Simulating and analyzing order book data: The queue-reactive model

Through the analysis of a dataset of ultra high frequency order book updates, we introduce a model which accommodates the empirical properties of the full order book together with the stylized facts of lower frequency financial data. To do so, we split the time interval of interest into periods in which a well chosen reference price, typically the mid price, remains constant. Within these periods, we view the limit order book as a Markov queuing system. Indeed, we assume that the intensities of the order flows only depend on the current state of the order book. We establish the limiting behavior of this model and estimate its parameters from market data. Then, in order to design a relevant model for the whole period of interest, we use a stochastic mechanism that allows for switches from one period of constant reference price to another. Beyond enabling to reproduce accurately the behavior of market data, we show that our framework can be very useful for practitioners, notably as a market simulator or as a tool for the transaction cost analysis of complex trading algorithms

Pregnancy Tests

10 p. Reprint from: Medical Sentinel, November, 192

Stratégies d'adaptation des organismes subventionnaires en sciences humaines et sociales au Canada et au Québec aux compressions budgétaires gouvernementales

This study sought to evaluate to what degree the strategies employed by Canadian and Québécois funding agencies in the social sciences and humanities were successful in dealing with government spending restrictions. More specifically, it examined whether the oriented research programs that were established and consolidated at the beginning of the 1990s have enabled funding agencies in the social sciences and humanities to supplement government funding, and it compared this revenue to that which was generated within equivalent programs in the natural sciences and engineering. It also examined to what extent professors in the social sciences and humanities have transformed their research practices to meet the increased number of grants available for oriented research. The results suggest that oriented research programs have not generated new revenues in the social sciences and humanities and that these programs have in fact amplified the existing financial inequalities between the social and natural sciences. The results also seem to indicate that professors did not in effect modify their research practices to adapt to the growing number of grants offered for oriented research.La présente étude vise à évaluer dans quelle mesure deux aspects de la stratégie déployée par les organismes subventionnaires (OS) canadiens et québécois en sciences humaines et sociales (SHS) pour faire face aux compressions budgétaires gouvernementales ont atteint leurs objectifs. Plus spécifiquement, nous avons vérifié si la mise sur pied et la consolidation des programmes de recherche orientée au début des années 90 ont permis aux OS en SHS de générer des revenus additionnels à ceux octroyés par les gouvernements, et comparer ces revenus à ceux générés en sciences naturelles et génie (SNG) dans le cadre de programmes équivalents. Nous avons également vérifié dans quelle mesure les professeurs en SHS ont transformé leurs pratiques de recherche afin de répondre favorablement à l'accroissement de l'offre de subventions de recherche orientée. Les résultats suggèrent que les programmes de recherche orientée n'ont pas véritablement générés de nouveaux revenus en SHS et, qu'en fait, ces programmes ont eu pour effet d'accentuer les inégalités économiques entre les SHS et les SNG. Pour ce qui est des professeurs, ceux-ci n'ont pas véritablement modifié leur pratiques de recherche dans le but de répondre à l'offre croissante de subventions de recherche orientée

Alien Registration- Mathieu, A Albert (Lewiston, Androscoggin County)

https://digitalmaine.com/alien_docs/28934/thumbnail.jp

Nanoscale Electrochemical Sensing and Processing in Microreactors

In this review, we summarize recent advances in nanoscale electrochemistry, including the use of nanoparticles, carbon nanomaterials, and nanowires. Exciting developments are reported for nanoscale redox cycling devices, which can chemically amplify signal readout. We also discuss promising high-frequency techniques such as nanocapacitive CMOS sensor arrays or heterodyning. In addition, we review electrochemical microreactors for use in (drug) synthesis, biocatalysis, water treatment, or to electrochemically degrade urea for use in a portable artificial kidney. Electrochemical microreactors are also used in combination with mass spectrometry, e.g., to study the mimicry of drug metabolism or to allow electrochemical protein digestion. The review concludes with an outlook on future perspectives in both nanoscale electrochemical sensing and electrochemical microreactors. For sensors, we see a future in wearables and the Internet of Things. In microreactors, a future goal is to monitor the electrochemical conversions more precisely or ultimately in situ by combining other spectroscopic techniques

Characterizing the Landscape of Musical Data on the Web: State of the Art and Challenges

Musical data can be analysed, combined, transformed and exploited for diverse purposes. However, despite the proliferation of digital libraries and repositories for music, infrastructures and tools, such uses of musical data remain scarce. As an initial step to help fill this gap, we present a survey of the landscape of musical data on the Web, available as a Linked Open Dataset: the musoW dataset of catalogued musical resources. We present the dataset and the methodology and criteria for its creation and assessment. We map the identified dimensions and parameters to existing Linked Data vocabularies, present insights gained from SPARQL queries, and identify significant relations between resource features. We present a thematic analysis of the original research questions associated with surveyed resources and identify the extent to which the collected resources are Linked Data-ready

L6DNet: Light 6 DoF Network for Robust and Precise Object Pose Estimation with Small Datasets

Estimating the 3D pose of an object is a challenging task that can be considered within augmented reality or robotic applications. In this paper, we propose a novel approach to perform 6 DoF object pose estimation from a single RGB-D image. We adopt a hybrid pipeline in two stages: data-driven and geometric respectively. The data-driven step consists of a classification CNN to estimate the object 2D location in the image from local patches, followed by a regression CNN trained to predict the 3D location of a set of keypoints in the camera coordinate system. To extract the pose information, the geometric step consists in aligning the 3D points in the camera coordinate system with the corresponding 3D points in world coordinate system by minimizing a registration error, thus computing the pose. Our experiments on the standard dataset LineMod show that our approach is more robust and accurate than state-of-the-art methods. The approach is also validated to achieve a 6 DoF positioning task by visual servoing.Comment: This work has been accepted at IEEE Robotics and Automation Letter