搜索结果：Neoplasia (New York, N.Y.)

Computer-aided detection (CADe) of early neoplasia in Barrett's esophagus is a low-prevalence surveillance problem in which clinically relevant findings are rare. Although many CADe systems report strong performance on balanced or enriched datasets, their behavior under realistic prevalence remains insufficiently characterized. The RARE25 challenge addresses this gap by introducing a large-scale, prevalence-aware benchmark for neoplasia detection. It includes a public training set and a hidden test set reflecting real-world incidence. Methods were evaluated using operating-point-specific metrics emphasizing high sensitivity and accounting for prevalence. Eleven teams from seven countries submitted approaches using diverse architectures, pretraining, ensembling, and calibration strategies. While several methods achieved strong discriminative performance, positive predictive values remained low, highlighting the difficulty of low-prevalence detection and the risk of overestimating clinical utility when prevalence is ignored. All methods relied on fully supervised classification despite the dominance of normal findings, indicating a lack of prevalence-agnostic approaches such as anoma

Cervical intraepithelial neoplasia (CIN) is the development of abnormal cells on the surface of the cervix, caused by a human papillomavirus (HPV) infection. Although in most of the cases it is resolved by the immune system, a small percentage of people might develop a more serious CIN which, if left untreated, can develop into cervical cancer. Cervical cancer is the fourth most common cancer in women globally, for which the World Health Organization (WHO) recently adopted the Global Strategy for cervical cancer elimination by 2030. With this research topic being more imperative than ever, in this paper, we develop a nonlinear mathematical model describing the CIN progression. The model consists of partial differential equations describing the dynamics of epithelial, dysplastic and immune cells, as well as the dynamics of viral particles. We use our model to explore numerically three important factors of dysplasia progression, namely the geometry of the cervix, the strength of the immune response and the frequency of viral exposure.

We examine the impact of New York City's congestion pricing program through automated analysis of traffic camera data. Our computer vision pipeline processes footage from over 900 cameras distributed throughout Manhattan and New York, comparing traffic patterns from November 2024 through the program's implementation in January 2025 until January 2026. We establish baseline traffic patterns and identify systematic changes in vehicle density across the monitored region.

Cytasters have been underestimated in terms of their potential relevance to embryonic development and evolution. From the perspective discussed herein, structures such as the multiciliated cells of comb rows and balancers supporting mineralized statoliths and macrocilia in Beroe ovata point to a past event of multiflagellate fusion in the origin of metazoans. These structures, which are unique in evolutionary history, indicate that early animals handled basal bodies and their duplication in a manner consistent with a "developmental program" originated in the Ctenophora. Furthermore, the fact that centrosome amplification leads to spontaneous tumorigenesis suggests that the centrosome regulation process was co-opted into a neoplastic functional module. Multicilia, cilia, and flagella are deeply rooted in the evolution of animals and Neoplasia. The fusion of several flagellated microgametes into a cell with a subsequent phase of zygotic (haplontic) meiosis might have been at the origin of both animal evolution and the neoplastic process. In the Ediacaran ocean, we also encounter evolutionary links between the Warburg effect and Neoplasia.

The notion of time in general relativity must arise from an internal clock, i.e., a degree of freedom in the gravitational theory internal to the system that can serve the role of a physical clock. One such internal notion of time is the York time, corresponding to constant extrinsic curvature slicing of spacetime. We study the Hartle-Hawking wavefunction of asymptotically $AdS_2$ JT gravity as a function of York time. Using both canonical quantization and the JT gravity path integral, we explicitly calculate this wavefunction and show that it satisfies a Schrodinger equation with respect to York time. We find the corresponding York Hamiltonian, which turns out to be manifestly Hermitian. Our analysis cleanly avoids operator ordering ambiguities. The dependence of the wavefunction on York time should be thought of as emerging from a unitary transformation of the gravitational length basis states, and not from a physical time evolution of the state in the dual boundary theory.

