Graph kernels based on tree patterns for molecules

Motivated by chemical applications, we revisit and extend a family of positive definite kernels for graphs based on the detection of common subtrees, initially proposed by Ramon et al. (2003). We propose new kernels with a parameter to control the complexity of the subtrees used as features to represent the graphs. This parameter allows to smoothly interpolate between classical graph kernels based on the count of common walks, on the one hand, and kernels that emphasize the detection of large common subtrees, on the other hand. We also propose two modular extensions to this formulation. The first extension increases the number of subtrees that define the feature space, and the second one removes noisy features from the graph representations. We validate experimentally these new kernels on binary classification tasks consisting in discriminating toxic and non-toxic molecules with support vector machines

The pharmacophore kernel for virtual screening with support vector machines

We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-points pharmacophores present in the 3D structures of molecul es, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demanding exact implementation of these kernels, as well as fast approximations related to the classical fingerprint-based approa ches. Experimental results suggest that this new approach outperforms state-of-the-art algorithms based on the 2D structure of mol ecules for the detection of inhibitors of several drug targets

Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data

Microbial identification is a central issue in microbiology, in particular in the fields of infectious diseases diagnosis and industrial quality control. The concept of species is tightly linked to the concept of biological and clinical classification where the proximity between species is generally measured in terms of evolutionary distances and/or clinical phenotypes. Surprisingly, the information provided by this well-known hierarchical structure is rarely used by machine learning-based automatic microbial identification systems. Structured machine learning methods were recently proposed for taking into account the structure embedded in a hierarchy and using it as additional a priori information, and could therefore allow to improve microbial identification systems. We test and compare several state-of-the-art machine learning methods for microbial identification on a new Matrix-Assisted Laser Desorption/Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) dataset. We include in the benchmark standard and structured methods, that leverage the knowledge of the underlying hierarchical structure in the learning process. Our results show that although some methods perform better than others, structured methods do not consistently perform better than their "flat" counterparts. We postulate that this is partly due to the fact that standard methods already reach a high level of accuracy in this context, and that they mainly confuse species close to each other in the tree, a case where using the known hierarchy is not helpful

First-order ambisonic coding with quaternion-based interpolation of PCA rotation matrices

International audienceConversational applications such as telephony are mostly restricted to mono. With the emergence of VR/XR applications and new products with spatial audio, there is a need to extend traditional voice and audio codecs to enable immersive communication.The present work is motivated by recent activities in 3GPP standardization around the development of a new codec called immersive voice and audio services (IVAS). The IVAS codec will address a wide variety of use cases, e.g. immersive telephony, spatial audio conferencing, live content sharing. There are two main design goals for IVAS. One goal is the versatility of the codec in terms of input (scene-based, channel-based, object-based audio…) and output (mono, stereo, binaural, various multichannel loudspeaker setups). The second goal is to re-use as much as possible and extend the enhanced voice services (EVS) mono codec.In this work, we focus on the first-order ambisonic (FOA) format which is a good candidate for the internal representation in an immersive audio codec at low bit rates, due to the flexibility of the underlying sound field decomposition. We propose a new coding method, which can extend existing core codecs such as EVS. The proposed method consists in adaptively pre-processing ambisonic components prior to multi-mono coding by a core codec.The first part of this work investigates the basic multi-mono coding approach for FOA, which is for instance used in the Opus codec (in the so-called channel mapping family 2). In this approach ambisonic components are coded separately with different instances of the (mono) core codec. We present results of a subjective test (MUSHRA), which shows that this direct approach is not satisfactory for low-bitrate coding. The signal structure is degraded which produces many spatial artifacts (e.g. wrong panning, ghost sources...). In the second part of this work, we propose a new method to exploit the correlation of ambisonic components. The pre-processing (prior to multi-mono coding) operates in time-domain to allow maximum compatibility with many codecs, especially low bit-rate codecs such as EVS and Opus, and to minimize extra delay.The proposed method applies Principal Components Analysis (PCA) on a 20 ms frame basis. For each frame, eigenvectors are computed and the eigenvector matrix is defined as a 4D rotation matrix. For complex sound scenes (with many audio sources, sudden changes…) rotation parameters may change dramatically between consecutive frames and audio sources may go from one principal component to another, which may cause discontinuities or other artifacts. Solutions such as the interpolation of eigenvectors (after inter-frame realignment) are not optimal. In the proposed method, we ensure smooth transitions between inter-frame PCA rotations thanks to two complementary methods. The first one is a matching algorithm for eigenvectors between the current and the previous frame, which avoids signal inversion and permutation across frames. The second one is an interpolation of the 4D rotation matrices in quaternion domain. We use the Cayley factorization of 4D rotation matrices into a double quaternion for the current and previous frame and apply quaternion spherical linear interpolation (QSLERP) interpolation on a subframe basis. The interpolated rotation matrices are then applied to the ambisonic components and the decorrelated components are coded with a multi-mono coding approach.We present results of a subjective evaluation (MUSHRA) for the proposed method showing that the proposed method brings significant improvements over naive multi-mono method, especially in terms of spatial quality

Philologie et historiographie du Caucase chrétien

Programme de l’année 2008-2009 : I. L’Arménie et les Arabes (639-884). — II. La royauté bagratide (884-1055)

Philologie et historiographie du Caucase chrétien

Programme de l’année 2019-2020 : I. Histoire primitive du Kartli et Listes royales dans la Conversion de la Géorgie. — II. Le roman des Saints Traducteurs et l’enseignement de la philosophie dans l’Arménie des VIe-XIe siècles

Philologie et historiographie du Caucase chrétien

Programme de l’année 2006-2007 : L’historiographie arménienne de la conversion des Albaniens

Philologie et historiographie du Caucase chrétien

Programme de l’année 2009-2010 : I. L’Arménie Majeure et la Cilicie entre les Seldjoukides et les Mongols (1064-1375). — II. Turcomans, Ottomans, Safavides (1375-1639)

Philologie et historiographie du Caucase chrétien

Programme de l’année 2007-2008 : L’Arménie artaxiade (iie s. avant notre ère)