Vertex Operator Superalgebras and Their Representations

After giving some definitions for vertex operator SUPERalgebras and their modules, we construct an associative algebra corresponding to any vertex operator superalgebra, such that the representations of the vertex operator algebra are in one-to-one correspondence with those of the corresponding associative algebra. A way is presented to decribe the fusion rules for the vertex operator superalgebras via modules of the associative algebra. The above are generalizations of Zhu's constructions for vertex operator algebras. Then we deal in detail with vertex operator superalgebras corresponding to Neveu-Schwarz algebras, super affine Kac-Moody algebras, and free fermions. We use the machinery established above to find the rationality conditions, classify the representations and compute the fusion rules. In the appendix, we present explicit formulas for singular vectors and defining relations for the integrable representations of super affine algebras. These formulas are not only crucial for the theory of the corresponding vertex operator superalgebras and their representations, but also of independent interest.Comment: 50 pages, to appear in Contemporary Mathematic

Quasifinite representations of classical Lie subalgebras of W_{1+infty}

We show that there are precisely two, up to conjugation, anti-involutions sigma_{\pm} of the algebra of differential operators on the circle preserving the principal gradation. We classify the irreducible quasifinite highest weight representations of the central extension \hat{D}^{\pm} of the Lie subalgebra of this algebra fixed by - sigma_{\pm}, and find the unitary ones. We realize them in terms of highest weight representations of the central extension of the Lie algebra of infinite matrices with finitely many non-zero diagonals over the truncated polynomial algebra C[u] / (u^{m+1}) and its classical Lie subalgebras of B, C and D types. Character formulas for positive primitive representations of \hat{D}^{\pm} (including all the unitary ones) are obtained. We also realize a class of primitive representations of \hat{D}^{\pm} in terms of free fields and establish a number of duality results between these primitive representations and finite-dimensional irreducible representations of finite-dimensional Lie groups and supergroups. We show that the vacuum module V_c of \hat{D}^+ carries a vertex algebra structure and establish a relationship between V_c for half-integral central charge c and W-algebras.Comment: Latex, 77 page

Whole-exome sequencing capture kit biases yield false negative mutation calls in TCGA cohorts.

The Cancer Genome Atlas (TCGA) provides a genetic characterization of more than ten thousand tumors, enabling the discovery of novel driver mutations, molecular subtypes, and enticing drug targets across many histologies. Here we investigated why some mutations are common in particular cancer types but absent in others. As an example, we observed that the gene CCDC168 has no mutations in the stomach adenocarcinoma (STAD) cohort despite its common presence in other tumor types. Surprisingly, we found that the lack of called mutations was due to a systematic insufficiency in the number of sequencing reads in the STAD and other cohorts, as opposed to differential driver biology. Using strict filtering criteria, we found similar behavior in four other genes across TCGA cohorts, with each gene exhibiting systematic sequencing depth issues affecting the ability to call mutations. We identified the culprit as the choice of exome capture kit, as kit choice was highly associated with the set of genes that have insufficient reads to call a mutation. Overall, we found that thousands of samples across all cohorts are subject to some capture kit problems. For example, for the 6353 samples using the Broad Institute\u27s Custom capture kit there are undercalling biases for at least 4833 genes. False negative mutation calls at these genes may obscure biological similarities between tumor types and other important cancer driver effects in TCGA datasets

Repeated Labeling Using Multiple Noisy Labelers

This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction. With the outsourcing of small tasks becoming easier, for example via Amazon's Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a set of robust techniques that combine different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire. For certain label-quality/cost regimes, the benefit is substantial.This work was supported by the National Science Foundation under Grant No. IIS-0643846, by an NSERC Postdoctoral Fellowship, and by an NEC Faculty Fellowship

Diversified in-domain synthesis with efficient fine-tuning for few-shot classification

Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to-image generation models. Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components. First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization. Second, we emphasize the importance of effective model fine-tuning in few-shot recognition, proposing to use Low-Rank Adaptation (LoRA) for joint adaptation of the text and image encoders in a Vision Language Model. We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few-shot classification. Code is available at https://github.com/vturrisi/disef.Comment: 14 pages, 6 figures, 8 table

Shanghai rising: health improvements as measured by avoidable mortality since 2000

Over the past two decades, Shanghai, the largest megacity in China, has been coping with unprecedented growth of its economy and population while overcoming previous underinvestment in the health system by the central and local governments. We study the evolution of Shanghai’s healthcare system by analyzing “Avoidable Mortality” (AM) – deaths amenable to public health and healthcare interventions, as previously defined in the literature. Based on analysis of mortality data, by cause of death, from the Shanghai Municipal Center for Disease Control and Prevention, we analyze trends over the period 2000–10 and compare Shanghai’s experience to other mega-city regions – New York, London and Paris. Population health status attributable to public health and healthcare interventions improved dramatically for Shanghai’s population with permanent residency status. The age-adjusted rate of AM, per 1,000 population, dropped from 0.72 to 0.50. The rate of decrease in age-adjusted AM in Shanghai (30%) was comparable to New York City (30%) and Paris (25%), but lower than London (42%). Shanghai’s establishment of the Municipal Center for Disease Control and Prevention and its upgrading of public health and health services are likely to have contributed to the large decrease in the number and rate of avoidable deaths, which suggests that investments in public health infrastructure and increasing access to health services in megacities – both in China and worldwide – can produce significant mortality declines. Future analysis in Shanghai should investigate inequalities in avoidable deaths and the extent to which these gains have benefitted the significant population of urban migrants who do not have permanent residency status

In Vivo Diagnosis of Melanoma and Nonmelanoma Skin Cancer Using Oblique Incidence Diffuse Reflectance Spectrometry

Early detection and treatment of skin cancer can significantly improve patient outcome. However, present standards for diagnosis require biopsy and histopathologic examinations that are relatively invasive, expensive, and difficult for patients with many early-stage lesions. Here, we show an oblique incidence diffuse reflectance spectroscopic (OIDRS) system that can be used for rapid skin cancer detection in vivo. This system was tested under clinical conditions by obtaining spectra from pigmented and nonpigmented skin lesions, including melanomas, differently staged dysplastic nevi, and common nevi that were validated by standard pathohistologic criteria. For diagnosis of pigmented melanoma, the data obtained achieved 90% sensitivity and specificity for a blinded test set. In a second analysis, we showed that this spectroscopy system can also differentiate nonpigmented basal cell or squamous cell carcinomas from noncancerous skin abnormalities, such as actinic keratoses and seborrheic keratoses, achieving 92% sensitivity and specificity. Taken together, our findings establish how OIDRS can be used to more rapidly and easily diagnose skin cancer in an accurate and automated manner in the clinic

Positron scattering from chiral enantiomers

We report on total cross section measurements for positron scattering from the chiral enantiomers (+)-methyl (R)-2-chloropropionate and (–)-methyl (S)-2-chloropropionate. The energy range of the present study was 0.1–50 eV, while the energy resolution of our incident positron beam was ∼0.25 eV (FWHM). As positrons emanating from β decay in radioactive nuclei have a high degree of spin polarization, which persists after moderation, we were particularly interested in probing whether the positron helicity differentiates between the measured total cross sections of the two enantiomers. No major differences were, however, observed. Finally, quantum chemical calculations, using the density functional theory based B3LYP-DGTZVP model within the gaussian 09 package, were performed as a part of this work in order to assist us in interpreting some aspects of our data