Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid approach of video indexing and machine learning for rapid indexing and highly precise object recognition

Video database and object recognition have been treated as separate problems in the past. Previous retrieval applications achieved rapid indexing and robust retrieval capabilities, but the precision of recognizing objects in video images is lower than object recognition using machine learning. In contrast with video retrieval, machine learning needs vast computational training time in advance and cannot handle similarity easily. To solve this problem, we present an image-based video recognition framework combined with video retrieval and object recognition. To develop an effective combination, we evaluated several retrieval methods and the support vector machine (SVM), which is one of the most popular supervised learning techniques. From experimental results, we found that the combination of extended color-pair retrieval and SVM using color location is the most effective pair for high precision and rapid indexing of a video recognition system.
Parents' Education and Their Adult Offspring's Other-Regarding Behavior

Does socioeconomic background when measured by parental educational attainment explain the heterogeneity in adults' other-regarding preferences? I test this by using data from two online experiments -- a Dictator Game and a Trust Game that were conducted with a broad sample of the Danish adult population. I match the experimental data with high-quality data from the Danish population registers about my subjects and their parents. Whereas previous studies have found socioeconomic status, including parental educational attainment, to be predictive for children's generosity, I find no such evidence among adults. This result is robust across age groups and genders. I provide two explanations for this. First, sociodemographic characteristics in general appear to be poor predictors of adults' other-regarding behavior. Second, by using Danish survey data, I find that Danish parents' educational attainment appears to be uncorrelated with how important they find it to teach their children to "think of others". More speculative explanations are also provided.
Staying Oriented with Software Terrain Maps

Developers often find themselves lost as they navigate around large programs, particularly when those programs are unfamiliar. This paper presents a new visualization, called a software terrain map, intended to keep a programmer oriented as she navigates around source code in the editor. The design is based on the metaphor of cartographic maps, which are continuous (no wasted space), have enough visual landmarks to allow the user to find her location perceptually rather than cognitively, and lend themselves to overlaying data. Although an optimal layout for software terrain maps is computationally intractable, the paper presents an efficient, heuristic algorithm that produces good
Implementing a simple continuous speech recognition system on an FPGA

Speech recognition is a computationally demanding task, particularly the stage which uses Viterbi decoding for converting pre-processed speech data into words or sub-word units. We present an FPGA implementations of the decoder based on continuous hidden Markov models (HMMs) representing monophones, and demonstrate that it can process speech 75 times real time, using 45% of the slices of a Xilinx Virtex XCV1000.
Multi-risk infants: predicting attachment security from sociodemographic, psychosocial, and health risk among African-American preterm infants.

BACKGROUND Ecological and transactional theories link child outcomes to accumulated risk. This study hypothesized that cumulative risk was negatively related to attachment, and that maternal sensitivity mediated linkages between risk and attachment. METHODS One hundred and twelve high-risk African-American premature infant-mother dyads participated. Psychosocial (maternal depression, stress and self-efficacy) and sociodemographic risk (poverty, maternal education, marital status) were maternal self-report (0-4 months). Infant health risk was obtained from hospital charts. Infant-mother attachment (12 months) and maternal sensitivity (4 months) were assessed with Q-sort measures. FINDINGS Psychosocial and sociodemographic risk, but not infant health risk, negatively related to attachment. Both were mediated by maternal sensitivity. CONCLUSIONS The impact of risk domains on attachment security was mediated by maternal sensitivity. Results emphasize the need for early intervention programs targeting premature infants to identify and address environmental and personal factors that place parenting at risk.
“Union Yes” at a Public Ivy

This study investigates predictors of prounion faculty voting intentions in a collective bargaining representational election. The research draws on a dataset developed during the course of the representational election campaign at the University of Vermont. The article explores the current context of higher education organizing in relation to the American labor movement. The literature review shows prounion attitudes, union instrumentality, demographic factors, job dissatisfaction, and active participation in the union campaign to be predictors of voting intentions. A general model of voting intentions emerges. Multiple logistic regression analysis is used to test the general model against assessments of voting intentions observed at four phases of the campaign. Specific models of voting intentions for each phase of the campaign materialize. An exploration of implications for both researchers and organizers follows.
Expression of the helix‐loop‐helix genes Id‐1 and NSCL‐1 during cerebellar development

