Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary mercury exposure, bioaccumulation, and effects in larvae of the southern leopard frog, Rana sphenocephala

Aufwuchs was collected from three reservoirs, a constructed wetland used for groundwater treatment and from mercury-enriched mesocosms to determine the relationship between inorganic mercury and methylmercury concentrations in the diet of tadpoles. Four diets were then formulated with mercury-enriched aufwuchs to concentrations that bracketed concentrations of mercury observed in aufwuchs from the field and reported in the literature from sites contaminated by non point-source emissions. The diets were fed to southern leopard frog tadpoles in the laboratory for the entire larval period (60-254 d). Metamorphs and tadpoles were analyzed for inorganic methylmercury and methylmercury content by gas chromatography-cold vapor atomic fluorescence spectrophotometry. Methylmercury concentration increased with total mercury concentration in aufwuchs, but the proportion of methylmercury to inorganic mercury decreased with increasing total mercury concentration. In the feeding experiment, there was an inverse relationship between mercury exposure concentration and bioaccumulation factor for each mercury species. We concluded that neither methylmercury nor inorganic mercury in aufwuchs are highly bioavailable to tadpoles and that bioaccumulation is not well explained by a simple partitioning model. This suggests that bioaccumulation factors as currently used are not the best predictors of dietary mercury bioaccumulation. INTRODUCTION Mercury (Hg) from atmospheric deposition tends to accumulate in biota inhabiting ephemeral wetlands [1] that are critical breeding sites for many species of amphibians and several studies have documented the occurrence of mercury in amphibians [2-4]. Unfortunately these studies are of limited utility because concentrations in tissues were not related to specific
Learning to Plan with Pointcloud Affordances for General-Purpose Dexterous Manipulation

—We aim to enable a robot to solve arbitrary manipulation tasks using dexterous manipulation primitives. This suggests the use of techniques from task-and-motion planning, which can sequence primitives by performing multistep reasoning and forward simulation. At the same time, we aim to solve such tasks when presented with objects of different shapes and sizes, suggesting the use of learned perceptual representation which enable generalization across geometries. However, it has remained challenging to incorporate these representations into systems that perform accurate forward simulation, a necessary component for reasoning toward long-horizon goals. We propose a framework, that utilizes deep generative models and segmented object point-clouds, that enables multistep planning using dexterous primitive manipulation skills in tasks involving a variety of object shapes and sizes. Our learned models, taking the form of biased sampling distributions, provide gains in planning efﬁciency over a manually designed baseline when integrated with a sampling-based planner. We also contribute a set of novel design choices in this framework which provide beneﬁts in generalization and sample quality.
Memristor Emulator Modeling Using Multisim

: In the forthcoming nano generation, the component memristor can play a key role in designing the analog circuits, digital logic circuits and neuromorphic systems. Memristor is a non-linear passive element which can store its preceding value of resistance. The physical memristor was not yet available in the market it is still at an experimental stage. Our motto is to design a memristor model emulator using multisim simulation and study its characteristics.
HAT: Hierarchical Alternative Training for Long Range Policy Transfer

Humans can master a new task within a few trials by drawing upon skills acquired through prior experience. To mimic this capability, hierarchical models combining primitive policies learned from prior tasks have been proposed. However, these methods fail short comparing to the human’s range of transferability. We propose Hierarchical Alternative Training (HAT), which leverages the hierarchical structure to train the combination function and adapt the primitive polices alternatively, to efficiently produce a range of complex behaviors on challenging new tasks. We demonstrate that our method outperforms recent policy transfer methods by combining and adapting these reusable primitives in tasks with continuous action space. The experiment results further show that HAT provides a broader transferring range.
A confirmatory factor analysis of the Observer alexithymia scale in treatment seeking alcohol- dependent patients Thorberg,

