dataesr/scientific-paragraphs-categorization huggingface dataset:
On the experimental side, high-throughput methods are reaching a maturity where they can provide large microstructure databases, which can precisely serve both the objective of improving the predictive power of fast physically-based models, and provide databases on which data mining tools and machine learning approaches can be applied to provide guidance for alloy design in areas of design space too complex to be tackled by modeling alone. Now it becomes necessary to combine these numerical and experimental approaches in collaborative case studies. Such an endeavor requires a multi-disciplinary team of experimentalists, modeling and optimization experts and could be applied in many alloy systems that are currently the stake of fast development such as new generation Ti alloys, multiple solutes-containing Al alloys, advanced steels, or high-entropy alloys.
Acknowledgements A. D. , I. -E. B. , F. D. G. and H. V. L. would like to thank  Constellium C-TEC  research centre, particularly  Dr. C. Sigli  and  Arcelor-Mittal Research SA , particularly  F. Bonnet , for financial support and fruitful discussions. F. Tancret would like to thank  Dr. A. Devaux  and  Dr. C. Crozet  at  Aubert & Duval  for stimulating discussions and for the provision of experimental alloys. A part of this work was performed within the framework of the  Centre of Excellence of Multifunctional Architectured Materials "CEMAM"  No. ANR-10-LABX-44-01. The technical staff of beamline  BM02/D2AM  of the  European Synchrotron Radiation Facility (ESRF)  is thanked for technical support. The previously published work presented in this paper is based on work with various contributors who we gratefully acknowledge:  Dr.
The SAAP database  (Al-Numair & Martin, 2013) , which registers structurally relevant variants of human proteins, may constitute a very helpful resource to do this. Fourth, while we based our approach on cell population distributions, tracking the response dynamics of individual cells over time is also possible  (Stockwell & Rifkin, 2017)  and can provide more information on the network response. In other contexts, such methods had been very useful to infer parameters associated with individual cells  (Llamosi et al, 2016). A variant may then be associated with one parameter by a whole distribution of values, which likely carries more information than a single scalar value as presented here. In addition, such time-lapse acquisitions can provide high temporal resolution of gene induction  (Aymoz et al, 2016).
Acknowledgements We thank  Orsolya Symmons  for discussions,  Olivier Gandrillon  for critical reading of the manuscript,  Nicolas Rochette  for sequence analysis,  Sandrine Mouradian  and  SFR Biosciences Gerland-Lyon Sud  ( UMS344/US8 ) for access to flow cytometers and technical assistance, the  Pôle Scientifique de Modélisation Numérique  and the CIMENT infrastructure (supported by the  Rhône-Alpes region , Grant  CPER07_13 CIRA ) for computing resources,  BioSyL Federation and Ecofect LabEx  ( ANR-11-LABX-0048 ) for inspiring scientific events, developers of R, bioconductor and Ubuntu for their software, and two anonymous reviewers for their comments. This work was supported by the  European Research Council  under the European Union's  Seventh Framework Programme   FP7/2007-2013 Grant Agreement no 281359.
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in humanrobot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.
We validate the proposed architecture following a humanrobot interaction scenario where the robot has to learn concepts related to its own body and its vicinity in a proactive manner and express those concepts in goal-oriented behavior. We show a complete implementation running in real-time on the iCub humanoid robot. The interaction depends on the internal dynamics of the architecture, the properties of the environment and the behavior of the human. We analyze a typical interaction in detail and provide videos showing the robustness of our system in various environments (https://github. com/robotology/wysiwyd). Our results show that the architecture autonomously drives the iCub to acquire a number of concepts about the present entities (objects, humans, and body parts), whilst proactively maintaining the interaction with a human and recruiting those concepts to express more complex goal-oriented behavior.
E. Language learning, autobiographical memory and narrative expression We have just described the main components that a robot requires to solve the SGP: a cognitive architecture able to process both low-level sensorimotor data and high-level symbolic representation, mechanisms for linking these two levels in abstract multimodal representations, as well autonomous behaviors for proactively interacting with the environment. The final challenge to address concerns the use of the acquired symbols for higher-level cognition, including language learning, autobiographical memory and narrative expression. Several works address the ability of language learning in robotics. The cognitive architecture of iTalk  [70]  focuses on modeling the emergence of language by learning about the robot's embodiment, learning from others, as well as learning linguistic capability.
