Recent developments in effective antioxidants : the structure and antioxidant properties

Since the last few years, the growing interest in the use of natural and synthetic antioxidants as functional food ingredients and dietary supplements, is observed. The imbalance between the number of antioxidants and free radicals is the cause of oxidative damages of proteins, lipids, and DNA. The aim of the study was the review of recent developments in antioxidants. One of the crucial issues in food technology, medicine, and biotechnology is the excess free radicals reduction to obtain healthy food. The major problem is receiving more effective antioxidants. The study aimed to analyze the properties of efficient antioxidants and a better understanding of the molecular mechanism of antioxidant processes. Our researches and sparing literature data prove that the ligand antioxidant properties complexed by selected metals may significantly affect the free radical neutralization. According to our preliminary observation, this efficiency is improved mainly by the metals of high ion potential, e.g., Fe(III), Cr(III), Ln(III), Y(III). The complexes of delocalized electronic charge are better antioxidants. Experimental literature results of antioxidant assays, such as diphenylpicrylhydrazyl (DPPH) and ferric reducing activity power assay (FRAP), were compared to thermodynamic parameters obtained with computational methods. The mechanisms of free radicals creation were described based on the experimental literature data. Changes in HOMO energy distribution in phenolic acids with an increasing number of hydroxyl groups were observed. The antioxidant properties of flavonoids are strongly dependent on the hydroxyl group position and the catechol moiety. The number of methoxy groups in the phenolic acid molecules influences antioxidant activity. The use of synchrotron techniques in the antioxidants electronic structure analysis was proposed

Puffer: Pop-Up Flat Folding Explorer Robot

A repeatably reconfigurable robot, comprising at least two printed circuit board (PCB) rigid sections, at least one PCB flexible section coupled to the at least two PCB rigid sections, at least one wheel, hybrid wheel propeller, wheel and propeller, or hybrid wheel screw propeller rotatably coupled to at least one of the at least two PCB rigid sections and at least one actuator coupled to the at least two PCB rigid sections, wherein the at least one actuator folds and unfolds the repeatably reconfigurable robot

Submarine Lava Deltas of the 2018 Eruption of Kīlauea Volcano

Hawaiian and other ocean island lava flows that reach the coastline can deposit significant volumes of lava in submarine deltas. The catastrophic collapse of these deltas represents one of the most significant, but least predictable, volcanic hazards at ocean islands. The volume of lava deposited below sea level in delta-forming eruptions and the mechanisms of delta construction and destruction are rarely documented. Here, we report on bathymetric surveys and ROV observations following the Kīlauea 2018 eruption that, along with a comparison to the deltas formed at Pu‘u ‘Ō‘ō over the past decade, provide new insight into delta formation. Bathymetric differencing reveals that the 2018 deltas contain more than half of the total volume of lava erupted. In addition, we find that the 2018 deltas are comprised largely of coarse-grained volcanic breccias and intact lava flows, which contrast with those at Pu‘u ‘Ō‘ō that contain a large fraction of fine-grained hyaloclastite. We attribute this difference to less efficient fragmentation of the 2018 ‘a‘ā flows leading to fragmentation by collapse rather than hydrovolcanic explosion. We suggest a mechanistic model where the characteristic grain size influences the form and stability of the delta with fine grain size deltas (Pu‘u ‘Ō‘ō) experiencing larger landslides with greater run-out supported by increased pore pressure and with coarse grain size deltas (Kīlauea 2018) experiencing smaller landslides that quickly stop as the pore pressure rapidly dissipates. This difference, if validated for other lava deltas, would provide a means to assess potential delta stability in future eruptions

A New Surrogating Algorithm by the Complex Graph Fourier Transform (CGFT)

