Miracle-dz/UniFAR-FTUs huggingface dataset:
Improve the Response Diversity of Multi-turn Dialogue System by Combining Knowledge

—Recently, neural network language models have been trained in an end-to-end and fully data-driven manner, generating more flexible responses. Traditional neuro-language models have some problems, such as generating meaningless security responses and containing less information, resulting in unattractive conversations. According to the above problems, we propose a knowledge-driven hierarchical recurrent attention network (Kd-HRAN) model. The Kd-HRAN model adds a knowledge entity generator to hierarchical recurrent attention network infrastructure and introduces a knowledge awareness gate in its decoder. The knowledge awareness gate can integrate the context-related external knowledge into the reply process, and determine whether the final output word is generated from the decoder or copied from the knowledge entity generator. The Kd-HRAN model ensures the consistency, richness, and sustainability of the dialogue system. After a large number of experimental verification, the Kd-HRAN model is superior to the baseline model and has diversified and robust responses.
Prenatal diagnosis of cystic fibrosis: I. Prospective study of 51 pregnancies

A prenatal diagnosis was performed in 51 pregnancies with a 1‐in‐4 risk of having a child with cystic fibrosis. The criteria for determining an affected fetus were based on the results of alkaline phosphatase (ALP) residual activity after inhibition by phenylalanine and by homoarginine, of total ALP activity, and of gamma‐glutamyltranspeptidase (GGTP) activity in the amniotic fluid taken between 16 and 19 weeks of pregnancy.
An X-ray and optical study of the supernova remnant W44

We report the results of a 8000 s observation of the supernova remnant W44 using the ROSAT Position Sensitive Proportional Counter (PSPC). The image shows the same centrally peaked morphology observed by the Einstein IPC and contrasts with the shell-like radio morphology. The eastern limb shows a lack of X-ray emission within the radio shell, probably due to the interaction between the Supernova Remnants (SNR) and a molecular cloud. No counterpart to the pulsar 1853 + 01 in W44 has been detected, with L(sub X) less than 1.3 x 10(exp 32) ergs/s in the 0.2 to 2.4 keV band. The spectral analysis of the central part of W44, combining EXOSAT ME and Einstein SSS data, shows that the shocked plasma has not reached ionization equilibrium. The best nonequilibrium fit to PSPC, ME, and SSS spectra gives Eta = 10(exp 51) ergs cm(exp -6), T(sub s) = 10(exp 7) K with T(sub e) = T(sub i), suggesting conditions are approaching ionization equilibrium. There is no evidence of enhanced abundances of Mg, Si, S, or Fe. The variation of temperature and column density was obtained region by region using the PSPC and Einstein IPC. The temperature is largely uniform over the remnant, but strong column density variations are found to be consistent with molecular clouds in the line of sight. An evaporation model with a two-phase interstellar medium structure of clumps and interclump gas (White & Long 1991) can explain the X-ray centrally peaked morphology of W44. The clumps remaining behind a SN shock provide a reservoir of material, and evaporat e to increase the density of X-ray emitting gas in the interior of a SNR. The uniform temperature distribution of W44 strongly supports the predictions of this model. In addition, mosaiced H alpha and (S II) images of W44, taken using the prime focus universal extragalactic instrument (PFUEI) camera on the Palomar 60 sec telescope, reveal the first discovery of optical filaments (both H alpha and (S II)) in the northwestern and southeastern portion of the remnant, within the X-ray emitting region. The optical filaments and the X-ray image showing locally brighter emission and clumps along the filaments suggest both are produced by the interaction between the supernova shock front and regions of enhanced ambient density.
Ecosystem-Level Impacts of Fisheries Bycatch on Marine Megafauna Biodiversity Conservation through Mitigation

