Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiuser Resource Allocation for OFDM Downlink with Terminal Bandwidth Limitation

In the next generation wireless systems, it's quite likely that a large number of user terminals are not able to transmit and/or receive over the entire system bandwidth, as much wider system bandwidth, e.g., 100MHz, is expected to be employed in the future. As a new and challenging problem, user terminal bandwidth limitation is considered in this paper, and multiuser resource allocation for orthogonal frequency division multiplexing (OFDM) downlink with terminal bandwidth limitation is discussed. The optimal multiuser resource allocation strategy is first presented; however, due to the prohibitive computational expense of the optimal strategy, two suboptimal resource allocation strategies with significantly reduced complexity are proposed as well. The performance of the two suboptimal strategies is given via simulations, and is discussed for various scenarios.
Heparanase Facilitates Cell Adhesion and Spreading by Clustering of Cell Surface Heparan Sulfate Proteoglycans

Heparanase is a heparan sulfate (HS) degrading endoglycosidase participating in extracellular matrix degradation and remodeling. Apart of its well characterized enzymatic activity, heparanase was noted to exert also enzymatic-independent functions. Non-enzymatic activities of heparanase include enhanced adhesion of tumor-derived cells and primary T-cells. Attempting to identify functional domains of heparanase that would serve as targets for drug development, we have identified heparin binding domains of heparanase. A corresponding peptide (residues Lys158-Asp171, termed KKDC) was demonstrated to physically associate with heparin and HS, and to inhibit heparanase enzymatic activity. We hypothesized that the pro-adhesive properties of heparanase are mediated by its interaction with cell surface HS proteoglycans, and utilized the KKDC peptide to examine this possibility. We provide evidence that the KKDC peptide interacts with cell membrane HS, resulting in clustering of syndecan-1 and syndecan-4. We applied classical analysis of cell morphology, fluorescent and time-lapse microscopy and demonstrated that the KKDC peptide efficiently stimulates the adhesion and spreading of various cell types, mediated by PKC, Src, and the small GTPase Rac1. These results support, and further substantiate the notion that heparanase function is not limited to its enzymatic activity.
A Stochastic Model for Message Assembly Buffering with a Comparison of Block Assignment Strategies

A stochastic model is developed for the process of dynamic buffering for inbound messages in a computer communications system. For a specific characterization of message traffic, complementary viewpoints--one considering the process as a queueing system, the other considering it as a compound process--lead to the same probability generating function. Two buffer assignment schemes, both dynamic but differing in binding strategy, are compared in terms of optimal buffer size and total buffer pool requirements for a given overflow criterion. A simple model of optimal blocking, earlier proposed by Gaver and Lewis, is extended to cover both buffer strategies and also heterogeneous message sources. Finally, the derived characterization of total storage requirements is compared by numerical example with conservative and nonconservative asymptotic treatments of the process.
A G ] 1 1 Ju l 2 00 6 Linear Free Divisors

A free divisor D in C is linear if its module of logarithmic vector fields has a basis of global vector fields of degree 0. It is then defined by a homogeneous polynomial of degree n and its complement is an open orbit of an algebraic subgroup GD in Gln(C). The best known example is the normal crossing divisor. Many other such divisors arise, for instance, from quiver representations. We give a large number of examples, and classify all linear free divisors for n ≤ 4. One says that the Logarithmic Comparison Theorem (LCT) holds for a reduced divisor D in a complex manifold X if the de Rham morphism Ω• X (log D) → Rj∗ CU , j : U = X r D →֒ X, is a quasi-isomorphism. This condition implies that the global de Rham morphism also induces an isomorphism H(Γ(C, Ω•(log D)) → H(C rD; C) We refer to this second property as the Global Logarithmic Comparison Theorem GLCT. We characterize GLCT for linear free divisors by the property that the complex cohomology of GD coincides with the Lie algebra cohomology with complex coefficients of the Lie algebra gD of G. From this, we conclude that GLCT holds for a linear free divisor D if gD is reductive. In particular this is the case when D is the discriminant in a quiver representation space. Subject Classification 32S20, 14F40, 20G10, 17B66.
Extirpation‐resistant species do not always compensate for the decline in ecosystem processes associated with biodiversity loss

