Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Hybrid Tracking using Edge and Texture Information

This paper proposes a real-time, robust and effective tracking framework for visual servoing applications. The algorithm is based on the fusion of visual cues and on the estimation of a transformation (either a homography or a 3D pose). The parameters of this transformation are estimated using a non-linear minimization of a unique criterion that integrates information both on the texture and the edges of the tracked object. The proposed tracker is more robust and performs well in conditions where methods based on a single cue fail. The framework has been tested for 2D object motion estimation and pose computation. The method presented in this paper has been validated on several video sequences as well as in visual servoing experiments considering various objects. Results show the method to be robust to occlusions or textured backgrounds and suitable for visual servoing applications.
Automorphisms, root systems, and compactifications of homogeneous varieties

Let G be a complex semisimple group and let H C G be the group of fixed points of an involutive automorphism of G. Then X = G/H is called a symmetric variety. In [CP], De Concini and Procesi have constructed an equivariant compactification X which has a number of remarkable properties, some of them being: i) The boundary is the union of divisors D1,. , Dr. ii) There are exactly 2r orbits. Their closures are the intersections Di1 n . nDi (even schematically). In particular, there is only one closed orbit. iii) In case G is of adjoint type, all orbit closures are smooth. It is called the wonderful embedding of X or a complete symmetric variety and is the foundation for most deeper results about X. Independently, Luna and Vust developed in [LV] a general theory of equivariant compactifications of homogeneous varieties under a connected reductive group G. In particular, they realized the reason which makes symmetric varieties behave so nicely: A Borel subgroup B has an open dense orbit in G/H. Varieties with this property are called spherical. Luna and Vust were able to describe all equivariant compactifications of them in terms of combinatorial data, very similar to torus embeddings which are actually a special case. They obtained in particular that every spherical embedding has only finitely many orbits. Nevertheless, the reason for the existence of a compactification with properties i)-iii) remained mysterious. Then Brion and Pauer established a relation with the automorphism group. They proved in [BP]: A spherical variety X = G/H possesses an equivariant compactification with exactly one closed orbit if and only if AutG X = NG (H)/H is finite. In this case there is a unique one which dominates all others: the wonderful compactification X. They also showed that the orbits of X correspond to the faces of a strictly convex polyhedral cone Z. Then properties i) and ii) above are equivalent to Z being simplicial. This fact is much deeper and was proved by Brion in [Brl]. In fact he showed much more. Let F be the set of characters of B which are the characters of a rational B-eigenfunction on X. This is a finitely generated free abelian group. Then the cone Z is a subset of the real vector space Hom(F, R). Brion showed that there is a finite reflection group Wx acting on F such that Z is one of its Weyl chambers. In case of a symmetric variety, Wx is its little Weyl group.
INFORMATION SYSTEMS EPISTEMOLOGY: AN HISTORICAL PERSPECTIVE

There are some important issues associated with knowledge and its acquisition which, if realized, could make quite a shift in our thinking about what constitutes valid research. They have a rich historical tradition and are fundamental to our understanding of nature and society. This paper takes an historical perspective on information systems epistemology; in so doing, it hopes to expose some of the hidden assumptions which lie behind our conception of valid research and valid research methods.
Earnings smoothing and the underpricing of seasoned equity offerings

Purpose - The purpose of this paper is to examine the impact of earnings smoothing on the underpricing of seasoned equity offerings (SEOs). It aims to investigate whether earnings smoothing can add value to firms by reducing the degree of SEO underpricing. Design/methodology/approach - The sample of US common stock seasoned equity offerings (SEOs) by non-regulated firms during 1989-2009 was used to conduct various cross-section, univariate, and multivariate tests, using several proxies for earnings smoothing, in order to confirm the impact of earnings smoothing on the degree of SEO underpricing. Three-stage least square estimation was used to address the possible endogeneity of pricing and earnings smoothing. Findings - Smooth earnings performance resulting from discretionary accruals is negatively related to SEO underpricing and improves earnings informativeness. Consistent with risk management and signaling theories, managers' efforts to produce smooth earning reports may add value to their firms. Based on the mean values for SEOs, such smoothing reduces underpricing by $0.33 per share offered and increases the value of the average offering by $1.65 million. Smoothed earnings also conveys information about the firms' future performance, as firms with a long historical pattern of smooth earnings prior to SEOs significantly outperform, for at least three years after the SEO, those with more volatile earnings, with respect to stock returns and operating performance. Originality/value - The paper contributes specifically to the current literature on earnings smoothing by demonstrating that high quality firms that expect larger quantity of cash flows in the near future are more likely to actively smooth earnings via discretionary accruals before SEOs to reduce underpricing. The paper contributes generally by showing that firms can signal their quality to outside investors by showing smooth earnings over a long period of time and such firms are more likely to experience a lower degree of underpricing through SEO episodes.
ROR1 Is Expressed in Human Breast Cancer and Associated with Enhanced Tumor-Cell Growth

