Miracle-dz/UniFAR-FTUs huggingface dataset:
CSE 252 c Fall 03 . Project Report . Calibration-Free Augmented Reality

Described as ”closed loop” system [2], vision-based emerges as the most potential solution for registration among other techniques like magnetic, optical or mechanical tracker. In [3], camera position is recovered based on frame-to-frame homographies. This algorithm demands the existence of planar surfaces in the scene. Although this requirement seems to be satisfied in most scenarios, the method may fail with the plain (non-texture) surface for it strongly depends on the corner detection. Besides, the paper does not give a clear solution to prevent registration error propagating through the sequence. Some approaches employ fiducial points to help tracking. State et al. [4] uses 12 special color landmarks. This system combines vision-based with magnetic tracker to reduce the registration error down to 1 pixels. However, cumbersome initialization, involving placing landmarks and calibration, and limitation of the magnetic tracker’s range makes this approach undesirable for general use. Kutulakos and Vallino [5] develop a method based on affine transformation. Without the need of camera calibration and require only few fiducial points, the system can achieve the accuracy within 15 pixels for 640x480 images. The major drawback of the algorithm is constraints of the affine space and the fiducial points must be visible at all times. Seo and Hong [6] extends this algorithm to the perspective case and able to compute shadow of the virtual object.
Accurate Stemming of Dutch for Text Classification

This paper investigates the use of stemming for classification of Dutch (email) texts. We introduce a stemmer, which combines dictionary lookup (implemented efficiently as a finite state automaton) with a rule-based backup strategy and,how, that it outperforms the Dutch Porter stemmer in terms of accuracy. while not being substantially slower. For text classification, the most important property of a stemmer is the number of words it (correctly) reduces to the same stem. Here the dictionary - based system also outperforms Porter. However, evaluation of a Bayesian text classification system with either no stemming or the Porter or dictionary-based stemmer on an email classification and a newspaper topic classification task does not lead to significant differences in accuracy. We conclude with an analysis of why this is the case.
Inhibition of glioblastoma malignancy by Lgl1

lethal giant larvae (lgl) was first identified as a tumor suppressor in Drosophila, where its loss repressed the differentiation and promoted the invasion of neuroblasts, the Drosophila equivalent of the neural stem cell. Recently we have shown that a human homolog of Lgl, Lgl1 (LLGL1), is constitutively phosphorylated and inactivated in glioblastoma cells; this occurs as a downstream consequence of PTEN loss, one of the most frequent genetic events in glioblastoma. Here we have investigated the consequences of this loss of functional Lgl1 in glioblastoma in vivo. We used a doxycycline-inducible system to express a non-phosphorylatable, constitutively active version of Lgl1 (Lgl3SA) in either a glioblastoma cell line or primary glioblastoma cells isolated under neural stem cell culture conditions from patients. In both types of cells, expression of Lgl3SA, but not wild type Lgl1, inhibited cell motility in vitro. Induction of Lgl3SA in intracerebral xenografts markedly reduced the in vivo invasion of primary glioblastoma cells. Lgl3SA expression also induced the differentiation of glioblastoma cells in vitro and in vivo along the neuronal lineage. Thus the central features of Lgl function as a tumor suppressor in Drosophila are conserved in human glioblastoma.
Performance optimization of a UWB-based network for safety-critical avionics

To reduce the aircraft weight and maintenance costs while guaranteeing system performance and reliability, an alternative avionic communication architecture based on Ultra Wide Band (UWB) and TDMA protocol is proposed to replace the back-up part of safety-critical avionics network. The analysis and performance optimization of such a proposal is tackled as follows. First, appropriate system modeling and timing analysis, using Network Calculus and Integer Linear Programing (ILP) approach, are provided to evaluate the end-to-end delays and verify system predictability. Then, an optimization approach to find the optimal TDMA cycle duration, which minimizes the end-to-end delays, is proposed. Finally, the efficiency of our proposal to enhance the system performance is validated through a realistic avionic case study.
Pivotal Advance: IgE accelerates in vitro development of mast cells and modifies their phenotype

