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Methyl 4-(1H-benzimidazol-2-yl)benzoate trihydrate

Parna Gupta, Soumik Mandal
2010 Acta Crystallographica Section E  
E66, o2754 [] Methyl 4-(1H-benzimidazol-2-yl)benzoate trihydrate Parna Gupta and Soumik Mandal S1. Comment Acta Cryst. (2010).  ... 
doi:10.1107/s1600536810039188 pmid:21588958 pmcid:PMC3009264 fatcat:rucexdveebf2tgwouane6crhau

Frustration induced inversion of magnetocaloric effect and enhanced cooling power in substituted Pyrochlore Iridates [article]

Vinod Kumar Dwivedi, Prabhat Mandal, Soumik Mukhopadhyay
2021 arXiv   pre-print
We investigate the effect of partial replacement of extended 5d Ir4+ sites by localized 3d Cr3+ moments on the magnetocaloric properties of Y2Ir2O7 (YIO) pyrochlore iridates. We find that Y2Ir2-xCrxO7 (YICO) undergoes cluster glass transition, possibly due to RKKY like interaction between localized Cr3+ moments occupying random sites in the pyrochlore network, mediated by 5d Ir conduction electrons. The coexistence of ferromagnetic and antiferromagnetic clusters give rise to the conventional
more » ... inverse magnetocaloric effect (MCE). We observe significant enhancement of conventional as well as inverse MCE with substitution. Although the value of conventional MCE and inverse MCE in substituted Iridates are not large, the effect spans over a giant working temperature window, thus leading to orders of magnitude enhancement of cooling power, the value being comparable to standard magnetocalric materials.
arXiv:2110.03570v1 fatcat:gbrtlmwhhnho5fhyk6f45ijjya

Reconciling modern machine learning practice and the bias-variance trade-off [article]

Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal
2019 arXiv   pre-print
Mikhail Belkin, Siyuan Ma and Soumik Mandal were supported by NSF RI-1815697. Daniel Hsu was supported by NSF CCF-1740833 and Sloan Research Fellowship.  ...  [4] Mikhail Belkin, Siyuan Ma, and Soumik Mandal. To understand deep learning we need to understand kernel learning.  ... 
arXiv:1812.11118v2 fatcat:b2o723pgdzejlkctgcminq24oi

Tea Waste Management: A Case Study from West Bengal, India

Anurag Chowdhury, Satyajit Sarkar, Akash Chowdhury, Soumik Bardhan, Palash Mandal, Monoranjan Chowdhury
2016 Indian Journal of Science and Technology  
Objectives: The purpose of the present study is to focus on the types of wastages that generated as a byproduct from tea processing industries. Quality and quantities of tea waste and their proper management or waste disposal method were determined in Terai and Duars region of West Bengal. There are very few companies or societies who buy a very little amount of tea waste that does not have any significant impact on the tea waste management as a whole. Lastly, there is a lack of comprehensive
more » ... d uniform guidelines towards tea waste management in this area. Methods: Random cluster sampling technique in selecting 20 study sites, out of the 30 tea factories that are spread in four major tea producing districts namely foothills of Darjeeling, Jalpaiguri, Alipurduar and a part of Cooch Behar were performed. Primary and secondary data are documented during data collection, using questionnaires, interviews, observation and necessary photographs were taken. Findings: Authors have attempted to bring out this work to develop some management strategy of tea waste. Our survey report indicated that effective management strategies would improvise socio-economic status of tea garden workers as well as owners by utilizing this waste in poultry and fish feed, garden manure and caffeine extraction. Applications/Improvement: Fibers from tea waste can now be converted into different industrially implemented products like low cost absorbent during removal of pollutants from waste water. New technologies are emerged through which factory tea waste might be utilized for the preparation of n-triacontanol, which is commercially valuable bio-nutrient and has important growth promoting activities of leaf primordia.
doi:10.17485/ijst/2016/v9i42/89790 fatcat:4nnyml7efbejxh4vst3avrkjie

To understand deep learning we need to understand kernel learning [article]

