Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Model Complexity Control Using Marginalized Discriminative Growth Functions

Selecting the model structure with the "appropriate" complexity is a standard problem for training large-vocabulary continuous-speech recognition (LVCSR) systems. State-of-the-art LVCSR systems are highly complex. A wide variety of techniques may be used which alter the system complexity and word error rate (WER). Explicitly evaluating systems for all possible configurations is infeasible; hence, an automatic model complexity control criterion is highly desirable. Most existing complexity control schemes can be classified into two types, Bayesian learning techniques and information theory approaches. An implicit assumption is made in both, that increasing the likelihood on held-out data decreases the WER. However, this correlation has been found quite weak for current speech recognition systems. This paper presents a novel discriminative complexity control technique, the marginalization of a discriminative growth function. This is a closer approximation to the true WER than standard approaches. Experimental results on a standard LVCSR Switchboard task showed that marginalized discriminative growth functions outperforms manually tuned systems and conventional complexity control techniques, such as Bayesian information criterion (BIC), in terms of WER
The Role of Fascia Iliaca Blocks in Hip Fractures: A Prospective Case-Control Study and Feasibility Assessment of a Junior-Doctor-Delivered Service

Hip fractures are common and the incidence is expected to increase. Systemic analgesics, often prescribed to relieve pain after hip fractures, have huge side effects and can delay surgery. We analyse the role and efficacy of alternative forms of analgesia like fascia-iliac blocks (FIB) and assess the feasibility of a service delivered by junior doctors. 104 consecutive hip fracture patients were prospectively recruited and equally divided into cases (patients receiving FIB) and controls (patients receiving systemic analgesia). Outcome measures included time of initial analgesia, total preoperative dose of analgesia, pain scores from admission to 24 hours preoperatively, and complications. The pain scores were significantly lower (P ≤ 0.05) in patients receiving FIB at 2 and 8 hours preoperatively. The timing of initial analgesia was also quicker in patients with FIB (25 compared to 40 minutes). FIB patients required fewer doses of systemic analgesia. The block was successful in 67% of patients. There were no complications. The implementation of EWTD, HAN, and shift-system and the reduction in the number of medical staff have increased the burden on emergency departments. This study demonstrates that FIB performed by junior doctors are not only safe and effective analgesia but also provide an opportunity for junior doctors to improve current clinical practice.
A General Evolution Mechanism Model for E-Commerce Network

In order to investigate the general formation of e-commerce market network, this chapter describes an analytical framework and builds an innovative model for explaining the evolutionary process, with several original factors—growth factor, select-order factor, preferential attachment mechanism, and global-local factor. The research reveals that the attraction mechanism impacts evolutionary trend and network structure to some extent, and also reveals that the global-local factor and select-order factor impact the evolutionary structure of the network: the smaller the probability, the smaller the concentration of networks and the more obvious the randomness is.
What happens when detectors are placed in both arms of a Mach-Zehnder interferometer?

If you want to quote this document, please quote the latest version with its date and when it was retrieved, along with a link to the current document. (The current link, which is not expected to change, is www.math.umb.edu/ sp/papers/gracom.pdf.) That way, if any errors turn up, I will have the chance to correct them. And of course, I shall be grateful for any suggestions or notification of errors.
The follicle-stimulating hormone receptor: biochemistry, molecular biology, physiology, and pathophysiology.

I. Introduction II. Biochemical Properties of the FSH Receptor: A Historical Prelude III. Molecular Structure of the FSH Receptor A. Cloning of the FSH receptor B. Predicted primary structure of the FSH receptor C. Molecular mass of the FSH receptor IV. The FSH Receptor Gene A. Chromosomal localization B. Structure and organization of the FSH receptor gene C. The promoter of the FSH receptor gene V. Expression of the FSH Receptor and Its Regulation A. FSH receptor gene expression B. Expression of the FSH receptor in the testis C. Expression of the FSH receptor in the ovary VI. Expression of the FSH Receptor in Cell Lines A. Cell lines expressing the recombinant FSH receptor B. Measurement of FSH by means of “recombinant” in vitro bioassays C. FSH receptor function in cell lines VII. Structure-Function Relationships and Models of FSH-FSH Receptor Interaction A. General features B. Structure-function relationships C. Models of FSH-FSH receptor interaction VIII. Signal Transduction and Postreceptor Events A.
Quality metrics for mashups

Mashups combine disparate resources in order to create content in new ways from heterogeneous resources. Mashups are created in an ad-hoc manner due to, among other things, missing metrics that should give Mashup composers feedback about their application. The aim of this paper is to discuss application areas of metrics for Mashups. In particular, we will discuss Mashup metrics for design engineering and for corporate environments.
Reverse Tree Clustering