Antigen‐dependent activation of IgE‐bound mast cells is critical for immediate hypersensitivity and other allergic disorders. Recent studies have revealed the effects of monomeric IgEs on mast cell survival and activation. Furthermore, IgE molecules exhibit a wide range of heterogeneity in the ability to induce mast cell activation in the absence of antigen. Highly cytokinergic (HC) IgEs can induce a variety of activation events including cell survival, degranulation, cytokine production, and migration, whereas poorly cytokinergic (PC) IgEs can do so inefficiently. Here, we show that culture of bone marrow cells in the presence of monomeric IgEs results in an increased number of mast cells compared with cultures grown without IgE. Furthermore, time in culture required to generate ≥80% pure mast cells is decreased. IgE molecules can directly influence mast cell progenitors to differentiate into mast cells. mRNA expression of several mast cell proteases and mast cell‐related transcription factors is higher in mast cells cultured with an HC IgE than those cultured with a PC IgE or without IgE. Expression of early growth response factor‐1, a transcription factor that is involved in the production of TNF‐α in mast cells, is enhanced in cultures containing high and low concentrations of HC IgE and a high concentration of PC IgE. Consistent with this, expression of TNF‐α is higher in mast cells cultured with HC IgE than PC IgE. Therefore, our results suggest that monomeric IgEs, especially HC IgEs, not only promote mast cell development but also modulate the mast cell phenotype.
Improving paddling efficiency through raising sitting height in female white water kayakers

Abstract The study compared female white water paddlers over two conditions: with seat raise and with no seat raise. The aim was to determine whether raising the sitting height would improve paddling efficiency. Sitting height of each participant was recorded in order to calculate the seat raise height required and three-dimensional kinematic data was collected for six participants over both conditions. Twelve measures of efficiency were utilised. The efficiency of all participants improved on the seat condition for ≥4 of the measures, with three participants showing improvement for ≥6 of the measures. The stern snaking measure had the highest value of significance (P = 0.1455) and showed an average of 11.98% reduction in movement between no seat and seat conditions. The results indicate that improvements were seen although these were individualistic. Therefore it can be concluded that it is worth experimenting with a seat raise for a female kayaker who is lacking efficiency, noting, however, that improvements might depend on anthropometrics and the seat height selected, and therefore could elicit differing results.
PATCH-BASED MARKOVMODELS FOR CHANGE DETECTION IN IMAGE SEQUENCE ANALYSIS

Change detection between two images is challenging and needed in a wide variety of imaging applications. Several approaches have been yet developed, especially methods based on difference image. In this paper, we propose an original patch-based Markov modeling framework to detect spatial irregularities in the difference image with low false alarm rates. Experimental results show that the proposed approach performs well for change detection, especially for images with low signal-to-noise ratios.
Early Proliferation Stage of Detecting Diabetic Retinopathy Using Bayesian Classifier Based Level Set Segmentation

This article presents Bayesian Classifier which controls the levels set segmentation and it detect the retinal clots at an early stage from the image captured from fundus camera. The classifier is a probabilistic and used for the control of level set contour propagation for the detection of class clot defined, extracting the retinal vessels even with minute deformation due to the clots. The algorithm is tested in MATLAB on fundus images taken at various stages of progression and results which proves the effectiveness of the proposed method.
Structure of YraM, a protein essential for growth of Haemophilus influenzae

Nontypeable Haemophilus influenzae is an obligate human parasite that often causes middle ear infections in children and exacerbates chronic obstructive pulmonary disorder, the fourth leading cause of death in the United States. There are no effective vaccines available for this strain. The lipoprotein YraM (gene HI1655) was identified as essential for the growth and viability of H. influenzae but its function is unknown. Sequence comparisons showed that YraM is a fusion of two protein modules. We grew crystals of the carboxyl‐terminal module of YraM comprising residues 257–573 (YraM‐C), phased the diffraction data by the multiwavelength anomalous diffraction technique, and refined the model to a crystallographic R‐factor of 0.16 (Rfree = 0.19) with data to 1.35 Å resolution. The two‐domain structure of YraM‐C adopts a fold similar to that observed for the open, unliganded forms of several periplasmic binding proteins (PBPs) involved in bacterial active transport. Sequence alignments of YraM homologues from other Gram‐negative species showed that the most conserved residues of YraM‐C cluster between the two domains in the location where other PBPs bind their cognate ligand. Modeling of YraM‐C into a closed conformation similar to the leucine‐bound form of the Leu/Ile/Val‐binding protein (LIVBP) shows a putative binding pocket larger than the leucine‐binding site in LIVBP. The pocket has both polar and nonpolar surfaces, with the latter located in the same area where a leucine side chain binds to LIVBP. We discuss possible biological functions of YraM considering its predicted location in the outer membrane, a novel place for such a binding protein. Proteins 2008. © 2008 Wiley‐Liss, Inc.
Character categorization via latent Dirichlet allocation for Kana sequence segmentation with conditional random fields