NOAA Fisheries, Center for Environmental Economics University of California San Diego (CEE), Institute for Global Cooperation and Conflict University of California (iGCC), International Sustainable Seafood Foundation (ISSF), Inter-American Tropical Tuna Commission (IATTC), The Nature Conservancy (TNC), Centre for Fisheries & Aquaculture Economics & Management (FAME), and the Fisheries Expert Group of the IUCN Commission on Ecosystem Management, IUCN/CEM/FEG.
The effect of irradiation on metakaolin-based geopolymer using Ti + ion implantation

The need for finding a replacement for concrete in nuclear power plants has increased dramatically due to concrete degradation caused by prolonged exposure to neutron irradiation. In this study, we mimicked the effect of neutron irradiation on metakaolin-based potassium geopolymer by using Ti+ as an implantation ion. Several techniques, including laser profilometry, scanning electron microscopy (SEM), backscattered electron (BSE), and nanoindentation, were employed to evaluate the geopolymer’s morphological, microstructural, and mechanical properties. X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy were used to identify chemical composition and molecular structure before and after irradiation. Results revealed the formation of new cracks at the geopolymer surface after irradiation due to exposure to Ti+ and localized heating, accompanied by water loss and microcracking, as shown by laser profilometry. SEM and BSE analyses indicated that unreacted metakaolin disappeared, and the geopolymer microstructure became denser after irradiation. The results from nanoindentation showed a 90% increase in microhardness, a 46% increase in the reduced modulus, and a 23% reduction in the contact depth after irradiation. The FTIR spectra demonstrated a reduction in the intensity of O-H stretching vibration and H-O-H peaks after irradiation. XRD patterns indicated no changes in the geopolymer structure following irradiation. However, Raman spectroscopy revealed a decline in intensity, peak shift, and an increase in full-width half maximum (FWHM) after irradiation, likely attributed to the presence of titanium ions in the target and the thermal shock due to the irradiation. The increase in FWHM suggests increased sample disorder and alterations in bond angles, bond distance, or the local environment. This research is crucial for developing eco-friendly and resilient nuclear reactor shielding materials using geopolymers.
A Simple Electrostatic Balance for the Milligram Range

We propose a method to realize the kilogram through electrical units, namely, the volt. The device is based on a simple plane plate electrostatic actuator used to counterbalance the weight force. The geometry of the electric field is characterized by changing the distance between the plates while maintaining a constant force and measuring the capacitance. This approach leads to the realization of a simple and compact device for the absolute measurement of small masses. The preliminary results show a relative uncertainty of 10−3 for mass above 10 mg. A series of uncertainty sources has been pointed out. An improvement of one order of magnitude is expected by correcting these sources.
Saccharomyces cerevisiae: A Nuclear Tetratricopeptide Repeat Protein with Two Mutable Domains

CDC23 is required in Saccharomyces cerevisiae for cell cycle progression through the G2/M transition. The CDC23 gene product contains tandem, imperfect repeats, termed tetratricopeptide repeats, (TPR) units common to a protein family that includes several other nuclear division CDC genes. In this report we have used mutagenesis to probe the functional significance of the TPR units within CDC23. Analysis of truncated derivatives indicates that the TPR block of CDC23 is necessary for the function or stability of the polypeptide. In-frame deletion of a single TPR unit within the repeat block proved sufficient to inactivate CDC23 in vivo, though this allele could rescue the temperature-sensitive defect of a cdc23 point mutant by intragenic complementation. By both in vitro and in vivo mutagenesis techniques, 17 thermolabile cdc23 alleles were produced and examined. Fourteen alleles contained single amino acid changes that were found to cluster within two distinct mutable domains, one of which encompasses the most canonical TPR unit found in CDC23. In addition, we have characterized CDC23 as a 62-kDa protein (p62cd"23) that is localized to the yeast nucleus. Our mutagenesis results suggest that TPR blocks form an essential domain within members of the TPR family.
Multidomain Features Fusion for Zero-Shot Learning