Relevant information throughout the interaction of the robot with humans is continuously stored in an autobiographical memory used for the generation of a narrative self, i. e. , a verbal description of the own robot's history over the long term (able to store and verbally describe interactions from a long time ago, e. g. a period of several months). In the next section, we describe how the above features are implemented in a coherent cognitive architecture made up of functional YARP  [77]  modules running in real-time on the iCub robot.
A. Layer and module overview In DAC-h3, the somatic layer consists of an iCub humanoid robot equipped with advanced motor and sensory abilities for interacting with humans and objects. The reactive layer ensures the autonomy of the robot through drive reduction mechanisms implementing proactive behaviors for acquiring and expressing knowledge about the current scene. This allows the bootstrapping of adaptive learning of multimodal representations about entities in the adaptive layer. More specifically, the adaptive layer learns high-level multimodal representations (visual, tactile, motor and linguistic) for the categorization of entities (objects, agents, actions and body parts) and associates them in unified representations. Those representations form the basis of an episodic memory for goaloriented behavior through planning in the contextual layer, which deals with goal representation and action planning.
g. pointing towards an object or moving a specific body part, while verbally referring to the considered entity). The implementation of these drives is realized through the three structural modules described below, interacting with each other as well as with the surrounding layers: 1) sensations, 2) allostatic controller, and 3) behaviors (see Figure  1 ). 1) Sensations: The sensations module deals with low-level sensing to provide relevant information for meaning extraction in the adaptive layer. Specifically, the module detects presence and position of other agents and their body parts (agent detector functional module; of interest are the head location for gazing at the partner and the location of the hands to detect pointing actions) based on the input of the RGB-D camera. Similarly, it detects objects based on a texture analysis and extracts their location using the stereo vision capabilities of the iCub  [78].
The prediction, anticipation, sensation, attention and response functional module (PASAR;  [79] ) calculates the saliency of agents based on their motion (increased velocity leads to increased saliency), and similarly the saliency for objects is increased if they move or the partner points at them. Finally, the speech recognition functional module extracts text from human speech sensed by a microphone using the Microsoft TM Speech API. The functionalities of the sensations module can, therefore, be summarized as dimensionality reduction and saliency computation, and the resulting data are used for bootstrapping knowledge in higher layers of the architecture. 2) Allostatic Controller: In many situations, several drives which may conflict with each other can be activated at the same time (in the case of this paper, the drive for knowledge acquisition and the drive for knowledge exploration).
D. Adaptive layer The adaptive layer oversees the acquisition of a state space of the agent-environment interaction by binding visual, tactile, motor and linguistic representations of entities. It integrates functional modules for maintaining an internal representation of the current scene, visually categorizing entities, recognizing and sensing body parts, extracting linguistic labels from human speech, and learning associations between multimodal representations. They are grouped in three structural modules described below: perceptions, associations and action selection (see Figure  1 ). 1) Perceptions: The object recognition functional module  [86]  is used to learn the categorization of objects directly from the visual information given by the iCub eyes with resort to the most recent deep convolutional networks.
The associations of representations can also be applied to the developmental robot itself (instead of external entities as above), to acquire motor capabilities or to learn the links between motor joints and skin sensors of its body  [93]. Learning self-related representations of the robot's own body schema is realized by the sensorimotor representation learning functional module dedicated to forward model learning  [94]. The module receives sensory data collected from the robot's sensors (e. g. cameras, skin, joint encoders) and allows accurate prediction of the next state given the current state and an action. Importantly, the forward model is learned based on sensory experiences rather than based on known mechanical properties of the robot's body. The kinematic structure learning functional module  [95] ,  [96]  estimates an articulated kinematic structure of arbitrary objects (including the robot's body parts and humans) using visual input videos of the iCub eye cameras.
This again is based on sensory experiences rather than known properties of the agents, which is important to autonomously identify the abilities of other agents. Based on the estimated articulated kinematic structures  [95] , we also allow the iCub to anchor two objects' kinematic structure joints by observing their movements  [96]  and formulating the problem of finding corresponding kinematic joint matches between two articulated kinematic structures. This allows the iCub to infer correspondences between its own body parts (its left arm and its right arm), as well as between its own body and the body of the human as retrieved by the agent detector  [93]  (see Figure  2 ). Finally, based on these correspondences, the perspective taking functional module  [97]  enables the robot to reason about the state of the world from the partner's perspective.