We propose an efficient Kana sequence segmentation as a component of faster and easier interfaces for e-learning systems. We assign categories to Kana characters via latent Dirichlet allocation (LDA) and use the categories to compose additional features for conditional random fields (CRF). We compare the categories our method gives and those manually prepared by their efficiency in Kana sequence segmentation.
Searching With Style: Authorship Attribution in Classic Literature

It is a truism of literature that certain authors have a highly recognizable style. The concept of style underlies the authorship attribution techniques that have been applied to tasks such as identifying which of several authors wrote a particular news article. In this paper, we explore whether the works of authors of classic literature can be correctly identified with either of two approaches to attribution, using a collection of 634 texts by 55 authors. Our results show that these methods can be highly accurate, with errors primarily for authors where it might be argued that style is lacking. And did Marlowe write the works of Shakespeare? Our preliminary evidence suggests not.
A complex choreography of cell movements shapes the vertebrate eye

Optic cup morphogenesis (OCM) generates the basic structure of the vertebrate eye. Although it is commonly depicted as a series of epithelial sheet folding events, this does not represent an empirically supported model. Here, we combine four-dimensional imaging with custom cell tracking software and photoactivatable fluorophore labeling to determine the cellular dynamics underlying OCM in zebrafish. Although cell division contributes to growth, we find it dispensable for eye formation. OCM depends instead on a complex set of cell movements coordinated between the prospective neural retina, retinal pigmented epithelium (RPE) and lens. Optic vesicle evagination persists for longer than expected; cells move in a pinwheel pattern during optic vesicle elongation and retinal precursors involute around the rim of the invaginating optic cup. We identify unanticipated movements, particularly of central and peripheral retina, RPE and lens. From cell tracking data, we generate retina, RPE and lens subdomain fate maps, which reveal novel adjacencies that might determine corresponding developmental signaling events. Finally, we find that similar movements also occur during chick eye morphogenesis, suggesting that the underlying choreography is conserved among vertebrates.
Multi Target Acoustic Source Tracking using Track Before Detect

Particle Filter-based Source Localisation algorithms attempt to track the position of a sound source - a person speaking in a room - based on the current data from a distributed microphone array as well as all previous data up to that point. This paper introduces a multi-target methodology for acoustic source tracking. The methodology is based upon the Track Before Detect (TBD) framework. The algorithm also implicitly evaluates the source activity using a variable appended to the state vector. Examples of typical tracking performance are given using a set of real speech recordings with two sources active simultaneously.
Wired to Connect: Neuroscience, Relationships, and Therapy

As family therapists, we are accustomed to considering the multisystemic contexts of our clients’ lives. Although we have extended our gaze to the macro systems in which families function, we do not generally have the knowledge to grasp the microsystemic level of our clients’ internal experience in terms of brain processes. In the last decade, due in part to advanced neuroimaging techniques such as the fMRI, neuroscientists’ understanding of the human brain has grown dramatically. The body of research data, which can be highly technical for nonspecialists, is becoming widely accessible thanks to several authors who integrate and present it in a compelling and even inviting manner. It is impossible to cover the vast and rapidly evolving field of neuroscience here. In this article, I will focus on what Daniel Siegel (1999) calls ‘‘interpersonal neurobiology,’’ highlighting relationships, emotion, and the brain. As I read this literature, I find a tremendous overlap between our systemic perspective and the picture of the human being that is emerging from neuroscience. Understanding the key ideas from this developing field can enhance our own work, both theoretically and clinically. I will explore as well some clinical interventions that may be useful in integrating interpersonal neurobiology into our work as family and couple therapists.
Implications of the Measured Image Size for the Radio Afterglow of GRB 030329

We use data on the image size of the radio afterglow of GRB 030329 to constrain the physical parameters of this explosion. Together with the observed broadband spectrum, this data overconstrains the physical parameters, thus enabling us to test different GRB jet models for consistency. We consider two extreme models for the lateral spreading of the jet: model 1, with relativistic expansion in the local rest frame, and model 2, with little lateral expansion as long as the jet is highly relativistic. We find that both models are consistent with the data for a uniform external medium, while for a stellar wind environment model 1 is consistent with the data but model 2 is disfavored by the data. Our derivations can be used to place tighter constraints on the dynamics and structure of GRB jets in future afterglows, following a denser monitoring campaign for the temporal evolution of their image size.
Real-time traffic classification based on statistical tests for matching signatures with packet length distributions