Given a novel class instance, the purpose of zero-shot learning (ZSL) is to learn a model to classify the instance by seen samples and semantic information transcending class boundaries. The difficulty lies in how to find a suitable space for zero-shot recognition. The previous approaches use semantic space or visual space as classification space. These methods, which typically learn visual-semantic or semantic-visual mapping and directly exploit the output of the mapping function to measure similarity to classify new categories, do not adequately consider the complementarity and distribution gap of multiple domain information. In this paper, we propose to learn a multidomain information fusion space by a joint learning framework. Specifically, we consider the fusion space as a shared space in which different domain features can be recovered by simple linear transformation. By learning a $n$-way classifier of fusion space from the seen class samples, we also obtain the discriminative information of the similarity space to make the fusion representation more separable. Extensive experiments on popular benchmark datasets manifest that our approach achieves state-of-the-art performances in both supervised and unsupervised ZSL tasks.
INTERNATIONAL JOURNAL OF HUMAN COMPUTER INTERACTION (IJHCI)

Search engines help the user to surf the web. Due to the vast number of web pages it is highly impossible for the user to retrieve the appropriate web page he needs. Thus, Web search ranking algorithms play an important role in ranking web pages so that the user could retrieve the page which is most relevant to the user's query. This paper presents a study of the applicability of two user-effort-sensitive evaluation measures on five Web search engines (Google, Ask, Yahoo, AOL and Bing). Twenty queries were collected from the list of most hit queries in the last year from various search engines and based upon that search engines are evaluated.
Graph Partitioning: A Heuristic Procedure to Partition Network Graphs

Graphs are mathematical structures used to model pair wise relationship between objects of a certain collection. It consists of collection of vertices or “nodes” and a collection of edges that connect these nodes. Graphs can be directed from one vertex to another or undirected. In our context, a graph denotes a network with computers distributed as nodes while the communication channel acting as the edges. These are directed graphs where each edge has a capacity which cannot be exceeded. In real life applications, it becomes very essential that graphs are partitioned in some way so as to satisfy certain conditions. For example, while placing components of electronic circuit on circuit boards or substrates, components that are highly dependent on each other (exchanging maximum information) should be placed on the same board. Also an important factor is the number of connections between these boards should be minimized. Similar situation arises in a computer network where computer systems are distributed over a wide geographic location. This is the basis of graph partitioning problem. The classical graph partitioning problem consists of dividing a graph into pieces, such that the pieces are of about same size and there exists very few connections between these pieces. The objective is to partition the nodes of a graph with costs on its edges into subsets so as to minimize the sum of the costs on all edges that are cut. Let G be graph with n nodes, of sizes (weights) wi > 0, i = 1, 2, …. , n. Let p be a positive number, such that 0 < wi < p for all i. Let C = (cij), i,j = 1, 2, ……, n be a weighted connectivity matrix describing the edges of G. Let k be a positive integer. A k-way partition of G is a set of disjoint subsets of G, v1, v2, …, vk such that A partition is admissible if for all i. The cost of partition is the summation of (cij), where i and j belong to different subsets. A strictly exhaustive procedure for finding the optimal partition is out of question because the problem of graph partitioning is NP-Hard problem. For a graph with 40 nodes and 4 partitions, the possible number of partitioned cases will be of the order of 1036. Hence, any direct approach to find an optimal solution from these many cases is not a feasible option. As a result heuristic approaches are employed in these cases. We use a heuristic partitioning algorithm that divides a network into 2 disjoint sets based on the distance between any two nodes. The network used is a real network termed ARPANET and is regarded as the origin of the Internet.
Transforming Growth Factor- -activated Kinase 1 Is an Essential Regulator of Myogenic Differentiation*