[EN] The essential step of surrogating algorithms is phase randomizing the Fourier transform while preserving the original spectrum amplitude before computing the inverse Fourier transform. In this paper, we propose a new method which considers the graph Fourier transform. In this manner, much more flexibility is gained to define properties of the original graph signal which are to be preserved in the surrogates. The complex case is considered to allow unconstrained phase randomization in the transformed domain, hence we define a Hermitian Laplacian matrix that models the graph topology, whose eigenvectors form the basis of a complex graph Fourier transform. We have shown that the Hermitian Laplacian matrix may have negative eigenvalues. We also show in the paper that preserving the graph spectrum amplitude implies several invariances that can be controlled by the selected Hermitian Laplacian matrix. The interest of surrogating graph signals has been illustrated in the context of scarcity of instances in classifier training.This research was funded by the Spanish Administration and the European Union under grant TEC2017-84743-P.Belda, J.; Vergara Domínguez, L.; Safont Armero, G.; Salazar Afanador, A.; Parcheta, Z. (2019). A New Surrogating Algorithm by the Complex Graph Fourier Transform (CGFT). Entropy. 21(8):1-18. https://doi.org/10.3390/e21080759S118218Schreiber, T., & Schmitz, A. (2000). Surrogate time series. Physica D: Nonlinear Phenomena, 142(3-4), 346-382. doi:10.1016/s0167-2789(00)00043-9Miralles, R., Vergara, L., Salazar, A., & Igual, J. (2008). Blind detection of nonlinearities in multiple-echo ultrasonic signals. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 55(3), 637-647. doi:10.1109/tuffc.2008.688Mandic, D. ., Chen, M., Gautama, T., Van Hulle, M. ., & Constantinides, A. (2008). 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Applied and Computational Harmonic Analysis, 40(2), 260-291. doi:10.1016/j.acha.2015.02.005Dong, X., Thanou, D., Frossard, P., & Vandergheynst, P. (2016). Learning Laplacian Matrix in Smooth Graph Signal Representations. IEEE Transactions on Signal Processing, 64(23), 6160-6173. doi:10.1109/tsp.2016.2602809Perraudin, N., & Vandergheynst, P. (2017). Stationary Signal Processing on Graphs. IEEE Transactions on Signal Processing, 65(13), 3462-3477. doi:10.1109/tsp.2017.2690388Yu, G., & Qu, H. (2015). Hermitian Laplacian matrix and positive of mixed graphs. Applied Mathematics and Computation, 269, 70-76. doi:10.1016/j.amc.2015.07.045Gilbert, G. T. (1991). Positive Definite Matrices and Sylvester’s Criterion. The American Mathematical Monthly, 98(1), 44-46. doi:10.1080/00029890.1991.11995702Merris, R. (1994). Laplacian matrices of graphs: a survey. Linear Algebra and its Applications, 197-198, 143-176. doi:10.1016/0024-3795(94)90486-3Shapiro, H. (1991). 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Combining Embeddings of Input Data for Text Classification

