Miracle-dz/UniFAR-FTUs huggingface dataset:
On use of the MM algorithm for channel estimation in MIMO OFDM

In this paper, the general channel estimator for MIMO OFDM is derived and MM algorithms are used to reduce computational complexity. The first M of MM stands for majorization (minorization) and the second M stands for minimization (maximization). It's well known that EM algorithms are powerful tools for channel estimation using iterative calculation. Indeed, every EM-type algorithm is a special case of the more general class of MM algorithms. To construct an EM-type algorithm is skillful and complicated. In contrast, the MM algorithms are easier to be understood and applied. In addition to constructing channel estimation algorithms based on the MM principle we also analyze the convergence property of the MM-type algorithms. Finally, the simulation results demonstrate the performance of MM-type channel estimation algorithms.
A deterministic small-world network

Small-world networks are ubiquitous in nature and society, describing such diverse systems as the World Wide Web, the neuronal network of the worm C. elegans, and the friendship network. Most small-world models are stochastic, that is, they create networks where the nodes appear to be randomly connected to each other. Here we propose a simple model that generates a small-world network in a simple deterministic fashion. Our model has a discrete exponential degree distribution. We solve the main characteristics of the model.
A Convergence Theorem for Improved Kernel Based Fuzzy C-Means Clustering Algorithm

In 2008,we proposed a clustering algorithm called improved kernel based fuzzy c-means clustering algorithm (IKFCM) to improve the performance of the original fuzzy c-means clustering algorithm. In this paper,we analyze the convergence of the IKFCM by means of Zangwill's convergence theorem.The result shows that arbitrary sequences generated by IKFCM always terminates at a local minimum or saddle point,or at worst,al-ways contains a subsequence which converges to a local minimum or saddle point of the IKFCM clustering model.
Design and implementation of a socket-level bandwidth aggregation mechanism for wireless networks

This paper proposes a mechanism to aggregate bandwidth of multiple network interfaces on a computer. Recently one single computer entails multiple different wireless network interfaces, such as IEEE 802.11a/b/g/n, Bluetooth, WiMAX, and so on. They can be combined, and we can aquire broad bandwidth. We designed and developed a Socket-level Bandwidth Aggregation Mechanism (SBAM). SBAM can aggregate multiple interfaces on a computer to offer broad bandwidth. In addition, implementation in socket layer causes easy deployment and adaptation to existing environment. We evaluated SBAM under actual wireless network environments. SBAM offers broad bandwidth by utilizing various wireless devices on a computer while it avoids modification to existing environment.
Fast visual saliency detection with bisection search to scale selection

Inan image, objects are meaningful entity only at certain scales; however appropriate scales of object cannot be predicted a priori. The description of the image at multiple scales is thus necessary for salient objects detection. Multiple computations involved in this process make the model slow. To reduce the computational complexity bisection search to scale selection is proposed in this paper. The saliency detection with Hypercomplex Fourier Transform is made faster with the proposed algorithm. Minimum and maximum scales act as distinct seeds. To begin with, saliency maps and their entropy are obtained for these seeds. A set of lower half or upper half scales is selected recursively based on minimum entropy for further search. A saliency map of approximate minimum entropy is obtained efficiently. The proposed method reduces computational complexity from O (N) to O (log2N+1) compared to the HFT method in the literature.
Does climatic seasonality produce seasonality in stock returns? Evidence from an emerging stock market

This paper investigates seasonality in the Kuwait stock market using monthly average data on the market index covering the period 1996-2005. While conventional regression analysis fails to detect seasonality, structural time series modelling reveals a significant June seasonal as well as the stochastic nature of seasonality. The main explanation put forward for this phenomenon is that harsh climatic conditions in the summer months of July and August forces a significant proportion of the Kuwaiti population (and hence stock traders) to leave the country for overseas holidays. Before leaving the country, traders accumulate stocks, putting upward pressure on prices and creating the June effect.
MobiDict: a mobility prediction system leveraging realtime location data streams

Mobility prediction is becoming one of the key elements of location-based services. In the near future, it will also facilitate tasks such as resource management, logistics administration and urban planning. To predict human mobility, many techniques have been proposed. However, existing techniques are usually driven by large volumes of data to train user mobility models computed over a long duration and stored in a centralized server. This results in inherently long waiting times before the prediction model kicks in. Over this large training data, small time bounded user movements are shadowed, due to their marginality, thus impacting the granularity of predictions. Transferring highly sensitive location data to third party entities also exposes the user to several privacy risks. To address these issues, we propose MobiDict, a realtime mobility prediction system that is constantly adapting to the user mobility behaviour, by taking into account the movement periodicity and the evolution of frequently visited places. Compared to the existing training approaches, our system utilises less data to generate the evolving mobility models, which in turn lowers the computational complexity and enables implementation on handheld devices, thus preserving privacy. We test our system using mobility traces collected around Lake Geneva region from 168 users and demonstrate the performance of our approach by evaluating MobiDict with six different prediction techniques. We find a satisfactory prediction accuracy as compared to the baseline results obtained with 70% of the user dataset for majority of the users.
Impact of a Single Bout of Aerobic Exercise on Regional Brain Perfusion and Activation Responses in Healthy Young Adults

