Gauge Theory, Geometry and the Large N Limit

We study the relationship between M theory on a nearly lightlike circle and U(N) gauge theory in p+1 dimensions. We define large N limits of these theories in which low energy supergravity is valid. The regularity of these limits implies an infinite series of nonrenormalization theorems for the gauge theory effective action, and the leading large N terms sum to a Born-Infeld form. Compatibility of two different large N limits that describe the same decompactified M theory leads to a conjecture for a relation between two limits of string theories.Comment: LaTeX, 17 pages, 1 figure, references added, shorter version to appear in Nucl. Phys.

RIXA - Explaining Artificial Intelligence in Natural Language

Natural language is the instinctive form of communication humans use among each other. Recently large language models have drastically improved and made natural language interfaces viable for all kinds of applications. We argue that the use of natural language is a great tool to make explainable artificial intelligence (XAI) accessible to end users. We present our concept and work in progress implementation of a new kind of XAI dashboard that uses a natural language chat. We specify 5 design goals for the dashboard and show the current state of our implementation. The natural language chat is the main form of interaction for our new dashboard. Through it the user should be able to control all important aspects of our dashboard. We also define success metrics we want to use to evaluate our work. Most importantly we want to conduct user studies because we deem them to be the best method of evaluation for end-user-centered application

Unified Quantification of Quantum Defects in Small-Diameter Single-Walled Carbon Nanotubes by Raman Spectroscopy

The covalent functionalization of single-walled carbon nanotubes (SWCNTs) with luminescent quantum defects enables their application as near-infrared single-photon sources, as optical sensors, and for in-vivo tissue imaging. Tuning the emission wavelength and defect density are crucial for these applications. While the former can be controlled by different synthetic protocols and is easily measured, defect densities are still determined as relative rather than absolute values, limiting the comparability between different nanotube batches and chiralities. Here, we present an absolute and unified quantification metric for the defect density in SWCNT samples based on Raman spectroscopy. It is applicable to a range of small-diameter nanotubes and for arbitrary laser wavelengths. We observe a clear inverse correlation of the D/G$^{+}$ ratio increase with nanotube diameter, indicating that curvature effects contribute significantly to the defect-activation of Raman modes. Correlation of intermediate frequency modes with defect densities further corroborates their activation by defects and provides additional quantitative metrics for the characterization of functionalized SWCNTs

Heterotic Flux Attractors

We find attractor equations describing moduli stabilization for heterotic compactifications with generic SU(3)-structure. Complex structure and K\"ahler moduli are treated on equal footing by using SU(3)xSU(3)-structure at intermediate steps. All independent vacuum data, including VEVs of the stabilized moduli, is encoded in a pair of generating functions that depend on fluxes alone. We work out an explicit example that illustrates our methods.Comment: 37 pages, references and clarifications adde

Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS

Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus

Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively

Conformal Symmetry for General Black Holes

We show that the warp factor of a generic asymptotically flat black hole in five dimensions can be adjusted such that a conformal symmetry emerges. The construction preserves all near horizon properties of the black holes, such as the thermodynamic potentials and the entropy. We interpret the geometry with modified asymptotic behavior as the "bare" black hole, with the ambient flat space removed. Our warp factor subtraction generalizes hidden conformal symmetry and applies whether or not rotation is significant. We also find a relation to standard AdS/CFT correspondence by embedding the black holes in six dimensions. The asymptotic conformal symmetry guarantees a dual CFT description of the general rotating black holes.Comment: 26 page