Confirmatory factor analyses evaluated the factorial validity of the Observer Alexithymia Scale (OAS) in an alcohol-dependent sample. Observation was conducted by clinical psychologists. All models exam-ined were rejected, given their poor fit. Given the psychometric limitations of the OAS shown in thisstudy, the OAS may not be the most appropriate measure to use early in treatment among alcohol-dependent individuals. Introduction Alexithymia is characterised by difficulties identifying and describing feelings, a lack of imagination and an externally oriented thinking style (Sifneos, 1973). Approximately 45– 67% of individuals with alcohol dependence also report alexithymia (Uzun et al., 2003; Thorberg et al., 2009). Furthermore, evidence suggests that individuals with alexithymia and alcohol dependence have a significantly younger age of onset, longer duration of abuse, stronger alcohol cravings, higher levels of dependence severity and more interpersonaldifficulties. This pattern of results suggests that those with alexithymia may be a very different group of people who have more severe alcohol problems than their nonalexithymic counterparts (Uzun et al., 2003; Thorberg et al., 2011a, b). To date, the most commonly used approach to assess alexithymia in alcohol populations are self-report methods (Thorberg et al., 2009), but alternative forms of assessment including observation and clinician ratings are important as it is considered contradictory to ask people to judge a capacity they may lack (Lane et al., 1997). Yet, studies utilising observer scales in alcohol populations are scarce except for a recent study examining the reliability and concurrent validity of the Observer Alexithymia Scale (OAS; Haviland et al., 2000) in an alcoholdependent sample (see, Thorberg et al., 2010). The OAS (Haviland et al., 2000) is an observational measure to be completed by friends, relatives or clinicians. Research in nonclinical samples and a psychiatric sample found adequate reliability and validity (Haviland et al., 2000, 2001; Berthoz et al., 2005; Yao et al., 2005), whereas other research has not (Meganck et al., 2010). As those with alexithymia may find it difficult to self-report feelings and alcohol-dependent individuals exposed to the debilitating neurotoxic effects of alcohol may have impaired cognitive flexibility (Butler et al., 2008), utilising an observational measure of alexithymia may be even more important among these patients. The objective of this study was to explore the underlying factor structure of the OAS in an alcoholdependent population. Based on the previous research, we hypothesised that the factor model with item parcels as reported by Haviland et al. (2000, 2001) would yield an acceptable fit, whereas the additional factor models tested would not.
Learning Long-term Dependencies Using Cognitive Inductive Biases in Self-attention RNNs

Attention and self-attention mechanisms, inspired by cognitive processes, are now central to state-of-the-art deep learning on sequential tasks. However, most recent progress hinges on heuristic approaches that rely on considerable memory and computational resources that scale poorly. In this work, we propose a relevancy screening mechanism, inspired by the cognitive process of memory consolidation, that allows for a scalable use of sparse self-attention with recurrence. We use simple numerical experiments to demonstrate that this mechanism helps enable recurrent systems on generalization and transfer learning tasks. Based on our results, we propose a concrete direction of research to improve scalability and generalization of attentive recurrent networks.
Assessment of parameters affecting H-type wind turbine performance

Due to the drastic effects of global warming and the eminent depletion of fossil fuels in future years, worldwide efforts are being expended to include renewable energy sources of dominated technologies in the energy matrices and intensify research and development of other promising sources. Solar and wind energy appear at the top of the list of well dominated technologies of reasonable implantation and energy generation costs. The horizontal axis wind turbines are well developed and many wind farms are operating in many countries supplying clean energy for the populations. On the other hand vertical axis wind turbines are not widely acceptable, few installations are in operation and their contribution in the low power range is insignificant. Different predicted behaviors of H-type vertical axis wind turbines, produced by changing the design and environmental variables were investigated using a low computational effort algorithm developed during this study. Among the input variables are the airfoil section, chord length, wind velocity, tip speed ratio, as well as the rotor solidity, radius, height and number of blades. Simulation parameters were also varied, including the initial guess for the axial induction factor and maximum number of iterations. The outputs of the numerical code include the wind extracted power, power coefficient, thrust, thrust coefficient, torque and torque coefficient. The power coefficient defines the aerodynamic efficiency of the rotor and the fluctuations of thrust and torque reflect the force balance, indicating the level of mechanical stress over the structure. Numerical simulations and performance predictions are important, since they may reduce the need of field tests, which are usually hard and costly to perform. Symmetrical NACA airfoils present good and similar balance of forces, with power coefficients that can exceed 0.30. At winds of 10 m/s, a 2 m radius and 4 m height rotor having 3 NACA 0012 blades with 0.67 m chord and tip speed ratio of 4.1 presents a power coefficient of 0.28 and delivers 2.7 kW of power. The level of unbalance is 46% (torque) and 76% (thrust), which may be acceptable. A high output power is due to the high wind velocity, rotor height and radius, and does not notably influence the power coefficient. More blades increase the balance, but decrease the power coefficient. The solidity and the tip speed ratio are important parameters that may be used for the power coefficient optimization. 1) Graduate Student 2) Full Professor The 2019 World Congress on Advances in Nano, Bio, Robotics and Energy (ANBRE19) Jeju Island, Korea, September 17 21, 2019
A new species of Efibula (Polyporales, Basidiomycota) from China

