Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficient Two-Way Non-Regenerative Relaying for Relays with Multiple Antennas

Energy efficient beamforming is studied for a MIMO two-way non-regenerative relaying system with multi-antenna users and a multi-antenna relay. The resulting optimization problem is non-convex. A Dinkelbach based alternating maximization (DAM) method is proposed to achieve an iterative design of the relay amplification matrix and the beamforming vectors at the users.
Calculation of Higher Moments in CreditRisk + with Applications

CreditRisk + is an influential and widely implemented model of portfolio credit risk. As a close variant of models long used for insurance risk, it retains the analytical tractability for which the insurance models were designed. Value-at-risk can be obtained via a recurrence-rule algorithm, so Monte Carlo simulation can be avoided. Little recognized, however, is that the algorithm is fragile. Under empirically realistic conditions, numerical error can accumulate in the execution of the recurrence rule and produce wildly inaccurate results for value-at-risk. This paper provides new tools for users of CreditRisk + based on the cumulant generating function (\cgf") of the portfolio loss distribution. Direct solution for the moments of the loss distribution from the cgf is almost instantaneous and is computationally robust. Thus, the moments provide a convenient, quick and independent diagnostic on the implementation and execution of the standard solution algorithm. Better still, with the cgf in hand we have an alternative to the standard algorithm. I show how tail percentiles of the loss distribution can be calculated quickly and easily by saddlepoint approximation. On a large and varied sample of simulated test portfolios, I nd a natural complementarity between the two algorithms: Saddlepoint approximation is accurate and robust in those situations for which the standard algorithm performs least well, and is less accurate in those situations for which the standard algorithm is fast and reliable.
Transient Calcium and Dopamine Increase PKA Activity and DARPP-32 Phosphorylation

Reinforcement learning theorizes that strengthening of synaptic connections in medium spiny neurons of the striatum occurs when glutamatergic input (from cortex) and dopaminergic input (from substantia nigra) are received simultaneously. Subsequent to learning, medium spiny neurons with strengthened synapses are more likely to fire in response to cortical input alone. This synaptic plasticity is produced by phosphorylation of AMPA receptors, caused by phosphorylation of various signalling molecules. A key signalling molecule is the phosphoprotein DARPP-32, highly expressed in striatal medium spiny neurons. DARPP-32 is regulated by several neurotransmitters through a complex network of intracellular signalling pathways involving cAMP (increased through dopamine stimulation) and calcium (increased through glutamate stimulation). Since DARPP-32 controls several kinases and phosphatases involved in striatal synaptic plasticity, understanding the interactions between cAMP and calcium, in particular the effect of transient stimuli on DARPP-32 phosphorylation, has major implications for understanding reinforcement learning. We developed a computer model of the biochemical reaction pathways involved in the phosphorylation of DARPP-32 on Thr34 and Thr75. Ordinary differential equations describing the biochemical reactions were implemented in a single compartment model using the software XPPAUT. Reaction rate constants were obtained from the biochemical literature. The first set of simulations using sustained elevations of dopamine and calcium produced phosphorylation levels of DARPP-32 similar to that measured experimentally, thereby validating the model. The second set of simulations, using the validated model, showed that transient dopamine elevations increased the phosphorylation of Thr34 as expected, but transient calcium elevations also increased the phosphorylation of Thr34, contrary to what is believed. When transient calcium and dopamine stimuli were paired, PKA activation and Thr34 phosphorylation increased compared with dopamine alone. This result, which is robust to variation in model parameters, supports reinforcement learning theories in which activity-dependent long-term synaptic plasticity requires paired glutamate and dopamine inputs.
On the efficiency of sequential auctions for spectrum sharing

In previous work we have studied the use of sequential second price auctions for sharing a wireless resource, such as bandwidth or power. The resource is assumed to be managed by a spectrum broker (auctioneer), who collects bids and allocates discrete units of the resource. It is well known that a second price auction for a single indivisible good has an efficient dominant strategy equilibrium; this is no longer the case when multiple units of a homogeneous good are sold in repeated iterations. Previous work attempted to bound this inefficiency loss for two users with non-increasing marginal valuations and full information. This work was based on studying a setting in which one agent's valuation for each resource unit is strictly larger than any of the other agent's valuations and assuming a certain property of the price paid by such a dominant user in any sub-game. Using this assumption it was shown that the worst-case efficiency loss was no more than e−1. However, here we show that this assumption is not satisfied for all non-increasing marginals with this dominance property. In spite of this, we show that it is always true for the worst-case marginals for any number of goods and so the worst-case efficiency loss for any non-increasing marginal valuations is still bounded by e−1
A phonological, lexical, and phonetic analysis of the new words that young children imitate