Satellite cells/myoblasts account for the majority of muscle regenerative potential in response to injury and muscular adaptation to exercise. Although the ability to influence this process would provide valuable benefits for treating a variety of patients suffering from muscle loss, the regulatory mechanisms of myogenesis are not completely understood.We have tested the hypothesis that transforming growth factor-activated kinase 1 (TAK1) is an important regulator of skeletal muscle formation. TAK1 is expressed in proliferating C2C12 myoblasts, and its levels are reduced upon differentiation of myoblasts into myotubes. In vivo, TAK1 is predominantly expressed in developing skeletal muscle of young mice. However, the expression of TAK1 was significantly up-regulated in regenerating skeletal muscle of adult mice. Overexpression of a dominant negative mutant of TAK1 or knockdown of TAK1 inhibited the proliferation and differentiation of C2C12 myoblasts. TAK1 was required for the expression of myogenic regulatory factors in differentiating myoblasts. Genetic ablation of TAK1 also inhibited the MyoD-driven transformation of mouse embryonic fibroblasts into myotubes. Inhibition of TAK1 suppressed the differentiation-associated activation of p38 mitogen-activated protein kinase (MAPK) and Akt kinase. Overexpression of a constitutively active mutant of MAPK kinase 6 (MKK6, an upstream activator of p38 MAPK) but not constitutive active Akt restored the myogenic differentiation in TAK1-deficient mouse embryonic fibroblasts. Insulin growth factor 1-induced myogenic differentiation was also found to involve TAK1. Collectively, our results suggest that TAK1 is an important upstream regulator of skeletal muscle cell differentiation.
Localization of coherently distributed source using a successive propagator method

In this letter, the localization of coherently distributed (CD) is considered. With regard to the direction finding of CD source, the conventional method distributed source parameter estimation (DSPE) can provide stable estimation performance, while it also requires a complex two-dimensional spectral peak search for both nominal direction-of-arrivals (DOAs) and angular spreads. Thus, with rank reduction criterion, we derived a successive propagator method (S-PM) which exploits three decoupled one-dimensional search to replace an extensive one-dimension search to reduce the heavy cost. And the proposed algorithm can avoid the high-complexity eigen-decomposition procedure by using the linear operator-propagator. When compared to the convention ESPRIT-CD [8], S-PM has a better estimation performance with a low complexity. Several simulation results illustrate the effectiveness and improvements of the proposed algorithm.
Retrieval of the aerosol asymmetry factor from Sun-sky radiometer measurements: application to almucantar geometry and accuracy assessment

The Devaux–Vermeulen–Li (DVL) method is a simple method that directly retrieves aerosol optical parameters based on single-wavelength solar–sky radiation observations, without the assumption of aerosol microphysical properties. Inheriting the previous retrieval of single-scattering albedo (SSA) and scattering phase function, the DVL method is modified to derive the aerosol asymmetric factor (g) parameter. Interpolation methods were proposed to estimate the phase function over the extreme forward and backward scattering regions where instrumental observations are missing; thus, g could be derived from the phase function over the entire scattering angle region. To evaluate the g accuracy from the DVL algorithm, especially for non-spherical aerosols, synthetic retrieval with typical aerosol models (water-soluble, biomass burning, dust-sphericity, and dust-spheroid models) and retrievals from AERONET observations (Beijing site, from January 2011 to March 2015) were implemented at four wavelengths (440, 675, 870, and 1020 nm). The numerical experiments showed that DVL retrieves g with errors less than ±0.02 under “error-free” conditions. When measurement uncertainties were present, all g errors were within ±0.03, except for the angular pointing error for the coarse mode-dominated dust-sphericity/spheroid aerosols. Most importantly, g retrievals were not sensitive to the aerosol optical depth (AOD) and sky radiance errors, which are important influencing factors for SSA retrieval. Comparison of DVL retrievals with AERONET version 2 level 2.0 products [with AOD (440 nm)>0.2] shows that the DVL g was well correlated with that of AERONET, especially for the 675, 870, and 1020 nm bands, with root mean square deviations (RMSDs) smaller than 0.02 and absolute values of mean bias deviation smaller than 0.01. Relatively larger deviations occurred at the 440 nm band, where g values were underestimated by approximately 0.03 compared to those of AERONET, with a higher RMSD of approximately 0.035. Both the synthetic retrieval and comparison with AERONET indicated that the algorithm performance for large, non-spherical particles is comparable to that of other spherical aerosol particles in retrieving g.
PREDICTING THE CONFUSION LEVEL OF TEXT EXCERPTS WITH SYNTACTIC, LEXICAL AND N-GRAM FEATURES