Efibula yunnanensis is proposed as a distinct new species based on morphological and molecular phylogenetic data. The species is characterized by an annual growth habit, resupinate basidiomata with smooth, cream to pale brown hymenial surface, a monomitic hyphal system with thin-walled, simple septate generative hyphae and ellipsoid, hyaline, thin-walled, smooth, IKI-, CB-basidiospores. The phylogenetic analyses based on ITS sequence data analyses revealed that E. yunnanensis was sister to a clade comprised E. clarkii and E. gracilis, and then grouped with E. americana and E. tuberculata.
Transfer Learning for Improving Singing-voice Detection in Polyphonic Instrumental Music

Detecting singing-voice in polyphonic instrumental music is critical to music information retrieval. To train a robust vocal detector, a large dataset marked with vocal or non-vocal label at frame-level is essential. However, frame-level labeling is time-consuming and labor expensive, resulting there is little well-labeled dataset available for singing-voice detection (S-VD). Hence, we propose a data augmentation method for S-VD by transfer learning. In this study, clean speech clips with voice activity endpoints and separate instrumental music clips are artificially added together to simulate polyphonic vocals to train a vocal/non-vocal detector. Due to the different articulation and phonation between speaking and singing, the vocal detector trained with the artificial dataset does not match well with the polyphonic music which is singing vocals together with the instrumental accompaniments. To reduce this mismatch, transfer learning is used to transfer the knowledge learned from the artificial speech-plus-music training set to a small but matched polyphonic dataset, i.e., singing vocals with accompaniments. By transferring the related knowledge to make up for the lack of well-labeled training data in S-VD, the proposed data augmentation method by transfer learning can improve S-VD performance with an F-score improvement from 89.5% to 93.2%.
Can We Spot the "Fake News" Before It Was Even Written?

Given the recent proliferation of disinformation online, there has been also growing research interest in automatically debunking rumors, false claims, and "fake news. " A number of fact-checking initiatives have been launched so far, both manual and automatic, but the whole enterprise remains in a state of crisis: by the time a claim is finally fact-checked, it could have reached millions of users, and the harm caused could hardly be undone. An arguably more promising direction is to focus on fact-checking entire news outlets, which can be done in advance. Then, we could fact-check the news before it was even written: by checking how trustworthy the outlets that published it is. We describe how we do this in the Tanbih news aggregator, which makes readers aware of what they are reading. In particular, we develop media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics.
S-vectors: Speaker Embeddings based on Transformer's Encoder for Text-Independent Speaker Verification

X-vectors have become the standard for speaker-embeddings in automatic speaker verification. X-vectors are obtained using a Time-delay Neural Network (TDNN) with context over several frames. We have explored the use of an architecture built on self-attention which attends to all the features over the entire utterance, and hence better capture speaker-level characteristics. We have used the encoder structure of Transformers, which is built on self-attention, as the base architecture and trained it to do a speaker classification task. In this paper, we have proposed to derive speaker embeddings from the output of the trained Transformer encoder structure after appropriate statistics pooling to obtain utterance level features. We have named the speaker embeddings from this structure as s-vectors. s-vectors outperform x-vectors with a relative improvement of 10% and 15% in % EER when trained on Voxceleb-1 only and Voxceleb-1+2 datasets. We have also investigated the effect of deriving s-vectors from different layers of the model.
GraphRepo: Fast Exploration in Software Repository Mining

Mining and storage of data from software repositories is typically done on a per-project basis, where each project uses a unique combination of data schema, extraction tools, and (intermediate) storage infrastructure. We introduce GraphRepo, a tool that enables a unified approach to extract data from Git repositories, store it, and share it across repository mining projects. GraphRepo usesNeo4j, an ACID-compliant graph database management system, and allows modular plug-in of components for repository extraction (drillers), analysis (miners), and export (mappers). The graph enables a natural way to query the data by removing the need for data normalisation. GraphRepo is built in Python and offers multiple ways to interface with the rich Python ecosystem and with big data solutions. The schema of the graph database is generic and extensible. Using GraphRepo for software repository mining offers several advantages versus creating project-specific infrastructure: (i) high performance for short-iteration exploration and scalability to large data sets (ii) easy distribution of extracted data(e.g., for replication) or sharing of extracted data among projects, and (iii) extensibility and interoperability. A set of benchmarks on four open source projects demonstrate that GraphRepo allows very fast querying of repository data, once extracted and indexed. More information can be found in the project's documentation (available at this https URL) and in the project's repository (available at this https URL). A video demonstration isalso available online (this https URL)
The MOSDEF survey: differences in SFR and metallicity for morphologically selected mergers at z ∼ 2

