Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiency improvement of human body detection with histograms of oriented gradients

In this paper we investigate improvements to the efficiency of human body detection using histograms of oriented gradients (HOG). We do this without compromising the performance significantly. This is especially relevant for embedded implementations in smart camera systems, where the on-board processing power and memory is limited. We focus on applications for indoor environments such as offices and living rooms. We present different experiments to reduce both the computational complexity as well as the memory requirements for the trained model. Since the HOG feature length is large, the total memory size needed for storing the model can become more than 50 MB. We use a feature selection based on Bayesian theory to reduce the feature length. Additionally we compare the performance of the full-body detector with an upper-body only detector. For computational complexity reduction we employ a ROI-based approach.
Estrogen Receptor-β: Recent Lessons from in Vivo Studies

The unexpected discovery of a second form of the estrogen receptor (ER), designated ERβ, surprised and energized the field of estrogen research. In the 9 yr since its identification, the remarkable efforts from academic and industrial scientists of many disciplines have made significant progress in elucidating its biology. A powerful battery of tools, including knockout mice as well as a panel of receptor-selective agonists, has allowed an investigation into the role of ERβ. To date, in vivo efficacy studies are limited to rodents. Current data indicate that ERβ plays a minor role in mediating estrogen action in the uterus, on the hypothalamus/pituitary, the skeleton, and other classic estrogen target tissues. However, a clear role for ERβ has been established in the ovary, cardiovascular system, and brain as well as in several animal models of inflammation including arthritis, endometriosis, inflammatory bowel disease, and sepsis. The next phase of research will focus on elucidating, at a molecular level.
Mining Behavior Graphs for "Backtrace" of Noncrashing Bugs

Analyzing the executions of a buggy software program is essentially a data mining process. Although many interesting methods have been developed to trace crashing bugs (such as memory violation and core dumps), it is still difficult to analyze noncrashing bugs (such as logical errors). In this paper, we develop a novel method to classify the structured traces of program executions using software behavior graphs. By analyzing the correct and incorrect executions, we have made good progress at the isolation of program regions that may lead to the faulty executions. The classification framework is built on an integration of closed graph mining and SVM classification. More interestingly, suspicious regions are identified through the capture of the classification accuracy change, which is measured incrementally during program execution. Our performance study and case-based experiments show that our approach is both effective and effi-
Safety and efficacy of nivolumab and standard chemotherapy drug combination in patients with advanced non-small-cell lung cancer: a four arms phase Ib study

In this phase Ib study, four combination therapies of nivolumab 10 mg/kg and standard chemotherapy (cisplatin/gemcitabine, cisplatin/pemetrexed, carboplatin/paclitaxel/bevacizumab, or docetaxel) showed acceptable toxicity profiles in patients with advanced non-small-cell lung cancer. Furthermore, these combination therapies presented encouraging antitumor activities.
Automated QT interval analysis on Diagnostic Electrocardiograms

In this study, we suggest a method for automatic measurement of the QT interval. The method derives from the standard technique of "selective beat averaging ". To overcome those situations in which the templates are noisy as they arise from averaging only a small number of beats, two separate patterns were used: one for the QRS complex and the other for the T wave. The templates were carefully matched with the beats by cross- correlation, using an iterative refinement process. The method was employed to automatically measure QT segments on the PTB diagnostic ECG database used for the 2006 Computers in cardiology challenge. A score in division 3 of 81.07 ms was obtained pointing out that the method needs further tuning and refinements. Excluding those beats for which the estimated QT values are outside the main physiological range (290-500 ms) a more promising tentative score of 41.62 ms might be obtained.
Watching, Thinking, Reacting: A Human-Centered Framework for Movie Content Analysis

In this paper, we propose a human-centered framework, “Watching, Thinking, Reacting”, for movie content analysis. The framework consists of a hierarchy of three levels. The low level represents human perception to external stimuli, where the Weber-Fechner Law-based human attention model is constructed to extract movie highlights. The middle level simulates human cognition to semantic, where semantic descriptors are modeled for automatic semantic annotation. The high level imitates human actions based on perception and cognition, where an integrated graph with content and contextual information is proposed for movie highlights correlation and recommendation. Moreover, three recommendation strategies are presented. The promising results of subjective and objective evaluation indicate that the proposed framework can make not only computers intelligently understand movie content, but also provide personalized service for movie highlights recommendation to effectively lead audiences to preview new movies in an individualized manner.
Semantic context transfer across heterogeneous sources for domain adaptive video search

