Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of the Error Probability for a Multilevel Modulation Scheme Using PSK-signaling1

We analyze a multilevel modulation scheme using PSK-signaling, when transmission takes place over a Gaussian channel. The receiver considered is the multistage receiver, using a suboptimal metric at each decoding stage. Upperbounding of the error probability for this scheme involves calculation of the Chernoff bounding parameter Z, to be used as argument in the code generating function. We argue that the conventional approximation of the Chernoff bounding parameter is not adequate, and new values of Z that tighten the error bounds without causing them to lose their validity are calculated. The capacity for this scheme is also calculated, and the result hereof is that the use of the suboptimal metric in multistage decoding causes very little degradation in capacity compared to when optimal metric is used in each stage of the multistage decoder.
Online Mapping and Perception Algorithms for Multi-robot Teams Operating in Urban Environments

Abstract : This thesis investigates some of the sensing and perception challenges faced by multi-robot teams equipped with LIDAR and camera sensors. Multi-robot teams are ideal for deployment in large, real-world environments due to their ability to parallelize exploration, reconnaissance or mapping tasks. However, such domains also impose additional requirements, including the need for a) online algorithms (to eliminate stopping and waiting for processing to finish before proceeding) and b) scalability (to handle data from many robots distributed over a large area). These general requirements give rise to specific algorithmic challenges, including 1) online maintenance of large, coherent maps covering the explored area, 2) online estimation of communication properties in the presence of buildings and other interfering structure, and 3) online fusion and segmentation of multiple sensors to aid in object detection. The contribution of this thesis is the introduction of novel approaches that leverage grid-maps and sparse multi-variate gaussian inference to augment the capability of multi-robot teams operating in urban, indoor-outdoor environments by improving the state of the art of map rasterization, signal strength prediction, colored point cloud segmentation, and reliable camera calibration. In particular, we introduce a map rasterization technique for large LIDAR-based occupancy grids that makes online updates possible when data is arriving from many robots at once. We also introduce new online techniques for robots to predict the signal strength to their teammates by combining LIDAR measurements with signal strength measurements from their radios. Processing fused LIDAR+camera point clouds is also important for many object-detection pipelines. We demonstrate a near linear-time online segmentation algorithm to this domain. However, maintaining the calibration of a fleet of 14 robots made this approach difficult to employ in practice.
The demise of Darwin's fishes: evidence of fishing down and illegal shark finning in the Galápagos Islands

The fauna of the Galapagos Island chain is characterized by high biodiversity and endemism. Thus, the conservation of its terrestrial and marine wildlife, including the sustainable management of local fisheries, is of paramount importance. Although the commercial exploitation of fish in the Galapagos did not intensify until the mid-1900s, issues of overexploitation and mismanagement are already of serious concern. However, to date, research on Galapagos fisheries has been largely species or island specific, and no long-term cumulative catch statistics exist. In this study, total landings associated with the industrial and artisanal fisheries of the Galapagos Islands were compiled and analysed in an effort to assess accurately the amount of seafood that has been extracted from this region over the last six decades. The total catch for all sectors from 1950–2010 was 797 000 t, of which industrially caught tuna made up 80%. The results also show a high degree of fishing down within the in-shore ecosystem catch, whereby planktivorous mullets have replaced high trophic level groupers within the past three decades. This shift has coincided with the spatial expansion of the Galapagos fishing fleet to areas further off-shore, where predatory species are not yet depleted. In addition to legally caught and exported seafood, Galapagos waters are also prone to illegal fishing. Of primary concern are shark finning practices that have escalated in intensity since the 1980s. Despite attempts at mitigation, this ecologically destructive and wasteful practice continues to occur in the Galapagos Marine Reserve. Copyright © 2014 John Wiley & Sons, Ltd.
Combining Top-Down and Ncut Methods for Figure-Ground Segmentation

To locate the object accurately in a scene for further vision processing, a novel approach for figure-ground segmentation is proposed, which combines the normalized-cut method (Ncut) and top-down method inspired by the trickle-up and trickle-down processing in primate visual pathways. Firstly, as the trickle-up stage, the Ncut method groups the pixels into multiple partitions based on the global criterion, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups. The computation of trickle-down includes mainly a covering operator, which covers the result of the trickle-up with the fragments of specific class. As one important computation in the trickle-down stage, the optimal method base on back-propagation neural network is utilized to improve the performance of the model. The proposed approach is applied to several segmentation experiments of clustering conditions. The results demonstrate that the performance of the proposed approach overpasses those achieved by previous top-down or bottom-up schemes on figure-ground segmentation. In addition to its application in computer vision, the success of this approach suggests a plausibility method, which combines the forward and backward processes for solving the visual perceptual grouping problem.
Reciprocal teaching : an exploration of its effectiveness in improving the vocabulary and reading comprehension of Key Stage Two pupils with and without English as an additional language

