Miracle-dz/UniFAR-FTUs huggingface dataset:
Bipartite Matching Based Speculative Execution to Improve Cloud MapReduce Performance

MapReduce is a software framework which canparallelize the job execution by dividing a job into a number ofmap and reduce tasks in cloud computing systems. Due to somereasons (hardware malfunction, input data skew, heterogeneouscloud environment, etc.), certain map (reduce) tasks may takeabnormally longer execution time than other tasks. As a result,the straggler map (reduce) tasks will significantly affect theentire job completion time, which is called the straggler problem.Speculative execution is a well-known method to solve thestraggler problem by re-running a straggler task in a fasternode. However, existing speculative execution schemes do not 1)preclude unsuitable idle slots for straggler tasks, 2) maximize theidle slot utilization to re-run straggler tasks as many as possible,and 3) make a re-run straggler tasks with the consistent executionprogress with non-straggler task. In this paper, we are basedon a well-known graph problem: bipartite matching to proposea new speculative execution scheme called the BM. First, theBM filters all idle slots to generate a backup slot set for eachstraggler tasks. Then, the straggler tasks and their correspondingbackup slot sets are used to model a bipartite graph. For eachstraggler task, we also estimate the progress difference betweenthe re-run straggler task and the non-straggler tasks as the edgeweight on the bipartite graph. Next, the optimal matching on theweighted bipartite graph is solved by formulating a correspondinginteger linear programming (ILP) model. Finally, the speculativeexecution of straggler tasks is scheduled by following the obtainedthe optimal matching solution. Simulation experiments are alsoperformed to demonstrate the improvement of the BM in theMapReudce speculative execution.
Generation of 3-D Models Based on Image Fusion of Range Data

This paper presents a method for measuring the entire three dimensional (3D) shape of object, which is applicable for 3D digitizing into a solid modeling CAD. The method measures the complete surface model of the object automatically and in the non-contact way. The method employs two range finders and a turn-table cooperatively: a fixed range finder using a turn table measures rough surface data of the objects, and a hand-eye range finder finds the deficit of surface data due to self occlusion and makes it up by changing to it. The 3D shape is represented in an extended octree in computer. The octree representation allows us to keep more precise shape in smaller memory storage. An experimental system based on the method shows us the good flexibility and performance of the method for 3D digitizing.
The Influence of Topic Difficulty, Relevance Level, and Document Ordering on Relevance Judging

Judging the relevance of documents for an information need is an activity that underpins the most widely-used approach in the evaluation of information retrieval systems. In this study we investigate the relationship between how long it takes an assessor to judge document relevance, and three key factors that may influence the judging scenario: the difficulty of the search topic for which relevance is being assessed; the degree to which the documents are relevant to the search topic; and, the order in which the documents are presented for judging. Two potential confounding influences on judgment speed are differences in individual reading ability, and the length of documents that are being assessed. We therefore propose two measures to investigate the above factors: normalized processing speed (NPS), which adjusts the number of words that were processed per minute by taking into account differences in reading speed between judges, and normalized dwell time (NDT), which adjusts the duration that a judge spent reading a document relative to document length. Note that these two measures have different relationships with overall judgment speed: a direct relationship for NPS, and an inverse relationship for NDT. The results of a small-scale user study show a statistically significant relationship between judgment speed and topic difficulty: for easier topics, assessors process more quickly (higher NPS), and spend less time overall (lower NDT). There is also a statistically significant relationship between the level of relevance of the document being assessed and overall judgment speed, with assessors taking less time for non-relevant documents. Finally, our results suggest that the presentation order of documents can also affect overall judgment speed, with assessors spending less time (smaller NDT) when documents are presented in relevance order than docID order. However, these ordering effects are not significant when also accounting for document length variance (NPS).
Design of Asymmetric Operators Using a Weighted Least-squares Approximation

A spatial convolution operator is designed so that it is stable and accurately matches the exact operator in the wavenumber domain in a desired wavenumber range. This can be done by a weighted least-squares optimization. Several applications require the use of an asymmetric operator. By introducing an asymmetric weight function, the properties of the operator can be in uenced to reach an optimal design.
Planetary subsurface explorer robot with propulsion units for peristaltic crawling

We have developed an exclusive planetary subsurface explorer robot with propulsion and excavation units. In this paper, we first explain the concept of our underground explorer robot. Next we develop an excavation unit with a tapered earth auger and a propulsion unit with dual pantograph. Several experiments are performed to demonstrate these features. Finally, we develop a subsurface explorer robot with propulsion and excavation units. Excavation experiments are successfully conducted using the excavator alone and at lighter weights. We show that the excavator can begin boring from a launcher, and in case of excavator of 1/6 of its own weight, it successfully excavates at the same depth as in its own weight. This demonstrates that this novel excavator is expected to accomplish future missions on other planets and the moon.
Pathological Voice Detection Using Efficient Combination of Heterogeneous Features

