48 research outputs found
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds.
We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced fantasy text-game—with a dataset of quests. These contain natural language motivations paired with in-game goals and human demonstrations; completing a quest might require dialogue or actions (or both). We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two. We conduct zero-shot evaluations using held-out human expert demonstrations, showing that our agents are able to act consistently and talk naturally with respect to their motivations
Haemodilution-induced profibrinolytic state is mitigated by fresh-frozen plasma: implications for early haemostatic intervention in massive haemorrhage
Background Fibrinolysis contributes to coagulopathy after major trauma and surgery. We hypothesized that progressive haemodilution is responsible, at least in part, for increased fibrinolytic tendency of blood clot. Methods The study was performed in two parts. First, whole blood (WB) samples collected from six healthy, consented volunteers were diluted in vitro with either saline or fresh-frozen plasma (FFP) to 40% and 15% of baseline. We quantified factor levels related to coagulation and fibrinolysis, and measured endogenous thrombin generation in undiluted control plasma samples and in samples diluted with saline or FFP. Additionally, thromboelastometry was used to assess susceptibility to fibrinolysis after adding tissue plasminogen activator in undiluted WB samples and in samples diluted with saline before and after substitution of fibrinogen or FFP. Secondly, as a model of in vivo haemodilution, we evaluated the same parameters before and after operation in nine consented patients undergoing off-pump coronary artery bypass surgery. Results The dilution with saline caused dose-dependent decreases in plasma levels of coagulation and antifibrinolytic factors, and in thrombin generation. In FFP-supplemented samples, factor levels and thrombin generation were maintained within normal ranges. Fibrinolytic tendency was significantly higher after haemodilution with saline independent of fibrinogen substitution compared with FFP. Similarly, increased tendency for fibrinolysis was also observed in the in vivo haemodilution. Conclusions We demonstrated in vitro and in vivo that progressive haemodilution decreases endogenous antifibrinolytic proteins including α2-antiplasmin and thrombin-activatable fibrinolysis inhibitor, resulting in increased fibrinolytic tendency. Therefore, early fluid replacement therapy with FFP might be advantageous after massive haemorrhag
Diffusion methods for wind power ramp detection
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38679-4_9Proceedings of 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Part IThe prediction and management of wind power ramps is currently receiving large attention as it is a crucial issue for both system operators and wind farm managers. However, this is still an issue far from being solved and in this work we will address it as a classification problem working with delay vectors of the wind power time series and applying local Mahalanobis K-NN search with metrics derived from Anisotropic Diffusion methods. The resulting procedures clearly outperform a random baseline method and yield good sensitivity but more work is needed to improve on specificity and, hence, precision.With partial support from Spain's grant TIN2010-21575-
C02-01 and the UAM-ADIC Chair for Machine Learning. The rst author is also
supported by an FPI-UAM grant and kindly thanks the Applied Mathematics
Department of Yale University for receiving her during her visits. The second
author is supported by the FPU-MEC grant AP2008-00167
Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization
We present a graph-based variational algorithm for classification of
high-dimensional data, generalizing the binary diffuse interface model to the
case of multiple classes. Motivated by total variation techniques, the method
involves minimizing an energy functional made up of three terms. The first two
terms promote a stepwise continuous classification function with sharp
transitions between classes, while preserving symmetry among the class labels.
The third term is a data fidelity term, allowing us to incorporate prior
information into the model in a semi-supervised framework. The performance of
the algorithm on synthetic data, as well as on the COIL and MNIST benchmark
datasets, is competitive with state-of-the-art graph-based multiclass
segmentation methods.Comment: 16 pages, to appear in Springer's Lecture Notes in Computer Science
volume "Pattern Recognition Applications and Methods 2013", part of series on
Advances in Intelligent and Soft Computin
Asynchronous Local-SGD Training for Language Modeling
Local stochastic gradient descent (Local-SGD), also referred to as federated
averaging, is an approach to distributed optimization where each device
performs more than one SGD update per communication. This work presents an
empirical study of {\it asynchronous} Local-SGD for training language models;
that is, each worker updates the global parameters as soon as it has finished
its SGD steps. We conduct a comprehensive investigation by examining how worker
hardware heterogeneity, model size, number of workers, and optimizer could
impact the learning performance. We find that with naive implementations,
asynchronous Local-SGD takes more iterations to converge than its synchronous
counterpart despite updating the (global) model parameters more frequently. We
identify momentum acceleration on the global parameters when worker gradients
are stale as a key challenge. We propose a novel method that utilizes a delayed
Nesterov momentum update and adjusts the workers' local training steps based on
their computation speed. This approach, evaluated with models up to 150M
parameters on the C4 dataset, matches the performance of synchronous Local-SGD
in terms of perplexity per update step, and significantly surpasses it in terms
of wall clock time
DiLoCo: Distributed Low-Communication Training of Language Models
Large language models (LLM) have become a critical component in many
applications of machine learning. However, standard approaches to training LLM
require a large number of tightly interconnected accelerators, with devices
exchanging gradients and other intermediate states at each optimization step.