Mikhail Belkin, Siyuan Ma, Soumik Mandal
2018 arXiv   pre-print
Generalization performance of classifiers in deep learning has recently become a subject of intense study. Deep models, typically over-parametrized, tend to fit the training data exactly. Despite this "overfitting", they perform well on test data, a phenomenon not yet fully understood. The first point of our paper is that strong performance of overfitted classifiers is not a unique feature of deep learning. Using six real-world and two synthetic datasets, we establish experimentally that kernel
more » ... machines trained to have zero classification or near zero regression error perform very well on test data, even when the labels are corrupted with a high level of noise. We proceed to give a lower bound on the norm of zero loss solutions for smooth kernels, showing that they increase nearly exponentially with data size. We point out that this is difficult to reconcile with the existing generalization bounds. Moreover, none of the bounds produce non-trivial results for interpolating solutions. Second, we show experimentally that (non-smooth) Laplacian kernels easily fit random labels, a finding that parallels results for ReLU neural networks. In contrast, fitting noisy data requires many more epochs for smooth Gaussian kernels. Similar performance of overfitted Laplacian and Gaussian classifiers on test, suggests that generalization is tied to the properties of the kernel function rather than the optimization process. Certain key phenomena of deep learning are manifested similarly in kernel methods in the modern "overfitted" regime. The combination of the experimental and theoretical results presented in this paper indicates a need for new theoretical ideas for understanding properties of classical kernel methods. We argue that progress on understanding deep learning will be difficult until more tractable "shallow" kernel methods are better understood.
arXiv:1802.01396v3 fatcat:r42tn3isxfhprfrn62xxdx7yr4

JU_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment Analysis

Braja Gopal Patra, Soumik Mandal, Dipankar Das, Sivaji Bandyopadhyay
2014 Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)  
The fast upswing of online reviews and their sentiments on the Web became very useful information to the people. Thus, the opinion/sentiment mining has been adopted as a subject of increasingly research interest in the recent years. Being a participant in the Shared Task Challenge, we have developed a Conditional Random Field based system to accomplish the Aspect Based Sentiment Analysis task. The aspect term in a sentence is defined as the target entity. The present system identifies aspect
more » ... m, aspect categories and their sentiments from the Laptop and Restaurants review datasets provided by the organizers.
doi:10.3115/v1/s14-2063 dblp:conf/semeval/PatraMDB14 fatcat:5dqq4qk3rjhmplgy5qtwq6zriu

Cyclometalated iridium(iii) complexes of (aryl)ethenyl functionalized 2,2′-bipyridine: synthesis, photophysical properties and trans–cis isomerization behavior

Soumalya Sinha, Soumik Mandal, Parna Gupta
2015 RSC Advances  
Acknowledgements Soumalya Sinha and Soumik Mandal are thankful to KVPY and CSIR, New Delhi for their Fellowship.  ... 
doi:10.1039/c5ra16214a fatcat:xl3rqbai5jalvhuwbu7z6dgzn4

Bis{μ-(E)-methyl 4-[(2-carbamothioylhydrazinylidene)methyl]benzoate-κ2S:S}bis[iodido(triphenylphosphane-κP)copper(I)]

Soumik Mandal, Vamsidhar Nethi, Parna Gupta
2011 Acta Crystallographica Section E  
E67, m1535 [doi:10.1107 Bis{µ-(E)-methyl 4-[(2-carbamothioylhydrazinylidene)methyl]benzoateκ 2 S:S}bis[iodido(triphenylphosphane-κP)copper(I)] Soumik Mandal, Vamsidhar Nethi and Parna Gupta S1.  ... 
doi:10.1107/s1600536811041845 pmid:22219780 pmcid:PMC3246960 fatcat:75ev5uydmbelhiayyboamxfph4

Reconciling modern machine-learning practice and the classical bias–variance trade-off

Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal
2019 Proceedings of the National Academy of Sciences of the United States of America  
Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central tenets of the field, the bias–variance trade-off, appears to be at odds with the observed behavior of methods used in modern machine-learning practice. The bias–variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple enough to
more » ... oid fitting spurious patterns. However, in modern practice, very rich models such as neural networks are trained to exactly fit (i.e., interpolate) the data. Classically, such models would be considered overfitted, and yet they often obtain high accuracy on test data. This apparent contradiction has raised questions about the mathematical foundations of machine learning and their relevance to practitioners. In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This "double-descent" curve subsumes the textbook U-shaped bias–variance trade-off curve by showing how increasing model capacity beyond the point of interpolation results in improved performance. We provide evidence for the existence and ubiquity of double descent for a wide spectrum of models and datasets, and we posit a mechanism for its emergence. This connection between the performance and the structure of machine-learning models delineates the limits of classical analyses and has implications for both the theory and the practice of machine learning.
doi:10.1073/pnas.1903070116 pmid:31341078 pmcid:PMC6689936 fatcat:6fbzu5o7ynhqjnkryssce3sy4e

