The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction

With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand. At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1 EPSRC Tier-2 capital grant EP/P020259/

Symbiotic interaction between humans and robot swarms

Comprising of a potentially large team of autonomous cooperative robots locally interacting and communicating with each other, robot swarms provide a natural diversity of parallel and distributed functionalities, high flexibility, potential for redundancy, and fault-tolerance. The use of autonomous mobile robots is expected to increase in the future and swarm robotic systems are envisioned to play important roles in tasks such as: search and rescue (SAR) missions, transportation of objects, surveillance, and reconnaissance operations. To robustly deploy robot swarms on the field with humans, this research addresses the fundamental problems in the relatively new field of human-swarm interaction (HSI). Four groups of core classes of problems have been addressed for proximal interaction between humans and robot swarms: interaction and communication; swarm-level sensing and classification; swarm coordination; swarm-level learning. The primary contribution of this research aims to develop a bidirectional human-swarm communication system for non-verbal interaction between humans and heterogeneous robot swarms. The guiding field of application are SAR missions. The core challenges and issues in HSI include: How can human operators interact and communicate with robot swarms? Which interaction modalities can be used by humans? How can human operators instruct and command robots from a swarm? Which mechanisms can be used by robot swarms to convey feedback to human operators? Which type of feedback can swarms convey to humans? In this research, to start answering these questions, hand gestures have been chosen as the interaction modality for humans, since gestures are simple to use, easily recognized, and possess spatial-addressing properties. To facilitate bidirectional interaction and communication, a dialogue-based interaction system is introduced which consists of: (i) a grammar-based gesture language with a vocabulary of non-verbal commands that allows humans to efficiently provide mission instructions to swarms, and (ii) a swarm coordinated multi-modal feedback language that enables robot swarms to robustly convey swarm-level decisions, status, and intentions to humans using multiple individual and group modalities. The gesture language allows humans to: select and address single and multiple robots from a swarm, provide commands to perform tasks, specify spatial directions and application-specific parameters, and build iconic grammar-based sentences by combining individual gesture commands. Swarms convey different types of multi-modal feedback to humans using on-board lights, sounds, and locally coordinated robot movements. The swarm-to-human feedback: conveys to humans the swarm's understanding of the recognized commands, allows swarms to assess their decisions (i.e., to correct mistakes: made by humans in providing instructions, and errors made by swarms in recognizing commands), and guides humans through the interaction process. The second contribution of this research addresses swarm-level sensing and classification: How can robot swarms collectively sense and recognize hand gestures given as visual signals by humans? Distributed sensing, cooperative recognition, and decision-making mechanisms have been developed to allow robot swarms to collectively recognize visual instructions and commands given by humans in the form of gestures. These mechanisms rely on decentralized data fusion strategies and multi-hop messaging passing algorithms to robustly build swarm-level consensus decisions. Measures have been introduced in the cooperative recognition protocol which provide a trade-off between the accuracy of swarm-level consensus decisions and the time taken to build swarm decisions. The third contribution of this research addresses swarm-level cooperation: How can humans select spatially distributed robots from a swarm and the robots understand that they have been selected? How can robot swarms be spatially deployed for proximal interaction with humans? With the introduction of spatially-addressed instructions (pointing gestures) humans can robustly address and select spatially- situated individuals and groups of robots from a swarm. A cascaded classification scheme is adopted in which, first the robot swarm identifies the selection command (e.g., individual or group selection), and then the robots coordinate with each other to identify if they have been selected. To obtain better views of gestures issued by humans, distributed mobility strategies have been introduced for the coordinated deployment of heterogeneous robot swarms (i.e., ground and flying robots) and to reshape the spatial distribution of swarms. The fourth contribution of this research addresses the notion of collective learning in robot swarms. The questions that are answered include: How can robot swarms learn about the hand gestures given by human operators? How can humans be included in the loop of swarm learning? How can robot swarms cooperatively learn as a team? Online incremental learning algorithms have been developed which allow robot swarms to learn individual gestures and grammar-based gesture sentences supervised by human instructors in real-time. Humans provide different types of feedback (i.e., full or partial feedback) to swarms for improving swarm-level learning. To speed up the learning rate of robot swarms, cooperative learning strategies have been introduced which enable individual robots in a swarm to intelligently select locally sensed information and share (exchange) selected information with other robots in the swarm. The final contribution is a systemic one, it aims on building a complete HSI system towards potential use in real-world applications, by integrating the algorithms, techniques, mechanisms, and strategies discussed in the contributions above. The effectiveness of the global HSI system is demonstrated in the context of a number of interactive scenarios using emulation tests (i.e., performing simulations using gesture images acquired by a heterogeneous robotic swarm) and by performing experiments with real robots using both ground and flying robots

