Networks for analysis of EEG signals, BCI application

Decoding brain signals has gained many attention and has found much applications in recent years such as Brain Computer Interfaces, communicating with controlling external devices using the user's intentions, occupies an emerging field with the potential of changing the world, with diverse applications from rehabilitation to human augmentation. This being said brain signal analysis, EEG brain signal analysis in particular, is a challenging task. With the advances and achievements in the field of deep learning in problem solving with using only raw data, few attempts has been carried in recent years, to apply deep learning to tackle EEG among other types of brain signals. In this study, we propose a novel loss function, called DeepCSP to extend the classical Common Spatial Patterns to a non linear, differentiable module to serve as the loss function to enforce linearly separable latent representations of EEG signals belonging to different classes in an end to end manner on raw signals without the need to perform extensive feature engineering. With recent generalizations of deep learning methods to work on arbitrarily structured graphs and the introduced loss we have proposed two light weight models to decode EEG signals and carried experiments to show their performance.
Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

In commercial buildings, about 40%–50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort. Due to the existence of unknown thermal dynamics models, parameter uncertainties (e.g., outdoor temperature, electricity price, and number of occupants), spatially and temporally coupled constraints associated with indoor temperature and CO2 concentration, a large discrete solution space, and a non-convex and non-separable objective function, it is very challenging to achieve the above aim. To this end, the above energy cost minimization problem is reformulated as a Markov game. Then, an HVAC control algorithm is proposed to solve the Markov game based on multi-agent deep reinforcement learning with attention mechanism. The proposed algorithm does not require any prior knowledge of uncertain parameters and can operate without knowing building thermal dynamics models. Simulation results based on real-world traces show the effectiveness, robustness and scalability of the proposed algorithm.
Improved protocol for isolation of genomic DNA from leaf tissues of Phyllanthus emblica Gaertn

Modified Cetyltrimethylammonium bromide (CTAB) protocol for DNA isolation was developed from leaf tissues of Phyllanthus emblica for obtaining high quality genomic DNA. Fresh leaves of three different maturity were analyzed for yield and quality of DNA. Acidity was determined in three different maturity of leaves viz. tender, intermediate and mature and their influence on DNA quality was determined. Drastic reduction of pH was the primary cause for poor quality of DNA. However, high quality DNA isolation was achieved by stabilizing the pH by addition of NaOH during different stages of DNA isolation process. The present protocol yielding high quality intact DNA for genetic fingerprinting as well as for amplification of chloroplast genes for molecular analysis.
Advances on research epigenetic change of hybrid and polyploidy in plants

Hybridization between different species, and subsequently polyploidy, play an important role in plant genome evolution, as well as it is a widely used approach for crop improvement. Recent studies of the last several years have demonstrated that, hybridization and subsequent genome doubling (polyploidy) often induce an array of variations that could not be explained by the conventional genetic paradigms. A large proportion of these variations are epigenetic in nature. Epigenetic can be defined as a change of the study in the regulation of gene activity and expression that are not driven by gene sequence information. However, the ramifications of epigenetic in plant biology are immense, yet unappreciated. In contrast to the ease with which the DNA sequence can be studied, studying the complex patterns inherent in epigenetic poses many problems. In this view, advances on researching epigenetic change of hybrid and polyploidy in plants will be initially set out by summarizing the latest researches and the basic studies on epigenetic variations generated by hybridization. Moreover, polyploidy may shed light on the mechanisms generating these variations. These advances will enhance our understanding on their evolutionary significances, as well as enable us to utilize these variations more effectively than before in crop breeding. Key words: Epigenetic change, hybridization and polyploidy, evolution, crop breeding.
Horseshoe co-orbitals of Earth: current population and new candidates

Most co-orbital objects in the Solar system are thought to follow tadpole-type orbits, behaving as Trojans. However, most of Earth's identified co-orbitals are moving along horseshoe-type orbits. The current tally of minor bodies considered to be Earth co-orbitals amounts to 18; of them, 12 are horseshoes, five are quasi-satellites, and one is a Trojan. The semimajor axis values of all these bodies librate between $0.983$ au and $1.017$ au. In this work, we have studied the dynamical behaviour of objects following orbits with semimajor axis within this range that may be in a 1:1 mean-motion resonance with Earth. Our results show that asteroids 2016 CO$_{246}$, 2017 SL$_{16}$, and 2017 XQ$_{60}$ are moving along asymmetrical horseshoe-type orbits; the asteroid 2018 PN$_{22}$ follows a nearly symmetric or regular horseshoe-type orbit. Asteroids 2016 CO$_{246}$, 2017 SL$_{16}$, and 2017 XQ$_{60}$ can remain in the horseshoe co-orbital state for about $900$ yr, $3300$ yr and $2700$ yr, respectively. Asteroid 2018 PN$_{22}$ has a more chaotic dynamical behaviour; it may not stay in a horseshoe co-orbital state for more than 200 yr. The horseshoe libration periods of 2016 CO$_{246}$, 2017 SL$_{16}$, 2017 XQ$_{60}$, and 2018 PN$_{22}$ are 280, 255, 411, and 125 yr, respectively.
Multilingual Jointly Trained Acoustic and Written Word Embeddings

