1,863 research outputs found
Hindi Visual Genome 1.0
No full textData
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Hindi Visual Genome 1.0, a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account. The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
Additionally, a challenge test set of 1400 segments will be released for the WAT2019 multi-modal task. This challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity.
Dataset Formats
--------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hindi Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28932 143178 136722
Dev 998 4922 4695
Test 1595 7852 7535
Challenge Test 1400 8185 8665 (Released separately)
------- --------- ---------------- -------------
Total 32925 164137 157617
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019,
title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}},
author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan},
journal={Computaci{\'o}n y Sistemas},
note={In print. Presented at CICLing 2019, La Rochelle, France},
year={2019},
Factors affecting the infant antibody response to measles immunisation in Entebbe-Uganda.
No full textBACKGROUND: Vaccine failure is an important concern in the tropics with many contributing elements. Among them, it has been suggested that exposure to natural infections might contribute to vaccine failure and recurrent disease outbreaks. We tested this hypothesis by examining the influence of co-infections on maternal and infant measles-specific IgG levels. METHODS: We conducted an observational analysis using samples and data that had been collected during a larger randomised controlled trial, the Entebbe Mother and Baby Study (ISRCTN32849447). For the present study, 711 pregnant women and their offspring were considered. Helminth infections including hookworm, Schistosoma mansoni and Mansonella perstans, along with HIV, malaria, and other potential confounding factors were determined in mothers during pregnancy and in their infants at age one year. Infants received their measles immunisation at age nine months. Levels of total IgG against measles were measured in mothers during pregnancy and at delivery, as well as in cord blood and from infants at age one year. RESULTS: Among the 711 pregnant women studied, 66% had at least one helminth infection at enrolment, 41% had hookworm, 20% M. perstans and 19% S. mansoni. Asymptomatic malaria and HIV prevalence was 8% and 10% respectively. At enrolment, 96% of the women had measles-specific IgG levels considered protective (median 4274 mIU/ml (IQR 1784, 7767)). IgG levels in cord blood were positively correlated to maternal measles-specific IgG levels at delivery (r = 0.81, p < 0.0001). Among the infants at one year of age, median measles-specific IgG levels were markedly lower than in maternal and cord blood (median 370 mIU/ml (IQR 198, 656) p < 0.0001). In addition, only 75% of the infants had measles-specific IgG levels considered to be protective. In a multivariate regression analysis, factors associated with reduced measles-specific antibody levels in infancy were maternal malaria infection, infant malaria parasitaemia, infant HIV and infant wasting. There was no association with maternal helminth infection. CONCLUSION: Malaria and HIV infection in mothers during pregnancy, and in their infants, along with infant malnutrition, may result in reduction of the antibody response to measles immunisation in infancy. This re-emphasises the importance of malaria and HIV control, and support for infant nutrition, as these interventions may have benefits for vaccine efficacy in tropical settings
Hydromagnetic flow of a heat radiating chemically reactive casson nanofluid past a stretching sheet with convective boundary conditions
No full textMalayalam Visual Genome 1.0
No full textData
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Malayalam Visual Genome (MVG for short) 1.0 has similar goals as Hindi Visual Genome (HVG) 1.1: to support the Malayalam language. Malayalam Visual Genome 1.0 is the first multi-modal dataset in Malayalam for machine translation and image captioning.
Malayalam Visual Genome 1.0 serves in "WAT 2021 Multi-Modal Machine Translation Task".
Malayalam Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Malayalam multimodal machine translation task and multimodal research. We follow the same selection of short English segments (captions) and the associated images from Visual Genome as HGV 1.1 has. For MVG, we automatically translated these captions from English to Malayalam and manually corrected them, taking the associated images into account.
The training set contains 29K segments. Further 1K and 1.6K segments are provided in development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
A third test set is called ``challenge test set'' and consists of 1.4K segments. The challenge test set was created for the WAT2019 multi-modal task by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word. For MVG, we simply translated the English side of the test sets to Malayalam, again utilizing machine translation to speed up the process.
Dataset Formats
----------------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Malayalam Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
-------------------
The statistics of the current release are given below.
Parallel Corpus Statistics
---------------------------------
Dataset Segments English Words Malayalam Words
---------- -------------- -------------------- -----------------
Train 28930 143112 107126
Dev 998 4922 3619
Test 1595 7853 5689
Challenge Test 1400 8186 6044
-------------------- ------------ ------------------ ------------------
Total 32923 164073 122478
The word counts are approximate, prior to tokenization.
Citation
-----------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019, title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}}, author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan}, journal={Computaci{\'o}n y Sistemas}, volume={23}, number={4}, pages={1499--1505}, year={2019}
Hindi Visual Genome 1.1
No full textData
----
Hindi Visual Genome 1.1 is an updated version of Hindi Visual Genome 1.0. The update concerns primarily the text part of Hindi Visual Genome, fixing translation issues reported during WAT 2019 multimodal task. In the image part, only one segment and thus one image were removed from the dataset.