The New York Times (NYT) games have found widespread popularity in recent years and reportedly account for an increasing fraction of the newspaper's readership. In this paper, we bring the computational lens to the study of New York Times games and consider four of them not previously studied: Letter Boxed, Pips, Strands and Tiles. We show that these games can be just as hard as they are fun. In particular, we characterize the hardness of several variants of computational problems related to these popular puzzle games. For Letter Boxed, we show that deciding whether an instance is solvable is in general NP-Complete, while in some parameter settings it can be done in polynomial time. Similarly, for Pips we prove that deciding whether a puzzle has a solution is NP-Complete even in some restricted classes of instances. We then show that one natural computational problem arising from Strands is NP-Complete in most parameter settings. Finally, we demonstrate that deciding whether a Tiles puzzle is solvable with a single, uninterrupted combo requires polynomial time.

Copyright infringement in frontier LLMs has received much attention recently due to the New York Times v. OpenAI lawsuit, filed in December 2023. The New York Times claims that GPT-4 has infringed its copyrights by reproducing articles for use in LLM training and by memorizing the inputs, thereby publicly displaying them in LLM outputs. Our work aims to measure the propensity of OpenAI's LLMs to exhibit verbatim memorization in its outputs relative to other LLMs, specifically focusing on news articles. We discover that both GPT and Claude models use refusal training and output filters to prevent verbatim output of the memorized articles. We apply a basic prompt template to bypass the refusal training and show that OpenAI models are currently less prone to memorization elicitation than models from Meta, Mistral, and Anthropic. We find that as models increase in size, especially beyond 100 billion parameters, they demonstrate significantly greater capacity for memorization. Our findings have practical implications for training: more attention must be placed on preventing verbatim memorization in very large models. Our findings also have legal significance: in assessing the relative m

In this work, we have concentrated our efforts on the interpretability of classification results coming from a fully convolutional neural network. Motivated by the classification of oesophageal tissue for real-time detection of early squamous neoplasia, the most frequent kind of oesophageal cancer in Asia, we present a new dataset and a novel deep learning method that by means of deep supervision and a newly introduced concept, the embedded Class Activation Map (eCAM), focuses on the interpretability of results as a design constraint of a convolutional network. We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis. In comparison to a baseline method which does not feature deep supervision but provides attention by grafting Class Activation Maps, we improve the F1-score from 87.3% to 92.7% and provide more detailed attention maps.

We estimate the number of street vendors in New York City. First, we summarize the process by which vendors receive licenses and permits to operate legally in New York City. We then describe a survey that was administered by the Street Vendor Project while distributing coronavirus relief aid to vendors operating in New York City both with and without a license or permit. Finally, we review ratio estimation and develop a theoretical justification based on the theory of point processes. We find approximately 23,000 street vendors operate in New York City: 20,500 mobile food vendors and 2,400 general merchandise vendors. One third are located in just six ZIP Codes: 11368 (16%), 11372 (3%), and 11354 (3%) in North and West Queens and 10036 (5%), 10019 (4%), and 10001 (3%) in the Chelsea and Clinton neighborhoods of Manhattan. Our estimates suggest the American Community Survey misses the majority of New York City street vendors.

Gradually typed programming languages, which allow for soundly mixing static and dynamically typed programming styles, present a strong challenge for metatheorists. Even the simplest sound gradually typed languages feature at least recursion and errors, with realistic languages featuring furthermore runtime allocation of memory locations and dynamic type tags. Further, the desired metatheoretic properties of gradually typed languages have become increasingly sophisticated: validity of type-based equational reasoning as well as the relational property known as graduality. Many recent works have tackled verifying these properties, but the resulting mathematical developments are highly repetitive and tedious, with few reusable theorems persisting across different developments. In this work, we present a new denotational semantics for gradual typing developed using guarded domain theory. Guarded domain theory combines the generality of step-indexed logical relations for modeling advanced programming features with the modularity and reusability of denotational semantics. We demonstrate the feasibility of this approach with a model of a simple gradually typed lambda calculus and prove th