Background: The English National Curriculum identifies the acquisition of vocabulary as key to learning (DfE, 2015). Rich contexts provided by text produce robust vocabulary learning (National Reading Panel, 2000). Considering this, as well as evidence that teaching metacognition and reading comprehension are low cost and high impact approaches (Higgins, Katsipataki, Kokotsaki, Coleman, Major, & Coe, 2014), a Reciprocal Teaching intervention (Palincsar & Brown, 1984) was selected for a group of children with known vocabulary and reading comprehension difficulties. A systematic literature search indicated that little research has focused on the effectiveness of Reciprocal Teaching on vocabulary development. The current study aimed to address this gap and to explore the impact of Reciprocal Teaching on the vocabulary development and reading comprehension of monolingual pupils and children with English as an Additional Language (EAL) in the context of the English education system. Method: A purposive sample of 22 participants (aged 8-11) from two mainstream primary schools were selected by teachers according to vocabulary and reading comprehension needs. Nine pupils were monolingual and 13 spoke English as an additional language. All took part in a Reciprocal Teaching intervention, based on approaches devised by Palincsar and Brown. A convergent mixed methods design was employed; whereby quantitative data were collected pre- and post-intervention to measure vocabulary and reading comprehension. Qualitative measures were conducted post-intervention to gain participants’ perspectives. Results: Educationally significant gains were observed in vocabulary for participants who received the greatest number of Reciprocal Teaching sessions and for monolingual children overall. No improvement was observed for reading comprehension. Thematic analysis produced themes related to child engagement and Reciprocal Teaching implementation. Implications: This study contributes to the developing evidence-base regarding the effectiveness of Reciprocal Teaching in England. Implications for Educational Psychologists in facilitating implementation of interventions in schools are discussed.
SSH Dictionary Attack Detection Based on Flow Analysis

SSH services are run on many hosts with various scopes other than just operation, so dictionary attack against the service is a common security threat. SANS has reported the emergence of distributed SSH dictionary attacks, which are very stealthy in comparison with a simple one. Since even one success of such an attack causes serious problems, administrators should implement countermeasures. SSH dictionary attacks have been detected in two basic ways that rely on either log files or network traffic. Both approaches, however, have limitations. The first approach imposes upon administrators heavy maintenance costs, which grow linearly with the number of hosts in networks. The second approach cannot distinguish between successful and unsuccessful attacks. Of more immediate concern, neither approach is effective against stealthy attacks because the login attempts of these attacks have little impact on log files or network traffic. An ideal method would be able to detect individual attacks and distinguish between an attack's success or failure, using information derived from only network traffic. In this paper, we describe such a method, which was developed by combining two novel elements. First, on the basis of our assumptions, we use two criteria: "existence of a connection protocol" and "difference in the inter-arrival time of an auth-packet". These criteria are not available, though, owing to the confidentiality and flexibility of the SSH protocol. Second, we resolve this problem by identifying transition points of a sub-protocol through flow features and machine learning algorithms. We evaluate the effectiveness of the proposed method through experiments on real traffic traces collected at the edge in our campus networks. The experimental results are encouraging for this research direction, though they are derived from reduced datasets of SSH dictionary attacks and under simplifying assumptions. The significant contribution is the demonstration that an ideal method for detecting SSH dictionary attacks seems feasible.
Influence of Residue and Nitrogen Fertilizer Additions on Carbon Mineralization in Soils with Different Texture and Cropping Histories