It is used for goal representation and as elements of the autobiographical memory. H5W have been argued to be the main questions any conscious being must answer to survive in the world  [99] ,  [100]. The concept of relations is the core of the H5W framework. It links up to five concepts and assigns them with semantic roles to form a solution to the H5W problem. We define a relation as a set of five edges connecting those nodes in a directed and labeled manner. The labels of those edges are chosen so that the relation models a typical sentence from the English grammar of the form: Relation → Subject Verb [Object] [Place] [Time]. The brackets indicate that the components are optional; the minimal relation is therefore composed of two entities representing a subject and a verb. 2) Goals and action plans: Goals can be provided to the iCub from human speech, and a meaning is extracted by the language reservoir handler, forming the representation of a goal in the goals module.
Each goal consequently refers to the appropriately predefined action plan, defined as a state transition graph with states represented by nodes and actions represented by edges of the graph. The action plans module extracts sequences of actions from this graph, with each action being associated with a pre-and a post-condition state. Goals and action plans can be parameterized by the name of a considered entity. For example, if the human asks the iCub to take the cube, this loads an action plan for the goal "Take an object" which consists of two actions: "Ask the human to bring the object closer" and "Pull the object". In this case, each action is associated with a pre-and post-condition state in the form of a region in the space where the object is located. In the action selection module of the adaptive layer, the plan is instantiated toward a specific object according to the knowledge retrieved from the associations module (e.
It stores data (e. g. objects locations, human presence) from the beginning to the end of an episode by taking snapshots of the environmental information from the episodic memory containing the pre-conditions and effects of episodes. This allows the generation of high-level concepts extracted by knowledge-based reasoning. In addition, the ABM captures continuous information during an episode (e. g. images from the camera, joints values)  [102] , which can be used by reasoning modules focusing on the action itself, leading to the production of a procedural memory (e. g. through learning from motor babbling or imitation)  [103]. The narrative structure learning module builds on the language processing and ABM capabilities. Narrative structure learning occurs in three phases: 1) First the iCub acquires experience in a given scenario, which generates the meaning representation in the ABM.
IV. EXPERIMENTAL RESULTS This section validates the cognitive architecture described in the previous section on a real demonstration with an iCub humanoid robot interacting with objects and a human. We first describe the experimental setup, followed by the behaviors provided to the robot (self-generated and human-requested). Finally, we analyze the DAC-h3 system in two ways: a complete version reporting the full complexity of the system through multiple video demonstrations and the detailed analysis of a particular interaction, as well as a simplified version showing the effect of the robot's proactivity level on naive users. The code for reproducing these experiments on any iCub robot is available open-source at https://github. com/robotology/ Figure  3. The setup consists of an iCub robot interacting with objects on the table and a human in front of it.
B. Self-generated behavior Two drives for knowledge acquisition and knowledge expression implement the interaction engine of the robot (see Section III-C). They regulate the knowledge acquisition process of the iCub and proactively maintain the interaction with the human. The generated sensorimotor data feeds the adaptive layer of the cognitive architecture to acquire multimodal information about the present entities (see Section III-D). In the current experiment, the entities are objects on the table, body parts (fingers of the iCub), human partners, and actions. The acquired multimodal information depends on the considered entity. Object representations are based on visual categorization and stereo-vision based 3D localization performed by the object recognition functional module. Body part representations associate motor and touch events.
After the attention has been attracted toward the desired entity, the iCub asks for the missing information (e. g. the name of an object or of the human, or in the case of a body part the name and touch information) and the human replies accordingly. In a third step, this information is passed to the adaptive layer and the knowledge of the robot is updated in consequence. Each time the drive level reaches the threshold, an entity is chosen in a pseudo-random way within the set of perceived entities with missing information, with a priority to request the name of a detected unknown human partner. Once a new agent enters the scene, the iCub asks for her/his name, which is stored alongside representations of its face in the synthetic sensory memory module. Similarly, the robot stores all objects it has previously encountered in its episodic memory implemented by the object property collector module.
When the chosen entity is an object, the robot asks the human to provide the name of interest while pointing at it. Then, the visual representation of the object computed by the object recognition module is mapped to the name. When the chosen entity is a body part (left-hand fingers), the iCub first raises its hand and moves a random finger to attract the attention of the human. Then it asks for the name of that body part. This provides a mapping between the robot's joint identifier and the joint's name. This mapping can be extended to include tactile information by asking the human to touch the body part which is being moved by the robot. Once a behavior has been triggered, the drive is reset to its default value and decays again as explained above (the amount of the decay being reduced according to what has been acquired).