Purpose Despite the generally accepted view that aerobic exercise can have positive effects on brain health, few studies have measured brain responses to exercise over a short time span. The purpose of this study was to examine the impact within one hour of a single bout of exercise on brain perfusion and neuronal activation. Methods Healthy adults (n = 16; age range: 20–35 yrs) were scanned using Magnetic Resonance Imaging (MRI) before and after 20 minutes of exercise at 70% of their age-predicted maximal heart rate. Pseudo-continuous arterial spin labeling (pcASL) was used to measure absolute cerebral blood flow (CBF) prior to exercise (pre) and at 10 min (post-10) and 40 min (post-40) post-exercise. Blood oxygenation level dependent (BOLD) functional MRI (fMRI) was performed pre and post-exercise to characterize activation differences related to a go/no-go reaction time task. Results Compared to pre-exercise levels, grey matter CBF was 11% (±9%) lower at post-10 (P<0.0004) and not different at post-40 (P = 0.12), while global WM CBF was increased at both time points post-exercise (P<0.0006). Regionally, the hippocampus and insula showed a decrease in perfusion in ROI-analysis at post-10 (P<0.005, FDR corrected), whereas voxel-wise analysis identified elevated perfusion in the left medial postcentral gyrus at post-40 compared to pre (pcorrected = 0.05). BOLD activations were consistent between sessions, however, the left parietal operculum showed reduced BOLD activation after exercise. Conclusion This study provides preliminary evidence of regionalized brain effects associated with a single bout of aerobic exercise. The observed acute cerebrovascular responses may provide some insight into the brain’s ability to change in relation to chronic interventions.
An approach for optimization of resource management in Hadoop

Many tools and frameworks have been developed to process data on distributed data centers. MapReduce most prominent among such frameworks has emerged as a popular distributed data processing model for processing vast amount of data in parallel on large clusters of commodity machines. The JobTracker in MapReduce framework is responsible for both managing the cluster's resources and executing the MapReduce jobs, a constraint that limits scalability, resource utilization. YARN the next-generation execution layer for Hadoop splits processing and resource management capabilities of JobTracker into separate entities and eliminates the dependency of Hadoop on MapReduce. This new model is more isolated and scalable compared to MapReduce, providing improved features and functionality. This paper discusses the design of YARN and significant advantages over traditional MapReduce.
2 Port-Hamiltonian systems 2 . 1 From the Euler-Lagrange and Hamiltonian equations to port-Hamiltonian systems

It is shown how port-based modeling of lumped-parameter complex physical systems (multi-body systems, electrical circuits, electromechanical systems, .) naturally leads to a geometrically defined class of systems, called port-Hamiltonian systems. The structural properties of these systems are discussed, in particular the existence of Casimir functions and their implications for stabilty. It is shown how a power-conserving interconnection of port-Hamiltonian systems defines another port-Hamiltonian system, and how this may be used for control by shaping the internal energy. Also implicit systems are incorporated in this framework. The link with H∞ control is established through the scattering representation. Extensions to the distributed-parameter case are briefly indicated.
Guarded Type Promotion Eliminating Redundant Casts in Java

In Java, explicit casts are ubiquitous since they bridge the gap between compile-time and runtime type safety. Since casts potentially throw a ClassCastException , many programmers use a defensive programming style of guarded casts . In this programming style casts are protected by a preceding conditional using the instanceof operator and thus the cast type is redundantly mentioned twice. We propose a new typing rule for Java called Guarded Type Promotion aimed at eliminating the need for the explicit casts when guarded. This new typing rule is backward compatible and has been fully implemented in a Java 6 compiler. Through our extensive testing of real-life code we show that guarded casts account for approximately one fourth of all casts and that Guarded Type Promotion can eliminate the need for 95 percent of these guarded casts.
Molecular dynamics and the accuracy of numerically computed averages