Common document clustering algorithms utilize models that either divide a corpus into smaller clusters or gather individual documents into clusters. Hierarchical Agglomerative Clustering, a common gathering algorithm runs in O(n) to O(n) time, depending on the linkage of documents. In contrast, Bisecting K-Means Clustering has been shown to run in linear time with respect to the number of documents to cluster, although other factors significantly affect run time. We propose a clustering algorithm bases on an inverted-index matrix of terms and an inverted term tree model.
Preliminary Dissertation Proposal

The topic of my dissertation research is Bayesian methods in the social sciences. There will be several parts to the dissertation. One part, the focus of this proposal, is a novel method called tuned Bayesian model averaging (tBMA) that modifies Bayesian model averaging (BMA) by using the data to inform the prior specification. BMA is a tool that accounts for model uncertainty in typical regression analyses (Hoeting et al., 1999). BMA requires that the analyst specify a prior distribution over the collection of models under consideration. Typically, users of BMA have put equal prior probability on all models. Since this specification puts nearly all of the prior mass on models with an intermediate number of terms, we find that this can result in poor predictive ability when the data generating model depends on a large proportion of the available covariates. The tBMA process specifies a method that results in a data-dependent prior over the model space. We define a hyper-parameter λ that favors small models when close to zero, and large models when close to one. This λ is chosen through leave-one-out methods like those that are used to select the tuning parameter in smoothing spline analyses (Wahba, 1990). We start with a generalized cross-validation (GCV) procedure that provides good results when the true data generating model contains a large proportion of the available predictors. We find that an approximation used in the derivation of this GCV criterion leads to poor performance in some cases, however. We then derive an ordinary cross-validation (OCV) version of tBMA that fixes this problem. Simulation results are presented that show out-of-sample prediction error can be reduced by a factor of two in some situations, relative to standard BMA. The second part is a Bayesian Heckman-type selection model for multinomial data. The Heckman model is commonly used in the social sciences
T-79.514 Special Course on Cryptology / Privacy-Preserving Data Mining: Database randomization via RRT

This survey is a review of randomized response techniques for privacy-preserving data mining as described in the papers ”Using Randomized Response Techniques for Privacy-Preserving Data Mining” by W. Du, and Z. Zhan [1] and ”Privacy Preserving Mining of Association Rule” by A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke.s [2]. There are two different approaches presented in the papers. One for the association rule mining and one for the data classification.
Genetic analysis of mammographic breast density in adult women: evidence of a gene effect.

BACKGROUND The appearance of the female breast viewed by mammography varies considerably from one individual to another because of underlying differences in the relative proportions of fat, connective tissue, and glandular epithelium that combine to create a characteristic pattern of breast density. An association between mammographic patterns and family history of breast cancer has previously been reported. However, this association has not been found in all studies, and few data are available on possible genetic components contributing to mammographic breast density. PURPOSE Our purpose was to estimate familial correlations and perform complex genetic segregation analyses to test the hypothesis that the transmission of a major gene influences mammographic breast density. METHODS As part of a cohort study (initiated in 1944) of families with a history of breast cancer, the probands' female relatives who were older than 40 years were asked to obtain a routine mammogram. The mammograms of 1370 women from 258 independent families were analyzed. The fraction of the breast volume occupied by radiographically dense tissue was estimated visually from video displays of left or right mediolateral oblique views by one radiologist experienced in mammography who had no knowledge of individual relationships to the probands. Data on breast cancer risk factors were obtained through telephone interviews and mailed questionnaires. Unadjusted and adjusted familial correlations in breast density were calculated, and complex genetic segregation analyses were performed. RESULTS Sister-sister correlations in breast density (unadjusted and adjusted for age and either body mass index, menopausal status, hormone replacement therapy, waist-to-hip ratio, number of live births, alcohol consumption, or cigarette smoking status) were all statistically significant (r = .16-.27; all P<.05 [two-sided]). Estimated mother-daughter correlations were smaller in magnitude (r = .01-.17) and not statistically significant. Segregation analyses indicate that a major autosomal gene influences breast density. The mendelian transmission of a dominant gene provided the best fit to the data; however, hypotheses involving the inheritance of either a recessive gene or a codominant gene could not be ruled out. The mendelian dominant hypothesis, accounting for 29% of the variability in breast density, suggests that approximately 12% of the population would be expected to carry at least one variant allele of this putative gene. Women who inherit the variant allele would have a mean breast density about twice that of the rest of the population. CONCLUSIONS Our preliminary findings suggest that, in this cohort of women at risk of breast cancer, mammographic breast density may be genetically influenced.
Perception of Nigerian students toward the role of Information and Communication Technology (ICT) in enhancing language learning

