Students of Haitian Descent in American Schools: Challenges and Issues

This paper examines the ways the reception of students of Haitian descent in this country has shaped their educational careers. Additionally, this paper explores the racial, cultural, and individual differences that need to be understood in order to help educators, parents, and students make their schooling a positive experience.

Substructuring in four populations of African descent

When a suspect's DNA profile is admitted into court as a match to evidence the probability of the perpetrator being another individual must be calculated from database allele frequencies. The two methods used for this calculation are phenotypic frequency and likelihood ratio. Neither of these calculations takes into account substructuring within populations. In these substructured populations the frequency of homozygotes increases and that of heterozygotes usually decreases. The departure from Hardy- Weinberg expectation in a sample population can be estimated using Sewall Wright's Fst statistic. Fst values were calculated in four populations of African descent by comparing allele frequencies at three short tandem repeat loci. This was done by amplifying the three loci in each sample using the Polymerase Chain Reaction and separating these fragments using polyacrylamide gel electrophoresis. The gels were then silver stained and autoradiograms taken, from which allele frequencies were estimated. Fst values averaged 0.007+- 0.005 within populations of African descent and 0.02+- 0.01 between white and black populations.

TAFRO Syndrome in a Patient of South-American Descent

A 31-year-old Caucasian woman of South-American descent was diagnosed with a variant of multicentric Castleman disease (MCD) that has been reported in Japan as Castleman-Kojima disease. This is a systemic inflammatory disorder known as TAFRO Syndrome which includes thrombocytopenia, polyserositis (ascites/pleural effusion), microcytic anemia, myelofibrosis, fever, renal dysfunction and organomegaly, with immunologic disorder, polyclonal hypergammaglobulinemia, and elevated levels of interleukin-6 (IL-6) and the vascular endothelial growth factor present in serum and/or effusions. Optimal therapies are not well established. The patient was treated with methylprednisolone and rituximab. Following the start of treatment, the patient has been asymptomatic for over 8 months.

The Descent of Pluto: Interactive dynamics, specialisation and reciprocity of roles in a Wikipedia debate

International audience

Lines of Descent d'Anthony K. Appiah : un regard oblique sur le panafricanisme

Sur les traces de l’auteur afro-américain W.E.B. Du Bois, le philosophe Anthony Kwame Appiah se préoccupe dans son Lines of Descent (2014) de l’articulation entre identité personnelle, nationalisme culturel et universel cosmopolitique, à la lumière du signifiant social de la race. Appiah se penche spécifiquement sur l’influence qu’a exercée la pensée allemande de la fin du 19e siècle sur le développement de la pensée de Du Bois. Dans la foulée de travaux antérieurs 1, il s’y intéresse à la question de l’identité raciale et à la place qu’elle occupe dans sa théorisation du panafricanisme. Après un survol biographique des auteurs abordés, cette note de lecture s’intéressera à ces trois thèses fortes en les soumettant à quelques réflexions critiques.

"Lines of Descent" d'Anthony K. Appiah : un regard oblique sur le panafricanisme.

Sur les traces de l’auteur afro-américain W.E.B. Du Bois, le philosophe Anthony Kwame Appiah se préoccupe dans son Lines of Descent (2014) de l’articulation entre identité personnelle, nationalisme culturel et universel cosmopolitique, à la lumière du signifiant social de la race. Appiah se penche spécifiquement sur l’influence qu’a exercée la pensée allemande de la fin du 19e siècle sur le développement de la pensée de Du Bois. Dans la foulée de travaux antérieurs 1, il s’y intéresse à la question de l’identité raciale et à la place qu’elle occupe dans sa théorisation du panafricanisme. Après un survol biographique des auteurs abordés, cette note de lecture s’intéressera à ces trois thèses fortes en les soumettant à quelques réflexions critiques.

Automating human thought processes for a UAV forced landing

This paper describes the current status of a program to develop an automated forced landing system for a fixed-wing Unmanned Aerial Vehicle (UAV). This automated system seeks to emulate human pilot thought processes when planning for and conducting an engine-off emergency landing. Firstly, a path planning algorithm that extends Dubins curves to 3D space is presented. This planning element is then combined with a nonlinear guidance and control logic, and simulated test results demonstrate the robustness of this approach to strong winds during a glided descent. The average path deviation errors incurred are comparable to or even better than that of manned, powered aircraft. Secondly, a study into suitable multi-criteria decision making approaches and the problems that confront the decision-maker is presented. From this study, it is believed that decision processes that utilize human expert knowledge and fuzzy logic reasoning are most suited to the problem at hand, and further investigations will be conducted to identify the particular technique/s to be implemented in simulations and field tests. The automated UAV forced landing approach presented in this paper is promising, and will allow the progression of this technology from the development and simulation stages through to a prototype system