Combination of mutually complementary features is necessary to cope with various changes in pattern classification between normal and pathological voices. This paper proposes a method to improve pathological/normal voice classification performance by combining heterogeneous features. Different combinations of auditory-based and higher-order features are investigated. Their performances are measured by Gaussian mixture models (GMMs), linear discriminant analysis (LDA), and a classification and regression tree (CART) method. The proposed classification method by using the CART analysis is shown to be an effective method for pathological voice detection, with a 92.7% classification performance rate. This is a noticeable improvement of 54.32% compared to the MFCC-based GMM algorithm in terms of error reduction.
High-Accuracy Total Variation With Application to Compressed Video Sensing

Numerous total variation (TV) regularizers, engaged in image restoration problem, encode the gradients by means of simple [-1, 1] finite-impulse-response (FIR) filter. Despite its low computational processing, this filter severely distorts signal's high-frequency components pertinent to edge/ discontinuous information and cause several deficiency issues known as texture and geometric loss. This paper addresses this problem by proposing an alternative model to the TV regularization problem via high-order accuracy differential FIR filters to preserve rapid transitions in signal recovery. A numerical encoding scheme is designed to extend the TV model into multidimensional representation (tensorial decomposition). We adopt this design to regulate the spatial and temporal redundancy in compressed video sensing problem to jointly recover frames from undersampled measurements. We then seek the solution via alternating direction methods of multipliers and find a unique solution to quadratic minimization step with capability of handling different boundary conditions. The resulting algorithm uses much lower sampling rate and highly outperforms alternative state-of-the-art methods. This is evaluated both in terms of restoration accuracy and visual quality of the recovered frames.
THE INFECTION OF FERRETS WITH SWINE INFLUENZA VIRUS

The experiments described confirm the earlier observation of Smith, Andrewes, and Laidlaw that the swine influenza virus is pathogenic for ferrets when administered intranasally. A disease that is clinically more severe and pathologically more extensive than that described by the above workers is obtained if inoculation with the virus is performed under ether anesthesia. Animals infected in this way show at autopsy an edematous type of pneumonia of lobar distribution which may terminate fatally. The virus maintains its pathogenicity for ferrets when stored in 50 per cent glycerol at refrigerator temperature for as long as 75 days. After serial passage through 16 ferrets the virus is still capable of inducing swine influenza when mixed with H. influenzae suis and administered intranasally to swine. Ferret passage causes no apparent attenuation of the virus for swine. Serum from pigs recovered from swine influenza is capable of neutralizing the ferret-passaged virus for either swine or ferrets. Likewise serum from recovered ferrets neutralizes the swine influenza virus for either ferrets or swine.
Information Retrieval from a Structured KnowledgeBase

With the inception of World Wide Web, the amount of data present on the internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is also on exploring the use of a structured and annotated knowledge base as opposed to an unstructured knowledge base. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.
How much off-center are centrality metrics for routing in opportunistic networks

The exploitation of social context for routing data in opportunistic networks is a relatively recent trend. Node centrality metrics, such as the betweenness centrality, quantify the relaying utility of network nodes and inform routing decisions, resulting in better performance than more naive routing approaches. Nevertheless,centrality-based routing is far from optimal for three main reasons: a) routing decisions are greedy and message destination-agnostic; b) its performance is highly sensitive to the contact graph over which the node centrality values are computed; c) the global network centrality values have for practical reasons to be approximated by their egocentric counterparts. Our paper experimentally assesses the impact of these three factors on the efficacy of centrality-based routing. Five centrality-based routing variants are compared with each other and against two schemes representing extreme instances of DTN routing complexity: the simple probabilistic forwarding protocol and an ideal scheme with perfect knowledge of future contacts that computes optimal message space-time paths over a novel graph construct with contacts as vertices and time-weighted edges. The results of this comparison are not always inline with intuition and indicate inherent weaknesses of centrality-based routing.
The system with exponentially degenerate vacuum state