[EN] The problem of automatic text classification is an essential part of text analysis. The improvement of text classification can be done at different levels such as a preprocessing step, network implementation, etc. In this paper, we focus on how the combination of different methods of text encoding may affect classification accuracy. To do this, we implemented a multi-input neural network that is able to encode input text using several text encoding techniques such as BERT, neural embedding layer, GloVe, skip-thoughts and ParagraphVector. The text can be represented at different levels of tokenised input text such as the sentence level, word level, byte pair encoding level and character level. Experiments were conducted on seven datasets from different language families: English, German, Swedish and Czech. Some of those languages contain agglutinations and grammatical cases. Two out of seven datasets originated from real commercial scenarios: (1) classifying ingredients into their corresponding classes by means of a corpus provided by Northfork; and (2) classifying texts according to the English level of their corresponding writers by means of a corpus provided by ProvenWord. The developed architecture achieves an improvement with different combinations of text encoding techniques depending on the different characteristics of the datasets. Once the best combination of embeddings at different levels was determined, different architectures of multi-input neural networks were compared. The results obtained with the best embedding combination and best neural network architecture were compared with state-of-the-art approaches. The results obtained with the dataset used in the experiments were better than the state-of-the-art baselines.This work is partially supported by MINECO under Grant DI-15-08169 and by Sciling under its R+D program. The authors would like to thank NVIDIA for their donation of a Titan Xp GPU, which allowed us to conduct this researchParcheta, Z.; Sanchis Trilles, G.; Casacuberta Nolla, F.; Rendahl, R. (2021). Combining Embeddings of Input Data for Text Classification. Neural Processing Letters. 53(5):3123-3151. https://doi.org/10.1007/s11063-020-10312-w31233151535Abadi M, Barham P, Chen J, Chen Z et al (2016) Tensorflow: a system for large-scale machine learning. In: Proceedings of 12th USENIX symposium on operating systems design and implementation (OSDI 16), pp 265–283Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of workshop at the international conference on learning representations (ICLR)Bergsma S, Kondrak G (2007) Alignment-based discriminative string similarity. In: Proceedings of the 45th annual meeting of the association of computational linguistics, pp 656–663Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135–146Bridle JS (1989) Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Fogelman Soulié F, Hérault J (eds) Neurocomputing. Springer, Berlin, Heidelberg, pp 227–236Chen D, Manning CD (2014) A fast and accurate dependency parser using neural networks. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 740–750Chollet F (2016) Using pre-trained word embeddings in a keras model. The Keras Blog, LondonChollet F, Falbel D, Allaire J, Tang YT, Van Der Bijl W, Studer M, Keydana S (2015) Keras: deep learning library for theano and tensorflow, vols 7, 8. https://keras.io/kConneau A, Kiela D, Schwenk H, Barrault L, Bordes A (2017) Supervised learning of universal sentence representations from natural language inference data. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 670–680Dai AM, Olah C, Le QV (2015) Document embedding with paragraph vectors. Preprint arXiv:1507.07998v1Devlin J, Chang M, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. Preprint arXiv:1810.04805Gage P (1994) A new algorithm for data compression. C Users J 12:23–38Goasduff L, Omale G (2018) Gartner survey finds consumers would use AI to save time and money. Gartner, BerlinGupta V, Karnick H, Bansal A, Jhala P (2016) Product classification in e-commerce using distributional semantics. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp 536–546Habernal I, Brychcín T (2013) Unsupervised improving of sentiment analysis using global target context. Proc Recent Adv Nat Lang Process 2013:122–128Hill F, Cho K, Korhonen A (2016) Learning distributed representations of sentences from unlabelled data. In:Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1367–1377Hövelmann L, Allee S, Friedrich CM (2017) Fasttext and gradient boosted trees at Germeval-2017 on relevance classification and document-level polarity. In: Shared task on aspect-based sentiment in social media customer feedback, pp 30–35Ionescu RT, Butnaru A (2019) Vector of locally-aggregated word embeddings (VLAWE): a novel document-level representation. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pp 363–369Jain G, Sharma M, Agarwal B (2019) Spam detection in social media using convolutional and long short term memory neural network. Ann Math Artif Intell 85(1):21–44Jauhiainen TS, Lui M, Zampieri M, Baldwin T, Lindén K (2019) Automatic language identification in texts: a survey. J Artif Intell Res 65:675–782Joulin A, Grave E, Bojanowski P, Mikolov T (2017) Bag of tricks for efficient text classification. In: Proceedings of conference of the European chapter of the association for computational linguistics (ACL), vol 2, pp 427–431Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. Preprint arXiv:1412.6980Kiros R, Zhu Y, Salakhutdinov R, Zemel RS, Torralba A, Urtasun R, Fidler S (2015) Skip-thought vectors. Preprint arXiv:1506.06726Koehn P (2004) Statistical significance tests for machine translation evaluation. In: Proceedings of the 2004 conference on empirical methods on natural language processing, pp 388–395Parcheta Z, Sanchis-Trilles G, Casacuberta F, Redahl R (2019) Multi-input CNN for text classification in commercial scenarios. In: Proceedings of the international work-conference on artificial neural networks. Springer, Berlin, pp 596–608Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), pp 1532–1543Sadr H, Pedram MM, Teshnehlab M (2019) A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks. Neural Process Lett 50(3):2745–2761Sayyed ZA, Dakota D, Kübler S (2017) IDS IUCL: investigating feature selection and oversampling for GermEval2017. Shared task on aspect-based sentiment in social media customer feedback, pp 43–48Sennrich R, Haddow B, Birch A (2016) Neural machine translation of rare words with subword units. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies (NAACL HLT), vol 1, pp 1715–1725Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631–1642Stein RA, Jaques PA, Valiati JF (2018) An analysis of hierarchical text classification using word embeddings. Preprint arXiv:1809.01771Strange W, Bohn OS, Nishi K, Trent SA (2005) Contextual variation in the acoustic and perceptual similarity of North German and American English vowels. J Acoust Soc Am 118(3):1751–1762Strange W, Bohn OS, Trent SA, Nishi K (2004) Acoustic and perceptual similarity of North German and American English vowels. J Acoust Soc Am 115(4):1791–1807Tiwary A (2017) Time is money and artificial intelligence can save you time. Digital CMO, LondonVaswani A, Bengio S, Brevdo E, Chollet F, Gomez AN, Gouws S, Jones L, Kaiser L, Kalchbrenner N, Parmar N, Sepassi R, Shazeer N, Uszkoreit J (2018) Tensor2tensor for neural machine translation. Preprint arXiv:1803.07416Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008Wojatzki M, Ruppert E, Holschneider S, Zesch T, Biemann C (2017) Germeval 2017: shared task on aspect-based sentiment in social media customer feedback. 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Data selection for NMT using Infrequent n-gram Recovery

