No Trade, Informed Trading, and Accuracy of Information.

We present a model in which there is uncertainty about realization of a risky asset value for an informed trader. Then, we show that the informed trader does not trade in equi- librium if the inside information the informed trader has is not sufficiently accurate. We use the framework presented by Glosten and Milgrom (1985) and extend the assumption that the informed trader knows the terminal value of the risky asset. Finally, we obtain the conditions under which the informed trader would not trade in equilibrium.

Mixed signal approach for rapid prototyping of a compact smart pebble for sediment transport monitoring in river beds

Low-cost accelerometers and gyro ICs were used to develop a smart sediment particle to study the sediment transport in rivers. With strap-down MEMS, battery, a processing subsystem and memory, this self contained unit captures semiprocessed data for durations up to 15 minutes. In a mixed-mode design, analog multiplier ICs with limited digital circuits transform the body frame data to a reference frame using Euler angles, with adequate accuracy despite cumulative errors. For 3D motion, up to nine sensor inputs from three orthogonal modules are coupled to a multiplexed analog processing module, and processed by a digital module for data conversion and storage. Despite the simplified mathematics used, experimental data from the proof-of-concept system provided adequate accuracy. Subsequent processing of the raw sensor data using an external PC program with smart algorithms allowed the comparison of accuracy of the mixed mode approach. The adopted mixed signal design approach helps the packaging requirements due to the specific nature of the problem with short recording durations

No Trade, Informed Trading, and Accuracy of Information

We present a model in which there is uncertainty about realization of a risky asset value for an informed trader. We introduce two states such that in the "narrow" state the informed trader has better information than in the "wide" state. Then, we show that the informed trader in the wide state does not trade in equilibrium if the information that the informed trader with better information has is sufficiently accurate and the probability of the narrow state is sufficiently high. We use the framework presented by Glosten and Milgrom (1985) and extend the assumption that the informed trader knows the terminal value of the risky asset. Finally, we obtain the conditions under which the informed trader would not trade in equilibrium.Market microstructure; Glosten-Milgrom; Price formation; Asymmetric information; Bid-ask spreads.

Finding Street Gang Members on Twitter

Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.Comment: 8 pages, 9 figures, 2 tables, Published as a full paper at 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Three-Phase Reduced Switch Topologies for AC-DC Front-End and Single-Stage Converters

Conventional three-phase ac-dc converters have two converter stages. They have a front-end converter that converts the input ac voltage into an intermediate dc bus voltage and a second, back-end converter that converts this dc bus voltage into the desired isolated dc output voltage. The front-end converter also performs power factor correction (PFC) and shapes the three-phase input currents so that they are nearly sinusoidal and in phase with the three-phase input voltages. This allows the ac power source to be used in the most efficient manner. The front-end ac-dc converter is typically implemented with six switches while the back-end dc-dc converter is typically implemented with a four switch dc-dc full-bridge topology. Power electronic researchers have been motivated to try to reduce the number of switches that are used in the conventional two-stage approach in order to reduce cost and simplify the overall ac-dc converter. There are two general approaches to doing this: This first approach is to reduce the number of switches in the front-end ac-dc converter. The second approach is to combine the ac-dc converter and the dc-dc converter in a single converter so that the overall ac-dc converter can be implemented in a single converter stage that can simultaneously perform ac-dc power conversion with PFC and dc-dc power conversion. The main focus of this thesis is on new power converter topologies that convert a three-phase ac input voltage into an isolated dc output voltage with a reduced number of switches. In the thesis, a new family of reduced switch front-end converter topologies is proposed, an example converter from this new family is selected for further study and a modified version of this topology is studied as well. In addition to these front-end converters, two new three-phase ac-dc single-stage converters are proposed and their properties and characteristics are compared. For each new converter that is investigated in detail, its modes of operation are explained, its steady-state characteristics are determined by mathematical analysis, and the results of the analysis are used to develop a design procedure that can be used to select key components. The design procedure of each new converter is demonstrated with an example that was used in the implementation of an experimental prototype that confirmed the feasibility of the converter. The thesis concludes by presenting that have been reached as a result of the work that was performed, stating its main contributions to the power electronics literature and suggesting future research that can be done based on the thesis work

Cinq stratégies pratiques pour réussir l’apprentissage coopératif virtuel en grand groupe dans programmes de médecine

The COVID-19 pandemic has led to the rapid replacement of in-person classroom learning with virtual large group learning. Done well, virtual large group learning can be an effective tool that provides flexibility, accessibility, and collaboration between learners. However, despite its potential benefits, human and technological challenges limit engagement and overall efficacy of large group virtual learning. The following account provides an evidence-based framework to maximize cooperative learning, learner engagement and retention of medical education in the virtual setting.La pandémie de la COVID-19 a rapidement entraîné le remplacement de l’apprentissage en classe en personne par l’apprentissage virtuel en grand groupe. Bien conçu, l’apprentissage virtuel en groupe peut être un outil efficace qui offre souplesse, accessibilité et possibilités de collaboration entre apprenants. Malgré ces avantages potentiels, les défis humains et technologiques limitent la participation à l’apprentissage virtuel en grand groupe et son efficacité générale. L’exposé suivant propose un cadre fondé sur des données probantes permettant de maximiser l’apprentissage coopératif, la participation des apprenants et la rétention des connaissances enseignées virtuellement dans les programmes de médecine

A Semantics-Based Measure of Emoji Similarity

Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test multiple embedding models to measure emoji similarity. To evaluate our work, we create a new dataset called EmoSim508, which assigns human-annotated semantic similarity scores to a set of 508 carefully selected emoji pairs. After validation with EmoSim508, we present a real-world use-case of our emoji embedding models using a sentiment analysis task and show that our models outperform the previous best-performing emoji embedding model on this task. The EmoSim508 dataset and our emoji embedding models are publicly released with this paper and can be downloaded from http://emojinet.knoesis.org/.Comment: This paper is accepted at Web Intelligence 2017 as a full paper, In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany: ACM, 201

Synthetic methods for catalytically active polyaniline/platinum composites

Polyaniline (PANI) is an organic semiconductor, which has attracted great attention in fundamental science and industries over the past decade due to its conductivity and electrochemical properties. The studies show that the three different synthetic methods produce PANI/Pt composites with different chemical compositions and electronic properties. The differences results from the overall oxidation state of the polymer and the interaction of nitrogen with the metallic Pt particles. The method of preparation influences the electrocatalytic oxidation of methanol and formic acid. Although, the electrochemically and chemically prepared PANI/Pt materials show some catalytic activity, they are not identical. PANI/Pt formed from the electrochemical reduction of PtCl 62- and PtCl42- shows enhanced catalytic activity relative to the other PANI/Pt composites. In addition, the catalytic activity of PANI/Pt prepared from the electrochemical reduction of PtCl42- is significantly enhanced relative to the PANI/Pt composite produced using PtCl62. (Abstract shortened by UMI.)