Miracle-dz/UniFAR-FTUs huggingface dataset:
AT&T Research at TRECVID 2006

TRECVID (TREC Video Retrieval Evaluation) is sponsored by NIST to encourage research in digital video indexing and retrieval. It was initiated in 2001 as a “video track” of TREC and became an independent evaluation in 2003. AT&T participated in three tasks in TRECVID 2006: shot boundary determination (SBD), search, and rushes exploitation. The proposed SBD algorithm contains a set of finite state machine (FSM) based detectors for pure cut, fast dissolve, fade in, fade out, dissolve, and wipe. Support vector machine (SVM) is applied to cut and dissolve detectors to further boost the SBD performance. AT&T collaborated with Columbia University in the search and rushes exploitation tasks. In this paper, we mainly focus on the SBD system and briefly introduce our effort on the search and the rushes exploitation. The AT&T SBD system is highly effective and its evaluation results are among the best.
Breast cancer cells induce stromal fibroblasts to express MMP-9 via secretion of TNF-α and TGF-β

We used 2D-cocultures employing fibroblasts of different genetic backgrounds and MCF10A-derived human breast epithelial cells of increasingly malignant potential to investigate tumor-stroma interactions in breast cancer and to identify possible signaling pathways involved. Tumor cells induced expression of matrix-metalloproteinase 9 (MMP-9) in fibroblasts in a pattern dependent on the degree of their malignancy. In-situ zymography localized the main gelatinolytic activity around stromal cells in cocultures and xenografted tumors. Use of Smad3 knockout fibroblasts, small molecule inhibitors, and neutralizing antibodies showed that MMP-9 expression was induced by tumor cell-derived TNF-α and TGF-β, dependent on Smad-, Ras-, and PI3-kinase-signaling, and likewise modulated by subsequent HGF- and EGF-signaling. Together, our results indicate that MMP-9 levels in tumor fibroblasts are regulated by a complex tumor-stroma cross-talk, involving multiple ligands and cellular signaling pathways.
Membrane junctions in xenopus eggs: their distribution suggests a role in calcium regulation

We have observed the presence of membrane junctions formed between the plasma membrane and cortical endoplasmic reticulum of mature, unactivated eggs of xenopus laevis. The parallel, paired membranes of the junction are separated by a 10-mn gap within which electron-dense material is present. This material occurs in patches with an average center-to-center distance of approximately 30 nm. These junctions are rare in immature (but fully grown) oocytes (approximately 2 percent of the plasma membrane is associated with junctions) and increase dramatically during progesterone-induced maturation. Junctions in the mature, unactivated egg are two to three times more abundant in the animal hemisphere (25-30 percent of the plasma membrane associated with junction) as compared with the vegetal hemisphere (10-15 percent). Junction density decreases rapidly to values characteristic of immature oocytes in response to egg activation. The plasma membrane-ER junctions of xenopus eggs are strikingly similar in structure to membrane junctions in muscle cells thought to be essential in the triggering of intracellular calcium release from the sarcoplasmic reticulum. In addition, the junctions’ distinctive, animal-vegetal polarity of distribution, their dramatic appearance during maturation, and their disapperance during activation are correlated with previously documented patterns of calcium-mediated events in anuran eggs. We discuss several lines of evidence supporting the hypothesis that these junctions in xenopus eggs are sites that transduce extracellular events into intracellular calcium release during fertilization and activation of development.
Particle Swarm Optimization Applied to the Dynamic Allocation Problem

Particle Swarm Optimization (PSO) is based on the analysis of emergent behavior of bird flocks. Though it was originally designed for continuous optimization, PSO has provided good results in some recent works when applied to static and discrete optimization problems. In this paper, the particle encoding scheme is based on permutations and the PSO algorithm is adapted to solve a real-world application (cabs-customers allocation) of the dynamic task assignment problem. In the proposed approach, as the optimal solution may change during the optimization process, different strategies to detect and react to changes are tested. The results show that combinations of traditional techniques achieve good solutions in tested instances defined with different sizes and scales of changes.
Comparative analysis of Kannada phoneme recognition using different classifiers

Information retrieval from audio and speech is very important in the present digital world. Phonetic search (phoneme level search) is an efficient technique for searching words or phrases from audio and speech recordings. In this paper, a baseline phoneme recognition system for Kannada language is developed using Deep Belief Networks (DBNs). Phonemes are segmented from broadcast/read mode Kannada speech. 16 MFCC features are extracted from each speech frame. These features are used as input to the recognizer. DBNs are relatively new area of machine learning. The learning procedure of DBN has two steps, an unsupervised pre-training stage and fine-tuning stage. The performance of DBN for recognition of 25 Kannada phonemes is compared with the conventional methods of speech recognition such as, Multi-Layer Feed Forward Neural Networks (ML-FFNNs) and Support Vector Machines (SVMs). Experimental results show that DBNs yield a high performance as compared to other techniques with Phoneme Error Rate (PER) of 23.6 %. In another experiment conducted, shows that DBN's performance is influenced by number of hidden units in the hidden layer chosen.
Describing and Integrating Competence Theories for Problem-Solving Components and Machine Learning algorithms