To improve our ability to predict SOC mineralization response to residue and N additions in soils with different inherent and dynamic organic matter properties, a 330-day incubation was conducted using samples from two long-term experiments (clay loam Mollisols in Iowa [IAsoil] and silt loam Ultisols in Maryland [MDsoil]) comparing conventional grain systems (Conv) amended with inorganic fertilizers with 3 yr (Med) and longer (Long), more diverse cropping systems amended with manure. A double exponential model was used to estimate the size (C a, C s) and decay rates (k a, k s) of active and slow C pools which we compared with total particulate organic matter (POM) and occluded-POM (OPOM). The high-SOC IAsoil containing highly active smectite clays maintained smaller labile pools and higher decay rates than the low-SOC MDsoil containing semi-active kaolinitic clays. Net SOC loss was greater (2.6 g kg−1; 8.6%) from the IAsoil than the MDsoil (0.9 g kg−1, 6.3%); fractions and coefficients suggest losses were principally from IAsoil’s resistant pool. Cropping history did not alter SOC pool size or decay rates in IAsoil where rotation-based differences in OPOM-C were small. In MDsoil, use of diversified rotations and manure increased k a by 32% and k s by 46% compared to Conv; differences mirrored in POM- and OPOM-C contents. Residue addition prompted greater increases in C a (340% vs 230%) and C s (38% vs 21%) and decreases in k a (58% vs 9%) in IAsoil than MDsoil. Reduced losses of SOC from residue-amended MDsoil were associated with increased OPOM-C. Nitrogen addition dampened CO2-C release. Clay type and C saturation dominated the IAsoil’s response to external inputs and made labile and stable fractions more vulnerable to decay. Trends in OPOM suggest aggregate protection influences C turnover in the low active MDsoil. Clay charge and OPOM-C contents were better predictors of soil C dynamics than clay or POM-C contents.
The Sphereprint: An approach to quantifying the conformability of flexible materials

The Sphereprint is introduced as a means to characterize hemispherical conformability, even when buckling occurs, in a variety of flexible materials such as papers, textiles, nonwovens, films, membranes, and biological tissues. Conformability is defined here as the ability to fit a doubly curved surface without folding. Applications of conformability range from the fit of a wound dressing, artificial skin, or wearable electronics around a protuberance such as a knee or elbow to geosynthetics used as reinforcements. Conformability of flexible materials is quantified by two dimensionless quantities derived from the Sphereprint. The Sphereprint ratio summarizes how much of the specimen conforms to a hemisphere under symmetric radial loading. The coefficient of expansion approximates the average stretching of the specimen during deformation, accounting for hysteresis. Both quantities are reproducible and robust, even though a given material folds differently each time it conforms. For demonstration purposes, an implementation of the Sphereprint test methodology was performed on a collection of cellulosic fibrous assemblies. For this example, the Sphereprint ratio ranked the fabrics according to intuition from least to most conformable in the sequence: paper towel, plain weave, satin weave, and single knit jersey. The coefficient of expansion distinguished the single knit jersey from the bark weave fabric, despite them having similar Sphereprint ratios and, as expected, the bark weave stretched less than the single knit jersey did during conformance. This work lays the foundation for engineers to quickly and quantitatively compare the conformance of existing and new flexible materials, no matter their construction.
Full duplex device-to-device communication in cellular networks

In this paper we investigate the applicability of inband full-duplex (FD) radios in device-to-device communication (D2D) in cellular systems. The key challenge in FD radio design is the large self-interference on receiver. It is not possible to completely cancel the self-interference in FD radios. Considering the residual of self-interference, we study the performance of system while D2D link is operating in half-duplex and full-duplex mode. A cellular system is considered while D2D users are exploiting the same resources with cellular users in uplink. We apply a power control method for D2D pair to limit the interference from them on base station. For interference management from cellular transmissions on D2D receivers, interference limited area method is used. Simulation results show that using the most recent FD radios in D2D systems can almost double the throughput of the D2D link.
Roles and Responsibilities of White Allies: Implications for Research, Teaching, and Practice

In this article the authors introduce the Major Contribution addressing the roles and responsibilities of White allies in counseling psychology and related fields. To provide context for the three articles that follow, the authors define White allies, describe potential pitfalls and roadblocks for psychologists involved in racial justice ally efforts, and note scholarly criticisms of White ally work. The authors provide a brief overview of each of the three articles that comprise the Major Contribution, all of which apply concepts associated with White allyship to multicultural psychology research, education, and clinical practice.
A Rule-Based Source-Side Reordering on Phrase Structure Subtrees

Since different languages put words in different orders, reordering is an important issue in statistical machine translation. The paper proposes a rule-based reordering method at the source side as a preprocessing step, which applies some syntactic reordering rules on the phrase structure subtree to reorder source language. The reordering rules integrate the phrase structure tree with part-of-speech tags, which can implement the reordering not only between words but also between words and phrases. And the problems of long-distance reordering and translation errors can be partly solved. Meanwhile, the interference between reordering rules of this method has been significantly reduced in this method. Experiments shows that our method can improve the performance of the state-of-the-art phrase translation models, achieving 1.71 BLEU score increase over the standard phrase-based machine translation system.
Investigation of negative sequence injection capability in H-bridge multilevel STATCOM