I suggest and examine artificial material which has exponentially degenerate vacuum state. The corresponding Hamiltonian contains only exotic four-spin interaction term. Each vacuum state is realized as a particular spin configuration separated from others by potential barriers. The benefit of such system in practical applications is that it can be used as high density magnetic recording system which can reduce storage of one bit information to $nm$ scale. The information is stored as a particular vacuum state of the system. The process of recording can be visualized as a process in which the system moves from one vacuum state to another. Storing information in the form of different vacuum states separated by potential barriers will allow to protect it from fluctuations and for a longer time. These materials can be realized as lattices of nuclear spins with specially adjusted interactions. The planes of flipped spins can in principle be of atomic scale.
Bangla text document categorization using Stochastic Gradient Descent (SGD) classifier

This paper describes the Bangla Document Categorization using Stochastic Gradient Descent (SGD) classifier. Here, document categorization is the task in which text documents are classified into one or more of predefined categories based on their contents. The proposed system can be divided into three steps: 1. feature extraction incorporating term frequency (TF) and inverse document frequency (IDF), 2. classifier design using the Stochastic Gradient Descent (SGD) algorithm by learning the distinct features, and 3. performance measure using F1-score. In the experiments on BDNews24 documents, it is observed that our proposed method provides higher accuracy in comparison with the methods based on Support Vector Machine (SVM) and Naive Bayesian (NB) classifier.
CloudStreamMedia: A Cloud Assistant Global Video on Demand Leasing Scheme

Cloud computing is a new computing paradigm that takes all resources as services, and it is not only agile, but also scalable. With the development of cloud computing, video on demand has become one of the most popular applications over the Internet. Currently, there is a trend of using cloud data centers and virtualization technologies to expand large-scale video streaming services with higher quality and lower expense. In this paper, we present CSM (Cloud Stream Media), a scheme that books the minimum resources from global data centers to match its demand and dynamically adjusts all resources to effectively meet the users' requests and guarantee a certain kind of quality of service, thus enhances the utilization and decreases the cost. CSM first predicts the stream media's future demand and data center's workload by using ARIMA model, and then performs a locality-aware resource booking (LARB) algorithm to lease the necessary resource from globalized cloud service providers in a long time. In order to handle prediction inaccuracy and the short-term demand peeks, CMS also introduces an inaccurate prediction handle strategy and performs auto scaling. We evaluate our scheme by combining both real world data and simulation. The results show good accuracy of our prediction and about 20% cut of total cost.
Kinetics and subunit composition of NMDA receptors in respiratory‐related neurons

NMDA receptors are involved in a variety of brainstem functions. The excitatory postsynaptic NMDA currents of pre‐Bötzinger complex interneurons and hypoglossal motoneurons, which are located in the medulla oblongata, show remarkably fast deactivation kinetics of approximately 30 ms compared with NMDA receptors in other types of neurons. Because structural heterogeneity might be the basis for physiological properties, we examined the expression of six NMDA receptor subunits (NMDAR1, NR2A−2D, and NR3A) plus eight NMDR1 splice variants in pre‐Bötzinger complex, hypoglossal and, for comparison, neurons from the nucleus of the solitary tract in young rats using single cell multiplex RT–PCR. Expression of NR2A, NR2B, and NR2D was observed in all three cell types while NR3A was much more abundant in pre‐Bötzinger complex interneurons, which belong to the rhythm generator of respiratory activity. In hypoglossal neurons, the NMDAR1 splice variants NMDAR1–4a and NMDAR1–4b were found. In neurons of the nucleus of the solitary tract, instead of NMDAR1–4b, the NMDAR1–2a splice variant was detected. This differential expression of modulatory splice variants might be the molecular basis for the characteristic functional properties of NMDA receptors, as neurons expressing a special NMDAR1 splice variant at the mRNA level show fast kinetics compared with neurons lacking this splice variant.
Broad-Range Bacterial Detection and the Analysis of Unexplained Death and Critical Illness

Broad-range rDNA polymerase chain reaction (PCR) provides an alternative, cultivation-independent approach for identifying pathogens. In 1995, the Centers for Disease Control and Prevention initiated population-based surveillance for unexplained life-threatening infections (Unexplained Death and Critical Illness Project [UNEX]). To address the causes of UNEX cases, we examined 59 specimens from 46 cases by using broad-range bacterial 16S rDNA PCR and phylogenetic analysis of amplified sequences. Specimens from eight cases yielded sequences from Neisseria meningitidis (cerebrospinal fluid from two patients with meningitis), Streptococcus pneumoniae (cerebrospinal fluid from one patient with meningitis and pleural fluid from two patients with pneumonia), or Stenotrophomonas maltophilia (bone marrow aspirate from one patient with pneumonia). Streptococcus pneumoniae rDNA sequence microheterogeneity was found in one pleural fluid specimen, suggesting the presence of multiple strains. In conclusion, known bacterial pathogens cause some critical illnesses and deaths that fail to be explained with traditional diagnostic methods.
Purchase cost model and optimization of distributed generators