Receptor-tyrosine-kinase-like orphan receptor 1 (ROR1) is expressed during embryogenesis and by certain leukemias, but not by normal adult tissues. Here we show that the neoplastic cells of many human breast cancers express the ROR1 protein and high-level expression of ROR1 in breast adenocarcinoma was associated with aggressive disease. Silencing expression of ROR1 in human breast cancer cell lines found to express this protein impaired their growth in vitro and also in immune-deficient mice. We found that ROR1 could interact with casein kinase 1 epsilon (CK1ε) to activate phosphoinositide 3-kinase-mediated AKT phosphorylation and cAMP-response-element-binding protein (CREB), which was associated with enhanced tumor-cell growth. Wnt5a, a ligand of ROR1, could induce ROR1-dependent signaling and enhance cell growth. This study demonstrates that ROR1 is expressed in human breast cancers and has biological and clinical significance, indicating that it may be a potential target for breast cancer therapy.
Lecturer: Yuval Rabani

i:x̄i∈Cj (1 − yi) ≥ zj for all j 0 ≤ yi ≤ 1 for all i 0 ≤ zj ≤ 1 for all j. We were investigating how well the algorithm does, that sets xi to true with probability yi, independently of the other xi’s, where y is an optimal solution to the lp-relaxation. Without loss of generality, consider the clause Cj = (x1 ∨ x2 ∨ · · · ∨ xk): P (Cj is not satisfied) = P ( x1, x2, . . . , xk are not satisfied) = k
Software Defect Prediction using Adaptive Neural Networks

We present a system which gives prior idea about the defective module. The task is accomplished using Adaptive Resonance Neural Network (ARNN), a special case of unsupervised learning. A vigilance parameter (θ) in ARNN defines the stopping criterion and hence helps in manipulating the accuracy of the trained network. To demonstrate the usefulness of ARNN, we used dataset from promisedata.org. This dataset contains 121 modules out of which 112 are not defected and 9 are defected. In this dataset modules are termed as defected on the basis of three measures that are LOC, HALSTEAD, MCCABE measures that have been normalized in the range of 0-1.We see that at θ=0.1858 the network has maximum Recall (i.e. true negative rate) is 100% and average Precision=54%. In case of ART n/w shortfalls are seen for Accuracy as this is a subjective measure. Keyword: Resonance, Clustering, Unsupervised learning,
Statistical Inference as a Model for Learning in ANNs

The applications of statistical inference for learning in Artiicial Neural Networks(ANNs) are reviewed in this article. ANNs are widely used to model both low level neural activities and high level cognitive functions. Statistical inference provides an objective way to derive learning algorithms for training ANNs and to evaluate the performance of the trained ANNs. The over-tting problem can be solved by model selection methods based on either a conventional statistical approach or a Bayesian approach. The learning of ANNs can be supervised and unsupervised. To train a multi-layer ANN by supervised learning is equivalent to nonlinear regression. The ensemble methods such as bagging and arching can be applied to combine ANNs to form a new classiier or predictor with a better performance. Unsupervised learning algorithms are derived by either the Hebbian law for bottom-up self-organization or global objective functions such as entropy and mutual information for top-down self-organization. 1 The Brain and ANNs Although the brain is an extremely large and complex system, from the brain organization point of view the hierarchy of the brain can be divided into eight levels: behavior, cortex, neural circuit, neuron, microcircuit, synpase, membrane and molecule. With advanced invasive and non-invasive measurement technologies, the brain can be observed at all these levels and a huge amount of data has been accumulated. Neural computational theories have been developed to account for the complex brain functions based on the accumulated data. Neural computational theories consist of neural models, neural dynamics, and learning theories. Mathematical modeling has been applied to each level in the hierarchy of the brain. From the brain function point of view, the brain is formed by three functional levels: 1. a cognitive function level related to behavior and cortex, 2. a neural activity level related to neural circuit, neuron, microcircuit and synpase, and 3. a sub-neural level related to the membrane and molecule. In this article, we only consider the rst and second functional levels. To focus on the information processing principles of the brain, we must simplify the neurons and synapses in real neural systems. ANNs are simpliied mathematical models for neural systems formed by massively interconnected computational units running in parallel. We shall discuss the applications of ANNs at both the neural activity level and the cognitive function level. 1 2 Applications of ANNs ANNs can model the brain functions at either the neural activity level or the cognitive function level. The …
An enhancement of the ZigBee wireless sensor network using bluetooth for industrial field measurement