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure

Characterizing Structure and Free Energy Landscape of Proteins by NMR-guided Metadynamics

In the last two decades, a series of experimental and theoretical advances has made it possible to obtain a detailed understanding of the molecular mechanisms underlying the folding process of proteins. With the increasing power of computer technology, as well as with the improvements in force fields, atomistic simulations are also becoming increasingly important because they can generate highly detailed descriptions of the motions of proteins. A supercomputer specifically designed to integrate the Newton's equations of motion of proteins, Anton, has been recently able to break the millisecond time barrier. This achievement has allowed the direct calculation of repeated folding events for several fast-folding proteins and to characterize the molecular mechanisms underlying protein dynamics and function. However these exceptional resources are available only to few research groups in the world and moreover the observation of few event of a specific process is usually not enough to provide a statistically significant picture of the phenomenon. In parallel, it has also been realized that by bringing together experimental measurements and computational methods it is possible to expand the range of problems that can be addressed. For example, by incorporating structural informations as structural restraints in molecular dynamics simulations it is possible to obtain structural models of these transiently populated states, as well as of native and non-native intermediates explored during the folding process. By applying this strategy to structural parameters measured by nuclear magnetic resonance (NMR) spectroscopy, one can determine the atomic-level structures and characterize the dynamics of proteins. In these approaches the experimental information is exploited to create an additional term in the force field that penalizes the deviations from the measured values, thus restraining the sampling of the conformational space to regions close to those observed experimentally. In this thesis we propose an alternative strategy to exploit experimental information in molecular dynamics simulations. In this approach the measured parameters are not used as structural restraints in the simulations, but rather to build collective variables within metadynamics calculations. In metadynamics , the conformational sampling is enhanced by constructing a time-dependent potential that discourages the explorations of regions already visited in terms of specific functions of the atomic coordinates called collective variables. In this work we show that NMR chemical shifts can be used as collective variables to guide the sampling of conformational space in molecular dynamics simulations. Since the method that we discuss here enables the conformational sampling to be enhanced without modifying the force field through the introduction of structural restraints, it allows estimating reliably the statistical weights corresponding to the force field used in the molecular dynamics simulations. In the present implementation we used the bias exchange metadynamics method, an enhanced sampling technique that allows reconstructing the free energy as a simultaneous function of several variables. By using this approach, we have been able to compute the free energy landscape of two different proteins by explicit solvent molecular dynamics simulations. In the application to a well-structured globular protein, the third immunoglobulin-binding domain of streptococcal protein G (GB3), our calculation predicts the native fold as the lowest free energy minimum, identifying also the presence of an on-pathway compact intermediate with non-native topological elements. In addition, we provide a detailed atomistic picture of the structure at the folding barrier, which shares with the native state only a fraction of the secondary structure elements. The further application to the case of the 40-residue form of Amyloid beta, allows us another remarkable achievement: the quantitative description of the free energy landscape for an intrinsically disordered protein. This kind of proteins are indeed characterized by the absence of a well-defined three-dimensional structure under native conditions and are therefore hard to investigate experimentally. We found that the free energy landscape of this peptide has approximately inverted features with respect to normal globular proteins. Indeed, the global minimum consists of highly disordered structures while higher free energy regions correspond to partially folded conformations. These structures are kinetically committed to the disordered state, but they are transiently explored even at room temperature. This makes our findings particularly relevant since this protein is involved in the Alzheimer's disease because it is prone to aggregate in oligomers determined by the interaction of the monomer in extended beta-strand organization, toxic for the cells. Our structural and energetic characterization allows defining a library of possible metastable states which are involved in the aggregation process. These results have been obtained using relatively limited computational resources. The total simulation time required to reconstruct the thermodynamics of GB3 for example is about three orders of magnitude less than the typical timescale of folding of similar proteins, simulated also by Anton. We thus anticipate that the technique introduced in this thesis will allow the determination of the free energy landscapes of wide range of proteins for which NMR chemical shifts are available. Finally, since chemical shifts are the only external information used to guide the folding of the proteins, our methods can be also successfully applied to the challenging purpose of NMR structure determination, as we have demonstrated in a blind prediction test on the last CASD-NMR target

