THE INFLUENCE OF NEO-HUMANIST EDUCATION: AN ANALYSIS OF SELECTED ANANDA MARGA SCHOOLS IN PURBA MEDINIPUR, WEST BENGAL, INDIA

Background: Neo-Humanist Education (NHE) emphasizes the holistic development of students by integrating moral values, ethics, and spirituality. However, empirical research on its practical implementation and impact is limited. Objectives: This study aimed to examine NHE principles in Ananda Marga schools, explore their real-world applications, and assess the role of schools in students' holistic personality development. Methods: A case study was conducted at three Ananda Marga schools in Purba Medinipur, West Bengal. Data were collected through observations, interviews with parents, and an analysis of school practices. Moral development, emotional intelligence, and social skills were measured using standardized indices. Findings: The results showed strong reflection on NHE principles in school environments, curricula, and teaching practices. Students demonstrated high levels of moral reasoning, emotional intelligence, and social skill development. Parents reported positive changes in their students' confidence, values, and overall personality. Conclusion: This study provides evidence of the positive impact of NHE on students' holistic development. This highlights the potential for integrating NHE principles into educational settings to foster well-rounded individuals and create more compassionate learning environments.   Article visualizations

Assessment of Urban Expansion and Associated Spatial Transformation of Chandannagar City, West Bengal

The Chandannagar city, as a former French colony and a historic trading centre, has witnessed a steady growth throughout the French colonial era, and the process is still in action even today. Such urban extension has altered the land use/cover (LULC) fabric both in the core and fringe areas by transforming the natural landscape. The prime goals of the study are to assess the magnitude of urban expansion of the city from 1991 to 2016 and its consequent spatial transformation by using geospatial techniques. Three indices, that is, Built-up Index (BUI), Normalised Difference Vegetation Index (NDVI) and Modified Normalised Difference Water Index (MNDWI) are employed to perceive the spatio-temporal dynamics of LULC from the remotely sensed data. Annual Growth Rate (AGR) and Land Use Integrated Index (LDI) are used to evaluate the rate, magnitude, and nature of changes. The results reveal that the rapid increase in built-up area from 7.9 sq. Km. in 1991 to 14.45 sq. Km. in 2016 has transformed nearly 51.52% of the non-forest vegetation covers and 58.18% of the water bodies of the city during the observation period

Local Identification of Subsets of Quantum states: A Stronger Quantum Nonlocality

Nonolocality makes quantum theory nontrivially sacred and useful in the paradigm of information theoretic tasks. Apart from Bell nonlocality, which deals with measurement outcome statistics of spatially separated agents, there is also another kind of quantum nonlocality, that is associated with perfect distinguishability of quantum states by local operations and classical communication (LOCC). We propose a distributed task: perfect identification of subsets of a known set of multipartite orthogonal states by LOCC, namely, local subset identification. Failure in accomplishing this task guarantees a new notion of quantum nonlocality, viz., local subset unidentifiability. Here, we show that both local distinguishability and local markability of quantum states implies local subset identifiability, but the converse is not necessarily true. This makes local subset unidentifiability a stronger quantum nonlocal phenomenon than its predecessors -- local indistinguishability and local unmarkability. Moreover, we also present an even stronger version of local subset unidentifiablity involving more than two spatially separated parties namely, genuine local subset unidentifiability, where a given subset becomes identifiable if and only if all the parties come together in a common lab.Comment: Initial draft, New results added, Comments are welcom

Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts

While automatic summarization techniques have made significant advancements, their primary focus has been on summarizing short news articles or documents that have clear structural patterns like scientific articles or government reports. There has not been much exploration into developing efficient methods for summarizing financial documents, which often contain complex facts and figures. Here, we study the problem of bullet point summarization of long Earning Call Transcripts (ECTs) using the recently released ECTSum dataset. We leverage an unsupervised question-based extractive module followed by a parameter efficient instruction-tuned abstractive module to solve this task. Our proposed model FLAN-FinBPS achieves new state-of-the-art performances outperforming the strongest baseline with 14.88% average ROUGE score gain, and is capable of generating factually consistent bullet point summaries that capture the important facts discussed in the ECTs.Comment: Accepted in SIGIR 202

Radiative production of invisible charginos in photon photon collision

If in a supersymmetric model, the lightest chargino is nearly degenerate with the lightest neutralino, the former can decay into the latter alongwith a soft pion (or a lepton-neutrino pair). Near degeneracy of the chargino and neutralino masses can cause the other decay products (the pion or the lepton) to be almost invisible. Photon-photon colliders offer a possibility of clean detection of such an event through a hard photon tag.Comment: 12 pages, 5 postscript figure

How COVID-19 has Impacted the Anti-Vaccine Discourse: A Large-Scale Twitter Study Spanning Pre-COVID and Post-COVID Era