Naphthoquinone-mediated Inhibition of Lysine Acetyltransferase KAT3B/p300, Basis for Non-toxic Inhibitor Synthesis

Mohankrishna Dalvoy Vasudevarao, Pushpak Mizar, Sujata Kumari, Somnath Mandal, Soumik Siddhanta, Mahadeva MM Swamy, Stephanie Kaypee, Ravindra C Kodihalli, Amrita Banerjee, Chandrabhas Naryana, Dipak Dasgupta, Tapas K. Kundu
2014 Journal of Biological Chemistry  
1,4-Naphthoquinone analogs, such as plumbagin, are toxic compounds due to their redox cycling and thiolreactive properties. Results: The p300 inhibitor PTK1, a plumbagin derivative with greatly reduced toxicity, was synthesized and characterized. Conclusion: PTK1 is a reversible, non-competitive inhibitor of p300 KAT activity with reduced toxicity. Significance: These studies provide insight into naphthoquinone-mediated KAT inhibition and describe the synthesis of a therapeutically important,
more » ... n-toxic inhibitor. Hydroxynaphthoquinone-based inhibitors of the lysine acetyltransferase KAT3B (p300), such as plumbagin, are relatively toxic. Here, we report that free thiol reactivity and redox cycling properties greatly contribute to the toxicity of plumbagin. A reactive 3rd position in the naphthoquinone derivatives is essential for thiol reactivity and enhances redox cycling. Using this clue, we synthesized PTK1, harboring a methyl substitution at the 3rd position of plumbagin. This molecule loses its thiol reactivity completely and its redox cycling ability to a lesser extent. Mechanistically, non-competitive, reversible binding of the inhibitor to the lysine acetyltransferase (KAT) domain of p300 is largely responsible for the acetyltransferase inhibition. Remarkably, the modified inhibitor PTK1 was a nearly nontoxic inhibitor of p300. The present report elucidates the mechanism of acetyltransferase activity inhibition by 1,4-naphthoquinones, which involves redox cycling and nucleophilic adduct formation, and it suggests possible routes of synthesis of the non-toxic inhibitor.
doi:10.1074/jbc.m113.486522 pmid:24469461 pmcid:PMC3953281 fatcat:vozny7jxo5gu5l3q3p5crcmz5y

Development of a cyclometalated iridium complex with specific intramolecular hydrogen-bonding that acts as a fluorescent marker for the endoplasmic reticulum and causes photoinduced cell death

