Paradoxical Desires

I present a paradoxical combination of desires. I show why it's paradoxical, and consider ways of responding. The paradox saddles us with an unappealing trilemma: either we reject the possibility of the case by placing surprising restrictions on what we can desire, or we deny plausibly constitutive principles linking desires to the conditions under which they are satisfied, or we revise some bit of classical logic. I argue that denying the possibility of the case is unmotivated on any reasonable way of thinking about mental content, and rejecting those desire-satisfaction principles leads to revenge paradoxes. So the best response is a non-classical one, according to which certain desires are neither determinately satisfied nor determinately not satisfied. Thus, theorizing about paradoxical propositional attitudes helps constrain the space of possibilities for adequate solutions to semantic paradoxes more generally

Two Ways to Want?

I present unexplored and unaccounted for uses of 'wants'. I call them advisory uses, on which information inaccessible to the desirer herself helps determine what she wants. I show that extant theories by Stalnaker, Heim, and Levinson fail to predict these uses. They also fail to predict true indicative conditionals with 'wants' in the consequent. These problems are related: intuitively valid reasoning with modus ponens on the basis of the conditionals in question results in unembedded advisory uses. I consider two fixes, and end up endorsing a relativist semantics, according to which desire attributions express information-neutral propositions. On this view, 'wants' functions as a precisification of 'ought', which exhibits similar unembedded and compositional behavior. I conclude by sketching a pragmatic account of the purpose of desire attributions that explains why it made sense for them to evolve in this way

Non‐Classical Knowledge

The Knower paradox purports to place surprising a priori limitations on what we can know. According to orthodoxy, it shows that we need to abandon one of three plausible and widely-held ideas: that knowledge is factive, that we can know that knowledge is factive, and that we can use logical/mathematical reasoning to extend our knowledge via very weak single-premise closure principles. I argue that classical logic, not any of these epistemic principles, is the culprit. I develop a consistent theory validating all these principles by combining Hartry Field's theory of truth with a modal enrichment developed for a different purpose by Michael Caie. The only casualty is classical logic: the theory avoids paradox by using a weaker-than-classical K3 logic. I then assess the philosophical merits of this approach. I argue that, unlike the traditional semantic paradoxes involving extensional notions like truth, its plausibility depends on the way in which sentences are referred to--whether in natural languages via direct sentential reference, or in mathematical theories via indirect sentential reference by Gödel coding. In particular, I argue that from the perspective of natural language, my non-classical treatment of knowledge as a predicate is plausible, while from the perspective of mathematical theories, its plausibility depends on unresolved questions about the limits of our idealized deductive capacities

Against Conventional Wisdom

Conventional wisdom has it that truth is always evaluated using our actual linguistic conventions, even when considering counterfactual scenarios in which different conventions are adopted. This principle has been invoked in a number of philosophical arguments, including Kripke’s defense of the necessity of identity and Lewy’s objection to modal conventionalism. But it is false. It fails in the presence of what Einheuser (2006) calls c-monsters, or convention-shifting expressions (on analogy with Kaplan’s monsters, or context-shifting expressions). We show that c-monsters naturally arise in contexts, such as metalinguistic negotiations, where speakers entertain alternative conventions. We develop an expressivist theory—inspired by Barker (2002) and MacFarlane (2016) on vague predications and Einheuser (2006) on counterconventionals—to model these shifts in convention. Using this framework, we reassess the philosophical arguments that invoked the conventional wisdom

Paradox in Thought and Natural Language

Around 600BC, Epimenides, a Cretan apparently discontented with thehonesty of his compatriots, lamented that all Cretans are liars.Together with a few innocent assumptions, well-entrenched principlesof logic entail that Epimenides' lamentation cannot be true, and yetcannot be untrue---a flat contradiction. What's gone wrong? In thisdissertation, I argue that the source of the problem has beenmisdiagnosed as one about language (especially formal languages). Theproblem runs deeper, and stems from the structure of thought itself. The dissertation proceeds in two main stages. The first stage(Chapter 2) makes the case that that the intuitions that underlie theparadoxes come from natural languages, not from formal/mathematicalones. The Liar and related paradoxes are generally presented asconstraints on the latter. Their lesson, the story goes, is that noformal theory strong enough to represent the primitive recursivefunctions can include a satisfactory truth predicate. I argue thatit's our natural-language competence with the truth predicate thatunderlies our understanding of what 'satisfactory' means here, whichshifts the focus of the project to natural language semantics. In thisdomain, it's tempting to think (and many have thought) that theproblem with Epimenides' utterance is that it fails to express aproposition, and this failure explains why we have trouble assigningit a truth-value. Or, perhaps it does express a proposition, but notthe one that it seems to express. Or, perhaps it can express aproposition, but which proposition it expresses depends on context. Iargue that all such responses fail, in part because they cannot makesense of related attitude attributions. I can believe or disbelieveEpimenides, which wouldn't be possible if his utterance didn't expressthe proposition it seems to express.In the second stage, I argue that such paradoxes arise, not from thelanguage/thought interface, but rather from thought itself. The firststep in this argument concerns knowledge attributions (Chapter 3),where I develop and defend a novel solution to the Knower paradox.Then I move from attitude attributions to attitudes themselves(Chapter 4). Just as sentential truth and knowledge predicates givesrise to paradoxical sentences, seemingly innocent combinations ofbeliefs and desires give rise to paradoxical propositions---even whenthose beliefs and desires are not expressed in language. Thepossibility of such pathological combinations isn't accounted for byany extant theory of mental content, and, I argue, provides supportfor a non-classical theory. Finally (Chapter 5) I consider anobjection to these putative combinations of desires. I introduce whatI call /advisory/ desire reports, which seem to exhibit the radicallyexternalist behavior that the previous chapter rejects. I conclude byoffering reasons to think that the availability of these readings doesnot undermine the case for non-classical accounts of attitudes

Counterlogicals as Counterconventionals

We develop and defend a new approach to counterlogicals. Non-vacuous counterlogicals, we argue, fall within a broader class of counterfactuals known as counterconventionals. Existing semantics for counterconventionals, 459–482 ) and, 1–27 ) allow counterfactuals to shift the interpretation of predicates and relations. We extend these theories to counterlogicals by allowing counterfactuals to shift the interpretation of logical vocabulary. This yields an elegant semantics for counterlogicals that avoids problems with the usual impossible worlds semantics. We conclude by showing how this approach can be extended to counterpossibles more generally

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.</jats:p