The 21st century has come with rapid advancement of technology. This has brought many changes into various spheres of our lives. ICT plays an important role in ensuring that the needs for these new changes are met.Consequently, many language educators are urged to integrate ICT into their language classes. One of the purpose of this, is for it to serve as a means of creating experiences for enhancing learning. This paper, therefore, tries to find out the perception of students towards the use of ICT in enhancing learning and English as Second Language (ESL)learning in particular. The challenges in using ICT for language learning in Nigeria are also examined. Four out of the 16 institutions existing in Kano state have been randomly selected. Then 140 students have also been randomly selected. A questionnaire is used. The data is analyzed using simple percentage and descriptive analysis. The findings have shown that the respondents believe that ICT can be used to enhance communicative competence and develop more language skills. Some of the challenges for using it in Nigeria are problems of power/electricity failure and of connectivity.
eDonkey & eMule's Kad: Measurements & Attacks

This article reports on the results of our measurement study of the Kad network. Although several fully decentralized peer-to-peer systems have been proposed in the literature, most existing systems still employ a centralized architecture. The Kad network is a notable exception. Since the demise of the Overnet network, the Kad network has become the most popular peer-to-peer system based on a distributed hash table. It is likely that its user base will continue to grow in numbers over the next few years due to the system's scalability and reliability. The contribution of the article is twofold. First, we compare the two networks accessed by eMule: the centralized paradigmof the eDonkey network and the structured, distributed approach pursued by the Kad network. We re-engineer the eDonkey server software and integrate two modified servers into the eDonkey network in order to monitor traffic. Additionally, we implement a Kad client exploiting a design weakness to spy on the traffic at arbitrary locations in the ID space. The collected data provides insights into the spacial and temporal distributions of the peers' activity. Moreover, it allows us to study the searched content. The article also discusses problems related to the collection of such data sets and investigates techniques to verify the representativeness of the measured data. Second, this article shows that today's Kad network can be attacked in several ways. Our simple attacks could be used either to hamper the correct functioning of the network itself, to censor content, or to harm other entities in the Internet not participating in the Kad network, such as ordinary web servers. While there are heuristics to improve the robustness of Kad, we believe that the attacks cannot be thwarted easily in a fully decentralized peer-to-peer system, i.e., without some kind of a centralized certification and verification authority. This result may be relevant in the context of the current debate on the design of a clean-slate network architecture for the Internet which is based on concepts known from the peer-to-peer paradigm.
Domain‐Adaptation Technique for Semantic Role Labeling with Structural Learning

Semantic role labeling (SRL) is a task in natural‐language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high‐performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state‐of‐the‐art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F‐score.
Analysis and monitoring of a high density traffic flow at T-intersection using statistical computer vision based approach

A reliable traffic flow monitoring and traffic analysis approach using computer vision techniques has been proposed in this paper. The exponential increase in traffic density at urban intersections in the past few decades has raised precious and challenging demands to computer vision algorithms and technological solutions. The focus of this paper is to suggest a statistical based approach to determine the traffic parameters at heavily crowded urban intersections. The algorithm in addition to accurate tracking and counting of freeway traffic also offers high efficiency for determining vehicle count at a high traffic density T-intersection. The system uses Intel Open CV library for image processing. The implementation of algorithm is done using C++. The real time video sequence is obtained from a stationary camera placed atop a high building overlooking the particular T intersection. This paper suggests a dynamic method where each vehicle at a T intersection is passed through a number of detection zones and the final count of vehicles is derived from a statistical equation.
Differential public physically unclonable functions: Architecture and applications

We have developed an ultra low power (well below 1 nanojoule per transaction), ultra high speed (less than 1 nanosecond), and low cost (a few hundred gates) public physically unclonable function (PPUF). We have also developed the first PPUF-based smart card (SC). We analyze and demonstrate the security of this new SC against several families of potential security attacks.
Automatic Image Annotation Based on Visual Cognitive Theory

This paper presents a new method of automatic image annotation based on visual cognitive theory that improves the accuracy of image recognition by taking two semantic levels of keywords that give feedback to each other into consideration. Our system first segments an image and recognizes objects in the K-Nearest Neighbor (KNN). It then recognizes contexts by using them from networked knowledge. After that, it re-recognizes objects depending on these contexts. We adopted natural images for experiments and verified the system's effectiveness. As a result, we obtained improved recognition rates compared with KNN. We proved that our system that takes the semantic levels of keywords into account has great potential for enhancing image recognition.
Acute Effects of Normobaric Hypoxia and Cold Water Hand Immersion on Thermoregulatory Response and Cognitive Function