Distance learning, offline presentations (presentations that are not being carried in a live fashion but were instead pre-recorded) and such activities whose main goal is to convey information are getting increasingly relevant with digital media such as Virtual Reality (VR) and Massive Online Open Courses (MOOCs). While MOOCs are a well-established reality in the learning environment, VR is also being used to promote learning in virtual rooms, be it in the academia or in the industry. Oftentimes these methods are based on written scripts that take the learner through the content, making them critical components to these tools. With such an important role, it is important to ensure the efficiency of these scripts. Confusion is a non-basic emotion associated with learning. This process often leads to a cognitive disequilibrium either caused by the content itself or due to the way it is conveyed when it comes to its syntactic and lexical features. We hereby propose a supervised model that can predict the likelihood of confusion an input text excerpt can cause on the learner. To achieve this, we performed syntactic and lexical analyses over 300 text excerpts and collected 5 confusion level classifications (0 – 6) per excerpt from 51 annotators to use their respective means as labels. These examples that compose the dataset were collected from random presentations transcripts across various fields of knowledge. The learning model was trained with this data with the results being included in the body of the paper. This model allows the design of clearer scripts of offline presentations and similar approaches and we expect that it improves the efficiency of these speeches. While this model is applied to this specific case, we hope to pave the way to generalize this approach to other contexts where clearness of text is critical, such as the scripts of MOOCs or academic abstracts.
Soft-bodied benthic algae in pristine Brazilian streams: from acid to karst 1

This study evaluated soft-bodied benthic algae from seven streams located in protected areas of central Brazil (Parque Nacional de Brasília, Parque Nacional da Chapada dos Veadeiros and Parque Estadual de Terra Ronca), and related them to abiotic factors. We addressed the following questions: A) how algal community structure in Cerrado streams is influenced by the marked seasonality typical of the region; and B) how karst geology influences algal communities in Parque Estadual de Terra Ronca, as compared to streams in other Cerrado areas. Samples were collected between 2012 and 2013 from a single site in each stream, and upstream and downstream of caves present in Parque Estadual de Terra Ronca. Altogether, 159 taxa were recorded. Algal community structure varied significantly among sites, but not between seasons, despite relatively lower abundances during the rainy season, likely due to a negative influence of the faster currents in streams during this period. Some alkaliphilic species were observed only at sites near caves ( e.g. the cyanobacterium Tolypothrix distorta ) , while acidophilic taxa were more frequent in the other streams ( e.g. desmids and Cyanobacteria such as Scytonema caldarium, Hapalosiphon stuhlmannii and Stigonema ocellatum ). We observed a rare green alga, Microthamnion kuetzingianum, and reported the cyanobacterium Rivularia aquatica for the first time in Brazil.
High-throughput prediction of stress–strain curves of thermoplastic elastomer model block copolymers by combining hierarchical simulation and deep learning

We achieved high-throughput prediction of the stress–strain (S–S) curves of thermoplastic elastomers by combining hierarchical simulation and deep learning. ABA triblock copolymer with a phase-separated structure was used as a thermoplastic elastomer model. The S–S curves of the ABA triblock copolymers were calculated from the hierarchical simulation of self-consistent field theory calculations and coarse-grained molecular dynamics simulations. Because such hierarchical simulations require considerable computational resources, we applied a deep learning technique to accelerate the prediction. Sets of phase-separated structures and the S–S curves obtained from the hierarchical simulation were used to train a 3D convolutional neural network. Using the trained network, we confirmed that the predicted S–S curves of the untrained structures accurately reproduced the simulation results. These results will enable us to design novel polymers and phase-separated structures with desired S–S curves by high-throughput screening of a wide variety of structures.