Automatic video search based on semantic concept detectors has recently received significant attention. Since the number of available detectors is much smaller than the size of human vocabulary, one major challenge is to select appropriate detectors to response user queries. In this paper, we propose a novel approach that leverages heterogeneous knowledge sources for domain adaptive video search. First, instead of utilizing WordNet as most existing works, we exploit the context information associated with Flickr images to estimate query-detector similarity. The resulting measurement, named Flickr context similarity (FCS), reflects the co-occurrence statistics of words in image context rather than textual corpus. Starting from an initial detector set determined by FCS, our approach novelly transfers semantic context learned from test data domain to adaptively refine the query-detector similarity. The semantic context transfer process provides an effective means to cope with the domain shift between external knowledge source (e.g., Flickr context) and test data, which is a critical issue in video search. To the best of our knowledge, this work represents the first research aiming to tackle the challenging issue of domain change in video search. Extensive experiments on 120 textual queries over TRECVID 2005-2008 data sets demonstrate the effectiveness of semantic context transfer for domain adaptive video search. Results also show that the FCS is suitable for measuring query-detector similarity, producing better performance to various other popular measures.
A Distributed Stealthy Coordination Mechanism for Worm Synchronization

Once a critical mass of nodes is infected by a worm it becomes very difficult to stop the worm from infecting a large fraction of vulnerable nodes. Therefore, the focus of strategies for worm defense has been to detect the worm before it reaches that critical mass. In this paper we present a novel distributed coordination technique for worm propagation and synchronization that can persist under the radar of detection mechanisms long enough to achieve critical mass for a full fledged attack. We discuss the stealthy worm propagation and synchronization approach exploiting a P2P file-sharing network
Real Time Detection and Recognition of Indian Traffic Signs using Matlab

— Traffic Sign recognition system is a part of driving assistance system that automatically alerts and informs the driver of the traffic signs ahead. In this paper an efficient real time sign detection system is proposed for Indian traffic signs. Car cameras that capture video are integrated with an in-vehicle computing device. Image frames may be blurred and corrupted by Gaussian noise due to motion of vehicle and atmospheric turbulence. Hence Image enhancement is done using median filter and nonlinear Lucy-Richardson for de-convolution. Colour segmentation using YCbCr colour space along with shape filtering through template matching of colour detected candidates are used to detect sign from images as colour and shape easily distinguishes a sign from its background. The classification module determines the type of detected road signs using Multi-layer Perceptron neural networks.
Evaluating Linear Regression for Temperature Modeling at the Core Level

Temperature issues have become a first-order concern for modern computing systems. There are several approaches for dynamic thermal management, including reacting based on some threshold temperature, predicting based on history, or estimating localized temperatures based on performance counters. One possibility for more proactive management is to predict temperatures based on the upcoming instruction stream or performance counters. A logical approach is to use linear regression to generate a model based on the instruction stream, performance counters, or a combination of the two to predict temperatures at the next time step. However, we show in this paper that linear regression is unsuitable for predicting temperatures at the core level. This is primarily due to the fact that temperature at a time step is dependent upon both the processor activity and the temperature at the previous time step. As a result, application phases have long periods with similar instruction streams and performance counter values per time step but with different temperatures, which leads to prediction errors elsewhere in the phase. Incorporating temperature history as an input to the regression essentially leads to a last value predictor, which predicts the temperature will always be the same as the last time step. Thus, neither approach is truly suitable for predictive thermal management.
Query and Document Translation by Automatic Text Categorization: A Simple Approach to Establish a Strong Textual Baseline for ImageCLEFmed 2006

In this paper, we report on the fusion of simple retrieval strategies with thesaural resources in order to perform document and query translation for cross{language retrieval in a collection of medical cases. The collection contains textual and visual contents. In this paper, we focus on the textual contents of the collection, which contains documents in three languages: French, English and German. The fusion of visual and textual content will also be treated. Unlike most automatic categorization systems, which rely on training data in order to infer text{to{concept relationships, our approach can be applied with any controlled vocabulary and does not use any training data. For the 2006 ImageCLEFmed experiments we use the Medical Subject Headings (MeSH), a terminology maintained by the National Library of Medicine and which exists in a dozen languages. The basic idea consists of annotating every textual content of the collection (documents and queries) with a set of MeSH concepts using an automatic text categoriser. Thus, allowing an interlingual mapping between queries and documents. For tuning purposes, the system uses a sample of MEDLINE from the OHSUMED collection. Our results, conrmed that such a simple approach is competitive with best performing cross-language retrieval methods for such a collection. Several simple linear approaches were used to combine textual and visual features
Plasmons on metal layers embedded in dielectric PBG cavity