A topic in the field of knowledge acquisition is the reuse of components that are described at the knowledge level. Problems concern the description, indexing and retrieval of components. In our case there is the additional feature of integrating so called automated building blocks in a knowledge level description. This paper describes what knowledge level descriptions of components for reuse should look like, and proposes a way to describe assumptions and requirements that are to be made explicit. In the paper an extension of the “normal” knowledge acquisition setting is made in the direction of machine learning components.
Platelet serotonin-binding and dexamethasone suppression test in melancholia and dysthymia

Summary Platelet serotonin-binding (Bmax), using tritiated-seroionin as the ligand, was determined in 75 patients suffering from major depression with melancholia and in 26 patients diagnosed from dysthymic disorder. Twenty-five normal subjects were used as a control group. The melancholic group had significantly lower Bmax values (mean: 6.7 ± 6.1 pmol/108 platelets) than either dysthymic (9.3 ± 3.9 pmol/108 platelets) or control (9.2 ± 4.8 pmol/108 platelets) groups, while there were no significant differences between the two latter groups. There was also a significant difference on postdexamethasone Cortisol between melancholic (6.3 ± 7.1 μg/dL) and dysthymic (1.4 ± 1.4 μg/dL) groups, with a higher rate of nonsuppressors in melancholic groups. Although both tests were abnormal in the melancholic group, no relationship was found between platelet serotonin-binding and the dexaniethasone suppression test.
Data Fusion with Record Linkage

Assuming that there are two sources (e.g. les), which consist of records with diierent informations about some units like people. We want to fusion the information (data) that belong to the same units. Very often in practice no identiication numbers | like the Social Security Number SSN | are available at both les, that's why there is some uncertainity, which records belong together. Anyway, we want to link the records of the sources together, hopefully the right ones. Record Linkage | based on the Likelihood-Ratio-Test | is one method, to link records in an eecient way, at most automatically, without a high amount of review. Thanks to Fellegi and Sunter (1969) we present the basics of Record Linkage they introduced at rst therein. Further on we discuss, how to use Record Linkage in practice.
The Fate of Linear DNA in Saccharomyces cerevisiae and Candida glabrata: The Role of Homologous and Non-Homologous End Joining

In vivo assembly of plasmids has become an increasingly used process, as high throughput studies in molecular biology seek to examine gene function. In this study, we investigated the plasmid construction technique called gap repair cloning (GRC) in two closely related species of yeast – Saccharomyces cerevisiae and Candida glabrata. GRC utilizes homologous recombination (HR) activity to join a linear vector and a linear piece of DNA that contains base pair homology. We demonstrate that a minimum of 20 bp of homology on each side of the linear DNA is required for GRC to occur with at least 10% efficiency. Between the two species, we determine that S. cerevisiae is slightly more efficient at performing GRC. GRC is less efficient in rad52 deletion mutants, which are defective in HR in both species. In dnl4 deletion mutants, which perform less non-homologous end joining (NHEJ), the frequency of GRC increases in C. glabrata, whereas GRC frequency only minimally increases in S. cerevisiae, suggesting that NHEJ is more prevalent in C. glabrata. Our studies allow for a model of the fate of linear DNA when transformed into yeast cells. This model is not the same for both species. Most significantly, during GRC, C. glabrata performs NHEJ activity at a detectable rate (>5%), while S. cerevisiae does not. Our model suggests that S. cerevisiae is more efficient at HR because NHEJ is less prevalent than in C. glabrata. This work demonstrates the determinants for GRC and that while C. glabrata has a lower efficiency of GRC, this species still provides a viable option for GRC.
Localization in a Reverberant Room : Effects of Nearby Walls and Room Learning

A behavioral study was performed to examine the influence of listener location in a reverberant environment on spatial auditory perception. Localization accuracy was measured for sources in the right frontal quadrant of the listener's horizontal plane, with source distances between 15 cm and 1 m from the head. Listeners were positioned at four different locations in the room: in the center, with their back close to a wall, with their left ear close to a wall, or in a corner. Localization bias and variability in perceived angle from interaural axis and perceived distance were analyzed. Small but statistically significant changes in the azimuthal bias were observed as a function of listener location. The bias was smallest in the center of the room and was slightly larger when listeners were positioned with one ear close to a wall. On the other hand, a wall behind the listener caused significant increases in azimuthal bias. Listener location did not influence bias in perceived distance, even though, in general, distance perception depends on reverberation. Consistent with expectations, variability in perceived azimuth and distance increases for listener locations in which early, strong echoes are present; the influence of room location on localization performance appears to depend on the amount of distortion that the echoes and reverberation cause in localization cues. In addition to an effect of listener position, a strong room-learning effect is observed, leading to improved localization performance. Kopão and Shinn-Cunningham: Localization in a Room (DRAFT: to be submitted to JASA) 3 1.
Journal of Emerging Trends in Computing and Information Sciences Role of Cognitive Radio on 4g Communications a Review