Scientific machine learning (SciML) methods such as physics-informed neural networks (PINNs) are used to estimate parameters of interest from governing equations and small quantities of data. However, there has been little work in assessing how well PINNs perform for inverse problems across wide ranges of governing equations across the mathematical sciences. We present a new and challenging benchmark problem for inverse PINNs based on a parametric sweep of the 2D Burgers' equation with rotational flow. We show that a novel strategy that alternates between first- and second-order optimization proves superior to typical first-order strategies for estimating parameters. In addition, we propose a novel data-driven method to characterize PINN effectiveness in the inverse setting. PINNs' physics-informed regularization enables them to leverage small quantities of data more efficiently than the data-driven baseline. However, both PINNs and the baseline can fail to recover parameters for highly inviscid flows, motivating the need for further development of PINN methods.

The advent of the sixth-generation (6G) networks presents another round of revolution for the mobile communication landscape, promising an immersive experience, robust reliability, minimal latency, extreme connectivity, ubiquitous coverage, and capabilities beyond communication, including intelligence and sensing. To achieve these ambitious goals, it is apparent that 6G networks need to incorporate the state-of-the-art technologies. One of the technologies that has garnered rising interest is fluid antenna system (FAS) which represents any software-controllable fluidic, conductive, or dielectric structure capable of dynamically changing its shape and position to reconfigure essential radio-frequency (RF) characteristics. Compared to traditional antenna systems (TASs) with fixed-position radiating elements, the core idea of FAS revolves around the unique flexibility of reconfiguring the radiating elements within a given space. One recent driver of FAS is the recognition of its position-flexibility as a new degree of freedom (dof) to harness diversity and multiplexing gains. In this paper, we provide a comprehensive tutorial, covering channel modeling, signal processing and estimatio

To reduce waste and improve public health and sanitation in New York City, innovative policies tailored to the city's unique urban landscape are necessary. The first program we propose is the Dumpster and Compost Accessibility Program. This program is affordable and utilizes dumpsters placed near fire hydrants to keep waste off the street without eliminating parking spaces. It also includes legal changes and the provision of compost bins to single/two-family households, which together will increase composting rates. The second program is the Pay-As-You-Throw Program. This requires New Yorkers living in single/two-family households to purchase stickers for each refuse bag they have collected by the city, incentivizing them to sort out compostable waste and recyclables. We conduct a weighted multi-objective optimization to determine the optimal sticker price based on the City's priorities. Roughly in proportion to the price, this program will increase diversion rates and decrease the net costs to New York City's Department of Sanitation. In conjunction, these two programs will improve NYC's diversion rates, eliminate garbage bags from the streets, and potentially save New York City m

Multiple-input multiple-output (MIMO) system has been the defining mobile communications technology in recent generations. With the ever-increasing demands looming towards the sixth generation (6G), we are in need of additional degrees of freedom that deliver further gains beyond MIMO. To this goal, fluid antenna system (FAS) has emerged as a new way to obtain spatial diversity using reconfigurable position-switchable antennas. Considering the case with more than one ports activated on a 2D fluid antenna surface at both ends, we take the information-theoretic approach to study the achievable performance limits of the MIMO-FAS. First of all, we propose a suboptimal scheme, referred to as QR MIMO-FAS, to maximize the rate at high signal-to-noise ratio (SNR) via joint port selection, transmit and receive beamforming and power allocation. We then derive the optimal diversity and multiplexing tradeoff (DMT) of MIMO-FAS. From the DMT, we highlight that MIMO-FAS outperforms traditional MIMO antenna systems. Further, we introduce a new metric, namely q-outage capacity, which can jointly consider rate and outage probability. Through this metric, our results indicate that MIMO-FAS surpasses