While it is difficult to build and maintain a single computing cluster hosting
many accelerators, it might be easier to find several computing clusters each
hosting a smaller number of devices. In this work, we propose a distributed
optimization algorithm, Distributed Low-Communication (DiLoCo), that enables
training of language models on islands of devices that are poorly connected.
The approach is a variant of federated averaging, where the number of inner
steps is large, the inner optimizer is AdamW, and the outer optimizer is
Nesterov momentum. On the widely used C4 dataset, we show that DiLoCo on 8
workers performs as well as fully synchronous optimization while communicating
500 times less. DiLoCo exhibits great robustness to the data distribution of
each worker. It is also robust to resources becoming unavailable over time, and
vice versa, it can seamlessly leverage resources that become available during
training
Regularized Linear Inversion with Randomized Singular Value Decomposition
In this work, we develop efficient solvers for linear inverse problems based
on randomized singular value decomposition (RSVD). This is achieved by
combining RSVD with classical regularization methods, e.g., truncated singular
value decomposition, Tikhonov regularization, and general Tikhonov
regularization with a smoothness penalty. One distinct feature of the proposed
approach is that it explicitly preserves the structure of the regularized
solution in the sense that it always lies in the range of a certain adjoint
operator. We provide error estimates between the approximation and the exact
solution under canonical source condition, and interpret the approach in the
lens of convex duality. Extensive numerical experiments are provided to
illustrate the efficiency and accuracy of the approach.Comment: 20 pages, 4 figure
Target Detection Performance Bounds in Compressive Imaging
This paper describes computationally efficient approaches and associated
theoretical performance guarantees for the detection of known targets and
anomalies from few projection measurements of the underlying signals. The
proposed approaches accommodate signals of different strengths contaminated by
a colored Gaussian background, and perform detection without reconstructing the
underlying signals from the observations. The theoretical performance bounds of
the target detector highlight fundamental tradeoffs among the number of
measurements collected, amount of background signal present, signal-to-noise
ratio, and similarity among potential targets coming from a known dictionary.
The anomaly detector is designed to control the number of false discoveries.
The proposed approach does not depend on a known sparse representation of
targets; rather, the theoretical performance bounds exploit the structure of a
known dictionary of targets and the distance preservation property of the
measurement matrix. Simulation experiments illustrate the practicality and
effectiveness of the proposed approaches.Comment: Submitted to the EURASIP Journal on Advances in Signal Processin
Differential Contributions of Intrinsic and Extrinsic Pathways to Thrombin Generation in Adult, Maternal and Cord Plasma Samples.
BACKGROUND:Thrombin generation (TG) is a pivotal process in achieving hemostasis. Coagulation profiles during pregnancy and early neonatal period are different from that of normal (non-pregnant) adults. In this ex vivo study, the differences in TG in maternal and cord plasma relative to normal adult plasma were studied. METHODS:Twenty consented pregnant women and ten consented healthy adults were included in the study. Maternal and cord blood samples were collected at the time of delivery. Platelet-poor plasma was isolated for the measurement of TG. In some samples, anti-FIXa aptamer, RB006, or a TFPI inhibitor, BAX499 were added to elucidate the contribution of intrinsic and extrinsic pathway to TG. Additionally, procoagulant and inhibitor levels were measured in maternal and cord plasma, and these values were used to mathematically simulate TG. RESULTS:Peak TG was increased in maternal plasma (393.6±57.9 nM) compared to adult and cord samples (323.2±38.9 nM and 209.9±29.5 nM, respectively). Inhibitory effects of RB006 on TG were less robust in maternal or cord plasma (52% vs. 12% respectively) than in adult plasma (81%). Likewise the effectiveness of BAX499 as represented by the increase in peak TG was much greater in adult (21%) than in maternal (10%) or cord plasma (12%). Further, BAX499 was more effective in reversing RB006 in adult plasma than in maternal or cord plasma. Ex vivo data were reproducible with the results of the mathematical simulation of TG. CONCLUSION:Normal parturient plasma shows a large intrinsic pathway reserve for TG compared to adult and cord plasma, while TG in cord plasma is sustained by extrinsic pathway, and low levels of TFPI and AT