The main node (router, coordinator, gateway) of ZigBee based wireless sensor network enhanced by adoption of Bluetooth module is proposed in this paper to provide easy way for industrial field measurement with low-cost and portability. Only a Bluetooth module and basic circuits are required to be added to the original ZigBee network and a simplified physical model is designed and verified in wireless sensor network. The developed main node can be monitored and controlled wirelessly in real time through the designed android based application, which can be setup in a smart phone for communication, monitoring and controlling. The measured distance between the main node and the user is up to 60 meters thus greatly facilitate the industrial field measurement based on the smart phone.
iPRICE: A collaborative pricing model for e-service bundle delivery

Information goods pricing is an essential and emerging topic in the era of information economy. Myriad researchers have devoted considerable attention to developing and testing methods of information goods pricing. Nevertheless, in addition; there are still certain shortcomings as the challenges to be overcome. This study encompasses several unexplored concepts that have attracted research attention in other disciplines lately, such as collaborative prototyping, prospect theory, ERG theory, and maintenance from design, economic, psychological, and software engineering respectively. This study proposes a novel conceptual framework for information goods pricing and investigates the impact of three advantages: (1) provides collaborative process that could generate several prototypes via trial and error in pricing process, (2) deliberates the belief of consumer and producer by maximizing utility and profit, and (3) offers an appropriate service bundle by interacting with consumer and discovering the actual needs.
Pseudo-stabilizing causal ordering

Causal ordering finds its usage in many distributed appications like replicated data management, distributed systems monitoring, synchronizing multimedia streams and other applications frequently involving human interactions. The concept of self-stabilization has gathered significant recognition in distributed systems. Many self-stabilizing algorithms have been proposed for various problems in a distributed system. A self-stabilizing distributed system ensures recovery from an illegitimate state to a legitimate state in a finite number of steps. Pseudo-stabilization is a weaker form of self-stabilization which ensures that a system when started in arbitrary state is guaranteed to eventually recover to a legitimate state. In this paper we propose a pseudo-stabilizing algorithm for causal order delivery of messages in a distributed system and prove its correctness.
Stream ancestor function: A mechanism for fine-grained provenance in stream processing systems

Applications that require continuous processing of high-volume data streams have grown in prevalence and importance. These systems process streaming data in real-time and provide instantaneous response to support precise and ontime decisions. In such systems, it is difficult to know exactly how a particular result is generated or more particularly how to precisely trace stream events that caused a particular result. However, such information is extremely important for validating stream processing results. Therefore, it is crucial that stream processing systems have a mechanism for capturing and querying provenance information - the information pertaining to the process that produced result data - at the level of individual stream events, which we refer to as fine-grained provenance. In this paper, we propose a novel fine-grained provenance solution called Stream Ancestor Function - a reverse mapping function used to express precise dependencies between input and output stream elements. We demonstrate how to utilize stream ancestor functions by means of a stream provenance query and replay execution algorithm. Finally, we evaluate the stream ancestor function in terms of storage consumption for provenance collection and system throughput, demonstrating significant reductions in storage size and reasonable processing overheads.
Adaptive task duplication using on-line bottleneck detection for streaming applications

In this paper we describe an approach to dynamically improve the progress of streaming applications on SMP multi-core systems. We show that run-time task duplication is an effective method for maximizing application throughput in face of changes in available computing resources. Such changes can not be fully handled by static optimizations. We derive a theoretical performance model to identify tasks in need of more computing resources. We propose two on-line algorithms that use indications from the performance model to detect computation bottlenecks. In these algorithms, a task can identify itself as a bottleneck using only its local data. The proposed technique is transparent to end programmers and portable to systems with fair scheduling. Our on-line detection algorithms can be applied to other dynamic scenarios, for example, involving run-time variation of workload. Our experiments using the StreamIt benchmarks [5] show that the proposed run-time task duplication achieves considerable speedups over the multi-threaded baseline on a 16-core machine and on the scenarios with dynamically changing number of processing cores. We also show that our algorithms achieve better application throughput than alternative approaches for task duplication.
Multivariate Density Estimation: a Support Vector Machine Approach

A Support Vector Machine (SVM) algorithm for multivariate density estimation is developed based on regularization principles and bounds on the convergence of empirical distribution functions. The algorithm is compared to Gaussian Mixture Models (GMMs). Our algorithm outperforms GMMs for data drawn from mixtures of gaussians in IR and IR. Our algorithm is also automated with respect to parameters. Copyright c Massachusetts Institute of Technology, 1998 This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and at the Arti cial Intelligence Laboratory at the Massachusetts Institute of Technology. This research is sponsored by a grant from the O ce of Naval Research under contract No. N00014-93-13085. Additional support is provided by Eastman Kodak Company, Daimler-Benz AG, Siemens Corporate Research Inc., AT&T, Digital Equipment and Central Research Institute of Electric Power Industry.
DNA sequencing: bench to bedside and beyond

