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Start-ups are increasingly using social media to signal quality and provide information to potential investors. However, the effectiveness of social media on venture capital (VC) financing is likely to be heterogeneous, differing by demographic and network characteristics of the founders. In this paper, we examine whether social media use can improve funding outcomes for firms founded by women and by other people also lacking connections to the investor network, two groups that face greater difficulties in securing VC financing. Using Twitter data and data on VC investment in start-ups from Crunchbase, we explore the interaction effect between Twitter usage and gender and between Twitter usage and the network constraint measure. Overall, we show that social media can mitigate some disparities in financing experienced by these firms through improving information access. We find that this effect is stronger for first-time entrepreneurs than for experienced ones, stronger for attracting new investors than repeat ones, and stronger in more competitive markets. Collectively, these results suggest that social media could primarily help women and less connected individuals obtain financing by alleviating information asymmetry between founders and investors. This paper was accepted by D. J. Wu, information systems. Funding: The authors thank Wharton Mack Institute of Innovation Management for funding support. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4728. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm's ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm's discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental "followon" drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts during both the development and the operational stages of the tool. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development. [ABSTRACT FROM AUTHOR]

INTRODUCTION Federated databases offer information integration on demand in dynamic environments, where data warehousing approaches are not feasible (Sheth & Larson, 1990; Geer, 2003). In modern relational database management systems, […], Modern database management systems are supporting the inclusion and querying of non-relational sources within a data federation environment via wrappers. Wrapper development for Web sources, however, is a convolution of code with extraction and query planning knowledge and becomes a daunting task. We use IBM DB2 federation engine to demonstrate the challenges of incorporating Web sources into a data federation. We, then, present a practical and general strategy for the inclusion and querying of Web sources without requiring any changes in the underlying data federation technology. This strategy separates the code and knowledge in wrapper development by introducing a general-purpose capabilities-aware mini query-planner and a data extraction engine. As a result, Web sources can be included in a data federation system faster, and maintained easier. [Article copies are available for purchase from InfoSci-on-Demand.com] Keywords: Capability Restriction; Federated Database; Wrapper; Query Planning

We report that osteopenia is a prominent and previously unappreciated clinical feature of patients with X-linked hyper-IgM syndrome, an inherited immune deficiency disorder caused by mutations in the gene encoding CD40 ligand (CD40L). We therefore conducted studies to determine the relationship between CD40L and osteoclastogenesis. Recognizing that activated T cells express surface receptor activator of NF-[kappa]B ligand (RANKL) and can induce osteodast differentiation of myeloid cells expressing RANK, we assessed the capacity of wild-type T cells and CD40[L.sup.-/-] T cells to induce osteoclastogenesis in vitro. Relative to wild-type T cells, activated CD40[L.sup.-/-] T cells from both humans and mice promoted robust osteoclast differentiation of myeloid cells. Whereas activated CD40[L.sup.-/-] T cells had normal expression of RANKL, they were deficient in IFN-[gamma] production. In subsequent studies, we cultured activated CD40[L.sup.-/-] T cells in the presence of IFN-[gamma] and we found that the osteoclastic capacity of CD40[L.sup.-/-] T cells could be greatly diminished. These results show that CD40L can influence RANKL signaling through T cell priming, and thus they demonstrate a regulatory role for CD40L in bone mineralization that is absent in patients with X-linked hyper-IgM syndrome. osteoclast | IFN-[gamma] | receptor activator of NF-[kappa]B ligand (RANKL) | bone homeostasis | T cells

This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters (∗ or ∙) that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work (In European Symposium on Research in Computer Security (2018) 263–280 Springer). In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper. [ABSTRACT FROM AUTHOR]