Accelerating rates of biodiversity loss may result in a rapid decline in important ecosystem processes such as carbon capture. Whether extirpation‐resistant species compensate for the decline in ecosystem processes associated with the loss of extirpation‐prone species is poorly understood. We apply a novel approach to answer this question using an assemblage of salt marsh plants. First, manipulations were performed to simulate a realistic sequence of species loss, based on observed sensitivity to disturbance. Then, changes in biomass and primary production of extirpation‐resistant species were monitored over three consecutive growing seasons. Extirpation‐resistant species did not compensate for the loss of either biomass or primary production associated with the removal of extirpation‐prone species. Factors that determine the potential for compensation within ecosystems are discussed. These include resource‐regulated compensation rates, the level of functional redundancy within an assemblage and the extirpation resistance of species which possess good compensation traits. Synthesis. These results suggest that we cannot assume extirpation‐resistant species will compensate for the decline in ecosystem processes associated with biodiversity loss across all ecosystems. Understanding those factors that influence the ability of ecosystems to compensate for declines in ecosystem processes associated with biodiversity loss constitutes a significant challenge.
Technical analysis of three stock oscillators testing MACD, RSI and KDJ rules in SH & SZ stock markets

This article designs models and uses computer technology to examine three oscillators in stock market: the Moving Average Convergence Divergence (MACD), the Relative Strength Index (RSI) and the Stochastic Oscillator (KDJ). Based on the 8-year data of Shanghai (SH) Stock Index and Shenzhen (SZ) Stock Index, a buying and selling stock model is built by satisfying all of the three oscillators above and the increasing and decreasing rate is calculated by assigning different time period from 3 days to a month. The result shows that the combination of the three oscillators can have a relatively higher possibility to predict the short term stock changes. Furthermore, it also proves that different periods of markets have less influence on the efficiency of the oscillators.
Efficient Numerical Shadowing Global Error Estimation for High Dimensional Dissipative Systems

Abstract Shadowing is a means of characterizing global errors in the numerical solution of initial value differential equations by allowing for small perturbations in the initial conditions. The method presented in this paper provides a technique for efficient estimation of the shadowing global error for systems that have a large number of exponentially decaying modes. The method is formulated for one-step methods and is applied to the spatial discretization of some dissipative PDEs.
Maps, languages, and manguages: Rival cognitive architectures?

Provided we agree about the thing, it is needless to dispute about the terms. —David Hume, A treatise of human nature, Book 1, section VII Map-like representations are frequently invoked as an alternative type of representational vehicle to a language of thought. This view presupposes that map-systems and languages form legitimate natural kinds of cognitive representational systems. I argue that they do not, because the collections of features that might be taken as characteristic of maps or languages do not themselves provide scientiﬁcally useful information above and beyond what the individual features provide. To bring this out, I sketch several allegedly distinctive features of maps, and show how they can easily be grafted onto a simple logical language, resulting in a hybrid “manguage. ” The ease with which these linguistic and map-like properties can be integrated into a single representational system raises the question of what work broader categories like language and map are doing. I maintain that, as an empirical matter of fact, they serve no particular purpose in cognitive science, and thus do not characterize distinct kinds of cognitive architectures.
GSM based Multi node Pressure Sensor in Barometric Altimeter – A Survey

--- Our proposed project was mainly based on GSM networks and controller. Barometer is a well-organized tool for determining atmospheric pressure. The altimeter is a tool which calculates the vertical distance in accordance with a reference level. The barometric altimeter, computes the altitude according to the atmospheric pressure. Accuracy and size are the major issues in altimetry. MEMS based Barometric Altimeter is implemented using the following two terminals of embedded barometer Module. Even though the size of the MEMS based Barometric Altimeter is reduced, it provides more accuracy. The measured parameters are transmitted through commands using GSM modem from the controller to the mobile network. In the other end the GSM data is received and the readings are compared and the corresponding adjustments are made by the controller by vertical wheel adjustments. This process will be continued to achieve the equal level. This methodology can be used to measure the altitude at different places by means of separate arrangement. Index Terms — GSM, MEMS, Barometeric altimeter.
Metaphorical affect sensing in an intelligent conversational agent

We report new developments of an affect detection component on the processing of several different types of metaphorical affective expressions. The component has been embedded in an intelligent conversational AI agent interacting with human users under loose scenarios. The detected affective states also play an important role in producing emotional animation for users' avatars. Evaluation for the affect detection component is provided. Our work contributes to the conference themes on human-robots interaction based on affect sensing, affective computing, learning and children, narrative storytelling and evaluation of affective social interaction.
Structure and function of the respiratory muscles in patients with COPD: impairment or adaptation?

