Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition

In handwritten character recognition, benchmark database plays an important role in evaluating the performance of various algorithms and the results obtained by various researchers. In Devnagari script, there is lack of such official benchmark. This paper focuses on the generation of offline benchmark database for Devnagari handwritten numerals and characters. The present work generated 5137 and 20305 isolated samples for numeral and character database, respectively, from 750 writers of all ages, sex, education, and profession. The offline sample images are stored in TIFF image format as it occupies less memory. Also, the data is presented in binary level so that memory requirement is further reduced. It will facilitate research on handwriting recognition of Devnagari script through free access to the researchers.Comment: 5 pages, 8 figures, journal pape

Short-hairpin RNA library: identification of therapeutic partners for gefitinib-resistant non-small cell lung cancer.

Somatic mutations of the epidermal growth factor receptor often cause resistance to therapy with tyrosine kinase inhibitor in non-small cell lung cancer (NSCLC). In this study, we aimed to identify partner drugs and pathways that can induce cell death in combination with gefitinib in NSCLC cells. We undertook a genome-wide RNAi screen to identify synthetic lethality with gefitinib in tyrosine kinase inhibitor resistant cells. The screening data were utilized in different approaches. Firstly, we identified PRKCSH as a candidate gene, silencing of which induces apoptosis of NSCLC cells treated with gefitinib. Next, in an in silico gene signature pathway analysis of shRNA library data, a strong correlation of genes involved in the CD27 signaling cascade was observed. We showed that the combination of dasatinib (NF-κB pathway inhibitor) with gefitinib synergistically inhibited the growth of NSCLC cells. Lastly, utilizing the Connectivity Map, thioridazine was identified as a top pharmaceutical perturbagen. In our experiments, it synergized with gefitinib to reduce p-Akt levels and to induce apoptosis in NSCLC cells. Taken together, a pooled short-hairpin library screen identified several potential pathways and drugs that can be therapeutic targets for gefitinib resistant NSCLC

Dzyaloshinskii-Moriya interaction and chiral magnetism in 3dd-5dd zig-zag chains: Tight-binding model and ab initio calculations

We investigate the chiral magnetic order in free-standing planar 3$d$-5$d$ bi-atomic metallic chains (3$d$: Fe, Co; 5$d$: Ir, Pt, Au) using first-principles calculations based on density functional theory. We found that the antisymmetric exchange interaction, commonly known as Dzyaloshinskii-Moriya interaction (DMI), contributes significantly to the energetics of the magnetic structure. We used the full-potential linearized augmented plane wave method and performed self-consistent calculations of homogeneous spin spirals, calculating the DMI by treating the effect of spin-orbit interaction (SOI) in the basis of the spin-spiral states in first-order perturbation theory. To gain insight into the DMI results of our ab initio calculations, we develop a minimal tight-binding model of three atoms and 4 orbitals that contains all essential features: the spin-canting between the magnetic $3d$ atoms, the spin-orbit interaction at the $5d$ atoms, and the structure inversion asymmetry facilitated by the triangular geometry. We found that spin-canting can lead to spin-orbit active eigenstates that split in energy due to the spin-orbit interaction at the $5d$ atom. We show that, the sign and strength of the hybridization, the bonding or antibonding character between $d$-orbitals of the magnetic and non-magnetic sites, the bandwidth and the energy difference between states occupied and unoccupied states of different spin projection determine the sign and strength of the DMI. The key features observed in the trimer model are also found in the first-principles results.Comment: 19 page

Modes of Cooperative R&D Commercialization by Start-Ups

This study empirically examines the determinants of heterogeneous firm-level cooperative R&D commercialization strategies. While the volume of interfirm collaboration has increased dramatically in recent decades, the determinants of firm-level choices among alternate modes of such cooperative activity remain relatively understudied. We develop a conceptual model of factors determining collaborative mode choice at the organizational portfolio level. These factors include the firm-level appropriation environment, in which deal-level choices have portfolio-level spillover implications, as well as governance capabilities developed by the firm over time. Using a random sample of innovating biotechnology start-ups, we assemble a firm-year panel dataset that aggregates transaction-level collaboration data to the firm-year level, allowing us to characterize firms\u27 portfolios of collaborative deals. We find broad empirical support for our model, suggesting that a firm\u27s appropriation environment and governance capabilities strongly influence portfolio-level collaboration mode choices. In addition, we explore the implications of governance capability development, finding that experience with particular modes, as well as deviations from existing capabilities, impact firm valuation

Going beyond persistent homology using persistent homology

Representational limits of message-passing graph neural networks (MP-GNNs), e.g., in terms of the Weisfeiler-Leman (WL) test for isomorphism, are well understood. Augmenting these graph models with topological features via persistent homology (PH) has gained prominence, but identifying the class of attributed graphs that PH can recognize remains open. We introduce a novel concept of color-separating sets to provide a complete resolution to this important problem. Specifically, we establish the necessary and sufficient conditions for distinguishing graphs based on the persistence of their connected components, obtained from filter functions on vertex and edge colors. Our constructions expose the limits of vertex- and edge-level PH, proving that neither category subsumes the other. Leveraging these theoretical insights, we propose RePHINE for learning topological features on graphs. RePHINE efficiently combines vertex- and edge-level PH, achieving a scheme that is provably more powerful than both. Integrating RePHINE into MP-GNNs boosts their expressive power, resulting in gains over standard PH on several benchmarks for graph classification.Comment: Accepted to NeurIPS 202