Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance analysis of pilot aided channel estimation methods for LTE system in time-selective channels

This paper deals with the performance analysis of channel estimation methods for LTE downlink system over time varying mobile environments. The analysis of channel estimation in the presence of interference is also done. Least square frequency domain (LS_Freq), least square time domain (LS_Time), maximum likelihood (ML) and minimum mean square error estimation (MMSE) techniques are focused for different bandwidth by varying the number of resource blocks used in OFDM structure of LTE system. As number of pilots increases with increasing bandwidth, performance of the estimator increases. Computer simulations are used in the comparisons. Linear Interpolation is performed to get the channel estimation of data symbols. Theoretical results are derived for LS_Time and ML techniques. MMSE performs well at low SNR. LS_Time and ML performs well at high SNR as they use the knowledge of number of channel taps. We achieve interference mitigation using channel estimation using repeated interference features obtained from the two types of pilot symbols used with different pilot density. This technique not only makes the channel estimation robust to the time-selectivity of the channel but also reduces the number of pilot subcarriers needed to estimate the channel.
The Discovery of Algorithmic Probability

This paper will describe a voyage of discovery — the discovery of Algorithmic Probability. But before I describe that voyage —a few words about motivation. Motivation in science is roughly of two kinds: In one, the motivation is discovery itself — the joy of “going where no one has gone before” — the excitement of creating new universes and exploring them. Another kind of motivation is the achievement of a previously defined larger goal, and there may be many subsidiary discoveries on the path to this goal. In my own case both kinds of motivation were very strong. I first experienced the pure joy of mathematical discovery when I was very young — learning algebra. I didn’t really see why one of the axioms was “obvious”, so I tried reversing it and exploring the resultant system. For a few hours I was in this wonderful never-never land that I myself had created! The joy of exploring it only lasted until it became clear that my new axiom wouldn’t work, but the motivation of the joy of discovery continued for the rest of my life. The motivation for the discovery of Algorithmic Probability was somewhat different. It was the result of “goal motivated discovery” — like the discovery of the double helix in biology, but with fewer people involved and relatively little political skullduggery. The goal I set grew out of my early interest in science and mathematics. I found that while the discoveries of the past were interesting to me, I was
On the theory of the reaction rate of vibrationally excited CO molecules with OH radicals.

The dependence of the rate of the reaction CO+OH-->H+CO2 on the CO-vibrational excitation is treated here theoretically. Both the Rice-Ramsperger-Kassel-Marcus (RRKM) rate constant kRRKM and a nonstatistical modification knon [W. -C. Chen and R. A. Marcus, J. Chem. Phys. 123, 094307 (2005). ] are used in the analysis. The experimentally measured rate constant shows an apparent (large error bars) decrease with increasing CO-vibrational temperature Tv over the range of Tv's studied, 298-1800 K. Both kRRKM(Tv) and knon(Tv) show the same trend over the Tv-range studied, but the knon(Tv) vs Tv plot shows a larger effect. The various trends can be understood in simple terms. The calculated rate constant kv decreases with increasing CO vibrational quantum number v, on going from v=0 to v=1, by factors of 1.5 and 3 in the RRKM and nonstatistical calculations, respectively. It then increases when v is increased further. These results can be regarded as a prediction when v state-selected rate constants become available.
Efficient Estimation of Moment Condition Models with Heterogenous Populations

A wide range of econometric and statistical models are specified through moment conditions. Efficient estimation of such models essentially employs two distinct ideas: optimally combining estimation equations (e.g., the optimal estimating equations of Godambe (1976), the generalized method of moments of Hansen (1982) and the empirical likelihood of Qin and Lawless (1994)), and optimally combining estimators (e.g., the weighted method of moments of Xiao (2010)). This paper extends these methods to moment condition models with heterogeneous populations. Comparison of the finite sample performance of the proposed estimators is conducted through Monte Carlo simulations.
The Effect of System Load on the Existence of Bit Errors in CDMA With and Without Parallel Interference Cancelation

In this correspondence, we study a lightly loaded code-division multiple-access (CDMA) system with and without multistage hard- and soft-decision parallel interference cancelation (HD-PIC and SD-PIC). Throughout this paper we will only consider the situation of a noiseless channel, equal powers and random spreading codes. For the system with no or a fixed number of steps of interference cancelation, we give a lower bound on the maximum number of users such that the probability for the system to have no bit-errors converges to one. Moreover, we investigate when the matched filter system, where parallel interference cancelation is absent, has bit errors with probability converging to one. This implies that the use of HD-PIC and SD-PIC significantly enhances the number of users the system can serve
A Hybrid Approach for Speech Enhancement Using MoG Model and Neural Network Phoneme Classifier

