On two sequential problems : the load planning and sequencing problem and the non-normal recurrent neural network

The work in this thesis is separated into two parts. The first part deals with the load planning and sequencing problem for double-stack intermodal railcars, an operational problem found at many rail container terminals. In this problem, containers must be assigned to a platform on which the container will be loaded, and the loading order must be determined. These decisions are made with the objective of minimizing the costs associated with handling the containers, as well as minimizing the cost of containers left behind. The deterministic version of the problem can be cast as a shortest path problem on an ordered graph. This problem is challenging to solve because of the large size of the graph. We propose a two-stage heuristic based on the Iterative Deepening A* algorithm to compute solutions to the load planning and sequencing problem within a five-minute time budget. Next, we also illustrate how a Deep Q-learning algorithm can be used to heuristically solve the same problem.The second part of this thesis considers sequential models in deep learning. A recent strategy to circumvent the exploding and vanishing gradient problem in recurrent neural networks (RNNs) is to enforce recurrent weight matrices to be orthogonal or unitary. While this ensures stable dynamics during training, it comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a parameterization of RNNs, based on the Schur decomposition, that mitigates the exploding and vanishing gradient problem, while allowing for non-orthogonal recurrent weight matrices in the model.Le travail de cette thèse est divisé en deux parties. La première partie traite du problème de planification et de séquencement des chargements de conteneurs sur des wagons, un problème opérationnel rencontré dans de nombreux terminaux ferroviaires intermodaux. Dans ce problème, les conteneurs doivent être affectés à une plate-forme sur laquelle un ou deux conteneurs seront chargés et l'ordre de chargement doit être déterminé. Ces décisions sont prises dans le but de minimiser les coûts associés à la manutention des conteneurs, ainsi que de minimiser le coût des conteneurs non chargés. La version déterministe du problème peut être formulé comme un problème de plus court chemin sur un graphe ordonné. Ce problème est difficile à résoudre en raison de la grande taille du graphe. Nous proposons une heuristique en deux étapes basée sur l'algorithme Iterative Deepening A* pour calculer des solutions au problème de planification et de séquencement de la charge dans un budget de cinq minutes. Ensuite, nous illustrons également comment un algorithme d'apprentissage Deep Q peut être utilisé pour résoudre heuristiquement le même problème. La deuxième partie de cette thèse examine les modèles séquentiels en apprentissage profond. Une stratégie récente pour contourner le problème de gradient qui explose et disparaît dans les réseaux de neurones récurrents (RNN) consiste à imposer des matrices de poids récurrentes orthogonales ou unitaires. Bien que cela assure une dynamique stable pendant l'entraînement, cela se fait au prix d'une expressivité réduite en raison de la variété limitée des transformations orthogonales. Nous proposons une paramétrisation des RNN, basée sur la décomposition de Schur, qui atténue les problèmes de gradient, tout en permettant des matrices de poids récurrentes non orthogonales dans le modèle

Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary. This ensures eigenvalues with unit norm and thus stable dynamics and training. However this comes at the cost of reduced expressivity due to the limited variety of orthogonal transformations. We propose a novel connectivity structure based on the Schur decomposition and a splitting of the Schur form into normal and non-normal parts. This allows to parametrize matrices with unit-norm eigenspectra without orthogonality constraints on eigenbases. The resulting architecture ensures access to a larger space of spectrally constrained matrices, of which orthogonal matrices are a subset. This crucial difference retains the stability advantages and training speed of orthogonal RNNs while enhancing expressivity, especially on tasks that require computations over ongoing input sequences

The Montreal model: an integrative biomedical-psychedelic approach to ketamine for severe treatment-resistant depression

BackgroundSubanesthetic ketamine has accumulated meta-analytic evidence for rapid antidepressant effects in treatment-resistant depression (TRD), resulting in both excitement and debate. Many unanswered questions surround ketamine’s mechanisms of action and its integration into real-world psychiatric care, resulting in diverse utilizations that variously resemble electroconvulsive therapy, conventional antidepressants, or serotonergic psychedelics. There is thus an unmet need for clinical approaches to ketamine that are tailored to its unique therapeutic properties.MethodsThis article presents the Montreal model, a comprehensive biopsychosocial approach to ketamine for severe TRD refined over 6 years in public healthcare settings. To contextualize its development, we review the evidence for ketamine as a biomedical and as a psychedelic treatment of depression, emphasizing each perspectives’ strengths, weaknesses, and distinct methods of utilization. We then describe the key clinical experiences and research findings that shaped the model’s various components, which are presented in detail.ResultsThe Montreal model, as implemented in a recent randomized clinical trial, aims to synergistically pair ketamine infusions with conventional and psychedelic biopsychosocial care. Ketamine is broadly conceptualized as a brief intervention that can produce windows of opportunity for enhanced psychiatric care, as well as powerful occasions for psychological growth. The model combines structured psychiatric care and concomitant psychotherapy with six ketamine infusions, administered with psychedelic-inspired nonpharmacological adjuncts including rolling preparative and integrative psychological support.DiscussionOur integrative model aims to bridge the biomedical-psychedelic divide to offer a feasible, flexible, and standardized approach to ketamine for TRD. Our learnings from developing and implementing this psychedelic-inspired model for severe, real-world patients in two academic hospitals may offer valuable insights for the ongoing roll-out of a range of psychedelic therapies. Further research is needed to assess the Montreal model’s effectiveness and hypothesized psychological mechanisms

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Large-scale sequencing identifies multiple genes and rare variants associated with Crohn’s disease susceptibility

peer reviewe

Info Syst for Emerg Resp

The Federal Emergency Management Agency (FEMA) had recently developed a Remote Sensing Resources webpage to display aerial imagery after disasters. Data on the page was scattered through many sections and prevented users from efficiently retrieving target information. The project goal was to redesign the Remote Sensing Resources page by improving its navigation and content delivery. To accomplish this goal, the team interviewed FEMA employees and partners to identify web page users and establish their needs. Using interview feedback, the team organized page sections, embedded content, and external resources using a content map. The team translated the content map into an HTML visual draft. In addition to the redesign, the team provided FEMA with a list of recommendations to assist in future page maintenance