Robot navigation in sensor space

This thesis investigates the problem of robot navigation using only landmark bearings. The proposed system allows a robot to move to a ground target location specified by the sensor values observed at this ground target posi- tion. The control actions are computed based on the difference between the current landmark bearings and the target landmark bearings. No Cartesian coordinates with respect to the ground are computed by the control system. The robot navigates using solely information from the bearing sensor space. Most existing robot navigation systems require a ground frame (2D Cartesian coordinate system) in order to navigate from a ground point A to a ground point B. The commonly used sensors such as laser range scanner, sonar, infrared, and vision do not directly provide the 2D ground coordi- nates of the robot. The existing systems use the sensor measurements to localise the robot with respect to a map, a set of 2D coordinates of the objects of interest. It is more natural to navigate between the points in the sensor space corresponding to A and B without requiring the Cartesian map and the localisation process. Research on animals has revealed how insects are able to exploit very limited computational and memory resources to successfully navigate to a desired destination without computing Cartesian positions. For example, a honeybee balances the left and right optical flows to navigate in a nar- row corridor. Unlike many other ants, Cataglyphis bicolor does not secrete pheromone trails in order to find its way home but instead uses the sun as a compass to keep track of its home direction vector. The home vector can be inaccurate, so the ant also uses landmark recognition. More precisely, it takes snapshots and compass headings of some landmarks. To return home, the ant tries to line up the landmarks exactly as they were before it started wandering. This thesis introduces a navigation method based on reflex actions in sensor space. The sensor vector is made of the bearings of some landmarks, and the reflex action is a gradient descent with respect to the distance in sensor space between the current sensor vector and the target sensor vec- tor. Our theoretical analysis shows that except for some fully characterized pathological cases, any point is reachable from any other point by reflex action in the bearing sensor space provided the environment contains three landmarks and is free of obstacles. The trajectories of a robot using reflex navigation, like other image- based visual control strategies, do not correspond necessarily to the shortest paths on the ground, because the sensor error is minimized, not the moving distance on the ground. However, we show that the use of a sequence of waypoints in sensor space can address this problem. In order to identify relevant waypoints, we train a Self Organising Map (SOM) from a set of observations uniformly distributed with respect to the ground. This SOM provides a sense of location to the robot, and allows a form of path planning in sensor space. The navigation proposed system is analysed theoretically, and evaluated both in simulation and with experiments on a real robot.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

Robust speech recognition using speech enhancement

Automatic Speech Recognition (ASR) has matured into a technology which is becoming more common in our everyday lives, and is emerging as a necessity to minimise driver distraction when operating in-car systems such as navigation and infotainment. In “noise-free” environments, word recognition performance of these systems has been shown to approach 100%, however this performance degrades rapidly as the level of background noise is increased. Speech enhancement is a popular method for making ASR systems more ro- bust. Single-channel spectral subtraction was originally designed to improve hu- man speech intelligibility and many attempts have been made to optimise this algorithm in terms of signal-based metrics such as maximised Signal-to-Noise Ratio (SNR) or minimised speech distortion. Such metrics are used to assess en- hancement performance for intelligibility not speech recognition, therefore mak- ing them sub-optimal ASR applications. This research investigates two methods for closely coupling subtractive-type enhancement algorithms with ASR: (a) a computationally-efficient Mel-filterbank noise subtraction technique based on likelihood-maximisation (LIMA), and (b) in- troducing phase spectrum information to enable spectral subtraction in the com- plex frequency domain. Likelihood-maximisation uses gradient-descent to optimise parameters of the enhancement algorithm to best fit the acoustic speech model given a word se- quence known a priori. Whilst this technique is shown to improve the ASR word accuracy performance, it is also identified to be particularly sensitive to non-noise mismatches between the training and testing data. Phase information has long been ignored in spectral subtraction as it is deemed to have little effect on human intelligibility. In this work it is shown that phase information is important in obtaining highly accurate estimates of clean speech magnitudes which are typically used in ASR feature extraction. Phase Estimation via Delay Projection is proposed based on the stationarity of sinusoidal signals, and demonstrates the potential to produce improvements in ASR word accuracy in a wide range of SNR. Throughout the dissertation, consideration is given to practical implemen- tation in vehicular environments which resulted in two novel contributions – a LIMA framework which takes advantage of the grounding procedure common to speech dialogue systems, and a resource-saving formulation of frequency-domain spectral subtraction for realisation in field-programmable gate array hardware. The techniques proposed in this dissertation were evaluated using the Aus- tralian English In-Car Speech Corpus which was collected as part of this work. This database is the first of its kind within Australia and captures real in-car speech of 50 native Australian speakers in seven driving conditions common to Australian environments.

Optimising figure of merit for phonetic spoken term detection

This paper introduces a novel technique to directly optimise the Figure of Merit (FOM) for phonetic spoken term detection. The FOM is a popular measure of sTD accuracy, making it an ideal candiate for use as an objective function. A simple linear model is introduced to transform the phone log-posterior probabilities output by a phe classifier to produce enhanced log-posterior features that are more suitable for the STD task. Direct optimisation of the FOM is then performed by training the parameters of this model using a non-linear gradient descent algorithm. Substantial FOM improvements of 11% relative are achieved on held-out evaluation data, demonstrating the generalisability of the approach.

Forced landing technologies for unmanned aerial vehicles : towards safer operations

While using unmanned systems in combat is not new, what will be new in the foreseeable future is how such systems are used and integrated in the civilian space. The potential use of Unmanned Aerial Vehicles in civil and commercial applications is becoming a fact, and is receiving considerable attention by industry and the research community. The majority of Unmanned Aerial Vehicles performing civilian tasks are restricted to flying only in segregated space, and not within the National Airspace. The areas that UAVs are restricted to flying in are typically not above populated areas, which in turn are the areas most useful for civilian applications. The reasoning behind the current restrictions is mainly due to the fact that current UAV technologies are not able to demonstrate an Equivalent Level of Safety to manned aircraft, particularly in the case of an engine failure which would require an emergency or forced landing. This chapter will preset and guide the reader through a number of developments that would facilitate the integration of UAVs into the National Airspace. Algorithms for UAV Sense-and-Avoid and Force Landings are recognized as two major enabling technologies that will allow the integration of UAVs in the civilian airspace. The following sections will describe some of the techniques that are currently being tested at the Australian Research Centre for Aerospace Automation (ARCAA), which places emphasis on the detection of candidate landing sites using computer vision, the planning of the descent path trajectory for the UAV, and the decision making process behind the selection of the final landing site.