In this paper, we present a single-microphone speech enhancement algorithm. A hybrid approach is proposed merging the generative mixture of Gaussians (MoG) model and the discriminative deep neural network (DNN). The proposed algorithm is executed in two phases, the training phase, which does not recur, and the test phase. First, the noise-free speech log-power spectral density is modeled as an MoG, representing the phoneme-based diversity in the speech signal. A DNN is then trained with phoneme labeled database of clean speech signals for phoneme classification with mel-frequency cepstral coefficients as the input features. In the test phase, a noisy utterance of an untrained speech is processed. Given the phoneme classification results of the noisy speech utterance, a speech presence probability (SPP) is obtained using both the generative and discriminative models. SPP-controlled attenuation is then applied to the noisy speech while simultaneously, the noise estimate is updated. The discriminative DNN maintains the continuity of the speech and the generative phoneme-based MoG preserves the speech spectral structure. Extensive experimental study using real speech and noise signals is provided. We also compare the proposed algorithm with alternative speech enhancement algorithms. We show that we obtain a significant improvement over previous methods in terms of speech quality measures. Finally, we analyze the contribution of all components of the proposed algorithm indicating their combined importance.
Topological design of multihop lightwave networks

One of the promising lightwave network architectures is the multihop lightwave networks based on wavelength division multiplexing (WDM). In this architecture, separate channels created by assigning different wavelengths between node pairs define the logical connectivity. This logical topology is independent of the underlying physical topology and can be adaptively changed according to the changing traffic patterns and connectivity requirements. One of the possible objectives for designing this logical topology is minimizing the maximum utilization on any link by choosing the logical connectivity and finding the optimum routing. This paper describes an algorithm based on simulated annealing for solving the joint logical topology design and routing problem. Different algorithms were implemented for the cases with bifurcated and shortest path routing. Computational experiments showed that the simulated annealing solutions are as good as or better than the solutions found in previous studies. <<ETX>>
Order-preserving factor discovery from misaligned data

We present a factor analysis method that accounts for possible temporal misalignment of the factor loadings across the population of samples. Our main hypothesis is that the data contains a subset of variables with similar but delayed profiles obeying a consistent precedence ordering relationship. Our model is motivated by the difficulty of gene expression analysis across subjects who have common patterns of immune response but show different onset times after a uniform innoculation time of a viral pathogen. The proposed method is based on a linear model with additional degrees of freedom that account for each subject's inherent delays. We present an algorithm to fit this model in a totally unsupervised manner and demonstrate its effectiveness on extracting gene expression factors affecting host response using a flu-virus human challenge study dataset.
The tandem affinity purification (TAP) method: a general procedure of protein complex purification.

Identification of components present in biological complexes requires their purification to near homogeneity. Methods of purification vary from protein to protein, making it impossible to design a general purification strategy valid for all cases. We have developed the tandem affinity purification (TAP) method as a tool that allows rapid purification under native conditions of complexes, even when expressed at their natural level. Prior knowledge of complex composition or function is not required. The TAP method requires fusion of the TAP tag, either N- or C-terminally, to the target protein of interest. Starting from a relatively small number of cells, active macromolecular complexes can be isolated and used for multiple applications. Variations of the method to specifically purify complexes containing two given components or to subtract undesired complexes can easily be implemented. The TAP method was initially developed in yeast but can be successfully adapted to various organisms. Its simplicity, high yield, and wide applicability make the TAP method a very useful procedure for protein purification and proteome exploration.
A comparison of selected mRNA and protein abundances of target genes involved in Alzheimer’s disease development

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IEEE 802.11s Wireless Mesh Networks for Last-Mile Internet Access: An Open-Source Real-World Indoor Testbed Implementation

Due to their easy-to-deploy and self-healing features, WMNs (Wireless Mesh Networks) are emerging as a new promising technology with a rich set of applications. While the IEEE standardization of this new technology is still in progress, its main traits are already set, e.g., architecture and MAC routing. WMNs are attracting considerable research in academia and industry as well, but the lack of open-source testbeds is restricting such a research to simulation tools. The main problem with simulation tools is that they do not reflect the complexity of RF propagation, especially in indoor environments, of which IEEE 802.11s WMNs are an example. This paper presents an open-source implementation of an indoor IEEE 802.11s WMN testbed. The implementation is transparent, easy-to-deploy, and both the source code and deployment instructions are available online. The implementation can serve as a blueprint for the WMN research community to deploy their own testbeds, negating the shortcomings of using simulation tools. By delving into the testbed implementation subtleties, this paper is shedding further light on the details of the ongoing IEEE 802.11s standard. Major encountered implementation problems (e.g., clients association, Internetworking, and supporting multiple gateways) are identified and addressed. To ascertain the functionality of the testbed, both UDP and TCP traffic are supported and operational. The testbed uses the default IEEE 802.11s HWMP (Hybrid Wireless Mesh Protocol) routing protocol along with the default IEEE 802.11s Airtime routing metric.
Towards a Functional Architecture for Intelligence Based on Generic Information Processing Tasks

