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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]

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]

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]

Purpose: Trebananib, an investigational recombinant peptide-Fc fusion protein, neutralizes the receptor-ligand interaction between Tie2 and angiopoietin-1/2. This phase II study was conducted to evaluate trebananib plus sunitinib, a vascular endothelial growth factor receptor inhibitor, in patients with metastatic clear cell renal cell carcinoma., Patients and Methods: Adults with metastatic renal cell carcinoma were enrolled sequentially onto two cohorts that received sunitinib 50 mg once per day for 4 weeks on and 2 weeks off and intravenous trebananib once per week at a dose of 10 mg/kg in cohort A or 15 mg/kg in cohort B. The primary end points were incidences of adverse events (AEs) and dose interruptions of sunitinib during the first 12 weeks of treatment. Secondary end points included objective response rate and progression-free survival., Results: Eighty-five patients were enrolled: 43 in cohort A, and 42 in cohort B. During the first 12 weeks of treatment, 58% and 57% of patients in cohorts A and B, respectively, had sunitinib dose interruptions (dose decrease, withholding, or withdrawal). The most frequent AEs were diarrhea (cohort A, 74%; cohort B, 67%), mucosal inflammation (cohort A, 49%; cohort B, 60%), and hypertension (cohort A, 52%; cohort B, 45%). AEs of grade 3 or greater occurred in 58% of patients in cohort A and in 69% of patients in cohort B. The objective response rate was 58% and 63% in cohorts A and B, respectively. The median progression-free survival time was 13.9 months (95% CI, 10.4 to 19.2) and 16.3 months (95% CI, 13.1 to 21.4) in cohorts A and B, respectively. The median overall survival time was 36 months (95% CI, 25.2 to not estimable) in cohort A and was not estimable (median follow-up, 25 months) in cohort B., Conclusion: Trebananib plus sunitinib seemed to increase toxicity at the tested doses. Efficacy results suggest a potential benefit for the addition of trebananib to sunitinib., (© 2015 by American Society of Clinical Oncology.)