Neurons throughout the central nervous system (CNS) undergo proliferation, migration, and differentiation during their histogenesis: Although numerous regulatory molecules are expressed in developing neurons, it is unknown whether most of these molecules have the same function throughout the CNS or play different roles in different neuronal populations. Previous studies have shown that Id‐1 and NSCL‐1 are expressed at high levels in the ventricular and subependymal zones, respectively, of the embryonic brain. In the present study, the expression of Id‐1 and NSCL‐1 was further investigated during postnatal development of the cerebellum. By Northern blot hybridization analysis, the expression levels of Id‐1 and NSCL‐1 mRNA were developmentally regulated in the cerebellum, with the highest mRNA levels coinciding with the time of maximal granule cell histogenesis. By in situ hybridization, NSCL‐1 mRNA was found in the premigratory zone of the external granule layer (EGL), a structure developmentally analogous to the subependymal zone of the embryonic brain. In normal mice, Id‐1 mRNA was found to be transiently expressed in the upper internal granule layer (IGL), a population of cells that recently completed their migration from the EGL. In the mouse mutant weaver, Id mRNA was only seen in granule cells that have reached their normal positions in the IGL. No Id‐1 hybridization signal was observed in the large numbers of granule cells remaining in the EGL of weaver mice, indicating that Id‐1 expression is controlled by spatial cues. The lack of Id‐1 expression in ectopic weaver granule cells is compatible with previous suggestions of arrested differentiation. These results support the idea that transcriptional regulators of the helix‐loop‐helix gene family play important roles in neuronal development, exhibiting region‐specific expression and function. Dev Dyn 208:107–114, 1997. © 1997 Wiley‐Liss, Inc.
Risking Connection

Abstract Risking Connection, a mental health training curriculum, empowers clinicians, counselors and other direct-service providers to respond to the complex needs of trauma survivors within the framework of growth-promoting relationships. The program enhances success by teaching providers to notice and use their own reactions to working with traumatized individuals and to attend to their own self-care, with a focus on empowerment, collaboration, hope, and optimism. Building on construc-tivist self-development theory (CSDT), Risking Connection teaches the philosophy of relational therapy in the context of accessible, experiential learning, enabling its ready transfer into practice. This paper will discuss how, by modeling the model, collaborative relationships have nurtured the development, application, and follow-up of Risking Connection.
Automatic mapping of uncalibrated pictures on dense 3D point clouds

This paper presents a solution for the automated texturing of models obtained from 3D scanners by using uncalibrated pictures taken with a digital camera. The proposed method exploits calibrated light intensity images given by the scanning device to automatically extract a set of correspondences with the uncalibrated pictures, such correspondences are used to give an accurate estimate for the camera pose. In a second phase the set of calibrated pictures is projected onto the model to build the texture, with a proper merging of overlapping images. The image blending is carried out by weighting the pixels of each picture depending on their depth and normal orientation. Results are proposed for sculpture datasets where a method to assess the accuracy of the mapping is applied.
Group emotion recognition with individual facial emotion CNNs and global image based CNNs

This paper presents our approach for group-level emotion recognition in the Emotion Recognition in the Wild Challenge 2017. The task is to classify an image into one of the group emotion such as positive, neutral or negative. Our approach is based on two types of Convolutional Neural Networks (CNNs), namely individual facial emotion CNNs and global image based CNNs. For the individual facial emotion CNNs, we first extract all the faces in an image, and assign the image label to all faces for training. In particular, we utilize a large-margin softmax loss for discriminative learning and we train two CNNs on both aligned and non-aligned faces. For the global image based CNNs, we compare several recent state-of-the-art network structures and data augmentation strategies to boost performance. For a test image, we average the scores from all faces and the image to predict the final group emotion category. We win the challenge with accuracies 83.9% and 80.9% on the validation set and testing set respectively, which improve the baseline results by about 30%.
Registration Techniques in Computer Assisted Orthopedic Surgery ( CAOS )

