Work Semantics. In Search of an Alternative Conceptual Matrix for Labour and Social Historians

The idea for the project presented in this volume began with an encounter and a discovery. When we – a medievalist and a sinologist – first met in autumn 2017, we realised that although we came from different disciplines and worked on different regions and time periods, we were struggling with the same problem: As historians working on slaving practices in the Venetian empire (14th–16th centuries) respectively servitude in late imperial China (15th–19th centuries), we were both spending much of our time explaining the contextual differences and similarities between the social configurations we were studying to the broader community of social, labour, and global historians. We both felt that our objects of study did not fit well within the much-debated subfield of “free and unfree labour”, and that the postcolonial critiques and the so-called global turn in history did not solve the conceptual problem we were facing. Integrating a medieval or Chinese case study into a conference panel or a special journal issue on household service or slavery helped to enlarge the horizon of the historiographical debates on the history of unfree labour relations, but the umbrella terms of these subfields of study and the limited conceptual referencesavailable did little to help us understand and properly convey the social taxonomies shaping the power relations we were studying.The idea for the project presented in this volume began with an encounter and a discovery. When we – a medievalist and a sinologist – first met in autumn 2017, we realised that although we came from different disciplines and worked on different regions and time periods, we were struggling with the same problem: As historians working on slaving practices in the Venetian empire (14th–16th centuries) respectively servitude in late imperial China (15th–19th centuries), we were both spending much of our time explaining the contextual differences and similarities between the social configurations we were studying to the broader community of social, labour, and global historians. We both felt that our objects of study did not fit well within the much-debated subfield of “free and unfree labour”, and that the postcolonial critiques and the so-called global turn in history did not solve the conceptual problem we were facing. Integrating a medieval or Chinese case study into a conference panel or a special journal issue on household service or slavery helped to enlarge the horizon of the historiographical debates on the history of unfree labour relations, but the umbrella terms of these subfields of study and the limited conceptual referencesavailable did little to help us understand and properly convey the social taxonomies shaping the power relations we were studying

Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks

Deep learning (DL) techniques have had unprecedented success when applied to images, waveforms, and texts to cite a few. In general, when the sample size (N) is much greater than the number of features (d), DL outperforms previous machine learning (ML) techniques, often through the use of convolution neural networks (CNNs). However, in many bioinformatics ML tasks, we encounter the opposite situation where d is greater than N. In these situations, applying DL techniques (such as feed-forward networks) would lead to severe overfitting. Thus, sparse ML techniques (such as LASSO e.g.) usually yield the best results on these tasks. In this paper, we show how to apply CNNs on data which do not have originally an image structure (in particular on metagenomic data). Our first contribution is to show how to map metagenomic data in a meaningful way to 1D or 2D images. Based on this representation, we then apply a CNN, with the aim of predicting various diseases. The proposed approach is applied on six different datasets including in total over 1000 samples from various diseases. This approach could be a promising one for prediction tasks in the bioinformatics field.Comment: Accepted at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/); In Proceedings of the NIPS ML4H 2017 Workshop in Long Beach, CA, USA

Heterogeneity in Kv2 Channel Expression Shapes Action Potential Characteristics and Firing Patterns in CA1 versus CA2 Hippocampal Pyramidal Neurons.

The CA1 region of the hippocampus plays a critical role in spatial and contextual memory, and has well-established circuitry, function and plasticity. In contrast, the properties of the flanking CA2 pyramidal neurons (PNs), important for social memory, and lacking CA1-like plasticity, remain relatively understudied. In particular, little is known regarding the expression of voltage-gated K+ (Kv) channels and the contribution of these channels to the distinct properties of intrinsic excitability, action potential (AP) waveform, firing patterns and neurotransmission between CA1 and CA2 PNs. In the present study, we used multiplex fluorescence immunolabeling of mouse brain sections, and whole-cell recordings in acute mouse brain slices, to define the role of heterogeneous expression of Kv2 family Kv channels in CA1 versus CA2 pyramidal cell excitability. Our results show that the somatodendritic delayed rectifier Kv channel subunits Kv2.1, Kv2.2, and their auxiliary subunit AMIGO-1 have region-specific differences in expression in PNs, with the highest expression levels in CA1, a sharp decrease at the CA1-CA2 boundary, and significantly reduced levels in CA2 neurons. PNs in CA1 exhibit a robust contribution of Guangxitoxin-1E-sensitive Kv2-based delayed rectifier current to AP shape and after-hyperpolarization potential (AHP) relative to that seen in CA2 PNs. Our results indicate that robust Kv2 channel expression confers a distinct pattern of intrinsic excitability to CA1 PNs, potentially contributing to their different roles in hippocampal network function