The debate around vaccines has been going on for decades, but the COVID-19 pandemic showed how crucial it is to understand and mitigate anti-vaccine sentiments. While the pandemic may be over, it is still important to understand how the pandemic affected the anti-vaccine discourse, and whether the arguments against non-COVID vaccines (e.g., Flu, MMR, IPV, HPV vaccines) have also changed due to the pandemic. This study attempts to answer these questions through a large-scale study of anti-vaccine posts on Twitter. Almost all prior works that utilized social media to understand anti-vaccine opinions considered only the three broad stances of Anti-Vax, Pro-Vax, and Neutral. There has not been any effort to identify the specific reasons/concerns behind the anti-vax sentiments (e.g., side-effects, conspiracy theories, political reasons) on social media at scale. In this work, we propose two novel methods for classifying tweets into 11 different anti-vax concerns -- a discriminative approach (entailment-based) and a generative approach (based on instruction tuning of LLMs) -- which outperform several strong baselines. We then apply this classifier on anti-vaccine tweets posted over a 5-year period (Jan 2018 - Jan 2023) to understand how the COVID-19 pandemic has impacted the anti-vaccine concerns among the masses. We find that the pandemic has made the anti-vaccine discourse far more complex than in the pre-COVID times, and increased the variety of concerns being voiced. Alarmingly, we find that concerns about COVID vaccines are now being projected onto the non-COVID vaccines, thus making more people hesitant in taking vaccines in the post-COVID era.Comment: This work has been accepted to appear at the 18th International AAAI Conference on Web and Social Media (ICWSM), 202

Challenges and Prospects of Apple Cultivation in Himachal Pradesh

The present endeavour aims to ascertain the current status and recent challenges of apple cultivation in Kinnaur district, Himachal Pradesh and to explore the prospects by framing suitable strategies through quantitative SWOT (Strengths, Weaknesses, Opportunities, Threats) and QSPM (Quantitative Strategic Planning Matrix) analysis. A total of 32 factors encompassing 20 internal [10 Strengths (S) and 10 Weakness (W)] and 12 external [6 Opportunities (O) and 6 Threats (T)] factors have been identified through empiric investigation and interaction with the stakeholders. Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) matrices have revealed that favourable agro-climatic conditions (S1, 3.60) and prevalence of diseases of the plants (W5, 3.6) are the most prioritised internal strength and weaknesses. At the same time, the establishment of adequate cold storage facilities (O5, 3.6) and recent changes in the prevailing climate (T1, 2.25) comprise the most concerning external opportunities and threats in the area, respectively. The results further reveal that implementing a well-managed gardening system and developing of infrastructural facilities (WT1, 124.7) may become the qualified alternative action plan to cope with the negative determinants. The establishment and expansion of apple orchard-based food processing units and tourism activities (SO2, 95%) may be considered the most suitable positive (SO) strategy to ensure further prosperity of apple production, which has been supported by most of the respondents. Adapting such a strategy will enrich the horticulture economy and promote the sustainable development of apple farming in the district

Effect of substitution at amine functionality of 2,6-Diaminopyridine-coupled rhodamine on metal-ion interaction and self-assembly

2,6-Diaminopyridine-coupled rhodamines 1 and 2 have been synthesized, and the effect of substitution on amine functionality toward metal-ion interactions and self-assembly is thoroughly investigated. Both the compounds effectively recognize different metal ions of biological significance fluorimetrically and colorimetrically with a high degree of selectivity and sensitivities. While compound 1 is sensitive to Fe3+ ions, compound 2 is responsive to both Fe3+ and Al3+ ions in aqueous CH3CN (4/1, v/v; 10 mM tris HCl buffer, pH 6.8). The sensing mechanism involves the metal-ion chelation-induced spirolactam ring opening of the rhodamine scaffold that results in both color and fluorescence changes, while the extent of interactions with the metal ions is truly governed by the chemical structure of the compounds. Both 1 and 2 are proficient in detecting Fe3+ and Al3+ ions in human lung cancer cells (A549). As new findings, unlike 1, compound 2 formed a faint pink gel in the toluene-hexane mixture solvent (1:1, v/v), and the gel state of 2 selectively recognizes Ag+ ions by exhibiting a phase change from gel to purple sol. Experimental findings establish the role of the formamide moiety in forming the self-assembly

Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling

We study the problem of automatically annotating relevant numerals (GAAP metrics) occurring in the financial documents with their corresponding XBRL tags. Different from prior works, we investigate the feasibility of solving this extreme classification problem using a generative paradigm through instruction tuning of Large Language Models (LLMs). To this end, we leverage metric metadata information to frame our target outputs while proposing a parameter efficient solution for the task using LoRA. We perform experiments on two recently released financial numeric labeling datasets. Our proposed model, FLAN-FinXC, achieves new state-of-the-art performances on both the datasets, outperforming several strong baselines. We explain the better scores of our proposed model by demonstrating its capability for zero-shot as well as the least frequently occurring tags. Also, even when we fail to predict the XBRL tags correctly, our generated output has substantial overlap with the ground-truth in majority of the cases.Comment: This work has been accepted to appear at North American Chapter of the Association for Computational Linguistics (NAACL), 202

Minimum Detection Efficiencies for Loophole-free Genuine Nonlocality Tests

The certification of quantum nonlocality, which has immense significance in architecting device-independent technologies, confronts severe experimental challenges. Detection loophole, originating from the unavailability of perfect detectors, is one of the major issues amongst them. In the present study we focus on the minimum detection efficiency (MDE) required to detect various forms of genuine nonlocality, originating from the type of causal constraints imposed on the involved parties. In this context, we demonstrate that the MDE needed to manifest the recently suggested $T_2$-type nonlocality deviates significantly from perfection. Additionally, we have computed the MDE necessary to manifest Svetlichny's nonlocality, with state-independent approach markedly reducing the previously established bound. Finally, considering the inevitable existence of noise we demonstrate the robustness of the imperfect detectors to certify $T_2$-type nonlocality.Comment: Comments are welcome