Transmission spectroscopy of TRAPPIST-1d with the new Palomar/WIRC+Spec instrument : a Karhunen-Loève transform based approach to extracting spectrophotometry

Le système TRAPPIST-1 offre une opportunité sans précédent de caractériser les premières planètes potentiellement habitables en dehors de notre système solaire. Dans ce mémoire est décrit le développement d’un pipeline de réduction de données personnalisé pour le mode WIRC+Spec de la caméra infrarouge à grand champ récemment mise à niveau à l’observatoire Palomar. Nous introduisons une nouvelle approche d’ajustement de la fonction d’étalement du point basée sur la transformation de Karhunen-Loève pour extraire des courbes de lumière photométrique et spectroscopique de sources de forme irrégulière, que nous appliquons aux observations de l’exoplanète TRAPPIST-1d pour mesurer ses spectres de transmission dans les bandes J (1.1 à 1.4 µm) et Ks (1.95 à 2.35 µm). Un guide détaillé est présenté pour l’implémentation d’un calcul de profils de température incluant l’équilibre radiatif et convectif pour une modélisation atmosphérique efficace et précise. En comparant une multitude de scénarios atmosphériques aux observations de TRAPPIST-1d, nous obtenons des contraintes sur la composition et la structure de son atmosphère, excluant les scénarios sans nuages avec des métallicités inférieures à 300 fois la valeur solaire à 3σ.The TRAPPIST-1 system provides an unprecedented opportunity to characterize the first potentially habitable planets outside our solar system. In this work we describe the development of a custom data reduction pipeline for the WIRC+Spec mode of the recently upgraded Wide Field Infrared Camera instrument on Palomar Observatory. We introduce a novel, Karhunen-Loève transform based approach to extract photometric and spectroscopic light curves from irregularly shaped sources, which we apply to observations of the TRAPPIST-1d exoplanet to measure the J band (1.1 to 1.4 µm) and Ks band (1.95 to 2.35 µm) transmission spectra. We also present a detailed guide into the implementation of a self-consistent, radiative-convective temperature structure calculation for efficient and accurate atmospheric modelling. Comparing a host of atmospheric scenarios to the observations of TRAPPIST-1d to date, we constrain its atmosphere, ruling out cloud-free atmospheres with metallicities lower than 300 times the solar value at 3σ confidence

Non-line-of-sight tracking of people at long range

A remote-sensing system that can determine the position of hidden objects has applications in many critical real-life scenarios, such as search and rescue missions and safe autonomous driving. Previous work has shown the ability to range and image objects hidden from the direct line of sight, employing advanced optical imaging technologies aimed at small objects at short range. In this work we demonstrate a long-range tracking system based on single laser illumination and single-pixel single-photon detection. This enables us to track one or more people hidden from view at a stand-off distance of over 50~m. These results pave the way towards next generation LiDAR systems that will reconstruct not only the direct-view scene but also the main elements hidden behind walls or corners

CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos

Temporal action localization is an important yet challenging problem. Given a long, untrimmed video consisting of multiple action instances and complex background contents, we need not only to recognize their action categories, but also to localize the start time and end time of each instance. Many state-of-the-art systems use segment-level classifiers to select and rank proposal segments of pre-determined boundaries. However, a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. To this end, we design a novel Convolutional-De-Convolutional (CDC) network that places CDC filters on top of 3D ConvNets, which have been shown to be effective for abstracting action semantics but reduce the temporal length of the input data. The proposed CDC filter performs the required temporal upsampling and spatial downsampling operations simultaneously to predict actions at the frame-level granularity. It is unique in jointly modeling action semantics in space-time and fine-grained temporal dynamics. We train the CDC network in an end-to-end manner efficiently. Our model not only achieves superior performance in detecting actions in every frame, but also significantly boosts the precision of localizing temporal boundaries. Finally, the CDC network demonstrates a very high efficiency with the ability to process 500 frames per second on a single GPU server. We will update the camera-ready version and publish the source codes online soon.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

Cycles of length three and four in tournaments

Linial and Morgenstern conjectured that, among all $n$-vertex tournaments with $d\binom{n}{3}$ cycles of length three, the number of cycles of length four is asymptotically minimized by a random blow-up of a transitive tournament with all but one part of equal size and one smaller part. We prove the conjecture for $d\ge 1/36$ by analyzing the possible spectrum of adjacency matrices of tournaments. We also demonstrate that the family of extremal examples is broader than expected and give its full description for $d\ge 1/16$