Rounding Methods for Discrete Linear Classification (Extended Version)

Learning discrete linear classifiers is known as a difficult challenge. In this paper, this learning task is cast as combinatorial optimization problem: given a training sample formed by positive and negative feature vectors in the Euclidean space, the goal is to find a discrete linear function that minimizes the cumulative hinge loss of the sample. Since this problem is NP-hard, we examine two simple rounding algorithms that discretize the fractional solution of the problem. Generalization bounds are derived for several classes of binary-weighted linear functions, by analyzing the Rademacher complexity of these classes and by establishing approximation bounds for our rounding algorithms. Our methods are evaluated on both synthetic and real-world data

Distributed Fair Allocation of Indivisible Goods

International audienceDistributed mechanisms for allocating indivisible goods are mechanisms lacking central control, in which agents can locally agree on deals to exchange some of the goods in their possession. We study convergence properties for such distributed mechanisms when used as fair division procedures. Specifically, we identify sets of assumptions under which any sequence of deals meeting certain conditions will converge to a proportionally fair allocation and to an envy-free allocation, respectively. We also introduce an extension of the basic framework where agents are vertices of a graph representing a social network that constrains which agents can interact with which other agents, and we prove a similar convergence result for envy-freeness in this context. Finally, when not all assumptions guaranteeing envy-freeness are satisfied, we may want to minimise the degree of envy exhibited by an outcome. To this end, we introduce a generic framework for measuring the degree of envy in a society and establish the computational complexity of checking whether a given scenario allows for a deal that is beneficial to every agent involved and that will reduce overall envy

On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory

This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks. Lipschitz regularity is now established as a key property of modern deep learning with implications in training stability, generalization, robustness against adversarial examples, etc. However, computing the exact value of the Lipschitz constant of a neural network is known to be NP-hard. Recent attempts from the literature introduce upper bounds to approximate this constant that are either efficient but loose or accurate but computationally expensive. In this work, by leveraging the theory of Toeplitz matrices, we introduce a new upper bound for convolutional layers that is both tight and easy to compute. Based on this result we devise an algorithm to train Lipschitz regularized Convolutional Neural Networks

The Power of Swap Deals in Distributed Resource Allocation

International audienceIn the simple resource allocation setting consisting in assigning exactly one resource per agent, the top trading cycle procedure stands out as being the undisputed method of choice. It remains however a centralized procedure which may not well suited in the context of multiagent systems, where distributed coordination may be problematic. In this paper, we investigate the power of dynamics based on rational bilateral deals (swaps) in such settings. While they may induce a high efficiency loss, we provide several new elements that temper this fact: (i) we identify a natural domain where convergence to a Pareto-optimal allocation can be guaranteed, (ii) we show that the worst-case loss of welfare is as good as it can be under the assumption of individual rationality, (iii) we provide a number of experimental results, showing that such dynamics often provide good outcomes, especially in light of their simplicity, and (iv) we prove the NP-hardness of deciding whether an allocation maximizing utilitarian or egalitarian welfare is reachable

Game Theory Models for Multi-Robot Patrolling of Infraestructures

Abstract This work is focused on the problem of performing multi‐robot patrolling for infrastructure security applications in order to protect a known environment at critical facilities. Thus, given a set of robots and a set of points of interest, the patrolling task consists of constantly visiting these points at irregular time intervals for security purposes. Current existing solutions for these types of applications are predictable and inflexible. Moreover, most of the previous centralized and deterministic solutions and only few efforts have been made to integrate dynamic methods. Therefore, the development of new dynamic and decentralized collaborative approaches in order to solve the aforementioned problem by implementing learning models from Game Theory. The model selected in this work that includes belief‐based and reinforcement models as special cases is called Experience‐Weighted Attraction. The problem has been defined using concepts of Graph Theory to represent the environment in order to work with such Game Theory techniques. Finally, the proposed methods have been evaluated experimentally by using a patrolling simulator. The results obtained have been compared with previous availabl

Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante

Autonomous robots require online trajectory planning capability to operate in the real world. Efficient offline trajectory planning methods already exist, but are computationally demanding, preventing their use online. In this paper, we present a novel algorithm called Guided Trajectory Learning that learns a function approximation of solutions computed through trajectory optimization while ensuring accurate and reliable predictions. This function approximation is then used online to generate trajectories. This algorithm is designed to be easy to implement, and practical since it does not require massive computing power. It is readily applicable to any robotics systems and effortless to set up on real hardware since robust control strategies are usually already available. We demonstrate the computational performance of our algorithm on flat-foot walking with the self-balanced exoskeleton Atalante