Survey of Low-Resource Machine Translation

International audienceWe present a survey covering the state of the art in low-resource machine translation (MT) research. There are currently around 7,000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the challenge of producing useful translation models when very little translated training data is available. We present a summary of this topical research field and provide a description of the techniques evaluated by researchers in several recent shared tasks in low-resource MT

Findings of the 2019 Conference on Machine Translation (WMT19)

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation

Spatial learning and memory in brain-injured and non-injured mice: investigating the roles of diacylglycerol lipase-α and -β.

A growing body of evidence implicates the importance of the endogenous cannabinoid 2-arachidonyl glycerol (2-AG) in memory regulation. The biosynthesis of 2-AG occurs primarily through the diacylglycerol lipases (DAGL-α and -β), with 2-AG serving as a bioactive lipid to both activate cannabinoid receptors and as a rate limiting precursor for the production of arachidonic acid and subsequent pro-inflammatory mediators. Gene deletion of DAGL-α shows decrements in synaptic plasticity and hippocampal neurogenesis suggesting this biosynthetic enzyme may be important for processes of normal spatial memory. Additionally, 2-AG is elevated in response to pathogenic events such as traumatic brain injury (TBI), suggesting its regulatory role may extend to conditions of neuropathology. As such, this dissertation investigates the in vivo role of DAGL-α and -β to regulate spatial learning and memory in the healthy brain and following neuropathology (TBI). The first part of this dissertation developed a mouse model of learning and memory impairment following TBI, using hippocampal-dependent tasks of the Morris water maze (MWM). We found modest, but distinct differences in MWM performance between left and right unilateral TBI despite similar motor deficits, histological damage, and glial reactivity. These findings suggest that laterality in mouse MWM deficit might be an important consideration when modeling TBI-induced functional consequences. The second part of this dissertation work evaluated DAGL-β as a target to protect against TBI-induced learning and memory deficit given its selective expression on microglia and the role of 2-AG as a precursor for eicosanoid production. The gene deletion of DAGL-β did not protect against TBI-induced MWM or motor deficits, but unexpectedly produced a survival protective phenotype. These findings suggest that while DAGL-β does not contribute to injury-induced memory deficit, it may contribute to TBI-induced mortality. The third and final set of experiments investigated the role of DAGL-α in mouse spatial learning and memory under physiological conditions (given the predominantly neuronal expression of DAGL-α). Complementary pharmacological and genetic manipulations produced task specific impaired MWM performance, as well as impaired long-term potentiation and alterations to endocannabinoid lipid levels. These results suggest that DAGL-α may play a selective role in the integration of new spatial information in the normal mouse brain. Overall, these data point to DAGL-α, but not DAGL-β, as an important contributor to hippocampal-dependent learning and memory. In contrast, DAGL-β may contribute to TBI-induced mortality