Respiratory muscles are essential to alveolar ventilation. These muscles work against increased mechanical loads due to airflow limitation and geometrical changes of the thorax derived from pulmonary hyperinflation. Respiratory muscle fibres show several degrees of impairment in cellular and subcellular structures which, in many cases, are proportional to the severity of the disease and accompanying conditions (ageing, deconditioning, starvation, comorbidity). This structural impairment translates, from the functional point of view, to a loss of strength (capacity to generate tension) and an increased susceptibility to failure in the face of a particular load (early onset of fatigue). On the other hand, there is accumulating evidence that the diaphragm and other respiratory muscles are also able to express adaptive changes in response to the chronic mechanical load imposed by the disease. In most cases, impairment and adaptation of the respiratory muscles reaches a balance that permits enough ventilation for patients' survival. However, this balance can be altered for additional increments of the mechanical or metabolic load on the muscles (e.g. abdominal or thoracic surgeries, pneumonia, pulmonary embolism, etc.). Moreover, loss of balance is not always associated with extreme situations. Many patients develop ventilatory failure and require hospital admission even if the cause of the exacerbation is less dramatic (bronchial infections, pain of any nature, electrolyte disturbances, etc.). Although the physiopathology of chronic obstructive pulmonary disease exacerbations is multifactorial, the above-mentioned fragility suggests the existence of a “fragile balance” between respiratory muscle overload and respiratory muscle adaptations. Assessment of respiratory muscle function through specific tests evaluating the strength and endurance could offer valuable information about this particular susceptibility to muscle imbalance. Identification of patients possessing a fragile respiratory muscle balance could have important implications for the application of specific strategies such as respiratory muscle training, nutrition, or anabolic treatment.
Comparative study of clustering techniques for short text documents

We compare various document clustering techniques including K-means, SVD-based method and a graph-based approach and their performance on short text data collected from Twitter. We define a measure for evaluating the cluster error with these techniques. Observations show that graph-based approach using affinity propagation performs best in clustering short text data with minimal cluster error.
Learning Aggregation Functions for Expert Search

Machine learning techniques are increasingly being applied to problems in the domain of information retrieval and text mining. In this paper we present an application of evolutionary computation to the area of expert search. Expert search in the context of enterprise information systems deals with the problem of finding and ranking candidate experts given an information need (query). A difficult problem in the area of expert search is finding relevant information given an information need and associating that information with a potential expert. We attempt to improve the effectiveness of a benchmark expert search approach by adopting a learning model (genetic programming) that learns how to aggregate the documents/information associated with each expert. In particular, we perform an analysis of the aggregation of document information and show that different numbers of documents should be aggregated for different queries in order to achieve optimal performance. We then attempt to learn a function that optimises the effectiveness of an expert search system by aggregating different numbers of documents for different queries. Furthermore, we also present experiments for an approach that aims to learn the best way to aggregate documents for individual experts. We find that substantial improvements in performance can be achieved, over standard analytical benchmarks, by the latter of these approaches.
Steady-State Visual Evoked Potential based Brain controlled wheelchair system

Brain controlled wheelchair system is a Brain-computer interface (BCI) allowing people with severe neuromuscular disorder to control the navigation by themselves. Indeed, it replaces muscular activities by neurophysiological ones. The advantages of the Steady State Visual Evoked Potential (SSVEP) make it a favorable choice to be used in the BCI. The aim of the present study is to design a brain controlled wheelchair system which is cheap and easy to use. Two signal processing methods for SSVEP frequencies detection are presented and discussed. The first method is based on the calculation of the normalized amplitude spectrum. The second method is based on the comparison between the signals' signal to noise ratio calculated on specific frequencies. A comparison of the accuracy of these methods with a frequency resolution of 0.1Hz allows the identification of the most precise method among them both. Results show that by using the second method, the system can reach for 97% of the four directions' navigation's accuracy.
Estimating Nadir Objective Vector Quickly Using Evolutionary Approaches

Nadir point plays an important role in multi-objective optimization because of its importance in estimating the range of objective values corresponding to desired Pareto-optimal solutions and also in using many classical interactive optimization techniques. Since this point corresponds to the worst Pareto-optimal solution of each objective, the task of estimating the nadir point necessitates information about the whole Pareto optimal frontier and is reported to be a difficult task using classical means. In this paper, for the first time, we have proposed a couple of modifications to an existing evolutionary multi-objective optimization procedure to focus its search towards the extreme objective values front-wise. On two to 20-objective optimization problems, both proposed procedures are found to be capable of finding a near nadir point quickly and reliably. Simulation results are interesting and should encourage further studies and applications in estimating the nadir point, a process which should lead to a better interactive procedure of finding and arriving at a desired Pareto-optimal solution.
Extended Kalman lter in recurrent neural networktraining and pruningJohn

Recently, extended Kalman lter (EKF) based training has been demonstrated to be eeective in neural network training. However, its conjunction with pruning methods such as weight decay and optimal brain damage (OBD) has not yet been studied. In this paper, we will elucidate the method of EKF training and propose a pruning method which is based on the results obtained by EKF training. These combined training-pruning method is applied to a time series prediction problem.
Restoring or Re-storying the Lake District: Applying Responsive Cohesion to a Current Problem Situation