We introduce an analytic pipeline to model and simulate youth trajectories through the New York state foster care system. Our goal in doing so is to forecast how proposed interventions may impact the foster care system's ability to achieve it's stated goals \emph{before these interventions are actually implemented and impact the lives of thousands of youth}. Here, we focus on two specific stated goals of the system: racial equity, and, as codified most recently by the 2018 Family First Prevention Services Act (FFPSA), a focus on keeping all youth out of foster care. We also focus on one specific potential intervention -- a predictive model, proposed in prior work and implemented elsewhere in the U.S., which aims to determine whether or not a youth is in need of care. We use our method to explore how the implementation of this predictive model in New York would impact racial equity and the number of youth in care. While our findings, as in any simulation model, ultimately rely on modeling assumptions, we find evidence that the model would not necessarily achieve either goal. Primarily, then, we aim to further promote the use of data-driven simulation to help understand the ramificat

To enable innovative applications and services, both industry and academia are exploring new technologies for sixth generation (6G) communications. One of the promising candidates is fluid antenna system (FAS). Unlike existing systems, FAS is a novel communication technology where its antenna can freely change its position and shape within a given space. Compared to the traditional systems, this unique capability has the potential of providing higher diversity and interference-free communications. Nevertheless, the performance limits of FAS remain unclear as its system properties are difficult to analyze. To address this, we approximate the outage probability and diversity gain of FAS in closed-form expressions. We then propose a suboptimal FAS with $N^{*}$ ports, where a significant gain can be obtained over FAS with $N^{*}-1$ ports whilst FAS with $N^{*}+1$ ports only yields marginal improvement over the proposed suboptimal FAS. In this paper, we also provide analytical and simulation results to unfold the key factors that affect the performance of FAS. Limited to systems with one active radio frequency (RF)-chain, we show that the proposed suboptimal FAS outperforms single-anten

Learning new tasks and skills in succession without losing prior learning (i.e., catastrophic forgetting) is a computational challenge for both artificial and biological neural networks, yet artificial systems struggle to achieve parity with their biological analogues. Mammalian brains employ numerous neural operations in support of continual learning during sleep. These are ripe for artificial adaptation. Here, we investigate how modeling three distinct components of mammalian sleep together affects continual learning in artificial neural networks: (1) a veridical memory replay process observed during non-rapid eye movement (NREM) sleep; (2) a generative memory replay process linked to REM sleep; and (3) a synaptic downscaling process which has been proposed to tune signal-to-noise ratios and support neural upkeep. We find benefits from the inclusion of all three sleep components when evaluating performance on a continual learning CIFAR-100 image classification benchmark. Maximum accuracy improved during training and catastrophic forgetting was reduced during later tasks. While some catastrophic forgetting persisted over the course of network training, higher levels of synaptic do

In the digital era, Extended Reality (XR) is considered the next frontier. However, XR systems are computationally intensive, and they must be implemented within strict latency constraints. Thus, XR devices with finite computing resources are limited in terms of quality of experience (QoE) they can offer, particularly in cases of big 3D data. This problem can be effectively addressed by offloading the highly intensive rendering tasks to a remote server. Therefore, we proposed a remote rendering enabled XR system that presents the 3D city model of New York City on the Microsoft HoloLens. Experimental results indicate that remote rendering outperforms local rendering for the New York City model with significant improvement in average QoE by at least 21%. Additionally, we clarified the network traffic pattern in the proposed XR system developed under the OpenXR standard.

Pointlike objects cause many of the divergences that afflict physical theories. For instance, the gravitational binding energy of a point particle in Newtonian mechanics is infinite. In general relativity, the analog of a point particle is a black hole and the notion of binding energy must be replaced by quasilocal energy. The quasilocal energy (QLE) derived by York, and elaborated by Brown and York, is finite outside the horizon but it was not considered how to evaluate it inside the horizon. We present a prescription for finding the QLE inside a horizon, and show that it is finite at the singularity for a variety of types of black hole. The energy is typically concentrated just inside the horizon, not at the central singularity.