Molecular dynamics is discussed from a mathematical perspective. The recent history of method development is briefly surveyed with an emphasis on the use of geometric integration as a guiding principle. The recovery of statistical mechanical averages from molecular dynamics is then introduced, and the use of backward error analysis as a technique for analysing the accuracy of numerical averages is described. This article gives the first rigorous estimates for the error in statistical averages computed from molecular dynamics simulation based on backward error analysis. It is shown that molecular dynamics introduces an appreciable bias at stepsizes which are below the stability threshold. Simulations performed in such a regime can be corrected by use of a stepsize-dependent reweighting factor. Numerical experiments illustrate the efficacy of this approach. In the final section, several open problems in dynamics-based molecular sampling are considered.
Photochemical Targeting of Epidermal Growth Factor Receptor: A Mechanistic Study

Purpose: Photoimmunotherapy may allow target-specific photodynamic destruction of malignancies and may also potentiate anticancer antibody therapies. However, clinical use of either of the two modalities is limited for different reasons. Antibody therapies suffer from being primarily cytostatic and the need for prolonged administration with consequent side effects. In the case of photoimmunotherapy, a major impediment has been the absence of well-characterized photosensitizer immunoconjugates (PIC). In this investigation, we suggest a strategy to overcome these limitations and present the successful targeting of epidermal growth factor receptor (EGFR) using a well-characterized PIC. Experimental Design: The PIC consisted of the EGFR-recognizing chimeric monoclonal antibody, C225, conjugated with a two-branched polyethylene glycol and benzoporphyrin derivative (BPD, Verteporfin). Mechanistic studies included photophysics, phototoxicity, cellular uptake, and catabolism experiments to yield dosimetric parameters. Target cells included two EGFR-overexpressing human cancer cell lines, OVCAR-5 and A-431. Nontarget cells included an EGFR-negative fibroblast cell line, 3T3-NR6, and a monocyte-macrophage cell line, J774. Results: BPD-C225 PICs targeted and photodynamically killed EGFR-overexpressing cells, whereas free BPD exhibited no specificity. On a per mole basis, PICs were less phototoxic than free BPD, but PICs were very selective for target cells, whereas free BPD was not. Phototoxicity of the PICs increased at prolonged incubations. Photodynamic dose calculations indicated that PIC photophysics, photochemistry, catabolism, and subcellular localization were important determinants of PIC phototoxic potency. Conclusions: This study shows the efficacy of EGFR targeting with PIC constructs and suggests approaches to improve PIC designs and targeting strategies for in vivo photoimmunotherapy. The approach offers the possibility of dual effects via antibody-mediated cytostasis and photoimmunotherapy-based cytotoxicity.
Robust target feature extraction based on modified cochlear filter analysis model

The performance of underwater target recognition systems depends on the consistency and adaptation of target feature in complex conditions during the training and testing stages. In this paper, we investigated the target feature extraction problems in a complex underwater environment and proposed a novel approach based on modified cochlear filter analysis model of the human auditory system. The frequency responses and distributions of the modified cochlear filter bank are similar to that of the basilar membrane in the cochlea. The fast forward and inverse transforms of modified cochlear filter bank are also presented for discrete-time signals to save the computational load. The experimental results using real ship radiated noise data verified that the modified cochlear filter analysis model is robust in background noise.
Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments

This work presents a methodology that integrates a nonsupervised learning approach (self-organizing map (SOM)) and a supervised one (a Bayesian classifier) for segmenting diseased plants that grow in uncontrolled environments such as greenhouses, wherein the lack of control of illumination and presence of background bring about serious drawbacks. During the training phase two SOMs are used: one that creates color groups of images, which are classified into two groups using K-means and labeled as vegetation and nonvegetation by using rules, and a second SOM that corrects classification errors made by the first SOM. Two color histograms are generated from the two color classes and used to estimate the conditional probabilities of the Bayesian classifier. During the testing phase an input image is segmented by the Bayesian classifier and then it is converted into a binary image, wherein contours are extracted and analyzed to recover diseased areas that were incorrectly classified as nonvegetation. The experimental results using the proposed methodology showed better performance than two of the most used color index methods.
Competitive interactions between corals and turf algae depend on coral colony form

Turf algae are becoming more abundant on coral reefs worldwide, but their effects on other benthic organisms remain poorly described. To describe the general characteristics of competitive interactions between corals and turf algae, we determined the occurrence and outcomes of coral–turf algal interactions among different coral growth forms (branching, upright, massive, encrusting, plating, and solitary) on a shallow reef in Vietnam. In total, the amount of turf algal interaction, i.e., the proportion of the coral boundary directly bordering turf algae, was quantified for 1,276 coral colonies belonging to 27 genera and the putative outcome of each interaction was noted. The amount of turf algal interaction and the outcome of these interactions differed predictably among the six growth forms. Encrusting corals interacted most often with turf algae, but also competed most successfully against turf algae. The opposite was observed for branching corals, which rarely interacted with turf algae and rarely won these competitive interactions. Including all other growth forms, a positive relationship was found between the amount of competitive interactions with neighboring turf algae and the percentage of such interaction won by the coral. This growth form dependent ability to outcompete turf algae was not only observed among coral species, but also among different growth forms in morphologically plastic coral genera (Acropora, Favia, Favites, Montastrea, Montipora, Porites) illustrating the general nature of this relationship.
Optimal power allocation for multiple beam satellite systems

