Disasters and Disclosures

Many securities fraud lawsuits follow corporate disasters of some sort or another, claiming that known risks were concealed prior to the crisis. Yet for a host of doctrinal, pragmatic and political reasons, there is no clear-cut duty to disclose these risks. The SEC has imposed a set of requirements that sometimes forces risk disclosure, but does so neither consistently nor adequately. Courts in 10b-5 fraud-on-the-market cases, in turn, have made duty mainly a matter of active rather than passive concealment and thus, literally, wordplay: there is no fraud-based duty to disclose risks unless and until the issuer has said enough to put the particular kind of risk “in play.” But when that is, and why, flummoxes them. This incoherence could be rationalized by a more thoughtful assessment of how words matter to investors and better appreciation of the variable role that managerial credibility plays in the process of disclosure and interpretation, which is the main focus of this article. Disasters are an ideal, if disturbing, setting for thinking through the micro-structure of corporate discourse—the implicit rules of interpretation for how marketplace actors interpret what issuers say and don’t say, whether in formal SEC disclosures, conference calls, press conferences and even executive tweets. But even if there is more thoughtfulness to the endeavor, it is fair to ask why wordplay should make so much of a difference as to duty in the first place, or whether instead our impoverished conception of duty and its links to scienter, reliance and causation deserve a more thorough makeover. The study of disasters and disclosures also offers a distinctive reference point for thinking about contemporary controversies associated with bringing matters of social responsibility (e.g., law abidingness) and sustainability (environmental compliance, cybersecurity, product safety, etc.) into the realm of securities law

Meeting users where they are: Delivering dynamic content and services through a campus portal

Campus portals are one of the most visible and frequently used online spaces for students, offering one-stop access to key services for learning and academic self-management. This case study reports how instruction librarians at the University of Vermont collaborated with portal developers in the registrar’s office to develop high-impact, point-of-need content for a dedicated “Library” page. This content was then created in LibGuides and published using the Application Programming Interfaces (APIs) for LibGuides boxes. Initial usage data and analytics show that traffic to the libraries’ portal page has been substantially and consistently higher than expected. The next phase for the project will be the creation of customized library content that is responsive to the student’s user profile

Focal adhesion kinase: Insight into molecular roles and functions in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Due to the high incidence of post-operative recurrence after current treatments, the identification of new and more effective drugs is required. In previous years, new targetable genes/pathways involved in HCC pathogenesis have been discovered through the help of high-throughput sequencing technologies. Mutations in TP53 and β-catenin genes are the most frequent aberrations in HCC. However, approaches able to reverse the effect of these mutations might be unpredictable. In fact, if the reactivation of proteins, such as p53 in tumours, holds great promise as anticancer therapy, there are studies arguing that chronic activation of these types of molecules may be deleterious. Thus, recently the efforts on potential targets have focused on actionable mutations, such as those occurring in the gene encoding for focal adhesion kinase (FAK). This tyrosine kinase, localized to cellular focal contacts, is over-expressed in a variety of human tumours, including HCC. Moreover, several lines of evidence demonstrated that FAK depletion or inhibition impair in vitro and in vivo HCC growth and metastasis. Here, we provide an overview of FAK expression and activity in the context of tumour biology, discussing the current evidence of its connection with HCC development and progression

Bayesian Persuasion for Algorithmic Recourse

When subjected to automated decision-making, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a favorable decision. In many practical situations, the underlying assessment rule is deliberately kept secret to avoid gaming and maintain competitive advantage. The resulting opacity forces the decision subjects to rely on incomplete information when making strategic feature modifications. We capture such settings as a game of Bayesian persuasion, in which the decision maker offers a form of recourse to the decision subject by providing them with an action recommendation (or signal) to incentivize them to modify their features in desirable ways. We show that when using persuasion, the decision maker and decision subject are never worse off in expectation, while the decision maker can be significantly better off. While the decision maker's problem of finding the optimal Bayesian incentive-compatible (BIC) signaling policy takes the form of optimization over infinitely-many variables, we show that this optimization can be cast as a linear program over finitely-many regions of the space of possible assessment rules. While this reformulation simplifies the problem dramatically, solving the linear program requires reasoning about exponentially-many variables, even in relatively simple cases. Motivated by this observation, we provide a polynomial-time approximation scheme that recovers a near-optimal signaling policy. Finally, our numerical simulations on semi-synthetic data empirically demonstrate the benefits of using persuasion in the algorithmic recourse setting.Comment: In the thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022