A couple of years ago, before the introduction of GPS systems in nearly every car and before the widespread availability of mobile phone networks in eastern Europe, I was visiting some friends in a foreign country. On the way home, it began snowing lightly, and for some odd reason, the snow sticked to nearly every traf! c sign, so it was impossible to read the guideposts, which told us the correct direction to Germany or at least the names of the villages we passed. Although we had the best road maps and we stuck only to major roads, at one point we had to realize, that we were lost. Since our language skills were approaching zero, mobile phone was not working, the sun had already set and we had no compass, there was absolutely no possibility to determine our present position and so we had no chance to get home, until the snow melted away the next morning and we were able to read the guideposts again. " is short story demonstrates one major problem of navigation: " ere is the real world (the foreign country’s streets and cities) and there is a virtual model of this real world (which is the roadmap, in our analogy). It is the navigator’s task, to correlate these two spaces, so that he will know where the object of navigation (his car) is and where he has to lead it. In computer-assisted surgery, the process of referencing a virtual model (e.g. a CT scan) to the real world (the patient) is called “registration”. During this process, the coordinate systems of both, the virtual and the real object, are digitized in some way, so that the navigation system is able to recognize them and to put them in relationship to the navigation system’s intrinsic coordinates. By many authors, registration is recognized as the most crucial process in computer assisted surgery (1). Registration has to be performed individually for every surgery, and any imprecision will carry forward throughout the whole navigation process.
The Properties of Volatility of Returns in Different Scales

Many empirical studies on the properties of different volatility models dedicate to find the best way to measure the volatility. In this paper, we specify and estimate volatility models for the jointed dynamics of different scales of returns by intra-daily high frequency data. As absolute return and high-low return are non-negative series, we adopt a multiplicative error model jointed with expanded GARCH model. For Shanghai composite index, both absolute return and high-low return are affected by asymmetric variables but with different extent. The results show that the inclusion of different scales of returns in estimating models will enhance the explanatory power in final estimation. Furthermore, we adopt ARFIMA process to model the long memory in volatility, the results show the existence of long memory in volatility of returns in Chinese stock market.
IR-UWB technology on next generation RFID systems

Radio-frequency identification (RFID) systems are widely used in our daily life. Although several proposed solutions are in production, limitations are still significant. In this paper, the current RFID technology is reviewed and major shortcomings are discussed. Our expected features on next generation RFID systems are described. Finally, we propose an impulse-radio (IR) ultra-wideband (UWB) RFID system and present how to improve the performance by using IR-UWB technology.
Re-organizable wireless mesh network using ARS

In recent year wireless communication is most widely used, but there is also traffic in surrounding environment, while transferring the data from source to destination. This can be possible due to channel interference, various obstacles, which result in link failure causes poor performance, require expensive network management for their recovery. To maintain the network performance Autonomous network reconfiguration system (ARS) is used which autonomously reconfigure and recover from local link failure. Through NS2 based simulation ARS has been implemented and evaluated. ARS improves the channel efficiency better than other recovery methods for example reroute and greedy channel.
Cylindrospermopsis raciborskii: review of the distribution, phylogeography, and ecophysiology of a global invasive species

Cylindrospermopsis raciborskii is a cyanobacterial species extensively studied for its toxicity, bloom formation and invasiveness potential, which have consequences to public and environmental health. Its current geographical distribution, spanning different climates, suggests that C. raciborskii has acquired the status of a cosmopolitan species. From phylogeography studies, a tropical origin for this species seems convincing, with different conjectural routes of expansion toward temperate climates. This expansion may be a result of the species physiological plasticity, or of the existence of different ecotypes with distinct environmental requirements. In particular, C. raciborskii is known to tolerate wide temperature and light regimes and presents diverse nutritional strategies. This cyanobacterium is also thought to have benefited from climate change conditions, regarding its invasiveness into temperate climates. Other factors, recently put forward, such as allelopathy, may also be important to its expansion. The effect of C. raciborskii in the invaded communities is still mostly unknown but may strongly disturb species diversity at different trophic levels. In this review we present an up-to-date account of the distribution, phylogeography, ecophysiology, as well some preliminary reports of the impact of C. raciborskii in different organisms.
Maximum mutual information neural networks for hybrid connectionist-HMM speech recognition systems

This paper proposes a novel approach for a hybrid connectionist-hidden Markov model (HMM) speech recognition system based on the use of a neural network as vector quantizer. The neural network is trained with a new learning algorithm offering the following innovations. (1) It is an unsupervised learning algorithm for perceptron-like neural networks that are usually trained in the supervised mode. (2) Information theory principles are used as learning criteria, making the network especially suitable for combination with a HMM-based speech recognition system. (3) The neural network is not trained using the standard error-backpropagation algorithm but using instead a newly developed self-organizing learning approach. The use of the hybrid system with the neural vector quantizer results in a 25% error reduction compared with the same HMM system using a standard k-means vector quantizer. The training algorithm can be further refined by using a combination of unsupervised and supervised learning algorithms. Finally, it is demonstrated how the new learning approach can be applied to multiple-feature hybrid speech recognition systems, using a joint information theory-based optimization procedure for the multiple neural codebooks, resulting in a 30% error reduction. >
A new indirect encoding method with variable length gene code to optimize neural network structures