Presently, multiple spot-beam satellites are being launched with the ability to allocate power dynamically and to increase throughput, because on-board resources (e.g., power, bandwidth) are scarce and expensive. It is critical to share them efficiently among as many users as possible. In this paper, we propose an innovative power allocation algorithm for the system. We use heuristic method to search the Lagrange multiplier and obtain the optimal power allocation for each spot beam in order to meet the total power constraint and individual SLA constraint together. Scilab simulation demonstrates that our algorithm maximizes the power utilization of the satellite successfully.
ABCB1 polymorphism as prognostic factor in breast cancer patients treated with docetaxel and doxorubicin neoadjuvant chemotherapy

Expression of the adenosine triphosphate‐binding cassette B1 (ABCB1) transporter and P‐glycoprotein are associated with resistance to anticancer drugs. The purpose of this study was to investigate the role of single nucleotide polymorphism in the ABCB1 and CYP3A genes in breast cancer patients who were treated with neoadjuvant chemotherapy. Stage II/III breast cancer patients were treated with three cycles of neoadjuvant, after which the patients received curative surgery and adjuvant chemotherapy. The polymorphisms of ABCB1 and CYP3A were genotyped. The correlation of polymorphism of ABCB1, CYP3A, and clinical outcomes was analyzed. Among the 216 patients, ABCB1 3435TT genotype had a longer overall survival (OS). than CC/CT. Multivariate analyses demonstrated that good PS, invasive ductal carcinoma, non‐triple negative phenotype and initial operable stage were significantly associated with a lower death risk. ABCB1 3435TT genotype had a higher AUC than CC/CT for docetaxel. These higher AUCs in the C3435TT was associated with increased toxicities of neutropenia and diarrhea. This study showed that the genetic polymorphism of ABCB1 C3435T might be associated with a longer OS. Our results also suggest that the prediction of docetaxel toxicity might be possible for C3435T polymorphism. This study results provides valuable information on individualized therapy according to genotypes.
The Post-Offering Price Performance of Closed-End Funds

0 The pricing of closed-end funds has intrigued investors, policymakers, and economists since the late 1920s, when these securities grew at a remarkable pace. Unlike open-end funds which continually offer new shares to the investing public, closed-end funds fix the total number of shares outstanding at the initial public offering (IPO). While investors in an open-end fund may redeem their shares directly to the mutual fund at net asset value (NAV), holders of closed-end funds buy and sell their shares in the open market. Since the share value of a closed-end fund is determined by the price in the stock market, investors have no guarantee that the shares will be worth the underlying net asset value. Researchers have been especially interested in the fact that many funds trade at a significant discount from NAV. Although several studies have examined the existence of discounts and premiums in closed-end funds, the pricing of these securities remains puzzling.1 A recent resurgence in IPOs of closed-end funds has refocused attention on these securities. In particular, both the business press and academic researchers note anomalies in the aftermarket price performance of closed-end funds shares.2
When Do Robots Have to Think

An intelligent robot would seem to be an obvious place to implement a cognitive architecture, yet most robotics applications generally have complex software systems that bear little resemblance to cognitive systems. Furthermore, architectures are usually heavily engineered for specific applications. In this paper, we discuss why general architectures that include deliberation and learning are rarely used in robotics and we pose some challenges for the designers of cognitive architectures to make them more suitable for robotic applications.
Monosodium glutamate delivered in a protein-rich soup improves subsequent energy compensation

Previous research suggests that monosodium glutamate (MSG) may have a biphasic effect on appetite, increasing appetite within a meal with its flavour-enhancing effect, but enhancing subsequent satiety due to its proposed role as a predictor of protein content. The present study explored this by assessing the impact of a 450 g soup preload differing in MSG concentration (1 % MSG added (MSG+) or no MSG (MSG–)) and nutrient content (low-energy control or high-energy carbohydrate or high-energy protein) on rated appetite and ad libitum intake of a test meal in thirty-five low-restraint male volunteers using a within-participant design. Protein-rich preloads significantly reduced intake at the test meal and resulted in more accurate energy compensation than did carbohydrate-rich preloads. This energy compensation was stronger in the MSG+ protein conditions when compared with MSG+ carbohydrate conditions. No clear differences in rated appetite were seen in MSG or the macronutrient conditions alone during preload ingestion or 45 min after intake. Overall, these findings indicate that MSG may act to further improve energy compensation when provided in a protein-rich context.