Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecology of Kelp Communities

A taxonomic account of the world's kelps is far too complex for the scope of this review, but Mann (146) has characterized the world's kelp forests by their dominant genera. Laminaria species are found in many parts of the world but dominate both sides of the Atlantic and the coasts of China and Japan. Ecklonia dominates some kelp forests in Australia, New Zealand, South Africa, and a few other southern hemisphere localities. The giant kelp, Macrocystispyrifera, forms dense forests in many parts of the world including areas of the northeast Pacific, the southern shores of South America, many Southern Ocean islands, and isolated areas of South Africa, Australia, and New Zealand. Macrocystis pyrifera does not occur in the eastern Pacific area north of San Francisco, California; however, this northern region has the highest species diversity of kelps in the world (63), with Macrocystis integrifolia and Nereocystis, Alaria, Pterygophora, and Laminaria, among other conspicuous genera.
Obliquity of an Earth-like Planet from Frequency Modulation of Its Direct-imaged Lightcurve: Mock Analysis from General Circulation Model Simulation

Direct-imaging techniques of exoplanets have made significant progress recently and will eventually enable monitoring of photometric and spectroscopic signals of Earth-like habitable planets. The presence of clouds, however, would remain as one of the most uncertain components in deciphering such direct-imaged signals of planets. We attempt to examine how the planetary obliquity produces different cloud patterns by performing a series of general circulation model simulation runs using a set of parameters relevant for our Earth. Then we use the simulated photometric lightcurves to compute their frequency modulation that is due to the planetary spin–orbit coupling over an entire orbital period, and we attempt to see to what extent one can estimate the obliquity of an Earth twin. We find that it is possible to estimate the obliquity of an Earth twin within the uncertainty of several degrees with a dedicated 4 m space telescope at 10 pc away from the system if the stellar flux is completely blocked. While our conclusion is based on several idealized assumptions, a frequency modulation of a directly imaged Earth-like planet offers a unique methodology to determine its obliquity.
The Resolution of Niho’s Last Conjecture Concerning Sequences, Codes, and Boolean Functions

A new method is used to resolve a long-standing conjecture of Niho concerning the crosscorrelation spectrum of a pair of maximum length linear recursive sequences of length <inline-formula> <tex-math notation="LaTeX">$2^{2m}-1$ </tex-math></inline-formula> with relative decimation <inline-formula> <tex-math notation="LaTeX">$d=2^{m+2}-3$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> is even. The result indicates that there are at most five distinct crosscorrelation values. Equivalently, the result indicates that there are at most five distinct values in the Walsh spectrum of the power permutation <inline-formula> <tex-math notation="LaTeX">$f(x)=x^{d}$ </tex-math></inline-formula> over a finite field of order <inline-formula> <tex-math notation="LaTeX">$2^{2m}$ </tex-math></inline-formula> and at most five distinct nonzero weights in the cyclic code of length <inline-formula> <tex-math notation="LaTeX">$2^{2m}-1$ </tex-math></inline-formula> with two primitive nonzeros <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\alpha ^{d}$ </tex-math></inline-formula>. The method used to obtain this result proves constraints on the number of roots that certain seventh degree polynomials can have on the unit circle of a finite field. The method also works when <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> is odd, in which case the associated crosscorrelation and Walsh spectra have at most six distinct values.
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked roads are closely related. In the temporal dimension, although there exists a tendency among adjacent time points, the importance of distant time points is not necessarily less than that of recent ones, since traffic flows are also affected by external factors. In this study, an attention temporal graph convolutional network (A3T-GCN) was proposed to simultaneously capture global temporal dynamics and spatial correlations in traffic flows. The A3T-GCN model learns the short-term trend by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network. Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy. Experimental results in real-world datasets demonstrate the effectiveness and robustness of the proposed A3T-GCN. We observe the improvements in RMSE of 2.51–46.15% and 2.45–49.32% over baselines for the SZ-taxi and Los-loop, respectively. Meanwhile, the Accuracies are 0.95–89.91% and 0.26–10.37% higher than the baselines for the SZ-taxi and Los-loop, respectively.
Electrical detection of the inverse Edelstein effect on the surface of 
SmB6