Perfect Reflection of Chiral Fermions in Gated Graphene Nanoribbons

We describe the results of a theoretical study of transport through gated metallic graphene nanoribbons using a non-equilibrium Green function method. Although analogies with quantum field theory predict perfect transmission of chiral fermions through gated regions in one dimension, we find \emph{perfect reflection} of chiral fermions in armchair ribbons for specific configurations of the gate. This effect should be measurable in narrow graphene constrictions gated by a charged carbon nanotube.Comment: 9 pages, 3 figures. Submitted to Nano Letter

Lost Synergies and M&A Damages: Considering Cineplex v Cineworld

What is the appropriate remedy when an M&A transaction fails to close because of the acquirer’s breach of contract? Even before the controversy surrounding Elon Musk’s proposed acquisition of Twitter in the US, this question arose recently in Canada. In Cineplex v Cineworld, the Ontario Superior Court of Justice awarded $1.24 billion in damages based upon the target’s loss of anticipated synergies. This article highlights the problems with this approach, including conceptual and reliability issues with calculating and apportioning synergies to one entity in a business combination and significant variation in the availability and size of damages depending on transaction structuring and the financial or strategic nature of the buyer or deal. To avoid many of these issues and provide more consistent outcomes, we argue that courts should award specific performance, where feasible, or alternatively loss of consideration to shareholders as the seller’s or target’s damages. This latter measure best approximates the target corporation’s lost bargain and expectations and has the least reliability issues

Stratified Type Theory

To exploit the expressivity of being able to refer to the type of types, such as for large elimination, dependent type systems will either employ a universe hierarchy or else contend with an inconsistent type-in-type rule. However, these are not be the only possible options. Taking inspiration from Stratified System F, we introduce Stratified Type Theory (StraTT), where rather than stratifying universes by levels, we stratify typing judgements and restrict the domain of dependent function types to some fixed level strictly lower than that of the overall type. Even in the presence of type-in-type, this restriction suffices to enforce consistency of the system. We explore the expressivity of several extensions atop this design. First, the subsystem subStraTT employs McBride's crude-but-effective stratification (also known as displacement) as a simple form of level polymorphism where top-level definitions can be displaced uniformly to any higher level as needed, which is valid due to level cumulativity and plays well with stratified judgements. Second, to recover some expressivity lost due to the restriction on dependent function domains, the full StraTT system includes a separate nondependent function type with floating domains, whose level instead matches that of the overall type. Finally, we have implemented a prototype type checker for StraTT extended with datatypes along with a small type checked core library. While it's possible to show that the subsystem is consistent, showing consistency for the full system with floating nondependent functions remains open. Nevertheless, we believe that the full system is also consistent and have mechanized a syntactic proof of subject reduction. Furthermore, we use our implementation to investigate various well-known type-theoretic type-in-type paradoxes. These examples all fail to type check in expected ways as evidence towards consistency.Comment: 14 pages, 3 figures, submitted to CPP 202

Gastro-Intestinal Tract Segmentation Using an Explainable 3D Unet

In treating gastrointestinal cancer using radiotherapy, the role of the radiation oncologist is to administer high doses of radiation, through x-ray beams, toward the tumor while avoiding the stomach and intestines. With the advent of precise radiation treatment technology such as the MR-Linac, oncologists can visualize the daily positions of the tumors and intestines, which may vary day to day. Before delivering radiation, radio oncologists must manually outline the position of the gastrointestinal organs in order to determine position and direction of the x-ray beam. This is a time consuming and labor intensive process that may substantially prolong a patient's treatment. A deep learning (DL) method can automate and expedite the process. However, many deep neural networks approaches currently in use are black-boxes which lack interpretability which render them untrustworthy and impractical in a healthcare setting. To address this, an emergent field of AI known as Explainable AI (XAI) may be incorporated to improve the transparency and viability of a model. This paper proposes a deep learning pipeline that incorporates XAI to address the challenges of organ segmentation.Comment: 5 pages, 8 figures, 13th Joint Symposium on Computational Intelligence (JSCI13

How it Matters Who Makes Corporate Rules

Corporate rules are often analysed without attending to the strengths and limitations of the body making, monitoring, or implementing those rules. However, corporate rule-making and implementation bodies (RMIBs) over which policymakers have the most influence—legislatures and public regulatory agencies, stock exchanges, and private/professional bodies with a degree of self-regulatory autonomy—have an important bearing on the effectiveness of rules. This Article advances a framework to understand how RMIBs influence the effectiveness of corporate rules by critically examining five core features of RMIBs: (a) their incentives for making and implementing the rules; (b) the nature and extent of regulatory competition; (c) available and relative resources; (d) rule-making speed and the certainty of those decisions; and (e) their legitimacy in the eyes of the regulated parties and relevant stakeholders. To illustrate the framework concretely, this Article conducts case studies exploring how it matters who makes the rules on climate-related risks disclosure and in the UK’s recently enacted Financial Services and Markets Act 2023