Neural Machine Translation (NMT) has achieved promising results comparable with Phrase-Based Statistical Machine Translation (PBSMT). However, to train a neural translation engine, much more powerful machines are required than those required to develop translation engines based on PBSMT. One solution to reduce the training cost of NMT systems is the reduction of the training corpus through data selection (DS) techniques. There are many DS techniques applied in PBSMT which bring good results. In this work, we show that the data selection technique based on infrequent n-gram occurrence described in (Gascó et al., 2012) commonly used for PBSMT systems also works well for NMT systems. We focus our work on selecting data according to specific corpora using the previously mentioned technique. The specific-domain corpora used for our experiments are IT domain and medical domain. The DS technique significantly reduces the execution time required to train the model between 87% and 93%. Also, it improves translation quality by up to 2.8 BLEU points. The improvements are obtained with just a small fraction of the data that accounts for between 6% and 20% of the total data

Implementing a neural machine translation engine for mobile devices: the Lingvanex use case

In this paper, we present the challenge entailed by implementing a mobile version of a neural machine translation system, where the goal is to maximise translation quality while minimising model size. We explain the whole process of implementing the translation engine on an English–Spanish example and we describe all the difficulties found and the solutions implemented. The main techniques used in this work are data selection by means of Infrequent n-gram Recovery, appending a special word at the end of each sentence, and generating additional samples without the final punctuation marks. The last two techniques were devised with the purpose of achieving a translation model that generates sentences without the final full stop, or other punctuation marks. Also, in this work, the Infrequent n-gram Recovery was used for the first time to create a new corpus, and not enlarge the in-domain dataset. Finally, we get a small size model with quality good enough to serve for daily use.Work partially supported by MINECO under grant DI-15-08169 and by Sciling under its R+D programme

Alaska Earthquake Center Quarterly Technical Report October-December 2024

This series of technical quarterly reports from the Alaska Earthquake Center (AEC) includes detailed summaries and updates on Alaska seismicity, the AEC seismic network and stations, fieldwork, our online presence, public outreach, and lists publications and presentations by AEC staff. Multiple AEC staff members contributed to this report.1. Introduction 2. Seismicity 3. Alaska Geophysical Network 4. Data quality assurance 4.1 Seismic data 4.2 Environmental data 5. Real-time earthquake detection system 6. Computer systems 6.1 Computer resources 6.2 Waveform storage 6.3 Metadata 6.4 Software development 7. Fieldwork 7.1 October 7.2 November 7.3 December 8. Website, social media, and outreach 8.1 Website 8.2 X 8.3 Facebook 8.4 Instagram 8.5 LinkedIn 8.6 K-12 and community outreach 8.7 Workforce development 9. Publications and presentations 9.1 Publications 9.2 Public presentations 9.3 GI Geoscience lunch seminar talks 10. References Appendix A: Data availability for broadband stations from the AK network Appendix B: Gaps for broadband stations from the AK network Appendix C: 2025 strategic prioritie

Efficient Oxidative Resolution of 1-Phenylphosphol-2-Ene and Diels–Alder Synthesis of Enantiopure Bicyclic and Tricyclic P-Stereogenic C-P Heterocycles

1-Phenylphosphol-2-ene 1-oxide is effectively resolved by L-menthyl bromoacetate to afford both SP and RP enantiomers of 1-phenylphosphol-2-ene 1-oxide on a multigram scale. The resolved 1-phenylphosphol-2-ene oxide has been found to undergo face-selective and endo-selective cycloadditions with a series of acyclic and cyclic dienes to produce enantiopure P-stereogenic C-P heterocycles of hexahydrophosphindole and hexahydrobenzophosphindole as well as phospha[5.2.1.02,6]decene and phospha[5.2.2.02,6]undecene structures. Conversions of these cycloadducts to the fully saturated heterocyclic systems as well as to their P (III), P = S, P = Se and P-BH3 derivatives have been demonstrated to occur with retention of configuration and preservation of configurational homogeneity at P. A perplexing case of stereomutation at P during reduction of a tricyclic β-hydroxy phosphine oxide by PhSiH3 at 80 °C has been recorded