Hindi Visual Genome 1.1 serves in "WAT 2020 Multi-Modal Machine Translation Task".
Hindi Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account.
The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
A third test set is called ``challenge test set'' consists of 1.4K segments and it was released for WAT2019 multi-modal task. The challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word.
Dataset Formats
--------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple
tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hindi Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28930 143164 145448
Dev 998 4922 4978
Test 1595 7853 7852
Challenge Test 1400 8186 8639
------- --------- ---------------- -------------
Total 32923 164125 166917
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019,
title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}},
author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan},
journal={Computaci{\'o}n y Sistemas},
volume={23},
number={4},
pages={1499--1505},
year={2019}
Effect of praziquantel treatment of Schistosoma mansoni during pregnancy on immune responses to schistosome antigens among the offspring: results of a randomised, placebo-controlled trial.
No full textBACKGROUND: Offspring of women with schistosomiasis may exhibit immune responsiveness to schistosomes due to in utero sensitisation or trans-placental transfer of antibodies. Praziquantel treatment during pregnancy boosts maternal immune responses to schistosome antigens and reduces worm burden. Effects of praziquantel treatment during pregnancy on responses among offspring are unknown. METHODS: In a trial of anthelminthic treatment during pregnancy in Uganda (ISRCTN32849447; http://www.controlled-trials.com/ISRCTN32849447/elliott), offspring of women with Schistosoma mansoni were examined for cytokine and antibody responses to schistosome worm (SWA) and egg (SEA) antigen, in cord blood and at age one year. Relationships to maternal responses and pre-treatment infection intensities were examined, and responses were compared between the offspring of women who did, or did not receive praziquantel treatment during pregnancy. RESULTS: Of 388 S. mansoni-infected women studied, samples were obtained at age one year from 215 of their infants. Stool examination for S. mansoni eggs was negative for all infants. Cord and infant samples were characterised by very low cytokine production in response to schistosome antigens with the exception of cord IL-10 responses, which were substantial. Cord and infant cytokine responses showed no association with maternal responses. As expected, cord blood levels of immunoglobulin (Ig) G to SWA and SEA were high and correlated with maternal antibodies. However, by age one year IgG levels had waned and were hardly detectable. Praziquantel treatment during pregnancy showed no effect on cytokine responses or antibodies levels to SWA or SEA either in cord blood or at age one year, except for IgG1 to SWA, which was elevated in infants of treated mothers, reflecting maternal levels. There was some evidence that maternal infection intensity was positively associated with cord blood IL-5 and IL-13 responses to SWA, and IL-5 responses to SEA, and that this association was modified by treatment with praziquantel. CONCLUSIONS: Despite strong effects on maternal infection intensity and maternal immune responses, praziquantel treatment of infected women during pregnancy had no effect on anti-schistosome immune responses among offspring by age one year. Whether the treatment will impact upon the offspring's responses on exposure to primary schistosome infection remains to be elucidated. TRIAL REGISTRATION: ISRCTN: ISRCTN32849447
The Natural Way Forward: Molecular Dynamics Simulation Analysis of Phytochemicals from Indian Medicinal Plants as Potential Inhibitors of SARS-CoV-2 Targets
No full textThe natural way forward: Molecular dynamics simulation analysis of phytochemicals from Indian medicinal plants as potential inhibitors of SARS-CoV-2 targetsPratap Kumar Parida 1#, Dipak Paul 1#, Debamitra Chakravorty 2*# 1 Noor Enzymes Private Limited, 37-B, Darga Road, Kolkata - 700 017, India2 Novel Techsciences (OPC) Private Limited, 37-B, Darga Road, 1st Floor, Kolkata - 700 017, India * Corresponding author:Debamitra Chakravorty, PhD (Project Lead - Computational Biology)Novel Techsciences (OPC) Private Limited, 37-B, Darga Road, 1st Floor, Kolkata - 700 017, IndiaE-mail: [email protected]#All the authors have contributed equally to the paper.AbstractThe pandemic COVID-19 has become a global panic and health issue forcing our lives towards a compromised "new normal". Research is still ongoing to develop effective antiviral drugs and vaccines against SARS-CoV-2. Thus, to address the current outbreak, development of natural inhibitors as a prophylactic measure is an attractive strategy due to their natural diversity and safety. Phytochemicals that target viral entry (Spike glycoprotein) and replication (3CLPro) are lucrative in terms of both economy and health for the treatment of the deadly virus. In this context, this work explored natural compounds from Indian medicinal plants as potential inhibitors for containing the spread SARS-CoV-2. The phytochemicals were rationally screened from 55 Indian medicinal plants in our previous work. All atom 100 ns molecular dynamics simulations were performed using high