Adversarial Agents For Attacking Inaudible Voice Activated Devices

The paper applies reinforcement learning to novel Internet of Thing configurations. Our analysis of inaudible attacks on voice-activated devices confirms the alarming risk factor of 7.6 out of 10, underlining significant security vulnerabilities scored independently by NIST National Vulnerability Database (NVD). Our baseline network model showcases a scenario in which an attacker uses inaudible voice commands to gain unauthorized access to confidential information on a secured laptop. We simulated many attack scenarios on this baseline network model, revealing the potential for mass exploitation of interconnected devices to discover and own privileged information through physical access without adding new hardware or amplifying device skills. Using Microsoft's CyberBattleSim framework, we evaluated six reinforcement learning algorithms and found that Deep-Q learning with exploitation proved optimal, leading to rapid ownership of all nodes in fewer steps. Our findings underscore the critical need for understanding non-conventional networks and new cybersecurity measures in an ever-expanding digital landscape, particularly those characterized by mobile devices, voice activation, and non-linear microphones susceptible to malicious actors operating stealth attacks in the near-ultrasound or inaudible ranges. By 2024, this new attack surface might encompass more digital voice assistants than people on the planet yet offer fewer remedies than conventional patching or firmware fixes since the inaudible attacks arise inherently from the microphone design and digital signal processing

The Importance of Time in Causal Algorithmic Recourse

The application of Algorithmic Recourse in decision-making is a promising field that offers practical solutions to reverse unfavorable decisions. However, the inability of these methods to consider potential dependencies among variables poses a significant challenge due to the assumption of feature independence. Recent advancements have incorporated knowledge of causal dependencies, thereby enhancing the quality of the recommended recourse actions. Despite these improvements, the inability to incorporate the temporal dimension remains a significant limitation of these approaches. This is particularly problematic as identifying and addressing the root causes of undesired outcomes requires understanding time-dependent relationships between variables. In this work, we motivate the need to integrate the temporal dimension into causal algorithmic recourse methods to enhance recommendations' plausibility and reliability. The experimental evaluation highlights the significance of the role of time in this field.Comment: Accepted for xAI Conference 202

FACETS: Allele-Specific Copy Number and Clonal Heterogeneity Analysis Tool Estimates for High-Throughput DNA Sequencing

Allele-specific copy number analysis (ASCN) from next generation sequenc- ing (NGS) data can greatly extend the utility of NGS beyond the iden- tification of mutations to precisely annotate the genome for the detection of homozygous/heterozygous deletions, copy-neutral loss-of-heterozygosity (LOH), allele-specific gains/amplifications. In addition, as targeted gene panels are increasingly used in clinical sequencing studies for the detection of “actionable” mutations and copy number alterations to guide treatment decisions, accurate, tumor purity-, ploidy-, and clonal heterogeneity-adjusted integer copy number calls are greatly needed to more reliably interpret NGS- based cancer gene copy number data in the context of clinical sequencing. We developed FACETS, an ASCN tool and open-source software with a broad application to whole genome, whole-exome, as well as targeted panel sequencing platforms. It is a fully integrated stand-alone pipeline that in- cludes sequencing BAM file post-processing, joint segmentation of total- and allele-specific read counts, and integer copy number calls corrected for tumor purity, ploidy and clonal heterogeneity, with comprehensive output and inte- grated visualization. We demonstrate the application of FACETS using the Cancer Genome Atlas (TCGA) whole-exome sequencing of lung adenocarci- noma samples. We also demonstrate its application to a clinical sequencing platform based on a targeted gene panel

Second-generation p-values: improved rigor, reproducibility, & transparency in statistical analyses

Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value - a second-generation p-value - that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses, or with alternative hypotheses, or when the data are inconclusive. Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priori specifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses.Comment: 29 pages, 29 page Supplemen