We study the properties of 55 morphologically-identified merging galaxy systems at z ∼ 2. These systems are flagged as mergers based on features such as tidal tails, double nuclei, and asymmetry. Our sample is drawn from the MOSFIRE Deep Evolution Field (MOSDEF) survey, along with a control sample of isolated galaxies at the same redshift. We consider the relationships between stellar mass, star formation rate (SFR), and gas-phase metallicity for both merging and non-merging systems. In the local universe, merging systems are characterized by an elevated SFR and depressed metallicity compared to isolated systems at a given mass. Our results indicate SFR enhancement and metallicity deficit for merging systems relative to non-merging systems for a fixed stellar mass at z ∼ 2, though larger samples are required to establish these preliminary results with higher statistical significance. In future work, it will be important to establish if the enhanced SFR and depressed metallicity in high-redshift mergers deviate from the ‘fundamental metallicity relation,’ as is observed in mergers in the local universe, and therefore shed light on gas flows during galaxy interactions.
Use of intercropping and infochemical releasers to control aphids in wheat

Reference-Haibo Zhou, Julian Chen, Frederic Francis, et al. Screening and identification of main germplasm resources and candidate cultivars in wheat resistance to aphids in China. African Journal of Biotechnology. 2011,Vol. 10(63), pp. 13930-13935. Chapter IV: Evaluation on the resistance to aphids of wheat germplasm resources 84 Abstract: A collection of more than 200 wheat lines from main wheat-producing areas of China was evaluated for resistance to wheat aphids using fuzzy recognition technique in five field experiments over 2 years. The results showed that susceptible to wheat aphids was exhibited in most of the lines tested, and no immune and highly resistance lines to wheat aphids was observed. The average percentage of wheat germplasm lines with resistant, lowly susceptible, moderately susceptible and highly susceptible to aphid were 9.30%, 23.15%, 42.32%, and 25.23%, respectively. 5 moderately resistant wheat germplasm lines to wheat aphids (Lantian18, Lantian20, Lantian22, Lantian00-30 and Shanmai175) were found in Jiangyou experimental station in 2009. More importantly, 2 wheat germplasm lines (Lantian20, Lantian22) with the continuous resistance to wheat aphid in the five experimental stations over 2 years were discovered. Although resistance of wheat germplasm lines had a close relation to their genetics and inheritance, we also found that the resistance of the same wheat germplasm lines was varied in different experimental stations. It would be helpful to make wheat germplasm selections for breeding programs, especially if they have unique genes that may provide resistance to future biotypes of wheat aphids. A valuable method for evaluating the potential of aphid-resistance for wheat germplasm lines was also confirmed.
GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis

Synthesizing medical images, such as PET, is a challenging task due to the fact that the intensity range is much wider and denser than those in photographs and digital renderings and are often heavily biased toward zero. Above all, intensity values in PET have absolute significance, and are used to compute parameters that are reproducible across the population. Yet, usually much manual adjustment has to be made in pre-/post- processing when synthesizing PET images, because its intensity ranges can vary a lot, e.g., between -100 to 1000 in floating point values. To overcome these challenges, we adopt the Bidirectional Encoder Representations from Transformers (BERT) algorithm that has had great success in natural language processing (NLP), where wide-range floating point intensity values are represented as integers ranging between 0 to 10000 that resemble a dictionary of natural language vocabularies. BERT is then trained to predict a proportion of masked values images, where its "next sentence prediction (NSP)" acts as GAN discriminator. Our proposed approach, is able to generate PET images from MRI images in wide intensity range, with no manual adjustments in pre-/post- processing. It is a method that can scale and ready to deploy.
(Almost) All of Entity Resolution

Whether the goal is to estimate the number of people that live in a congressional district, to estimate the number of individuals that have died in an armed conflict, or to disambiguate individual authors using bibliographic data, all these applications have a common theme-integrating information from multiple sources. Before such questions can be answered, databases must be cleaned and integrated in a systematic and accurate way, commonly known as structured entity resolution (record linkage or deduplication). Here, we review motivational applications and seminal papers that have led to the growth of this area. We review modern probabilistic and Bayesian methods in statistics, computer science, machine learning, database management, economics, political science, and other disciplines that are used throughout industry and academia in applications such as human rights, official statistics, medicine, and citation networks, among others. Last, we discuss current research topics of practical importance.