performance computing for 8 top scoring rationally screened phytochemicals from Withania somnifera and Azadirachta indica and two repurposed drugs against the spike glycoprotein and the main protease of SARS-CoV-2. MM/PBSA, Principal component analysis and hydrogen bond occupancy were analysed to characterize protein–ligand interactions and to find the binding free energy. Biological pathway enrichment analysis was also carried out to observe the therapeutic efficacy of these phytochemicals. The results revealed that Withanolide R (-141.96 KJ/Mol) and 2,3-Dihydrowithaferin A (-87.60 KJ/Mol) were with the lowest relative free energy of binding for main protease and the spike proteins respectively. It was also observed that the phytochemicals exhibit a remarkable multipotency with the ability to modulate various human biological pathways especially pathways in cancer. Conclusively we suggest that these compounds need further detailed in vivo experimental evaluation and clinical validation for implementation as potent therapeutic agent for combating SARS-CoV-2.</p
Study in India Website at INFLIBNET using BASISplus/TECHLIBplus and BASISwebserver : a proposal
No full textSuggests that development of an information system on academic programmes offered by various higher education and research institutions in India can help the students to select apt courses, field of study and institution as well as enable universities and institutes to assess the need for the introduction of specific programme in particular localities. Proposes the establishment of a ‘Study in India' website as part of the system. Defines the objectives of the system. Compares the similar existing information sources in print media like World of Learning and Universities Handbook with the proposed database. Points out that they can not serve the purpose due to their delayed publication. The study recommends that websites and computerized databases on facilities for higher studies and research in India should be developed by INFLIBNET with the help of packages like BASISplus/TECHLIBplus and BASISwebserver. Suggests that UGC/INFLIBNET should prescribe standards for development of computerized prospectus by the universities and colleges which will enable pooling of the data
Synthesis and characterization of lead-free sodium doped bismuth titanate
No full textIn this communication, the convenient solid state technique had been adopted for the preparation of sodium doped bismuth titanate (Bi0.5Na0.5TiO3) ceramic sample in a specified temperature. The classified sample has been found to be possessed a rhombohedral structure as obtained from the XRD analysis having a space group of R-3c (#167). Na dopants in the host bismuth titanate (BTO) are about 53.4 nm and 0.503 %, which being confirmed from the crystalline size of the ceramic sample and lattice strain at microscopic level. The transport properties studied through non-destructive impedance spectroscopy analysis thus revealing the non-Debye as well as the prepared sample confirms negative temperature coefficient resistance (NTCR) behaviour. The thermally activated ac conductivity depicts the activation energy which rises from 10.2 meV to 129.1 meV with simultaneous increase of temperature. The oxygen vacancies and defects leads to conduction mechanism in the thermally activated sample which enables the transport behaviour due to immobile charge carriers. The formation of the asymmetric curves while analysing modulus part of the sample confirm non-Debye conductivity behaviour. The formation of semi-circular arcs in Nyquist as well as Cole-Cole plots suggests the semiconducting nature of the synthesized sample. These materials are useful for sensors, actuators and energy storage devices
Measurement of WZ and ZZ production in pp collisions at √s = 8 TeV in final states with b-tagged jets
No full textOpen Access This article is distributed under the terms of the Creative
Commons Attribution License which permits any use, distribution, and
reproduction in any medium, provided the original author(s) and the
source are credited.
Funded by SCOAP3 / License Version CC BY 4.0.Measurements are reported of the WZ and ZZ production cross sections in proton-proton collisions at s √ =8 TeV in final states where one Z boson decays to b-tagged jets. The other gauge boson, either W or Z, is detected through its leptonic decay (either W→eν , μν or Z→e + e − , μ + μ − , or νν ¯ ). The results are based on data corresponding to an integrated luminosity of 18.9 fb −1 collected with the CMS detector at the Large Hadron Collider. The measured cross sections, σ(pp→WZ)=30.7±9.3(stat.)±7.1(syst.)±4.1(th.)±1.0(lum.)pb and σ(pp→ZZ)=6.5±1.7(stat.)±1.0(syst.)±0.9(th.)±0.2(lum.)pb , are consistent with next-to-leading order quantum chromodynamics calculationsBMWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil);
MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CS (Croatia); RPF (Cyprus); MoER, SF0690030s09 and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF(Germany);GSRT(Greece);OTKAand NIH(Hungary);DAEand DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF and WCU (Republic of Korea); LAS (Lithuania);MOE and UM(Malaysia); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland);
NSC (Taipei); ThEPCenter, IPST, STAR and NSTDA(Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA)
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