With the advance of electricity market, all distributed power generators will participate in electric power bidding, start-up and shutdown of the unit must be considered in objective function of purchasing electricity. Therefore, this paper establishes the mathematical model of purchasing electricity optimization from distributed generators, which takes total costs from many distributed generators as an objective function, and synthetically considers supply and demand, transmission ability constraints. The mathematical model is a constrained nonlinear mixed integer programming problem. This paper presents an improved hybrid PSO algorithm to solve the problem, which is hybridization of the binary PSO and chaotic optimization. The hybrid PSO algorithm enhances the global search ability of the algorithm, and has been successfully applied to solving the problem. The simulation results show that the hybrid methodology was successfully validated for eight distributed generators, which can reach the expected requirements.
A Survey on Ensemble Combination Schemes of Neural Network

The Neural network ensembles are the most effective approach to improve the neural network system. The combination of neural networks can provide more accurate result than a single network. The simple averaging, weighted averaging, majority voting and ranking are commonly used combination strategies, and from these strategies each method has its limitations like for which application area particular is suited . This paper present a survey on different ensemble combination schemes as invented in literature.
Leading Indicators of Balance-of-Payments Crises: A Partial Review

This paper reviews the theory of balance-of-payments crises, and its implications for identifying potential leading indicators of crises. It discusses and evaluates three different empirical approaches to balance-of-payments crises: the signalling, discrete-choice, and structural approaches. Despite claims of success in predicting currency crises, we note some serious theoretical and empirical qualifications which throw these claims into question. Nevertheless, we conclude that a range of indicators supported by theory may still be useful for policy-makers interested in preventing financial instability.
Prosody based emotion recognition for MEXI

This paper describes the emotion recognition from natural speech as realized for the robot head MEXI. We use a fuzzy logic approach for analysis of prosody in natural speech. Since MEXI often communicates with well known persons but also with unknown humans, for instance at exhibitions, we realized a speaker dependent mode as well as a speaker independent mode in our prosody based emotion recognition. A key point of our approach is that it automatically selects the most significant features from a set of twenty analyzed features based on a training database of speech samples. This is important according to our results, since the set of significant features differs considerably between the distinguished emotions. With our approach we reach average recognition rates of 84% in speaker dependent mode and 60% in speaker independent mode.
Probing of Individual Semiconductor Nanowhiskers by TEM-STM

Along with rapidly developing nanotechnology, new types of analytical instruments and techniques are needed. Here we report an alternative procedure for electrical measurements on semiconductor nanowhiskers, allowing precise selection and visual control at close to atomic resolution. We use a combination of two powerful microscope techniques, scanning tunneling microscopy (STM) and simultaneous viewing in a transmission electron microscope (TEM). The STM is mounted in the sample holder for the TEM. We describe here a method for creating an ohmic contact between the STM tip and the nanowhisker. We examine three different types of STM tips and present a technique for cleaning the STM tip in situ. Measurements on 1-μm-tall and 40-nm-thick epitaxially grown InAs nanowhiskers show an ohmic contact and a resistance of down to 7 kΩ.
Statistical Inference for Partially Hidden Markov Models

ABSTRACT In this article we introduce a new missing data model, based on a standard parametric Hidden Markov Model (HMM), for which information on the latent Markov chain is given since this one reaches a fixed state (and until it leaves this state). We study, under mild conditions, the consistency and asymptotic normality of the maximum likelihood estimator. We point out also that the underlying Markov chain does not need to be ergodic, and that identifiability of the model is not tractable in a simple way (unlike standard HMMs), but can be studied using various technical arguments.
High-efficiency RB-IGBT based low-voltage PWM current-source converter for PMSG wind energy conversion systems

A low-voltage (LV) pulse-width modulated current-source converter (CSC) using reverse-blocking insulated gate bipolar transistor (RB-IGBT) devices is proposed in this paper for megawatt wind energy conversion systems (WECSs) with a permanent magnet synchronous generator. Benefiting from using the latest generation of reverse-blocking power semiconductors, the presented configuration is able to push the switching frequency to a higher range and overcome the traditional drawback of low efficiency in LV CSCs. Design of the configuration, switching scheme, and system control are briefly introduced. Semiconductor and converter loss models are developed for detailed efficiency study of the proposed system. The overall high-efficiency performance of the LV CSC based WECS is verified by simulation results and comparison with the state-of-the-art solution using voltage-source converters.