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Knowledge Representation and Reasoning with Deep Neural Networks
Knowledge representation and reasoning is one of the central challenges of artificial intelligence, and has important implications in many fields including natural language understanding and robotics. Representing knowledge with symbols, and reasoning via search and logic has been the dominant paradigm for many decades. In this work, we use deep neural networks to learn to both represent symbols and perform reasoning end-to-end from data. By learning powerful non-linear models, our approach generalizes to massive amounts of knowledge and works well with messy real-world data using minimal human effort. First, we show that recurrent neural networks with an attention mechanism achieve state-of-the-art reasoning on a large structured knowledge graph. Next, we develop Neural Programmer, a neural network augmented with discrete operations that can be learned to induce latent programs with backpropagation. We apply Neural Programmer to induce short programs on two datasets: a synthetic dataset requiring arithmetic and logic reasoning, and a natural language question answering dataset that requires reasoning on semi-structured Wikipedia tables. We present what is to our awareness the first weakly supervised, end-to-end neural network model to induce such programs on a real-world dataset. Unlike previous learning approaches to program induction, the model does not require domain-specific grammars, rules, or annotations. Finally, we discuss methods to scale Neural Programmer training to large databases
Multilevel regulation of growth rate in yeast revealed using systems biology
The effect of changing growth rates on the transcriptome, proteome and metabolome has been systematically studied. Measurements made under varying nutrient conditions, corresponding to biochemical pathways that correlate primarily with growth rate, reveal a central role for mitochondrial metabolism and the TOR (target of rapamycin) signaling pathway
Combining Task-level and System-level Scheduling Modes for Mixed Criticality Systems
Different scheduling algorithms for mixed criticality systems have been
recently proposed. The common denominator of these algorithms is to discard low
critical tasks whenever high critical tasks are in lack of computation
resources. This is achieved upon a switch of the scheduling mode from Normal to
Critical. We distinguish two main categories of the algorithms: system-level
mode switch and task-level mode switch. System-level mode algorithms allow low
criticality (LC) tasks to execute only in normal mode. Task-level mode switch
algorithms enable to switch the mode of an individual high criticality task
(HC), from low (LO) to high (HI), to obtain priority over all LC tasks. This
paper investigates an online scheduling algorithm for mixed-criticality systems
that supports dynamic mode switches for both task level and system level. When
a HC task job overruns its LC budget, then only that particular job is switched
to HI mode. If the job cannot be accommodated, then the system switches to
Critical mode. To accommodate for resource availability of the HC jobs, the LC
tasks are degraded by stretching their periods until the Critical mode
exhibiting job complete its execution. The stretching will be carried out until
the resource availability is met. We have mechanized and implemented the
proposed algorithm using Uppaal. To study the efficiency of our scheduling
algorithm, we examine a case study and compare our results to the state of the
art algorithms.Comment: \copyright 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
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this work in other work
Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
The process of screening molecules for desirable properties is a key step in
several applications, ranging from drug discovery to material design. During
the process of drug discovery specifically, protein-ligand docking, or chemical
docking, is a standard in-silico scoring technique that estimates the binding
affinity of molecules with a specific protein target. Recently, however, as the
number of virtual molecules available to test has rapidly grown, these
classical docking algorithms have created a significant computational
bottleneck. We address this problem by introducing Deep Surrogate Docking
(DSD), a framework that applies deep learning-based surrogate modeling to
accelerate the docking process substantially. DSD can be interpreted as a
formalism of several earlier surrogate prefiltering techniques, adding novel
metrics and practical training practices. Specifically, we show that graph
neural networks (GNNs) can serve as fast and accurate estimators of classical
docking algorithms. Additionally, we introduce FiLMv2, a novel GNN architecture
which we show outperforms existing state-of-the-art GNN architectures,
attaining more accurate and stable performance by allowing the model to filter
out irrelevant information from data more efficiently. Through extensive
experimentation and analysis, we show that the DSD workflow combined with the
FiLMv2 architecture provides a 9.496x speedup in molecule screening with a <3%
recall error rate on an example docking task. Our open-source code is available
at https://github.com/ryienh/graph-dock.Comment: Published as workshop paper at NeurIPS 2022 (AI for Science
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