Abstract As children learn language, they spontaneously imitate the speech of those around them. This article investigates the new words that five children imitated between 1 and 2 years of age. Children were more likely to imitate new words as they aged and as their productive language developed. After controlling for age, children also were more likely to imitate new words that were shorter and with high neighborhood densities, and that contained sounds the children had previously produced accurately. Together, the findings demonstrate that both the patterns of the target words and children's productive abilities are predictors of children's imitative speech. This supports models of language development where there are influences stemming not only from phonological and lexical representations, but also from phonetic representations. Résumé Au cours de leur développement langagier, les enfants imitent spontanément le discours des gens qui les entourent. La présente recherche s'intéresse à certains mots nouveaux imités par cinq enfants âgés de 1 à 2 ans. Les enfants imitent plus souvent les mots nouveaux avec l'avancée en âge et avec le développement de leurs compétences en production. Après avoir tenu compte du facteur ‘âge’, les enfants sont également plus enclins à imiter des mots nouveaux courts et avec une haute densité de voisinage, et contenant des sons que les enfants avaient produits correctement auparavant. Les résultats montrent que les modèles lexicaux des mots cibles et les capacités productives des enfants peuvent prédire leur discours imitatif. Cela soutient les théories de développement linguistique selon lesquelles les influences proviennent non seulement des représentations phonologiques et lexicales, mais également des représentations phonétiques.
Proxy protocol and PMIPv6 based mobility management for IEEE 802.11s wireless mesh networks

In Wireless Mesh Networks (WMNs), the mobility management for mesh clients is one of the most challenging issues since they need to be provided with continuous network access service, regardless of their movements. However, the IEEE 802.11s, standard protocol for layer-2 WMNs, overlooks mobility management, which can disturb the smooth operation of the network in a real environment. Therefore, in this paper, we propose a novel mobility management scheme that is compatible with the existing IEEE 802.11s standard. Our scheme performs intra-Mesh Basic Service Set (intra-MBSS) and inter-MBSS mobility management based on the proxy protocol in IEEE 802.11s and PMIPv6, without any modification of protocol messages and their usages. We implemented an IEEE 802.11s WMN test-bed and a mobility management protocol based on the open80211s implementation. We used this test-bed to verify that our proposed scheme achieves good performance in terms of TCP throughput and packet loss rate between mesh clients.
Beyond recognition: using gesture variation for continuous interaction

Gesture-based interaction is widespread in touch screen interfaces. The goal of this paper is to tap the richness of expressive variation in gesture to facilitate continuous interaction. We achieve this through novel techniques of adaptation and estimation of gesture characteristics. We describe two experiments. The first aims at understanding whether users can control certain gestural characteristics and if that control depends on gesture vocabulary. The second study uses a machine learning technique based on particle filtering to simultaneously recognize and measure variation in a gesture. With this technology, we create a gestural interface for a playful photo processing application. From these two studies, we show that 1) multiple characteristics can be varied independently in slower gestures (Study 1), and 2) users find gesture-only interaction less pragmatic but more stimulating than traditional menu-based systems (Study 2).
The Pure Characteristics Discrete Choice Model of Differentiated Products Demand still preliminary

In this paper we consider a class of discrete choice models in which consumers care about a finite set of product characteristics. These models have been used extensively in the theoretical literature on product differentiation, but have not as yet been translated into a form that is useful for empirical work. Most recent econometric applications of discrete choice models implicitly let the dimension of the characteristic space increase with the number of products. The models in this paper have very different theoretical properties. After developing those properties and comparing them to the properties of models where there is a taste for the product per se, we provide an algorithm for an estimator for the parameters of our model. In this version of the paper, we particularly consider how the modeling choices discussed in the paper affect the calculation of ideal consumer price indices, especially in the presence of new goods.
Human Prostate Sphere-Forming Cells Represent a Subset of Basal Epithelial Cells Capable of Glandular Regeneration in Vivo

Prostate stem/progenitor cells function in glandular development and maintenance. They may be targets for tumor initiation, so characterization of these cells may have therapeutic implications. Cells from dissociated tissues that form spheres in vitro often represent stem/progenitor cells. A subset of human prostate cells that form prostaspheres were evaluated for self‐renewal and tissue regeneration capability in the present study.
Diffusion Reflection and Fluorescence Lifetime Imaging Microscopy Study of Fluorophore-Conjugated Gold Nanoparticles or Nanorods in Solid Phantoms