Acoustic word embeddings (AWEs) are vector representations of spoken word segments. AWEs can be learned jointly with embeddings of character sequences, to generate phonetically meaningful embeddings of written words, or acoustically grounded word embeddings (AGWEs). Such embeddings have been used to improve speech retrieval, recognition, and spoken term discovery. In this work, we extend this idea to multiple low-resource languages. We jointly train an AWE model and an AGWE model, using phonetically transcribed data from multiple languages. The pre-trained models can then be used for unseen zero-resource languages, or fine-tuned on data from low-resource languages. We also investigate distinctive features, as an alternative to phone labels, to better share cross-lingual information. We test our models on word discrimination tasks for twelve languages. When trained on eleven languages and tested on the remaining unseen language, our model outperforms traditional unsupervised approaches like dynamic time warping. After fine-tuning the pre-trained models on one hour or even ten minutes of data from a new language, performance is typically much better than training on only the target-language data. We also find that phonetic supervision improves performance over character sequences, and that distinctive feature supervision is helpful in handling unseen phones in the target language.
Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence

Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications, thanks to their ability to emulate spiking neural networks in real-time. The fine-grain parallelism offered by this approach allows such neural circuits to process the sensory data efficiently by adapting their dynamics to the ones of the sensed signals, without having to resort to the time-multiplexed computing paradigm of von Neumann architectures. To reduce power consumption even further, we present a set of mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes. Specifically, we explore the options of advanced FDSOI technologies to address analog design issues and optimize the design of the synapse integrator and of the adaptive neuron circuits accordingly. We present circuit post-layout simulation results and demonstrate the circuit’s ability to produce biologically plausible neural dynamics with compact designs, optimized for the realization of large-scale spiking neural networks in neuromorphic processors.
A causal view of compositional zero-shot recognition

People easily recognize new visual categories that are new combinations of known components. This compositional generalization capacity is critical for learning in real-world domains like vision and language because the long tail of new combinations dominates the distribution. Unfortunately, learning systems struggle with compositional generalization because they often build on features that are correlated with class labels even if they are not "essential" for the class. This leads to consistent misclassification of samples from a new distribution, like new combinations of known components. Here we describe an approach for compositional generalization that builds on causal ideas. First, we describe compositional zero-shot learning from a causal perspective, and propose to view zero-shot inference as finding "which intervention caused the image? ". Second, we present a causal-inspired embedding model that learns disentangled representations of elementary components of visual objects from correlated (confounded) training data. We evaluate this approach on two datasets for predicting new combinations of attribute-object pairs: A well-controlled synthesized images dataset and a real world dataset which consists of fine-grained types of shoes. We show improvements compared to strong baselines.
Effect of Dilute Magnetism in a Topological Insulator

Three-dimensional (3D) topological insulator (TI) has emerged as a unique state of quantum matter and generated enormous interests in condensed matter physics. The surfaces of a 3D TI consist of a massless Dirac cone, which is characterized by the Z2 topological invariant. Introduction of magnetism on the surface of a TI is essential to realize the quantum anomalous Hall effect and other novel magneto-electric phenomena. Here, by using a combination of first-principles calculations, magneto-transport and angle-resolved photoemission spectroscopy (ARPES), we study the electronic properties of gadolinium (Gd)-doped Sb2Te3. Our study shows that Gd doped Sb2Te3 is a spin-orbit-induced bulk band-gap material, whose surface is characterized by a single topological surface state. Our results provide a new platform to investigate the interactions between dilute magnetism and topology in magnetic doped topological materials.
Exploring the Lower Mass Gap and Unequal Mass Regime in Compact Binary Evolution