C. Human-requested behavior The representations which are acquired through satisfying the drives introduced above allow a more complex interaction through goal-oriented behavior managed by the contextual layer (see Figure  1  and Section III-E). Goals can be provided to the iCub from human speech and a meaning is extracted by the language reservoir handler, forming the representation of a goal in the goals module. Each goal is associated with an action plan on the form of a sequence of actions together with their pre-and post-conditions in the action plans module. The action selection module takes care of the execution of the plan according to the associations known to the robots, triggering.
The available goals are described below. 1) Give or take an object: These goals are generated from a human verbal request, e. g. "Give me the octopus" or "Take the cube". Here, the goal is represented as a region on the table, either the human area H (for the "Give" goal) or the iCub area I (for the "Take" goal), where the mentioned object should be placed. Action plans are generated from the state-transition graph shown in Figure  4. State perception is updated in realtime according to the perceived location of the object computed through stereo-vision in the object recognition module. 2) Point to an object: This goal is generated through a verbal request, e. g. "Point to the octopus". If the mentioned object is not known to the iCub, it will first ask the human to point to it to learn the new association between the name and the object's visual representation.
Once the name is known, or if it was already known, the iCub will point to the object. 3) Say the name of a recognized action: This goal is generated through a verbal request, e. g. "How do you call this action?" formulated just after the human has performed an action on an object. Six actions can be recognized by the synthetic sensory memory module: "push", "pull", "lift", "drop", "wave", and "point". The reply from the iCub provides the name of the action and the object as well as the hand used by the human, e. g. "You pushed the cube with your left hand".
4) Say what happened during a past interaction with a human: This goal is generated through a verbal request, e. g. "What have you done the other day?". Based on its past interactions with the environment and with humans, the iCub has stored all the relevant information in its autobiographical memory (see Section III-E), including its own drives, motivations, and actions, as well of the actions of the human (both, spoken and physically enacted). The narrative handler functional module can generate a narrative discourse from the content of the autobiographical memory and to generate an action plan in the form of a sequence of sentences. The human can request more detailed information about an event using sentences like "What happened next?" or "Why did you do that?", this later question being answered according to the stored drive levels and goal states of the considered events.
D. Scenario Progression We first show how the full DAC-h3 system we have just described is able to generate a complex human-robot interaction by providing videos of live interactions (see https://github. com/ robotology/wysiwyd) and a detailed description of a particular interaction. Then, in the next subsection, we will analyze more specifically the effect of the robot's proactivity level on naive users. In both cases, we consider a mixed-initiative scenario, where the iCub behaves autonomously as described in Section IV-B, and so does the human. The human can interrupt the robot behavior by formulating verbal requests as described in Section IV-C. The scenario can follow various paths according to the interaction between the iCub's internal drive dynamics, its perception of the environment, and the behavior of the human.
Here, we describe one particular instance of the scenario. Figure  6  shows the corresponding drive dynamics and humanrobot interactions, and Figure  1  shows the connections between the modules of the cognitive architecture. Each of the numbered items below refers to its corresponding number in Figure  6. 1) At the beginning of the interaction, the iCub has only limited knowledge about the current scene. In the sensations module, the agent detector detects the presence of a human and extracts its skeleton. The object recognition module performs blob detection for extracting objects on the table from the visual input of the eye cameras and Each drive starts at its default value and decays following the dynamics described in Section IV-B. When reaching a given threshold (dashed horizontal line) the associated behavior is triggered (green rectangles), the corresponding drive level is reset to its default value and both drive levels are frozen for the duration of the behavior.
Human behavior is indicated by the red rectangles, being either a reply to a question asked by the iCub (small rectangles) or a request to the iCub triggering goal oriented behavior (here: "Take the cube"). The numbers refer to the description of the scenario progression in the main text. trains a classifier to categorize them. The 3D locations of the present objects are also computed in object recognition through stereo vision. This forms a first incomplete representation of the scene in the episodic memory where the object property collector registers the location and type of each detected entity (here objects and an agent). It also contains slots for unknown body parts of the iCub, here the five fingers of its right hand. 2) The presence of a large amount of missing information (presence of unknown objects, human and body parts) in the sensations module makes the drive for knowledge acquisition to decay rapidly in the allostatic controller and the drive for knowledge expression is kept constant (since there is no knowledge to express yet).