We report the measurement of spin current-induced charge accumulation, the inverse Edelstein effect (IEE), on the surface of single-crystal candidate topological Kondo insulator $\mathrm{Sm}{\mathrm{B}}_{6}$. The robust surface conduction channel of $\mathrm{Sm}{\mathrm{B}}_{6}$ has been shown to exhibit a large degree of spin-momentum locking, and the spin-polarized current through an external ferromagnetic contact induces spin-dependent charge accumulation on the surface of $\mathrm{Sm}{\mathrm{B}}_{6}$. The dependence of the IEE signal on the bias current, an external magnetic field direction, and temperature are consistent with an anticlockwise spin texture of the $\mathrm{Sm}{\mathrm{B}}_{6}$ surface band in momentum space. The direction and magnitude of this effect, compared with the normal Edelstein signal, are clearly explained by the Onsager reciprocal relation. Furthermore, we estimate the spin-to-charge conversion efficiency, i.e., the IEE length, to be 4.46 nm, which is an order of magnitude larger than the efficiency found in other typical Rashba interfaces, implying that the Rashba contribution to the IEE signal may be small. Building upon existing reports on the surface charge and spin conduction nature of this material, our results provide additional evidence that the surface of $\mathrm{Sm}{\mathrm{B}}_{6}$ supports a spin-polarized conduction channel.
Metasurface-Coated Devices: A New Paradigm for Energy-Efficient and Secure Communications in 5G and Beyond

The power-hungry transceiver chains in 5G mobile communication systems result in huge power consumption due to the complex signal processing algorithms and transmission in millimeter wave bands. In this article, we report energy efficiency (EE), energy harvesting (EH), and secure performance via the new paradigm of power-collecting metasurface-coated devices enabling ultra-low-power (ULP) transmissions. Contrary to smart surfaces (a.k.a. reconfigurable intelligent surfaces, RIS), where the reflected signal can be combined at the receiver by being treated as transmitted from a relay, the proposed metasurface claddings can be deployed both at the transmitter and receiver sides. The passive metasurface-coated devices can achieve ultra-high-EE and EH besides signal detection, combined with an enhanced secrecy rate at the legitimate user and/or improved spying capabilities of the eavesdroppers under ULP transmission. In particular, we provide a holistic model for the utilization of the metasurface shells and preliminary simulation results reveal the superiority of the proposed concept compared to the RIS ones. Furthermore, we discuss the main advantages of the new concept, potential opportunities, and challenges in beyond 5G mobile communication networks.
MAD-HTLC: Because HTLC is Crazy-Cheap to Attack

Smart Contracts and transactions allow users to implement elaborate constructions on cryptocurrency blockchains like Bitcoin and Ethereum. Many of these constructions, including operational payment channels and atomic swaps, use a building block called Hashed Time-Locked Contract (HTLC). In this work, we distill from HTLC a specification (HTLC-Spec), and present an implementation called Mutual-Assured-Destruction Hashed Time-Locked Contract (MAD-HTLC). MAD-HTLC employs a novel approach of utilizing the existing blockchain operators, called miners, as part of the design. If a user misbehaves, MAD-HTLC incentivizes the miners to confiscate all her funds. We prove MAD-HTLC’s security using the UC framework and game-theoretic analysis. We demonstrate MAD-HTLC’s efficacy and analyze its overhead by instantiating it on Bitcoin’s and Ethereum’s operational blockchains.Notably, current miner software makes only little effort to optimize revenue, since the advantage is relatively small. However, as the demand grows and other revenue components shrink, miners are more motivated to fully optimize their fund intake. By patching the standard Bitcoin client, we demonstrate such optimization is easy to implement, making the miners natural enforcers of MAD-HTLC.Finally, we extend previous results regarding HTLC vulnerability to bribery attacks. An attacker can incentivize miners to prefer her transactions by offering high transaction fees. We demonstrate this attack can be easily implemented by patching the Bitcoin client, and use game-theoretic tools to qualitatively tighten the known cost bound of such bribery attacks in presence of rational miners. We identify bribe opportunities occurring on the Bitcoin and Ethereum main networks where a few dollars bribe could yield tens of thousands of dollars in reward (e.g., $2 for over $25K).
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i.e., learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples. While current state-of-the-art methods employ importance reweighting to design various risk estimators, they ignored the learning capability of the model itself, which could have provided reliable supervision. This motivates us to propose a novel Self-PU learning framework, which seamlessly integrates PU learning and self-training. Self-PU highlights three "self"-oriented building blocks: a self-paced training algorithm that adaptively discovers and augments confident positive/negative examples as the training proceeds; a self-calibrated instance-aware loss; and a self-distillation scheme that introduces teacher-students learning as an effective regularization for PU learning. We demonstrate the state-of-the-art performance of Self-PU on common PU learning benchmarks (MNIST and CIFAR-10), which compare favorably against the latest competitors. Moreover, we study a real-world application of PU learning, i.e., classifying brain images of Alzheimer's Disease. Self-PU obtains significantly improved results on the renowned Alzheimer's Disease Neuroimaging Initiative (ADNI) database over existing methods. The code is publicly available at: this https URL.
Distinguishing between Wet and Dry Atmospheres of TRAPPIST-1 e and f