This paper describes the analysis of the effects induced on the resonant mode originating inside a Bragg waveguide by the presence of an Ag metal layer. Since it exhibits a negative value of the dielectric permittivity and a relatively low absorption in the visible and NIR ranges, the Ag metal layer is able to support surface modes. By exploiting surface plasmon features we investigate on an alternative fashion of tuning and enhancing resonant modes for LED applications. The analysis has been performed using FDTD method, where the experimental data of the Ag permittivity are introduced using Drude model.
The Cyclotomic Birman-Murakami-Wenzl Algebras

----- Please see the pdf file for the actual abstract and important remarks, which could not be put here due to the arXiv length restrictions. ----- This thesis presents a study of the cyclotomic BMW (Birman-Murakami-Wenzl) algebras, introduced by Haring-Oldenburg as a generalization of the BMW algebras associated with the cyclotomic Hecke algebras of type G(k,1,n) (also known as Ariki-Koike algebras) and type B knot theory involving affine/cylindrical tangles. They are shown to be free of rank k^n (2n-1)! and to have a topological realization as a certain cylindrical analogue of the Kauffman Tangle algebra. Furthermore, the cyclotomic BMW algebras are proven to be cellular, in the sense of Graham and Lehrer. This Ph.D. thesis, completed at the University of Sydney, was submitted September 2007 and passed December 2007.
A statistical significance of differences in classification accuracy of crop types using different classification algorithms

Abstract Crop classification is needed to understand the physiological and climatic requirement of different crops. Kernel-based support vector machines, maximum likelihood and normalised difference vegetation index classification schemes are attempted to evaluate their performances towards crop classification. The linear imaging self-scanning (LISS-IV) multi-spectral sensor data was evaluated for the classification of crop types such as barley, wheat, lentil, mustard, pigeon pea, linseed, corn, pea, sugarcane and other crops and non-crop such as water, sand, built up, fallow land, sparse vegetation and dense vegetation. To determine the spectral separability among crop types, the M-statistic and Jeffries–Matusita (J–M) distance methods have been utilised. The results were statistically analysed and compared using Z-test and χ2-test. Statistical analysis showed that the accuracy results using SVMs with polynomial of degrees 5 and 6 were not significantly different and found better than the other classification algorithms.
Load Testing, Benchmarking, and Application Performance Management for the Web

Web-based applications are becoming mission-critical for many organizations and their performance has to be closely watched. This paper discusses three important activities in this context: load testing, benchmarking, and application performance management. Best practices for each of these activities are identified. The paper also explains how basic performance results can be used to increase the efficiency of load testing procedures.
Laser-based technologies for photonic device integration

As demand is growing for devices capable of performing new and increased numbers of operations within an ever shrinking physical volume the laser has become an increasingly important tool helping to overcome the limitations of conventional microfabrication technologies. In addition to “classical” applications of lasers such as via hole drilling, trimming or pulsed laser deposition of thin films, there have been new developments of laser-based technologies for the fabrication of advanced microand nano-devices. Of particular interest for photonic applications is the potential of the laser for the fabrication of integrated and monolithically integrated photonic devices and circuits.
Global Sensitivity Analysis: An Introduction

This presentation aims to introduce global sensitivity analysis (SA), targeting an audience unfamiliar with the topic, and to give practical hints about the associated advantages and the effort needed. To this effect, we shall review some techniques for sensitivity analysis, including those that are not global, by applying them to a simple example. This will give the audience a chance to contrast each method’s result against the audience’s own expectation of what the sensitivity pattern for the simple model should be. We shall also try to relate the discourse on the relative importance of model input factors to specific questions, such as “Which of the uncertain input factor(s) is so noninfluential that we can safely fix it/them? ” or “If we could eliminate the uncertainty in one of the input factors, which factor should we choose to reduce the most the variance of the output? ” In this way, the selection of the method for sensitivity analysis will be put in relation to the framing of the analysis and to the interpretation and presentation of the results. The choice of the output of interest will be discussed in relation to the purpose of the model based analysis. The main methods that we present in this lecture are all related with one another, and are the method of Morris for factors’ screening and the variancebased measures. All are model-free, in the sense that their application does not rely on special assumptions on the behaviour of the model (such as linearity, monotonicity and additivity of the relationship between input factor and model output). Monte Carlo filtering will be also be discussed to demonstrate the usefulness of global sensitivity analysis in relation to estimation.
Silencing Motifs in the Clr2 Protein from Fission Yeast, Schizosaccharomyces pombe

