Early Classifying Multimodal Sequences

Often pieces of information are received sequentially over time. When did one collect enough such pieces to classify? Trading wait time for decision certainty leads to early classification problems that have recently gained attention as a means of adapting classification to more dynamic environments. However, so far results have been limited to unimodal sequences. In this pilot study, we expand into early classifying multimodal sequences by combining existing methods. We show our new method yields experimental AUC advantages of up to 8.7%.Comment: 7 pages, 5 figure

Programming language features, usage patterns, and the efficiency of generated adjoint code

International audienceThe computation of gradients via the reverse mode of algorithmic differentiation is a valuable technique in modeling many science and engineering applications. This technique is particularly efficient when implemented as a source transformation, as it may use static data-flow analysis. However, some features of the major programming languages are detrimental to the efficiency of the transformed source code. This paper provides an overview of the most common problem scenarios and estimates the cost overhead incurred by using the respective language feature or employing certain common patterns. An understanding of these topics is crucial for the efficiency or even feasibility of adjoint computations, particularly for large-scale numerical simulations e.g. in geosciences. While one cannot hope to cover all effects observable with a given programming language in a given run time environment, the paper aims at providing a reasonable guide for the users of C/C++ and Fortran source transformation tools for algorithmic differentiation

Timescales and regions of the sensitivity of Atlantic meridional volume and heat transport: Toward observing system design

A dual (adjoint) model is used to explore elements of the oceanic state influencing the meridional volume and heat transports (MVT and MHT) in the sub-tropical North Atlantic so as to understand their variability and to provide the elements of useful observational program design. Focus is on the effect of temperature (and salinity) perturbations. On short timescales (months), as expected, the greatest sensitivities are to local disturbances, but as the timescales extend back to a decade and longer, the region of influence expands to occupy much of the Atlantic basin and significant areas of the global ocean, although the influence of any specific point or small area tends to be quite weak. The propagation of information in the dual solution is a clear manifestation of oceanic teleconnections. It takes place through identifiable “dual” Kelvin, Rossby, and continental shelf-waves with an interpretable physics, in particular in terms of dual expressions of barotropic and baroclinic adjustment processes. Among the notable features are the relatively fast timescales of influence (albeit weak in amplitude) between 26°N and the tropical Pacific and Indian Ocean, the absence of dominance of the sub-polar North Atlantic, significant connections to the Agulhas leakage region in the southeast Atlantic on timescales of 5–10 years, and the marked sensitivity propagation of Doppler-shifted Rossby waves in the Southern Ocean on timescales of a decade and beyond. Regional, as well as time-dependent, differences between MVT and MHT sensitivities highlight the lack of a simple correspondence between their variability. Some implications for observing systems for the purpose of climate science are discussed.National Oceanographic Partnership Program (U.S.) (‘‘Estimating the Circulation and Climate of the Ocean’’ (ECCO) and the ‘‘Atlantic MOC Observing System Studies Using Adjoint Models’’ projects)National Science Foundation (U.S.) (NSF Collaboration in Mathematics and Geoscience (CMG) project ‘‘Uncertainty Quanti- fication in Geophysical State Estimation’’

An optimized treatment for algorithmic differentiation of an important glaciological fixed-point problem

International audienceWe apply an optimized method to the adjoint generation of a time-evolving land ice model through algorithmic differentiation (AD). The optimization involves a special treatment of the fixed-point iteration required to solve the nonlinear stress balance, which differs from a straightforward application of AD software, and leads to smaller memory requirements and in some cases shorter computation times of the adjoint. The optimization is done via implementation of the algorithm of 5 Christianson [1994] for reverse accumulation of fixed-point problems, with the AD tool OpenAD. For test problems, the optimized adjoint is shown to have far lower memory requirements, potentially enabling larger problem sizes on memory-limited machines. In the case of the land ice model, implementation of the algorithm allows further optimization by having the adjoint model solve a sequence of linear systems with identical (as opposed to varying) matrices, greatly improving per-10 formance. The methods introduced here will be of value to other efforts applying AD tools to ice models, particularly ones which solve a " hybrid " shallow ice / shallow shelf approximation to the Stokes equations

DIALGEN: Collaborative Human-LM Generated Dialogues for Improved Understanding of Human-Human Conversations

Applications that could benefit from automatic understanding of human-human conversations often come with challenges associated with private information in real-world data such as call center or clinical conversations. Working with protected data also increases costs of annotation, which limits technology development. To address these challenges, we propose DIALGEN, a human-in-the-loop semi-automated dialogue generation framework. DIALGEN uses a language model (ChatGPT) that can follow schema and style specifications to produce fluent conversational text, generating a complex conversation through iteratively generating subdialogues and using human feedback to correct inconsistencies or redirect the flow. In experiments on structured summarization of agent-client information gathering calls, framed as dialogue state tracking, we show that DIALGEN data enables significant improvement in model performance

Atlantic Salmon Reovirus Infection Causes a CD8 T Cell Myocarditis in Atlantic Salmon (Salmo salar L.)

Heart and skeletal inflammation (HSMI) of farmed Atlantic salmon (Salmo salar L.) is a disease characterized by a chronic myocarditis involving the epicardium and the compact and spongious part of the heart ventricle. Chronic myositis of the red skeletal muscle is also a typical finding of HSMI. Piscine reovirus (PRV) has been detected by real-time PCR from farmed and wild salmon with and without typical changes of HSMI and thus the causal relationship between presence of virus and the disease has not been fully determined [1]. In this study we show that the Atlantic salmon reovirus (ASRV), identical to PRV, can be passaged in GF-1 cells and experimental challenge of naïve Atlantic salmon with cell culture passaged reovirus results in cardiac and skeletal muscle pathology typical of HSMI with onset of pathology from 6 weeks, peaking by 9 weeks post challenge. ASRV replicates in heart tissue and the peak level of virus replication coincides with peak of heart lesions. We further demonstrate mRNA transcript assessment and in situ characterization that challenged fish develop a CD8+ T cell myocarditis