The nearby TRAPPIST-1 planetary system is an exciting target for characterizing the atmospheres of terrestrial planets. The planets e, f, and g lie in the circumstellar habitable zone and could sustain liquid water on their surfaces. During the extended pre–main-sequence phase of TRAPPIST-1, however, the planets may have experienced extreme water loss, leading to a desiccated mantle. The presence or absence of an ocean is challenging to determine with current and next-generation telescopes. Therefore, we investigate whether indirect evidence of an ocean and/or a biosphere can be inferred from observations of the planetary atmosphere. We introduce a newly developed photochemical model for planetary atmospheres, coupled to a radiative-convective model, and validate it against modern Earth, Venus, and Mars. The coupled model is applied to the TRAPPIST-1 planets e and f, assuming different surface conditions and varying amounts of CO2 in the atmosphere. As input for the model we use a constructed spectrum of TRAPPIST-1, based on near-simultaneous data from X-ray to optical wavelengths. We compute cloud-free transmission spectra of the planetary atmospheres and determine the detectability of molecular features using the Extremely Large Telescope (ELT) and the James Webb Space Telescope (JWST). We find that under certain conditions the existence or nonexistence of a biosphere and/or an ocean can be inferred by combining 30 transit observations with ELT and JWST within the K band. A nondetection of CO could suggest the existence of an ocean, whereas significant CH4 hints at the presence of a biosphere.
Efficient text generation of user-defined topic using generative adversarial networks

This study focused on efficient text generation using generative adversarial networks (GAN). Assuming that the goal is to generate a paragraph of a user-defined topic and sentimental tendency, conventionally the whole network has to be re-trained to obtain new results each time when a user changes the topic. This would be time-consuming and impractical. Therefore, we propose a User-Defined GAN (UD-GAN) with two-level discriminators to solve this problem. The first discriminator aims to guide the generator to learn paragraph-level information and sentence syntactic structure, which is constructed by multiple-LSTMs. The second one copes with higher-level information, such as the user-defined sentiment and topic for text generation. The cosine similarity based on TF-IDF and length penalty are adopted to determine the relevance of the topic. Then, the second discriminator is re-trained with the generator if the topic or sentiment for text generation is modified. The system evaluations are conducted to compare the performance of the proposed method with other GAN-based ones. The objective results showed that the proposed method is capable of generating texts with less time than others and the generated text is related to the user-defined topic and sentiment. We will further investigate the possibility of incorporating more detailed paragraph information such as semantics into text generation to enhance the result.
Avaliação de genótipos de bananeira em diferentes ambientes

Superior banana hybrids, resistant to diseases and with good fruits, generated or introduced in Embrapa-National Center of Cassava and Tropical Fruits Research, have been evaluated in different environments. The work was carried out to evaluate the behavior of tetraploid hybrids (AAAB) Pioneira, PV03-44, FHIA-01, SH3640 and FHIA-18 and varieties Nam (AAA), Caipira (AAA), Grande Naine (AAA) and Prata Ana (AAB), in Guanambi - BA, Cruz das Almas - BA, Vicosa - MG, Lavras - MG and Jaiba - MG, during two cycles. The characters analyzed were plant height, bunch weight, number of fruits, fruit length and cycle period. The average of 25 plants per plot and the standard deviation were used as variables. Considering five environments and two cycles, ‘Grande Naine' was the smaller genotype (218,7 cm), presenting, together with SH3640, the largest fruit length (respectively 20,3 e 18,8 cm) and the largest weight bunch (24,1 e 21,8 kg) and, together with ‘Caipira', the largest number of fruits (respectively 142,2 e 146,1). The introduced hybrids from Honduras of ‘Prata Ana' - FHIA-01, FHIA-18 and SH3640 - overcame the female genitor in bunch weight and in fruit length in five environments and two cycles, while ‘Pioneira' was the most precocious genotype. While Lavras - MG had the smallest standard deviations, Jaiba - MG presented the most favorable soil and climatic characteristics for the culture, propitiating to genotypes precocity and superior productivity.
Estimating Properties of Social Networks via Random Walk considering Private Nodes

Accurately analyzing graph properties of social networks is a challenging task because of access limitations to the graph data. To address this challenge, several algorithms to obtain unbiased estimates of properties from few samples via a random walk have been studied. However, existing algorithms do not consider private nodes who hide their neighbors in real social networks, leading to some practical problems. Here we design random walk-based algorithms to accurately estimate properties without any problems caused by private nodes. First, we design a random walk-based sampling algorithm that comprises the neighbor selection to obtain samples having the Markov property and the calculation of weights for each sample to correct the sampling bias. Further, for two graph property estimators, we propose the weighting methods to reduce not only the sampling bias but also estimation errors due to private nodes. The proposed algorithms improve the estimation accuracy of the existing algorithms by up to 92.6% on real-world datasets.
An Extremely Bright QSO at z = 2.89