T h e level o f abs t rac t ion of m u c h of the w o r k in know ledge-based systems ( the ru le , f rame, logic level) is too low to prov ide a r ich enough vocabu la ry for knowledge and con t ro l . I prov ide an overv iew of a f r amework cal led the Generic Task approach that proposes t h a t knowledge systems shou ld be b u i l t out of b u i l d i n g blocks, each of wh ich is app rop r i a te for a basic t ype of p rob lem so lv ing. Each generic task uses forms of knowledge and cont ro l strategies that are character is t ic to i t , and are general ly conceptua l ly closer to doma in knowledge. Th i s fac i l i ta tes knowledge acqu is i t i on , and can produce a more perspicuous exp lana t ion of p rob lem so lv ing . The re la t ionsh ip of the const ructs at the generic task level to the ru le f rame level is analogous to t h a t between h igh level p r o g r a m m i n g languages and assembly languages. 1 describe a set of generic tasks t ha t have been found pa r t i cu la r l y useful in cons t ruc t i ng d iagnost ic , design and p lann ing systems; d iagnost ic reasoning is used to i l lus t ra te the approach . 1 describe the Generic Task Toolset for cons t ruc t i ng knowledge systems, w h i c h embodies the Generic Task app roach . I conclude w i t h the imp l i ca t i ons of this approach for the func t iona l a rch i tec ture of in te l l igence. 1. Overview of the Paper T h e f i rst pa r t of the paper is a c r i t i que of the level o f abs t rac t ion in much of the cu r ren t discussion on knowledge-based systems. I t w i l l be argued t ha t the level of ru leslog icf rames-networks is i napp rop r i a te for d iscussing m a n y issues of knowledge o rgan iza t ion and c o n t r o l . We advocate instead the level of abs t rac t ion associated w i t h the language of generic tasks, types of knowledge, and types of con t ro l
Off-line variable substitution for scaling points-to analysis

Most compiler optimizations and software productivity tools rely oninformation about the effects of pointer dereferences in a program.The purpose of points-to analysis is to compute this informationsafely, and as accurately as is practical. Unfortunately, accuratepoints-to information is difficult to obtain for large programs,because the time and space requirements of the analysis becomeprohibitive. We consider the problem of scaling flow- and context-insensitivepoints-to analysis to large programs, perhaps containing hundreds ofthousands of lines of code. Our approach is based on a variable substitution transformation, which is performed off-line, i.e.,before a standard points-to analysis is performed. The general idea ofvariable substitution is that a set of variables in a program can be replaced by a single representative variable, thereby reducing the input size of the problem. Our main contribution is a linear-time algorithm which finds a particular variable substitution that maintains the precision of the standard analysis, and is also very effective in reducing the size of the problem. We report our experience in performing points-to analysis on largeC programs, including some industrial-sized ones. Experiments show thatour algorithm can reduce the cost of Andersen's points-to analysis substantially: on average, it reduced the running time by 53% and the memory cost by 59%, relative to an efficient baseline implementation of the analysis.
Kids, raccoons, and roos: awkward encounters and mixed affects

Within the Western cultural imaginary, child–animal relations are characteristically invoked with fond nostalgia and sentimentality. They are often represented as natural and innocent relations, thick with infantilizing and anthropomorphizing ‘cute’ emotions. Our multispecies ethnographic research – which is conducted in the everyday, lived common worlds of Australian and Canadian children and animals – reveals a very different political and emotional landscape. We find these embodied child–animal relations to be non-innocently entangled, fraught, and messy. In this article, we focus on some awkward encounters of mixed affect when kids and raccoons co-inhabit an urban forest setting in Vancouver, and when kids and kangaroos bodily encounter each other in a bush setting in Canberra. We trace the imbroglio of child–animal curiosities, warinesses, risks, inconveniences, revulsions, attachments, and confrontations at these sites as generative of new ethical logics.
A Task Scheduling Algorithm for Hadoop Platform