In this paper we report the optical properties of fluorescein-conjugated gold nanoparticles (GNPs) in solid phantoms using diffusion reflection (DR) and fluorescence lifetime imaging microscopy (FLIM). The GNPs attached with fluorescein in solution were studied by fluorescence correlation spectroscopy. The intensity decays were recorded to reveal the fluorescence lifetime of fluorescein while in the near-field vicinity of the GNPs. The DR method was used to explore the solid phantoms containing GNPs, indicating the light propagation from the surface of solid phantoms. The resulting DR slopes of the reflected intensity showed the higher the GNP concentration, the bigger the slope. Fluorescence intensity, lifetime, and anisotropy images of solid phantoms were investigated by FLIM. The exploration of optical properties and molecular imaging combined with DR and FLIM methods is a new approach that has not been established until now. The combined DR–FLIM technique is expected to provide discrimination based on unique spectroscopic fingerprints of GNPs that could be utilized for cell imaging. This paper includes a combined study with a variety of methods, which may lead to multimodal imaging for surfaces (by FLIM) and deep penetration (up to cm by the DR) together.
Distributed filtering in sensor networks with hybrid communication constraints

This paper is concerned with the distributed H∞ filtering problem for a class of sensor networks with hybrid communication constraints. Three kinds of communications constraints are considered during the process of information transmission among the sensors: (i) the estimation of sensors needs to be sampled before transmitting; (ii) at each sampling instant, the packet dropouts would happen to the sampled data of sensors; (iii) the noise and disturbance exist. It should be noted that the constraints of sampled information and data packet dropouts would lead that less information can be employed for each sensor, which makes the filtering problem in sensor networks more challenging and practical. Some criteria concerning the connection gains are derived and used to design efficient distributed H∞ filter to achieve the following objectives: (i) the filtering error system is exponentially mean-square stable in the absence of disturbance and noise; (ii) the prescribed H∞ performance constraint is satisfied. A numerical example is utilized to illustrate the effectiveness of the theoretical results.
Quaternion-Valued Stochastic Gradient-Based Adaptive IIR Filtering

A learning algorithm for the training of quaternion valued adaptive infinite impulse (IIR) filters is introduced. This is achieved by taking into account specific properties of stochastic gradient approximation in the quaternion domain and the recursive nature of the sensitivities within the IIR filter updates, to give the quaternion-valued stochastic gradient algorithm for adaptive IIR filtering (QSG-IIR). Further, to reduce computational complexity, a variant of the QSG-IIR is introduced, which for small stepsizes makes better use of the available information. Stability analysis and simulations on both synthetic and real world 4D data support the approach.
Adaptive Algorithms to Mitigate Inefficiency in Reliability Differentiation Mechanisms for Wireless Sensor Networks

This short paper presents practical algorithms for mitigating inefficiency in reliability differentiation service based on geographical stateless routing, which has great potential applications in large scale wireless sensor networks (WSN). Based on available algorithm, the paper presents a mathematical formalization to systematic understanding the multipath redundancy problem of WSN. With the information beyond one hop and the load balancing mechanism, the data retransmission and reliability differentiation reassignments are reduced. The illustration and simulation demonstrate the proposed algorithmspsila effectiveness and the capability to provide reliability differentiation service.
Understanding the Ethical Behavior of Healthcare Personnel : A Perspective of Human Resource Management

Healthcare ethical behavior is an important topic in health industry. This study proposes a conceptual model and eight propositions from a perspective of human resource management, and provides with useful managerial implications for reference of management in healthcare. The proposed model suggests that healthcare ethical behavior differs among individuals, and is influenced directly by subjective norm, attitude, perceived behavioral control and ethical judgments, while also being influenced indirectly by idealism and relativism through the mediation of perceived behavioral control and ethical judgment. Last, conclusion and limitation of this situation are provided.
Localized fusion of Shape and Appearance features for 3D Human Pose Estimation

This paper presents a learning-based method for combining the shape and appearance feature types for 3D human pose estimation from single-view images. Our method is based on clustering the 3D pose space into several modular regions and learning the regressors for both feature types and their optimal fusion scenario in each region. This way the complementary information of the individual feature types is exploited, leading to improved performance of pose estimation. We train and evaluate our method using a synchronized video and 3D motion dataset. Our experimental results show that the proposed feature combination method gave more accurate pose estimation than that from each individual feature type.
Assessment of genetic diversity in natural populations of Calamus guruba Buch.-Ham. ex Mart. using ISSR marker