A new encoding method for optimizing neural network structures is proposed. It is based on an indirect encoding method with variable length gene code. The search ability for finding an optimal solution is higher than the direct encoding methods because redundant information in gene code is reduced and the search space is also reduced. The proposed method easily operates adding and deleting hidden units. The performance of the proposed method is evaluated through computer simulations.
Elemental isotopic abundance determinations of magnesium in biological materials by secondary ion mass spectrometry.

Secondary ion mass spectrometry is applied to the determination of stable magnesium isotopes. The method involves the high mass solution determinations of the isotopes from air-dried films of untreated and acid-treated biological fluids. Calibration curves are used for the isotopic quantifications. Agreements of less than 2.5% enrichments of /sup 26/Mg were determined for spiked and unspiked acid-treated samples. Differences of about 5%, 2%, and 1% enrichments can be determined with 95% certainty in analyses times of 2, 15, and 40 min, respectively. 23 references, 3 figures, 6 tables.
Training Data Annotation for Segmentation Classification in Simultaneous Translation

Segmentation of the incoming speech stream and translating segments incrementally is a commonly used technique that improves latency in spoken language translation. Previous work of Oda et al. 2014 [1] has explored creating training data for segmentation by finding segments that maximize translation quality with a user-defined bound on segment length. In this work we provide a new algorithm that uses Pareto-optimality to find good segment boundaries that can balance the trade-off between latency versus translation quality. We compare against the state-of-the-art greedy algorithm from Oda et al. 2014. Our experimental results show that we can improve latency by up to 12% without harming the Bleu score for the same average segment length. Another benefit is that for any segment size, Pareto-optimal segments maximize both latency and translation quality.
Path optimisation of a mobile robot using an artificial neural network controller

This article proposed a novel approach for design of an intelligent controller for an autonomous mobile robot using a multilayer feed forward neural network, which enables the robot to navigate in a real world dynamic environment. The inputs to the proposed neural controller consist of left, right and front obstacle distance with respect to its position and target angle. The output of the neural network is steering angle. A four layer neural network has been designed to solve the path and time optimisation problem of mobile robots, which deals with the cognitive tasks such as learning, adaptation, generalisation and optimisation. A back propagation algorithm is used to train the network. This article also analyses the kinematic design of mobile robots for dynamic movements. The simulation results are compared with experimental results, which are satisfactory and show very good agreement. The training of the neural nets and the control performance analysis has been done in a real experimental setup.
Classification based on distribution of average matching degree and Gaussian function and its application to intrusion detection

With the rapid development of the Internet, Internet security is becoming an important problem recently. Therefore, many techniques for intrusion detection have been proposed to protect networks effectively. In this paper, a new classification model, named classification with average matching degree and gaussian function, is proposed and combined with the class association rule mining of Genetic Network Programming (GNP). The proposed classification algorithm can efficiently classify a new access data into a class of normal, misuse or anomaly. The simulations are based on NSL-KDD data set.
Incidence and impact of light chain associated (AL) amyloidosis on the prognosis of patients with multiple myeloma treated with autologous transplantation.

Little is known about the incidence of clinically occult AL amyloid in patients with multiple myeloma and its impact on prognosis of these patients. To address these issues, subcutaneous fat pad aspirates (SAFA) and bone marrow biopsies were evaluated for the presence of amyloid in a cohort of newly diagnosed patients with multiple myeloma prior to enrollment on a phase II study including tandem transplants. Organ directed biopsies were performed when clinically indicated. Presence of amyloid at > or = 1 site was noted in 32 patients (38%). SAFA was positive in 25 (31%), bone marrow in 8 patients (10%) and other organ sites in 7 patients. Patients with and without amyloid did not differ in disease characteristics, in particular no lambda predominance was observed in patients with amyloid. Event free survival (59+ vs 52 months; p = .9) and overall survival (59+ vs 66+ months; p = .9) were similar in both groups. Even the seven patients with symptomatic organ involvement with AL amyloid had a median overall survival of 38+ months. In conclusion, AL amyloidosis occurs more often than previously reported, but its presence does not influence the outcome of these patients after transplantation.