There is a rapid advancement in wireless communication technology providing the network services anywhere and anytime. 4G communication systems are being developed to solve the various problems the current communication systems (3G, 2.5G) are facing.4G will be an intelligent technology that will reduce the number of different technologies to a single global standard. Cognitive Radio (CR) is the key enabling technology for next generation networks. Cognitive Radio techniques provide the capability to use or share the spectrum in an opportunistic manner. With the use of CR, 4G wireless networks will support global roaming across multiple wireless and mobile networks. In this paper, the Role of CR in 4G Communications is reviewed. The recent research being done on Cognitive Radio and 4G technologies is identified. The main possibilities of implementing Cognitive Radios in 4G Communication Systems are surveyed. IEEE 802.22 networks are cognitive technology based networks which will enhance the performance of 4G Communication systems. IEEE 802.16h (WiMAX) provide extensions to support unlicensed coexistence.
Testing term structure estimation methods: evidence from the UK STRIPs market

Prices and yields of UK government zero-coupon bonds are used to test alternative yield curve estimation models. Zero-coupon bonds permit a more pure comparison, as the models are providing only the interpolation service and also not making estimation feasible. It is found that better yield curves estimates are obtained by fitting to the yield curve directly rather than fitting first to the discount function. A simple procedure to set the smoothness of the fitted curves is developed, and a positive relationship between over-smoothness and the fitting error is identified. A cubic spline function fitted directly to the yield curve provides the best overall balance of fitting error and smoothness, both along the yield curve and within local maturity regions. Copyright (c) 2008 The Ohio State University.
"Nursevendor Problem": Personnel Staffing in the Presence of Endogenous Absenteeism

The problem of determining nurse staffing levels in a hospital environment is a complex task because of variable patient census levels and uncertain service capacity caused by nurse absenteeism. In this paper, we combine an empirical investigation of the factors affecting nurse absenteeism rates with an analytical treatment of nurse staffing decisions using a novel variant of the newsvendor model. Using data from the emergency department of a large urban hospital, we find that absenteeism rates are consistent with nurses exhibiting an aversion to higher levels of anticipated workload. Using our empirical findings, we analyze a single-period nurse staffing problem considering both the case of constant absenteeism rate (exogenous absenteeism) as well as an absenteeism rate that is a function of the number of nurses scheduled (endogenous absenteeism). We provide characterizations of the optimal staffing levels in both situations and show that the failure to incorporate absenteeism as an endogenous effect results in understaffing. This paper was accepted by Yossi Aviv, operations management.
Behold : a content based image search engine for the World Wide Web

This short report outlines the indexing techniques and the user interface of a prototype image search engine called Behold. It complements traditional image metadata with automatically generated image annotations and an image similarity network to make images more accessible. The user supplies the initial query in the form of one or more keywords and is then able to locate the desired images more precisely in the image network through a browsing interface.
Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications

Although users of high-performance computing are most interested in raw performance both energy and power consumption has become critical concerns. One approach to lowering energy and power is to use high-performance cluster nodes that have several power-performance states so that the energy-time trade-off can be dynamically adjusted. This paper analyzes the energy-time trade-off of a wide range of applications-serial and parallel-on a power-scalable cluster. We use a cluster of frequency and voltage-scalable AMD-64 nodes, each equipped with a power meter. We study the effects of memory and communication bottlenecks via direct measurement of time and energy. We also investigate metrics that can, at runtime, predict when each type of bottleneck occurs. Our results show that, for programs that have a memory or communication bottleneck, a power-scalable cluster can save significant energy with only a small time penalty. Furthermore, we find that, for some programs, it is possible to both consume less energy and execute in less time by increasing the number of nodes while reducing the frequency-voltage setting of each node
Learning a Statistical Model of Product Aspects for Sentiment Analysis

sticas de Productos Abstract: In this paper, we introduce a new methodology for modeling product aspects from a collection of free-text customer reviews. The proposal relies on a lan- guage modeling framework and is domain independent. It combines both a kernel- based model of opinion words and a stochastic translation model between words to approach the aspect model of products. We also present a ranking-based met- hodology to model the sentiments expressed about the aspects. The experiments carried out over several collections of customer reviews show encouraging results in the modeling of product aspects and their sentiments even from individual customer reviews.
PAIN AND EMOTIONAL PROCESSING IN PSYCHOLOGICAL TRAUMA SCHMERZ UND EMOTIONALER VERARBEITUNG IM PSYCHOLOGISCHEN TRAUMATA