The fission yeast, Schizosaccharomyces pombe, is a well-established model for heterochromatin formation, but the exact sequence of events for initiation remains to be elucidated. The essential factors involved include RNA transcribed from repeated sequences together with the methyltransferase Clr4. In addition, histone deacetylases, like Clr3, found in the SHREC complex are also necessary for transcriptional silencing. Clr2 is another crucial factor required for heterochromatin formation found in the SHREC complex. The function of Clr2 has been difficult to establish due to the lack of conserved domains or homology to proteins of known molecular function. Using a bioinformatics approach, three conserved motifs in Clr2 were identified, which contained amino acids important for transcriptional repression. Analysis of clr2 mutant strains revealed a major role for Clr2 in mating-type and rDNA silencing, and weaker effects on centromeric silencing. The effect on mating-type silencing showed variegation in several of the strains with mutated versions of Clr2 indicating an establishment or maintenance defect. Moreover, the critical amino acids in Clr2 were also necessary for transcriptional repression in a minimal system, by the tethering of Clr4 upstream of a reporter gene, inserted into the euchromatic part of the genome. Finally, in silico modeling suggested that the mutations in Clr2 cause disruption of secondary structures in the Clr2 protein. Identification of these critical amino acids in the protein provides a useful tool to explore the molecular mechanism behind the role of Clr2 in heterochromatin formation.
Image-to-Image Retrieval Using Computationally Learned Bases and Color Information

New methods for joint compression and Image-to-image retrieval (12I retrieval) are presented. The novelty exists in the usage of computationally learned image bases besides color distributions. The bases are obtained by the Principal Component Analysis and/or the Independent Component Analysis. On the image compression, PCA and ICA outperform the JPEG's DCT This superiority holds even if the bases and superposition coefficients are quantized and encoded. On the 12I retrieval, the precision-recall curve is used to measure the performance. It is found that adding the basis information always increases the baseline ability of the color information. Besides the retrieval evaluation, a unified image format called RIM (Retrieval-aware IMage format) for effective packing of codewords including bases is specified. Furthermore, an image search viewer called Wisvi (Waseda Image Search Viewer) is developed and exploited. A beta-version of all source codes can be down-loaded from a web site given in the text.
A Unifying View of Some Linear Herbrand Procedures

The linked conjunct, resolution, matrix reduction, and model elimination proof procedures constitute a nearly exhaustive list of the basic Herbrand proof procedures introduced in the 1960's. Each was introduced as a hopefully efficient complete procedure for the first order predicate calculus for the purpose of mechanical theorem proving. This paper contains a demonstration that versions of these procedures can be highly related in their design. S-linear resolution, a particular strategy of resolution previously proposed, is seen to possess a natural refinement isomorphic at ground level to a refinement of the model elimination procedure. There is also an isomorphism at the general level between a less natural s-linear resolution refinement and the model elimination refinement. The model elimination procedure is also interpreted within the linked conjunct and matrix reduction procedures. An alternate interpretation of these results is that, very roughly, the procedures other than resolution can be viewed as forms of linear resolution. K E Y W O R D S A N D PHRASES: mechanical theorem proving, Herbrand proof procedure, linked coniunct, resolution, model elimination, matrix reduction procedure, linear resolution procedure CR CATEGORIES: 3.64, 3.66, 5.21
Acquire or Perish - Unique Factors Motivating M&A in the Software Industry

Business models used in the software industry constantly evolve. Organizations reconfigure their business models through various means and mergers and acquisitions (M&As) is one of the way. Recent past has witnessed a high frequency of M&As in the software industry. There are some unique factors that potentially influence M&As in the software industry. Using theoretical arguments, this study develops a model that explains the M&As and articulates propositions specific to the software industry. This study also intends to examine the proposition with the M&As that took place in the enterprise content management software area. Findings of this study provide unique insights into the phenomenon of M&As in the software industry and provide an understanding on the evolving business models in this industry sector.
Finite element calculation of wave propagation and excitation in periodic piezoelectric systems

Many sensors and actuators in technical systems consist of quasiperiodic structures which are constructed by successive repetition of a base cell. Typical examples are piezoelectric composites used as ultrasonic transducers or surface acoustic wave (SAW) devices utilized in telecommunication systems. The precise numerical simulation of such devices including all physical effects is currently beyond the capacity of high end computation. Therewith, we have to restrict the numerical analysis to the periodic substructure and have to introduce special boundaries to account for the periodicity. Due to the fact, that wave propagation phenomena have to be considered for SAW applications the periodic boundary condition (PBC) has to be able to model each possible mode within the periodic structure. That means this condition must hold for each phase difference existing at the periodic boundaries. To fulfill this difficult criteria we have introduced the Floquet theorem to the PBCs offering two different solution strategies. The first method leads to a quadratic eigenvalue problem. Therein, a huge matrix including all nodes not belonging to the periodic boundaries has to be inverted due to a Schur-Complement