Fifteen years elapsed between the discovery of the double helix (1953) and the first DNA sequencing (1968). Modern DNA sequencing began in 1977, with development of the chemical method of Maxam and Gilbert and the dideoxy method of Sanger, Nicklen and Coulson, and with the first complete DNA sequence (phage ϕX174), which demonstrated that sequence could give profound insights into genetic organization. Incremental improvements allowed sequencing of molecules >200 kb (human cytomegalovirus) leading to an avalanche of data that demanded computational analysis and spawned the field of bioinformatics. The US Human Genome Project spurred sequencing activity. By 1992 the first ‘sequencing factory’ was established, and others soon followed. The first complete cellular genome sequences, from bacteria, appeared in 1995 and other eubacterial, archaebacterial and eukaryotic genomes were soon sequenced. Competition between the public Human Genome Project and Celera Genomics produced working drafts of the human genome sequence, published in 2001, but refinement and analysis of the human genome sequence will continue for the foreseeable future. New ‘massively parallel’ sequencing methods are greatly increasing sequencing capacity, but further innovations are needed to achieve the ‘thousand dollar genome’ that many feel is prerequisite to personalized genomic medicine. These advances will also allow new approaches to a variety of problems in biology, evolution and the environment.
Object Matching for Information Integration: A Profiler-Based Approach

Object matching is a fundamental problem that arises in numerous information integration scenarios. Virtually all existing solutions to this problem have assumed that the objects to be matched share the same set of attributes, and that they can be matched by comparing the similarities of the attributes. We consider the more general problem where the objects can also have disjoint attributes, such as matching tuples that come from relational tables with schemas (age,name) and (name,salary), respectively. We describe PROM, a solution that also exploits the disjoint attributes to improve matching accuracy. In the above example, PROM begins by matching any two given tuples based on the shared attribute name. Then it applies a set of profilers, each of which contains some knowledge about what constitutes a typical person. The profilers examine the tuple pair to see if it can plausibly make up a person. For example, a profiler may state that because the age is 9 and the salary is 200K, the tuples do not make up a person and thus do not match. Profilers can be manually specified by domain experts, learned from training data, transferred from other matching tasks, or constructed from external data. Thus, the PROM approach is distinguished in that it not only can exploit disjoint attributes to improve matching accuracy, but can also reuse knowledge from previous object matching tasks.
Regulation of Histone Deacetylase 4 by Binding of 14-3-3 Proteins

ABSTRACT Histone (de)acetylation is important for the regulation of fundamental biological processes such as gene expression and DNA recombination. Distinct classes of histone deacetylases (HDACs) have been identified, but how they are regulated in vivo remains largely unexplored. Here we describe results demonstrating that HDAC4, a member of class II human HDACs, is localized in the cytoplasm and/or the nucleus. Moreover, we have found that HDAC4 interacts with the 14-3-3 family of proteins that are known to bind specifically to conserved phosphoserine-containing motifs. Deletion analyses suggested that S246, S467, and S632 of HDAC4 mediate this interaction. Consistent with this, alanine substitutions of these serine residues abrogated 14-3-3 binding. Although these substitutions had minimal effects on the deacetylase activity of HDAC4, they stimulated its nuclear localization and thus led to enhanced transcriptional repression. These results indicate that 14-3-3 proteins negatively regulate HDAC4 by preventing its nuclear localization and thereby uncover a novel regulatory mechanism for HDACs.
Traffic sign detection based on HOG and PHOG using binary SVM and k-NN

Traffic signs serve important functions on the road. Drivers can easily determine their directions and vehicle speeds by paying attention to traffic signs. However, it is only natural that sometimes drivers misjudge the position and meaning of traffic signs that they ignore them and in the worst case scenario, got involved in accidents. Therefore, technological improvements allow the development of a traffic sign detection system that assists drivers in their commute. Some challenges in this kind of research are weather condition, traffic signs positions, and obstructed view of traffic signs by the other vehicles and advertisement boards. This research has developed a model of traffic sign detection system using color segmentation by transforming the RGB color range into HSI to detect traffic signs based on the colors of red, yellow, blue and green, which are further morphologically processed to minimize searching scope with erosion and labeling. In this method, earlier processes reveal traffic sign candidates that make a Region of Interest (ROI) for its features to be extracted using the Histogram of Oriented Gradient (HOG) or the Pyramid Histogram of Oriented Gradient (PHOG). These are then ensued by a classification process using the Support Vector Machine (SVM) or k-NN to finally determine whether those candidates are traffic signs or not. Results show that this system has traffic sign detection accuracies of 79.03% with PHOG + SVM, 82.01% with HOG + SVM, 81.19% using PHOG + k-NN, and 77.81% using HOG + k-NN.