SUMMARY Extreme psychological and physical traumas cause dramatic symptom patterns which are insufficiently described by the psychiatric diagnostic criteria of post traumatic stress disorders (PTSD). Additionally, due to the neurobiological proximity and similarity of processing mechanisms of physical and psychological pain stimulation and extremely negative emotions, the patients often suffer from persistent pains even after the somatic healing process is completed. Epidemiological studies confirm the joint occurrence of pain and PTSD. The close relationship and the etiological and behavioral similarities of both disorders have led to the development of joined vulnerability and mutual maintenance models. The particular suffering of patients with PTSD due to chronic pain necessitates pain-therapeutic interventions. On the other hand, in chronic pain patients, the etiological role of severe traumas should be considered.
The mOSAIC Benchmarking Framework: Development and Execution of Custom Cloud Benchmarks

A natural consequence of the pay-per-use business model of Cloud Computing is that cloud users need to evaluate and to compare different cloud providers in order to choose the best offerings in terms of trade-off between performance and cost. But at the state of the art, in cloud environments no real grants are offered by providers about the quality of the resources offered and no clear ways exists to compare two different offerings. Moreover, the high elasticity of cloud resources (virtual machines can be added or removed in few minutes) makes the evaluation of such systems a hard task. In this paper we propose to build ad-hoc benchmark applications, whose behavior is strictly related to user needs and which can be used to compare different providers. The proposal is based on the use of the mOSAIC framework, which offers a deployable platform and an API for building provider-independent applications. Due to such independence, we are able to compare directly multiple cloud offers. The paper details the proposed approach and the framework architecture implemented in order to apply it. Simple case studies illustrate how the framework works in practice. Moreover the paper presents a detailed analysis of the state of the art and of the problem of benchmarking in cloud environment.
Fast Spectral Projection Algorithms for Density-Matrix Computations

We present a fast algorithm for the construction of a spectral projector. This algorithm allows us to compute the density matrix, as used in, e.g., the Kohn? Sham iteration, and so obtain the electron density. We compute the spectral projector by constructing the matrix sign function through a simple polynomial recursion. We present several matrix representations for fast computation within this recursion, using bases with controlled space? spatial-frequency localization. In particular we consider wavelet and local cosine bases. Since spectral projectors appear in many contexts, we expect many additional applications of our approach.
Size Control Via Size Fair Genetic Operators In The PushGP Genetic Programming System

The growth of program size during evolution (code "bloat") is a well-documented and well-studied problem in genetic programming. This paper examines the use of "size fair" genetic operators to combat code bloat in the PushGP genetic programming system. Size fair operators are compared to naive operators and to operators that use "node selection" as described by Koza. The effects of the operator choices are assessed in runs on symbolic regression, parity and multiplexor problems (2,700 runs in total). The results show that the size fair operators control bloat well while producing unusually parsimonious solutions. The computational effort required to find a solution using size fair operators is about equal to, or slightly better than, the effort required using the comparison operators.
Relational and overt aggression in preschool.

This research was designed as an initial attempt to assess relational aggression in preschool-age children. Our goal was to develop reliable measures of relational aggression for young children and to use these instruments to address several important issues (e.g., the relation between this form of aggression and social-psychological adjustment). Results provide evidence that relationally aggressive behaviors appear in children's behavioral repertoires at relatively young ages, and that these behaviors can be reliably distinguished from overtly aggressive behaviors in preschool-age children. Further, findings indicate that preschool girls are significantly more relationally aggressive and less overtly aggressive than preschool boys. Finally, results show that relational aggression is significantly related to social-psychological maladjustment (e.g., peer rejection) for both boys and girls.
The state-of-the-art of active filters for power conditioning

Unlike traditional passive filters, active filters for power conditioning have multiple functions such as harmonic filtering/damping/isolation/termination, load balancing, reactive-power control for power factor correction and voltage regulation, voltage-flicker reduction, or their combinations. Significant cost reductions in both power semiconductor devices and signal-processing devices have inspired many manufactures in Japan, Europe and the United States to put active filters on the market. This paper deals with active filters for power conditioning, and specifically hybrid active filters for harmonic filtering of three-phase diode rectifiers
Particle Swarm Optimization of Neural Network Architectures and Weights

The optimization of architecture and weights of feed forward neural networks is a complex task of great importance in problems of supervised learning. In this work we analyze the use of the Particle Swarm Optimization algorithm for the optimization of neural network architectures and weights aiming better generalization performances through the creation of a compromise between low architectural complexity and low training errors. For evaluating these algorithms we apply them to benchmark classification problems of the medical field. The results showed that a PSOPSO based approach represents a valid alternative to optimize weights and architectures of MLP neural networks.