Soumik Mandal, Dipak K. Poria, Ritabrata Ghosh, Partho Sarothi Ray, Parna Gupta
2014 Dalton Transactions  
Synthesis of ligands 3, 5, 6 and 8 3: 420.4 mg (2.0 mmol) 1,10-phenanthroline-5,6-dione was dissolved in 20 ml glacial acetic acid, added 5.78 g (75.0 mmol) and refluxed. To this refluxing solution 276.3 mg (2.0 mmol) 2,3-dihydroxybenzaldehyde in 15 ml warm glacial acetic acid was added and refluxed for two hours. Then the resulting yellow coloured solution was cooled to 0C and neutralised with aqueous ammonia solution (pH=7). The yellow precipitate was filtered, washed with water, methanol
more » ... dried in vacuum. Yield: 550 mg (82%). 1 H NMR (400 MHz, DMSO-d6): = 6.87-6.92 (m, 2H), 7.60-7.62 (d, 1H, J = 7.64Hz), 7.79-7.82 (q, 2H, J = 4.60 Hz), 8.85-8.87 (d, 2H, J = 8.40 Hz), 9.01-9.02 (d, 2H, J = 3.80 Hz). ESI-MS: m/z 328.3510 [M + ] (calcd M + = 328.0960). Anal. Calcd for C 19 H 12 N 4 O 2 : C, 69.51; H, 3.68; N, 17.06. Found: C, 69.65; H, 3.61; N, 17.10. 5: The procedure was similar to that for 3, except that 4-methoxy benzaldehyde ( 272.3 mg, 2.0 mmol) was used in place of 2,3-dihydroxybenzaldehyde. Yield: 581 mg (89%); 1 H NMR (25°C, 400 MHz, DMSO-d6): = 3.86 (s, 3H), 7.16-7.18 (d, 2H, J = 8.40 Hz), 7.81-7.84 (m, 2H), 8.24-8.26 (d, 2H, J = 8.40 Hz), 8.91-8.94 (dd, 2H, J = 8.40 Hz, 1.56 Hz), 9.02-9.03 (dd, 2H, J = 6.08 Hz, 1.52 Hz). ESI-MS: m/z 327.3351 [M + H] (calcd M + = 326.1168). Anal. 6: The procedure was similar to that for 3, except that 2-methoxy benzaldehyde (272.3 mg, 2.0 mmol) was used in place of 2,3-dihydroxybenzaldehyde. Yield: 542 mg (83%); 1 H NMR (25°C, 400 MHz, DMSO-d6): = 4.01 (s, 3H), 7.14-7.17 (t, 1H, J = 7.24 Hz), 7.25-7.27 (d, 1H, J = 8.40 Hz), 7.48-7.52 (t, 1H, J = 8.02 Hz), 7.80-7.82 (m, 2H), 8.21-8.23 (d, 1H, J = 7.64 Hz), 9.00-9.01 (m, 2H), 9.05-9.07 (d, 2H, J = 7.64 Hz). ESI-MS: m/z 327.3439 [M + H] (calcd M + = 326.1168). Anal. Calcd for C 20 H 14 N 4 O: C, 73.61; H, 4.32; N, 17.17. Found: C, 73.71; H, 4.27; N, 17.12. 8: The procedure was similar to that for 3, except that 2-hydroxy-3-methoxybenzaldehyde (304.3 mg, 2.0 mmol) was used in place of 2,3-dihydroxybenzaldehyde. Yield: 571 mg (84%); 1 H NMR (25°C, 400 MHz, DMSO-d6): = 1.87 (s, 3H), 6.88-6.92 (t, 1H, J = 8.02 Hz), 6.97-6.99 (d, 1H, J = 6.88 Hz), 7.76-7.79 (q, 2H, J = 3.05 Hz), 7.82-7.84 (dd, 1H, J = 7.64 Hz, 1.52 Hz), 8.33 (s, 1H), 8.85-8.87 (dd, 2H, J = 8.40 Hz, 1.52 Hz), 8.95-Electronic Supplementary Material (ESI) for Dalton Transactions. This journal is Synthesis of iridium(III) complexes C1, C3 -C10 and rhenium(I) complex C11 C1: 107.2 mg (0.1 mmol) [Ir(ppy) 2 Cl] 2 and 62.5 mg (0.2 mmol) ligand 1 were refluxed in (1:1:1) methanol : dichloromethane : acetonitrile for 3h. The resulting orange-yellow coloured solution was purified using thin layer chromatography using (1:19) methanol : dichloromethane as eluent. Yield: 113.2 mg (67%). 1 H NMR (25°C, 400 MHz, CDCl 3 ): =6.28-6.30 (d, 2H, J = 5.80 Hz), 6.94-6.97 (t, 2H, J = 6.30 Hz), 6.98-7.02 (t, 4H, J = 6.44 Hz), 7.04-7.07 (t, 2H, J = 6.06 Hz), 7.49 (s, 2H), 7.86-7.89 (t, 2H, J = 5.80 Hz), 7.94-7.96 (d, 2H, J = 5.80 Hz), 8.06-8.07 (m, 2H), 8.12-8.14 (m, 2H), 8.20-8.22 (d, 2H, J = 6.80 Hz), 8.25-8.27 (d, 2H, J = 6.56 Hz), 9.15-9.17 (d, 1H, J = 6.04 Hz), 9.33-9.35 (d, 1H, J = 6.80 Hz), 10.11 (b, 1H), 14.37 (b, 1H). ESI-MS: m/z 812.9058 [M + − Cl] (calcd M + − Cl = 813.1954). Anal. Calcd for C 41 H 28 ClIrN 6 O: C, 58.05; H, 3.33; N, 9.91. Found: C, 58.15; H, 3.27; N, 9.98. IR (cm -1 ): 758, 1477, 1608. C3: The procedure was similar to that for C1, except 3 (65.7 mg, 0.2 mmol) was used in place of 1. Yield: 98.2 mg (57%); 1 H NMR (25°C, 400 MHz, CDCl 3 ): =6.39-6.41 (d, 2H, J = 6.88 Hz), 6.87 (s, 2H), 6.93-6.97 (t, 3H, J = 7.26 Hz), 7.04-7.08 (t, 3H, J = 7.24 Hz), 7.46-7.47 (d, 2H, J = 4.56 Hz), 7.64-7.72 (m, 8H), 7.90-7.92 (d, 3H, J = 7.64 Hz), 8.09-8.11 (d, 2H, J = 4.60 Hz). ESI-MS: m/z 828.9341 [M + − Cl] (calcd M + − Cl = 829.1903). Anal. Calcd for C 41 H 28 ClIrN 6 O 2 : C , 56.97; H, 3.27; N, 9.72. Found: C, 57.02; H, 3.21; N, ): 759, 1384, 1580. C5: The procedure was similar to that for C1, except 1e (65.3 mg, 2.0 mmol) was used in place of 1. Yield: 108.4 mg (63%); 1 H NMR (25°C, 400 MHz, CDCl 3 ): = 3.83 (s, 3H), 6.40-6.42 (d, 2H, J = 6.88Hz), 6.85-6.90 (m, 2H), 6.95-6.99 (m, 4H), 7.07-7.10 (t, 2H, J = 7.62 Hz), 7.37 (s, 2H), 7.68-7.75 (m, 8H), 7.92-7.94 (d, 2H, J = 8.40 Hz), 8.12-8.13 (m, 2H), 8.35-8.38 (d, 2H, J = 8.40 Hz), 14.60 (b, 1H). ESI-MS: m/z 827.0107 [M + − Cl] (calcd M + − Cl = 827.2110). Anal. Calcd for C 42 H 30 C6: The procedure was similar to that for C1, except 1f (65.3 mg, 0.2 mmol) was used in place of 1. Yield: 122.5 mg (71%); 1 H NMR (25°C, 400 MHz, CDCl 3 ): = 4.18 (s, 3H), 6.40-6.42 (d, 2H, J = 6.88 Hz), 6.80-6.84 (t, 2H, J = 6.48 Hz), 6.95-6.98 (t, 2H, J = 7.26 Hz), 7.06-7.10 (m, 4H), 7.33-7.34 ( d, 2H, J = 5.32 Hz), 7.40-7.44 (t, 1H, J = 8.02 Hz), 7.67-7.77 (m, 8H), 7.91-7.93 (d, 2H, J = 8.40 Hz), 8.10-8.11 (d, 2H, J = 4.56 Hz), 8.29-8.31 (d, 1H, J =
doi:10.1039/c4dt00845f pmid:25341053 pmcid:PMC4289920 fatcat:gdy5rurjpnb6dlvmqxhvp5dwwq