This paper examines the role of ethics in addressing aspects of ecological restoration in culturally-saturated landscapes. Do we have the ethical tools to respond to the complex questions that restoration poses? We can see valued landscapes, such as the English Lake District, as culturally rich or as ecologically denuded. This paper will juxtapose the demands of retaining rich cultural narratives and those of rewilding (which would allow for greater self-sustaining biological diversity and space for unrestrained nature). Using the ethical theory of responsive cohesion, this paper will explore the cultural narrative vs wildness question in the context of the English Lake District.
The impact of frequency on radiation pattern of bowtie and spiral antenna based on RWG elements

Some antennas like bowtie, spiral, log-periodic array and conical spiral are known as frequency independent in context of antenna parameters like input impedance, reflection coefficient, gain and radiation pattern. In this paper, the work is done on bowtie and spiral antenna due to their easy construction and implementation. Although both the antennas are considered as frequency independent but shows some variation in their patterns with wide variation of frequency. Objective of this paper is to find the variation of two and three dimensional radiation pattern of both type of antenna based on RWG (Rao-Wilton-Glisson) element.
Social tagging: Semantics are actually used

This paper describes the results of a study whose goal is to analyze, evaluate and understand the use of semantics in social networks. As a paradigmatic example, the study concentrates on a fairly large portion of the tags used in del.icio. us (more than 5,000 tags). The results show that semantics are pervasively used. In particular, the large majority (in our experiment, 75%) of the tags are subject related and directly codify the semantics of the resource. Furthermore, the number of tags which are effectively used is only a small proportion of the overall tag set (in our experiment around 30% of the tags covers around 95% of the resources), and these tags tend to remain stable in time, despite the continuous growth of the number of tags and resources being indexed. Finally, it is possible to identify an implicit use of hierarchical relationships (i.e., lightweight ontologies) among the concepts denoted by the terms used.
Efficient Multistream Classification Using Direct Density Ratio Estimation

Traditional data stream classification techniques assume that the stream of data is generated from a single non-stationary process. On the contrary, a recently introduced problem setting, referred to as Multistream Classification involves two independent non-stationary data generating processes. One of them is the source stream that continuously generates labeled data instances. The other one is the target stream that generates unlabeled test data instances from the same domain. The distributions represented by the source stream data is biased compared to that of the target stream. Moreover, these streams may have asynchronous concept drifts between them. The multistream classification problem is to predict the class labels of target stream instances, while utilizing labeled data available from the source stream. In this paper, we propose an efficient solution for multistream classification by fusing drift detection into online data shift adaptation. Experiment results on benchmark data sets indicate significantly improved performance over the only existing approach for multistream classification.
A comparison of field and laboratory polygraphs in the detection of deception.

This study compared the effectiveness of field and laboratory polygraphs in discriminating guilty and innocent subjects tested using the Control Question Technique (CQT). Subjects were 48 prisoners examined regarding a mock theft in a peer threat context; physiological responses were recorded simultaneously on a Lafayette field polygraph and a Sensor Medics laboratory polygraph. Overall hit rates were essentially the same whether classifications were based on quantitative measures from the laboratory polygraph or on numerical scores from the field polygraph (i.e., 73% and 79%, respectively), and in both cases the best discriminating measures were electrodermal activity and indices of respiration suppression. Further analyses revealed that errors of classification were mostly due to the failure of the available measures to differentiate between relevant and control questions for innocent subjects. These results indicate that the CQT is susceptible to false positive errors when subjects are tested under field-like circumstances, a problem that is not likely to be eliminated by refinements in instrumentation and scoring.
Shop-Type Recommendation Leveraging the Data from Social Media and Location-Based Services

It is an important yet challenging task for investors to determine the most suitable type of shop (e.g., restaurant, fashion) for a newly opened store. Traditional ways are predominantly field surveys and empirical estimation, which are not effective as they lack shop-related data. As social media and location-based services (LBS) are becoming more and more pervasive, user-generated data from these platforms are providing rich information not only about individual consumption experiences, but also about shop attributes. In this paper, we investigate the recommendation of shop types for a given location, by leveraging heterogeneous data that are mainly historical user preferences and location context from social media and LBS. Our goal is to select the most suitable shop type, seeking to maximize the number of customers served from a candidate set of types. We propose a novel bias learning matrix factorization method with feature fusion for shop popularity prediction. Features are defined and extracted from two perspectives: location, where features are closely related to location characteristics, and commercial, where features are about the relationships between shops in the neighborhood. Experimental results show that the proposed method outperforms state-of-the-art solutions.