MapReduce is a kind of software framework for easily writing applications which process vast amounts of data on large clusters of commodity hardware. In order to get better allocation of tasks and load balancing, the MapReduce work mode and task scheduling algorithm of Hadoop platform is analyzed in this paper. According to this situation that the number of tasks of the smaller weight job is more, while that of the larger weight job is less, this paper introduces the idea of weighted round-robin scheduling algorithm into the task scheduling of Hadoop and puts forward the weight update rules through analyzing all the situations of weight update. Experimental result indicates that it is effective in making task allocation and achieving good balance when it is applied into the Hadoop platform which uses only JobTracker scheduling.
Influence of hemolysis on routine clinical chemistry testing

Abstract Background: Preanalytical factors are the main source of variation in clinical chemistry testing and among the major determinants of preanalytical variability, sample hemolysis can exert a strong influence on result reliability. Hemolytic samples are a rather common and unfavorable occurrence in laboratory practice, as they are often considered unsuitable for routine testing due to biological and analytical interference. However, definitive indications on the analytical and clinical management of hemolyzed specimens are currently lacking. Therefore, the present investigation evaluated the influence of in vitro blood cell lysis on routine clinical chemistry testing. Methods: Nine aliquots, prepared by serial dilutions of homologous hemolyzed samples collected from 12 different subjects and containing a final concentration of serum hemoglobin ranging from 0 to 20.6g/L, were tested for the most common clinical chemistry analytes. Lysis was achieved by subjecting whole blood to an overnight freeze-thaw cycle. Results: Hemolysis interference appeared to be approximately linearly dependent on the final concentration of blood-cell lysate in the specimen. This generated a consistent trend towards overestimation of alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, creatine kinase (CK), iron, lactate dehydrogenase (LDH), lipase, magnesium, phosphorus, potassium and urea, whereas mean values of albumin, alkaline phosphatase (ALP), chloride, γ-glutamyltransferase (GGT), glucose and sodium were substantially decreased. Clinically meaningful variations of AST, chloride, LDH, potassium and sodium were observed in specimens displaying mild or almost undetectable hemolysis by visual inspection (serum hemoglobin <0.6g/L). The rather heterogeneous and unpredictable response to hemolysis observed for several parameters prevented the adoption of reliable statistic corrective measures for results on the basis of the degree of hemolysis. Conclusion: If hemolysis and blood cell lysis result from an in vitro cause, we suggest that the most convenient corrective solution might be quantification of free hemoglobin, alerting the clinicians and sample recollection.
Linearized Kernel Dictionary Learning

In this paper, we present a new approach of incorporating kernels into dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an improvement in classification performance, with relation to its linear counterpart K-SVD. However, this algorithm requires the storage and handling of a very large kernel matrix, which leads to high computational cost, while also limiting its use to setups with small number of training examples. We address these problems by combining two ideas: first, we approximate the kernel matrix using a cleverly sampled subset of its columns using the Nyström method; second, as we wish to avoid using this matrix altogether, we decompose it by SVD to form new “virtual samples,” on which any linear dictionary learning can be employed. Our method, termed “Linearized Kernel Dictionary Learning” (LKDL) can be seamlessly applied as a preprocessing stage on top of any efficient off-the-shelf dictionary learning scheme, effectively “kernelizing” it. We demonstrate the effectiveness of our method on several tasks of both supervised and unsupervised classification and show the efficiency of the proposed scheme, its easy integration and performance boosting properties.
Use it or Lose it: Selective Memory and Forgetting in a Perpetual Learning Machine

In a recent article we described a new type of deep neural network - a Perpetual Learning Machine (PLM) - which is capable of learning 'on the fly' like a brain by existing in a state of Perpetual Stochastic Gradient Descent (PSGD). Here, by simulating the process of practice, we demonstrate both selective memory and selective forgetting when we introduce statistical recall biases during PSGD. Frequently recalled memories are remembered, whilst memories recalled rarely are forgotten. This results in a 'use it or lose it' stimulus driven memory process that is similar to human memory.
Hermes: Architecting a top-performing fault-tolerant routing algorithm for Networks-on-Chips

Networks-on-Chips (NoCs) are experiencing escalating susceptibility to wear-out and reduced reliability, with the risk of becoming the key point of failure in an entire multicore chip. Aiming towards seamless NoC operation in the presence of faulty communication links, in this paper we propose Hermes, a highly-robust, distributed and lightweight fault-tolerant routing algorithm, whose performance degrades gracefully with increasing faulty link counts. Hermes is a deadlock-free hybrid routing algorithm, utilizing load-balancing routing on fault-free paths to sustain high-performance, while providing pre-reconfigured escape path selection in the vicinity of faults. Additionally, Hermes identifies non-communicating network partitions in scenarios where faulty links are topologically densely distributed. An extensive experimental evaluation, including utilizing traffic benchmarks gathered from full-system chip multi-processor simulations, shows that Hermes improves network throughput by up to 3× when compared against prior-art.