Continual Reinforcement Learning in 3D Non-stationary Environments

High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques. Artificial agents, nowadays, are often trained off-line in very static and controlled conditions in simulation such that training observations can be thought as sampled i.i.d. from the entire observations space. However, in real world settings, the environment is often non-stationary and subject to unpredictable, frequent changes. In this paper we propose and openly release CRLMaze, a new benchmark for learning continually through reinforcement in a complex 3D non-stationary task based on ViZDoom and subject to several environmental changes. Then, we introduce an end-to-end model-free continual reinforcement learning strategy showing competitive results with respect to four different baselines and not requiring any access to additional supervised signals, previously encountered environmental conditions or observations.Comment: Accepted in the CLVision Workshop at CVPR2020: 13 pages, 4 figures, 5 table

Learning Visual Representations via Language-Guided Sampling

Although an object may appear in numerous contexts, we often describe it in a limited number of ways. Language allows us to abstract away visual variation to represent and communicate concepts. Building on this intuition, we propose an alternative approach to visual representation learning: using language similarity to sample semantically similar image pairs for contrastive learning. Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity instead of hand-crafted augmentations or learned clusters. Our approach also differs from image-text contrastive learning by relying on pre-trained language models to guide the learning rather than directly minimizing a cross-modal loss. Through a series of experiments, we show that language-guided learning yields better features than image-based and image-text representation learning approaches.Comment: Accepted to CVPR 2023. v2 is camera-ready version with additional ImageNet evaluations. Project page: https://github.com/mbanani/lgss

Selection of amine combination for CO2 capture in a packed bed scrubber

This investigation was to test different blends of tertiary amine; triethanolamine (TEA) into primary amine; Monoethanolamine (MEA) used to capture CO2 in packed bed scrubber with recycle stream. Four different operating parameters: Amine Combination (A), Dilution Water (B), Liquid Flow rate (C), and Gas Flow rate (D) were varied to study the behavior of the system. Moreover, Taguchi method was employed to establish the order of importance of different parameters in the process. A 4 factor and 3 level was chosen for the study and it was explored using L9 (34 ) orthogonal array design. According to 3-level design 0%, 20% and 30% were chosen for A, 10%, 20% and 30% for B, 1 Lmin−1, 1.5 Lmin−1 and 2 Lmin−1 for C, 8 Lmin−1, 16 Lmin−1 and 20 Lmin−1 for D. To understand the effectiveness order of different operating parameters, three factors namely Absorption efficiency (E), Absorption Rate (RA), and Scrubbing Factor (E) were calculated upon which the order was compared. The highest efficiency of 92.2% was achieved with 20% TEA. However, with 30% of TEA and 20% solvent mix maximum scrubbing factor (E) of 0.63 mol-CO2/mol-Solvent was achieved. As per Taguchi analysis the significance sequence for absorption efficiency (ϕ) was B > C > D > A; for absorption rate C > B > D > A and for scrubbing factor it was C > B > D > A. The blending of tertiary amine seemed advantageous for carbon dioxide capture process

Simultaneous resource recovery and ammonia volatilization minimization in animal husbandry and agriculture

The study demonstrates that the minimization of ammonia volatilization and urea recovery could be coupled through the use of physical adsorption processes in continuous packed-bed columns. The potential of using microwave activated coconut shell based activated carbon toward the recovery of urea from cattle urine was investigated. The prepared carbon was immobilized onto etched glass beads to investigate the effect of initial concentration, flow rate and size of carbon support in a continuous, down-flow mode packed column. Further, to describe the sorption behavior, the experimental data were tested against different kinetic models. The analysis of the breakthrough curves allowed identification of the favorable operating parameters as: sorbate flow (8 L·h−1), initial urea concentration (60%) and glass bead support size (ϕ 1.5 cm). An equilibrium sorption of 802.8 mg·g−1 and up to 80% urea recovery was observed. Regeneration studies allowed for nearly 95% urea recovery with sorbent capacity decreasing by 5% over seven cycles of sorption/desorption

Hyperbolic Image-Text Representations

Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept ``dog'' entails all images that contain dogs. Despite being intuitive, current large-scale vision and language models such as CLIP do not explicitly capture such hierarchy. We propose MERU, a contrastive model that yields hyperbolic representations of images and text. Hyperbolic spaces have suitable geometric properties to embed tree-like data, so MERU can better capture the underlying hierarchy in image-text data. Our results show that MERU learns a highly interpretable representation space while being competitive with CLIP's performance on multi-modal tasks like image classification and image-text retrieval.Comment: Technical repor

nocaps: novel object captioning at scale

Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed 'nocaps', for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the OpenImages validation and test sets. The associated training data consists of COCO image-caption pairs, plus OpenImages image-level labels and object bounding boxes. Since OpenImages contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work on this task

Extra-hepatic comorbidity burden significantly increases 90-day mortality in patients with cirrhosis and high model for endstage liver disease

Background We examined how extra-hepatic comorbidity burden impacts mortality in patients with cirrhosis referred for liver transplantation (LT). Methods Adults with cirrhosis evaluated for their first LT in 2012 were followed through their clinical course with last follow up in 2019. Extra-hepatic comorbidity burden was measured using the Charlson Comorbidity Index (CCI). The endpoints were 90-day transplant free survival (Cox-Proportional Hazard regression), and overall mortality (competing risk analysis). Results The study included 340 patients, mean age 56 ± 11, 63% male and MELD-Na 17.2 ± 6.6. The CCI was 0 (no comorbidities) in 44%, 1–2 in 44% and > 2 (highest decile) in 12%, with no differences based on gender but higher CCI in patients with fatty and cryptogenic liver disease. Thirty-three (10%) of 332 patients not receiving LT within 90 days died. Beyond MELD-Na, the CCI was independently associated with 90-day mortality (hazard ratio (HR), 1.32 (95% confidence interval (CI) 1.02–1.72). Ninety-day mortality was specifically increased with higher CCI category and MELD ≥18 (12% (CCI = 0), 22% (CCI = 1–2) and 33% (CCI > 2), (p = 0.002)) but not MELD-Na ≤17. At last follow-up, 69 patients were alive, 100 underwent LT and 171 died without LT. CCI was associated with increased overall mortality in the competing risk analysis (Sub-HR 1.24, 95%CI 1.1–1.4). Conclusions Extra-hepatic comorbidity burden significantly impacts short-term mortality in patients with cirrhosis and high MELD-Na. This has implications in determining urgency of LT and mortality models in cirrhosis and LT waitlisting, especially with an ageing population with increasing prevalence of fatty liver disease