Clarifying user's information need in conversational information retrieval

Soumik Mandal
Best wishes, Soumik Mandal Preface Parts of this dissertation are in various stages of publications by Soumik Mandal. iv 1.  ...  Mandal, and I am a PhD candidate at the department of Library and Information Science in SC&I, Rutgers, New Brunswick campus.  ... 
doi:10.7282/t3-dtbx-wa64 fatcat:dzcz6saunndcxa63pqsdp46v4y

APSYM 2020 Cover Page

2020 2020 International Symposium on Antennas & Propagation (APSYM)  
36 Soumik Dey 101, 114 Debashish Chakravarty 75, 129 Sowfia M 118 Debdeep Sarkar 32, 36 Steffy Benny 25 Deven G.  ...  Mukundan 40, 62 Vinesh P.V 67 Kaushik Mandal 7, 89 Vinod Kumar P 105 M Y Mohammad Abdul Shukoor 93, 97 Yahia M. M.  ... 
doi:10.1109/apsym50265.2020.9350736 fatcat:oci336yyprctxn3m5jcf7u2k2y

APSYM 2020 Cover Page

2020 2020 International Symposium on Antennas & Propagation (APSYM)  
Soumik Dey and Sukomal Dey ?? 25. Dual-Band Flat-Top Pattern Synthesis Using Phase Gradient Metasur- face 105 ?? Vinod Kumar P and Basudeb Ghosh ??  ...  Shankhadip Mandal and Basudeb Ghosh ?? 31. Design of Stepped Impedance Stub Loaded Wide-Band 90-Degree Phase Shifter 129 ?? Anindya Ghosh and Debashish Chakravarty ??  ... 
doi:10.1109/apsym50265.2020.9350724 fatcat:7voxglmh4nglln5evf5zy4yrum

ICCE 2020 List Reviewer Page

2020 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)  
Debasis Barman, Netaji Subhash Engineering College Debottam Mukherjee, Indian Institute of Technology (BHU) Varanasi Dikshita Nath, Jorhat Engineering College Dipak Chandra Das, NIT Agratala Dipak Kumar Mandal  ...  International University Siddhartha Bhattacharyya, RCC Institute of Information Technology Somasree Bhadra, University of Calcutta Somnath Das, Swami Vivekananda Institute of Science & Technology Soumik  ... 
doi:10.1109/icce50343.2020.9290593 fatcat:t7tvnod5avgrzgsa3nwt4wxnxe
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