Rattan palms are one of the important nontimber forest produce which forms an integral part of the tribal population and contribute significantly in the rural economy. The demand of the rattan resources is substantial but most of the raw material is being collected from natural stands; and therefore, there is tremendous pressure on natural populations. Calamus guruba is one of the important rattan species of India and extensively harvested for a wide range of end products. In the present study, three populations of C. guruba naturally growing in Mizoram were characterized for their genetic diversity and population structure using Inter-Simple Sequence Repeat (ISSR) markers. A total of 197 bands generated in PCR amplification of 67 individuals with 10 ISSR primers. Among all the primers UBC 808 displayed maximum 25, while UBC 807 and UBC 827 showed the lowest 13 numbers of bands. Relatively high genetic diversity (PPB = 98.98 %, h = 0.271, I = 0.341) was recorded for the species with a moderate level of genetic differentiation (PhiPT = 0.282). Analysis of molecular variance revealed that the genetic differentiation among the populations is the significant and large proportion of the genetic variation (72%) resides among the individuals within a population, whereas only 28 % were found among the populations. UPGMA tree, PCoA plot and STRUCTURE analysis revealed that two ancestral groups captured the entire genetic variability. The baseline study on C. guruba is of paramount importance in devising programs for conservation and improvement.
What's Hot in The Theme: Query Dependent Emerging Topic Extraction from Social Streams

Analyzing emerging topics from social media enables users to overview social movement and several web services to adopt current trends. Although existing studies mainly focus on extracting global emerging topics, efficient extraction of local ones related to a specific theme is still a challenging and unavoidable problem in social media analysis. We focus on extracting emerging social topics related to user-specified query words, and propose an extraction framework that uses a non-negative matrix factorization (NMF) modified for detecting temporal concentration and reducing noises. We conduct preliminary experiments for verifying our method using a Twitter dataset.
Fastica-Based Blind Signal Separation and its Application to Radio Surveillance

This paper presents an efficient blind signal separation scheme for radio surveillance of digital modulation signals using Fast-ICA algorithm. FastICA (Fast Independent Component Analysis) enables very fast optimum weight calculation without any prior information in separating signals by array processing. We first analyze the undesired phase rotation of separated signals caused by FastICA, and then study the compensation technique for the rotation. Moreover, we evaluate signal separation accuracy and computational cost of the proposed algorithm in comparison with conventional algorithms through computer simulation and experiments.
Technical Issues Involved in Obtaining Reliable Data from Microarray Experiments

Microarrays show great promise in advancing the understanding of many biological phenomena, including toxicity and effectiveness of the many products regulated by the U.S. Food and Drug Administration. In addition, devices based on the microarray technology have the potential to individualize diagnosis and treatment of disease, as well as monitor efficacy of treatment regimens. To realize these expectations, reliable and reproducible measurements are essential. Currently, there are no generally accepted standards for performing and analyzing a microarray study. Without such quality assurance standards among the microarray community, it will be difficult to move this technology into the regulatory framework where it holds such promise. Here, we describe observations and approaches undertaken at the NCTR Center for Functional Genomics to examine and optimize steps in the complex microarray procedure with the aim of decreasing variability in order to generate reliable data. These observations illustrate some of the subtle technical issues that can easily be overlooked in microarray experiments. Among the factors that can influence microarray experiments are microarray printing procedures, oligonucleotide characteristics, RNA quality, and environmental factors. REGULATORY RESEARCH PERSPECTIVES Volume 6, Issue 1 March 2006 DHHS/FDA/Jefferson Labs National Center for Toxicological Research Impact on Public Health
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts

We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims to discover the number of latent classes in the patients population, and train a mixture of Gaussian Process (GP) experts, where each expert models the physiological data streams associated with a specific class. Self-taught transfer learning is used to transfer the knowledge of latent classes learned from the domain of clinically stable patients to the domain of clinically deteriorating patients. For new patients, the posterior beliefs of all GP experts about the patient's clinical status given her physiological data stream are computed, and a personalized risk score is evaluated as a weighted average of those beliefs, where the weights are learned from the patient's hospital admission information. Experiments on a heterogeneous cohort of 6,313 patients admitted to Ronald Regan UCLA medical center show that our risk score outperforms the currently deployed risk scores, such as MEWS and Rothman scores.
The Role of Interactive Computer Graphics to Augment the Learning Experience of Cultural Heritage Within Museums and Expositions

In the past years the adoption of computer graphics to improve learning experience has seen a rising success. The wide availability of dedicated graphics hardware at low cost, mostly fostered by the increasing popularity of videogames, has contributed to the widespread adoption of 3D graphics technologies specifically developed for the general public in the context of cultural heritage. This chapter illustrates the role that 3D graphics has played and still plays today in improving the learning experience and it does so from different perspectives through the results from three projects. The first project illustrates the use of high-tech virtual reality facilities as means to improve the learning experience in the context of an archaeological site. The second project describes the use of standard desktop as well as Web-based 3D game-based technologies to provide interactive access to cultural heritage content. Finally, the third project shows how the use of technologies borrowed from other domains, such as in the case of 3D Geobrowsers, can bring significant benefit if adopted in the context of cultural heritage.