Classifying network traffic constitutes one of the most defying research topics nowadays. On the one hand, traffic classification is critical for assuring Quality of Service (QoS), the correct functioning and the security of the networks. On the other hand, the increasing complexity and throughput of networks, added to the problems caused by encryption and other evasive techniques, make of traffic classification a difficult and expensive task to perform. This paper describes a traffic classification in the dark mechanism based on matching several empirical distributions representing computer applications with the one of the target traffic. The classifier combines two methods for performing such matching in real-time and on a packet-by-packet manner: one based on the Kolmogorov-Smirnov test, and another one based on the Chi-Squared test. The experimental results included show that the mechanism is accurate and suitable for implementations demanding real-time operation.
A Team Formation and Project-based Learning Support Service for Social Learning Networks

The Internet affords new approaches to learning. Geographically dispersed self- directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks do not readily support effective, coherence-creating and motivating learning settings. It is argued that providing project-based learning opportunities and team formation services can help overcome these shortcomings. A review of existing team formation tools evidences that a new design for team formation and the initiation of project- based learning is required before these can be supported in social learning networks. A design is proposed which identifies 'knowledge', 'personality' and 'preferences' as categories in which data is needed to form teams, and it specifies how the required data are gathered and assessed. The design defines rules deduced from team formation principles from prior team formation research to optimise team formations towards increased productivity, creative solutions or higher learning outcomes. The rules are implemented in three team formation expressions each calculating one of the desired team formations. The expressions are deployed on a set of test data, demonstrating the effectiveness of the team formation service design. The article includes a discussion of the results and provides indications for future research.
Using machine learning to predict the driving context whilst driving

This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.
A SURVEY ON SENTIMENT ANALYSIS AND OPINION MINING

Sentiment analysis is a machine learning approach i n which machines analyze and classify the human’s s entiments, emotions, opinions etc about some topic which are expressed i n the form of either text or speech. The textual da ta available in the web is increasing day by day. In order to enhance the sale s of a product and to improve the customer satisfac tion, most of the on-line shopping sites provide the opportunity to customers to write reviews about products. These reviews are l rge in number and to mine the overall sentiment or opinion polarity from all of them, sentiment analysis can be used. Manua l an lysis of such large number of reviews is practically impossible. Theref o automated approach of a machine has significant role in solving this hard problem. The major challenge of the area of Sentime nt analysis and Opinion mining lies in identifying the emotions expressed in these texts. This literature survey is done to stud y the sentiment analysis problem in-depth and to fa mili rize with other works done on the subject.
Hypergraph Based Visual Segmentation and Retrieval

This chapter explores original techniques for the construction of hypergraph models for computer vision applications. A hypergraph is a generalization of a pairwise simple graph, where an edge can connect any number of vertices. The expressive power of the hypergraph models places a special emphasis on the relationship among three or more objects, which has made hypergraphs better models of choice in a lot of problems. This is in sharp contrast with the more conventional graph representation of visual patterns where only pairwise connectivity between objects is described. This chapter draws up from three aspects to carry the discussion of hypergraph based methods in computer vision: (i) The advantage of the hypergraph neighborhood structure is analyzed. The author argues that the summarized local grouping information contained in hypergraphs causes an ‘averaging’ effect which is beneficial to the clustering problems; (ii)The chapter discusses how to build hypergraph incidence structures and how to solve the related unsupervised and semi-supervised problems for two different computer vision scenarios: video object segmentation and image retrieval; (iii) For the application of image retrieval, the chapter introduces a novel hypergraph model — probabilistic hypergraph to exploit the structure of the data manifold by considering not only the local grouping information, but also the similarities between vertices in hyperedges.
On boundary effects in 3-D wavelet video coding

3-D wavelet-based scalable video coding provides a viable alternative to standard MC-DCT coding. However, many current 3-D wavelet coders experience severe boundary effects across group of picture (GOP) boundaries. This paper proposes a memory efficient transform technique via lifting that effectively computes wavelet transforms of a video sequence continuously on the fly, thus eliminating the boundary effects due to limited length of individual GOPs. Coding results show that the proposed scheme completely eliminates the boundary effects and gives superb video playback quality.
A Dual Approach to Semidefinite Least-Squares Problems

In this paper, we study the projection onto the intersection of an affine subspace and a convex set and provide a particular treatment for the cone of positive semidefinite matrices. Among applications of this problem is the calibration of covariance matrices. We propose a Lagrangian dualization of this least-squares problem, which leads us to a convex differentiable dual problem. We propose to solve the latter problem with a quasi-Newton algorithm. We assess this approach with numerical experiments which show that fairly large problems can be solved efficiently.