On 2019 August 14, the LIGO and Virgo detectors observed GW190814, a gravitational-wave signal originating from the merger of a black hole (BH) with a compact object. GW190814's compact-binary source is atypical both in its highly asymmetric masses and in its lower-mass component lying between the heaviest known neutron star (NS) and lightest known BH in a compact-object binary. If formed through isolated binary evolution, the mass of the secondary is indicative of its mass at birth. We examine the formation of such systems through isolated binary evolution across a suite of assumptions encapsulating many physical uncertainties in massive-star binary evolution. We update how mass loss is implemented for the neutronization process during the collapse of the proto-compact object to eliminate artificial gaps in the mass spectrum at the transition between NSs and BHs. We find it challenging for population modeling to match the empirical rate of GW190814-like systems while simultaneously being consistent with the rates of other compact binary populations inferred from gravitational-wave observations. Nonetheless, the formation of GW190814-like systems at any measurable rate requires a supernova engine model that acts on longer timescales such that the proto-compact object can undergo substantial accretion immediately prior to explosion, hinting that if GW190814 is the result of massive-star binary evolution, the mass gap between NSs and BHs may be narrower or nonexistent.
Optimal Control Concerns Regarding the COVID-19 (SARS-CoV-2) Pandemic in Bahia and Santa Catarina, Brazil

A pandemia COVID-19 apresenta-se como a mais profunda crise do século 21. O vírus SARS-CoV-2 chegou ao Brasil em Março de 2020 e seus efeitos sociais e econômicos têm sido catastróficos desde então. Neste artigo, investigamos como Controle Preditivo baseado em modelo (CPM) pode pautar políticas públicas eficientes para o distanciamento social, visando mitigar os efeitos da pandemia, no que diz respeito aos estados brasileiros da Bahia e de Santa Catarina. Abordamos o problema através da identificação dos parâmetros do modelo SIRD via otimização. A variável manipulada é a meta de isolamento social passada à população; duas estratégias de CPM são desenvolvidas: a) um CPM centralizado, que coordena o isolamento em ambos os estados através do mesmo sinal de controle; e b) um CPM distribuído, no qual dois problemas de otimização são resolvidos, resultando em uma lei de controle individual para cada estado. Resultados de simulação ilustram, comparam e discutem ambas estratégias
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks

With increasing expressive power, deep neural networks have significantly improved the state-of-the-art on image classification datasets, such as ImageNet. In this paper, we investigate to what extent the increasing performance of deep neural networks is impacted by background features? In particular, we focus on background invariance, i.e., accuracy unaffected by switching background features and background influence, i.e., predictive power of background features itself when foreground is masked. We perform experiments with 32 different neural networks ranging from small-size networks to large-scale networks trained with up to one Billion images. Our investigations reveal that increasing expressive power of DNNs leads to higher influence of background features, while simultaneously, increases their ability to make the correct prediction when background features are removed or replaced with a randomly selected texture-based background.
Rice OASA1D, a mutant anthranilate synthase α subunit gene, is an effective selectable marker for transformation of Arabidopsis thaliana

We have previously developed an antibiotic-free system for the selection of plant transformants that is based on a gene (OASA1D) for a mutant α subunit of rice anthranilate synthase. The product of this gene shows a reduced sensitivity to negative feedback regulation by tryptophan. Whereas 5-methyltryptophan (5MT) is lethal for normal plant cells because it causes tryptophan deficiency, expression of OASA1D confers resistance to this tryptophan analog. We used this OASA1D-5MT system for the transformation of Arabidopsis thaliana. An expression vector containing OASA1D under the control of the 35S promoter of cauliflower mosaic virus was introduced into Arabidopsis by Agrobacterium-mediated transformation. Transgenic plants that harbored OASA1D exhibited resistance to 5MT but did not manifest any other differences in growth, morphology, or fertility. The OASA1D-5MT selection system performed as well as the HPT-hygromycin system for the transformation of Arabidopsis. Given the limited number of conventional marker genes currently available, OASA1D should prove to be a useful tool in Arabidopsis transformation, especially for the generation of plants carrying multiple transgenes.
Stratified Stochastic Variational Inference for High-Dimensional Network Factor Model

Abstract There has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on Markov chain Monte Carlo can be extremely slow and show poor mixing, thereby motivating research on alternative algorithms that scale well in high-dimensional settings. In this article, we focus on the latent factor model, a widely used approach for latent space modeling of network data. We develop scalable algorithms to conduct approximate Bayesian inference via stochastic optimization. Leveraging sparse representations of network data, the proposed algorithms show massive computational and storage benefits, and allow to conduct inference in settings with thousands of nodes. An R package with an efficient c++ implementation of the proposed algorithms is provided.