The aim of this paper is to investigate the ability of star- and delta-connected H-bridge multilevel STATCOMs to exchange negative-sequence current with the grid. Zero-sequence injection is utilized for capacitor voltage balancing. It is shown that a singularity for these configurations exists, leading to limitations in the utilization of the compensator for asymmetry compensation purposes.
The Development and Implementation of the Technological Creativity Course : An Interdisciplinary Approach

The teachers’ experiences on developing an interdisciplinary course, “Open-Ended Creative Mechanical Engineering Design,” for technological creativity cultivation are discussed in this paper. The paper will cover three essential aspects of the course: (1) the rationale to incorporate technological creativity in engineering education, (2) the design of the course, including hands-on activities, instructional strategies, and supplemental materials on technological creativity, and (3) the effects of interdisciplinary teaching on students. This course is jointly designed and taught by four professors of interdisciplinary backgrounds: creative learning, thermo-fluid, design, education, and computer network based learning. The learning modules developed include: (1) utilizing mechanical engineering design procedures, i.e. brainstorming, evaluating, and implementing the team project; (2) experiencing the four phases of technological creativity problem solving, i.e. preparation, incubation, illumination, and implementation; and (3) executing the eight essential procedures of a hands -on creativity contest and project design. The course has been developed and taught for two years by engineering professors. Since this year, quantitative and qualitative data of student interviews, questionnaires, classroom observations and discussions on web-bbs have been collected and analyzed with the participation of faculties in education in order to understand learning difficulties and to look for inspiration of improvement.
A Large Deviations Result for Aggregation of Independent Noisy Observations

Sensing and aggregation of noisy observations should not be considered as separate issues. The quality of collective estimation involves a difficult tradeoff between sensing quality which increases by increasing the number of sensors, and aggregation quality which typically decreases if the number of sensors is too large. We examine a strategy for optimal aggregation for an ensemble of independent sensors with constrained system capacity. We show that in the large capacity limit larger scale aggregation always outperforms smaller scale aggregation at higher noise levels, while below a critical value of noise, there exist moderate scale aggregation levels at which optimal estimation is realized.
Efficient visual hull computation for real-time 3D reconstruction using CUDA

In this paper we present two efficient GPU-based visual hull computation algorithms. We compare them in terms of performance using image sets of varying size and different voxel resolutions. In addition, we present a real-time 3D reconstruction system which uses the proposed GPU-based reconstruction method to achieve real-time performance (30 fps) using 16 cameras and 4 PCs.
Automatic facial action analysis

This thesis provides a fully automatic framework to analyze the facial actions and head gestures in real time. This framework can be used in scenarios where the machine needs a perceptual ability to recognize, model and analyze the facial actions and head gestures in real time without any manual intervention. Rather than trying to recognize speci c prototypical emotional expressions like joy, anger, surprise and fear, this system aims to recognize the head gestures and the upper facial action units such as eyebrow raises, frowns and squints. These facial action units (AUs) are enumerated in Paul Ekman's Facial Action Coding System (FACS) [17] and are essentially building blocks, which can be assembled to form facial expressions. The system rst robustly tracks the pupils using an infrared sensitive camera equipped with infrared LEDs. For each frame, the pupil positions are used to localize regions of eyes and eyebrow, which are analyzed using statistical techniques to recover parameters that relate to the shape of the facial features. These parameters are used as input to classi ers based on Support Vector Machines to recognize upper facial action units and their all possible combinations. The system detects head gestures using Hidden Markov Models that use pupil positions in consecutive frames as observations. The system is evaluated on completely natural dataset with lots of head movements, pose changes and occlusions. The system can successfully detect head gestures 78.46% of time. Recognition accuracy of 67.83% for each individual AU is reported and the system can correctly identify all possible AU combinations with an accuracy of 61.25%. Thesis Supervisor: Rosalind W. Picard, Associate Professor of Media Arts and Sciences. This research was supported by NSF ROLE grant 0087768 and also is an output from a research project funded by Media Lab Asia. Media Lab Asia is funded in part by the Ministry of Information Technology, Government of India. The research was carried out in support of the Media Lab Asia Program by Massachusetts Institute of Technology's Media Laboratory itself. Media Lab Asia does not accept responsibility for any information provided or views expressed.