We report the discovery and confirmation of a bright quasi-stellar object (QSO), 2MASS J13260399 + 7023462, at . This QSO is the first spectroscopically confirmed candidate from an ongoing search using the combination of Gaia and Wide-field Infrared Survey Explorer photometry to identify bright QSOs at z > 2, the redshift regime for which the Lyα forest is accessible with ground-based facilities. With a Gaia apparent magnitude G = 16.07, 2MASS J13260399 + 7023462 is one of the brightest QSOs known at z > 2, with only 15 currently known brighter QSOs. Given its inferred magnitude and redshift, it is among the most luminous objects in the universe; the inferred black hole mass and corresponding Eddington ratio are (2.7 ± 0.4) × 1010 and 1.3 ± 0.3, respectively. Follow-up Hubble observations confirm it is not gravitationally lensed.
Battery Second-Life for Dedicated and Shared Energy Storage Systems Supporting EV Charging Stations

Power systems are facing increasing strain due to the worldwide diffusion of electric vehicles (EVs). The need for charging stations (CSs) for battery electric vehicles (BEVs) in urban and private parking areas (PAs) is becoming a relevant issue. In this scenario, the use of energy storage systems (ESSs) could be an effective solution to reduce the peak power request by CSs in PAs to the grid. Moreover, II-Life battery modules are a potential approach for cutting costs and implementing sustainable solutions. We propose a method to size ESSs coupled to CSs by using II-Life battery modules. Our methodology is based on the estimation of the residual cycles and the decrease in the supplied power due to the battery aging for defining the number of EV battery packs required for an ESS use case. Then, economic evaluations are presented to compare II-Life with the equivalent I-Life storage system.
Evaluation of Synthetic Pheromones for Monitoring Forest Tent Caterpillar (Lepidoptera: Lasiocampidae) Populations

Abstract To effectively monitor forest tent caterpillar (Malacosoma disstria Hübner) populations, the use of synthetic sex pheromones to trap adults was evaluated in outbreak and nonoutbreak population levels. Three pheromone blends were field-tested, consisting of 1) (Z,E)-5,7-dodecadienal, 2) 100:1 (Z,E)-5,7-dodecadienal: (Z,Z)-5,7-dodecadienal and 3) 100:1:10 (Z,E)-5,7-dodecadienal: (Z,Z)-5,7-dodecadienal: (Z)-7- dodecanal. The tertiary blend (3) was superior for capturing forest tent caterpillar moths, and was best able to resolve population density levels. Lures dosed with 11 μg or less of either the binary (2) or tertiary blends failed to capture moths at nonoutbreak population levels, while lures dosed at 390 μg gave the highest mean trap catch and the lowest rate of zero-captures.
An Improved Relevance Feedback in CBIR

Relevance Feedback in Content-Based Image Retrieval is a method where the feedback of the performance is being used to improve itself. Prior works use feature re-weighting and classification techniques as the Relevance Feedback methods. This paper shows a novel addition to the prior methods to further improve the retrieval accuracy. In addition to all of these, the paper also shows a novel idea to even improve the 0-th iteration retrieval accuracy from the information of Relevance Feedback.
On the Impact of Non-Synchronous Devices over the Critical Clearing Time of Low-Inertia Systems

This paper discusses the impact of the inertia of conventional power plants as well as of the frequency control of non-synchronous devices on the Critical Clearing Time (CCT) following a three-phase fault in a benchmark power system model. The nonsynchronous devices studied in this paper are wind power plants, energy storage devices, and an average model of thermostatically controlled loads. The case study consists in a parametric analysis where the total inertia of the system is varied and the dynamic response of the system is evaluated by considering different scenarios and control strategies. Simulation results lead to the non-intuitive conclusion that, for any given level of inertia, non-synchronous devices always lead to an increase in the CCT of the system.
Impact of the WTO Membership, Regional Economic Integration, and Structural Change on China's Trade and Growth

The growing economic and political importance of China in the global economy in recent years has been discussed by academic and business economists, trade and investment experts, transnational corporate planners, government advisers, and politicians worldwide. While the discussions have been useful for global knowledge enhancement, and regional and national strategic purposes, they have often been regarded as “hypothetical or with fuzzy outcomes” due to their lack of substantive support. The paper addresses the issue by applying the recent generalized gravity theory to construct a new multi-equation econometric model of trade–growth causality for China. Using historical trade and growth data and advanced estimation, the model provides substantive evidence for the intertwining impact of China's trade and economic activities, WTO membership, regional economic integration, national, regional and global shocks, and gradual policy reforms on China's trade, growth, and economic relations. Some resulting major strategic trade, development and cooperation issues will also be discussed.