Cold-induced vasodilation (CIVD) is a mechanism that protects the peripheries from cold-related injury. PURPOSE: The purpose of the current study was to investigate the effects of normobaric hypoxia on the thermoregulatory and CIVD response before and following submaximal exercise. METHODS: 10 apparently healthy men (23±3 years) volunteered for two experimental trials and differing O2 saturations. These two trials (13% O2, 21% O2) were counterbalanced and blinded from the participant. Following a 60-min. acclimation the experimental trials consisted of two 15-min. exposures to 5oC water of the non-dominant hand. The exposures were separated by a 30min. bout of submaximal exercise producing the equivalent of 400 watts (W) of metabolic heat. Mean body temperature (MBT), oxygen saturation (SaO2), and thermal sensation (TS) of the arm were collected during the final 5 min. of each stage. CIVD was measured preand post-exercise during each of the cold water exposures on the nailbed of the middle finger on the non-dominant hand. RESULTS: No significant difference across time or condition exists for MBT. ANOVA revealed a significant time (baseline, acclimation, CIVDpre, exercise, and CIVDpost) by condition (13% O2, 21% O2) interaction for SaO2 (F = 38.4, p < 0.001). Significant differences (p < 0.001) between conditions existed at all time-points with the exception of baseline (p = 1.0). No significant difference across time or condition was found for overall TS. A main effect of
Modulation of Olfactory Perception by Visual Cortex Stimulation

When attempting to identify an object based on smell alone, people often visualize the perceived source of the odorant. This close association between olfactory and visual functions is supported by neuroimaging studies demonstrating activation of visual cortex during performance of purely olfactory tasks. Such activation might simply reflect the correlation between olfactory percepts and the corresponding visual images, or it might reflect a causal contribution of visual processing to olfactory perception. Here we provide evidence in support of the latter possibility. Using repetitive transcranial magnetic stimulation, we show that stimulating human visual cortex improves performance on a task requiring discrimination among different odor qualities. No significant improvement is found for tasks involving discrimination between intensities of the same odor, from stimulation of auditory cortex, or from “sham” stimulation. These results are thus consistent with a specific visual cortical influence on high-level olfactory perception. They also demonstrate that unimodal perceptual tasks are influenced by processing within cortical areas of other, seemingly unrelated, sensory systems.
Session S1E THE DILDIS-PROJECT- USING APPLETS FOR MORE DEMONSTRATIVE LECTURES IN DIGITAL SYSTEMS DESIGN AND TEST

This paper presents a new teaching concept supporting the learning process by several features. It supports the possibility of distance learning as well as a web-based computer-aided teaching, ofers a set of tooh ("interactive modules'? to inspect the teaching topics and carries out laboratoty research. A big reservoir of examples and the possibility to generate own ones makes the learning process more interesting and allows learning at an individual depth and duration. The interactive modules are focused on correct solutions, easy action and reaction, multilingual descriptions, learning by doing, a game-like use, and fostering students in critical thinking, problem solving skills and creativity.
Clothes change detection using the Kinect sensor

This paper describes a methodology for detecting when a human has changed clothes. Changing clothes is a basic activity of daily living which makes the methodology valuable for tracking the functional status of elderly people, in the context of a non-contract unobtrusive monitoring system. Our approach uses Kinect and the OpenNI SDK, along with a workflow of basic image analysis steps. Evaluation has been conducted on a set of real recordings under various illumination conditions, which is publicly available along with the source code of the proposed system at http://users.iit. demokritos.gr/ tyianak/ClothesCode.html.
Regularity and unitarity of bilinear time-frequency signal representations

Two structural properties of bilinear time-frequency representations (BTFRs) of signals are introduced and studied. The definition of these properties is based on a linear-operator description of BTFRs. The first property, termed regularity, has important implications with respect to the recovery of signals from the BTFR outcome, the derivation of other bilinear signal representations from a BTFR, the BTFRs reaction to linear signal transformations, and the construction of bases of induced BTFR-domain spaces. The second property, called unitarity, is equivalent to validity of Moyal's formula (1949). Unitarity is thus necessary and sufficient for a closed-form solution of optimal signal synthesis and for a BTFR formulation or optimal detection/estimation methods. Unitarity also allows the systematic construction of BTFR product relations like the Wigner distribution's interference formula and the ambiguity function's self-transformation property. Unitarity permits the construction of induced orthogonal projection operators and guarantees the orthonormality of induced basis functions. >