Miracle-dz/UniFAR-FTUs huggingface dataset:
Is Traditional Power Management + Prefetching = = DRPM for Server Disks ?

The I/O subsystem on servers consumes considerable energy leading to cost, reliability and environmentalconcerns. Hence, there is a need for reduction of energy consump tion of server disks. Most of the idle periods for server disks are shorter than the total time taken to spin down the disks and bring them back up. Traditional Power Management (TPM) schemes, which completely shut the disks down during period s of inactivity, are therefore ineffective. In a previous study, it has been shown that Dynamic Rotations per Minute (DRPM), a power management scheme that modulates disk rotation speed based on request arrival patterns is an effective solution t o this problem. However, DRPM disks do not exist yet. This paper intends to evaluate both TPM schemes combined with I/O Prefetching and DRPM. Using both synthetic and real workloads and both idealistic and realistic versions of TPM, DRPM and Prefetching, we have a conducted simulations which reitera te the necessity of alternate techniques such as DRPM for serve r power management.
Serum levels of interleukin-6 and tumor necrosis factor-alpha in exacerbation and remission phase of schizophrenia.

BACKGROUND The variations in proinflamatory cytokine levels have been associated with schizophrenia (SCH), duration of illness, psychopathology and treatment. The aim of the study was to investigate serum levels of interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) in schizophrenic patients during exacerbation and remission, and its association with course of illness and therapy. SUBJECTS AND METHODS We measured serum levels of IL-6 and TNF-α in 43 schizophrenic patients in exacerbation and remission and compared them to 29 healthy controls, matched by sex, age, body mass index (BMI) and smoking habits. The severity of psychopathology was assessed using the Positive and Negative Syndrome Scale (PANSS). RESULTS There was no difference in levels of IL-6 and TNF-α in exacerbation compared to remission in schizophrenic patients. IL-6 was higher and TNF-α was lower in schizophrenic patients in both exacerbation and remission in comparison with healthy controls. TNF-α in exacerbation was in negative correlation with IL-6 in remission. No statistical significance was found between levels of cytokines and sex, age, BMI, smoking habits, antipsychotic medication, duration of treatment and duration of illness. IL-6 levels were in positive correlation with the age of onset and the duration of untreated psychosis. In schizophrenic patients on adjunctive treatment with mood stabilizers, TNF-α levels increased in remission. CONCLUSION Our results suggest that the connection between schizophrenia, cytokines and medication is multifaceted, and not necessarily linear. Adjunct mood stabilizers not only ameliorate psychopathology, but might convey immunomodulatory effects as well. Further longitudinal studies could elucidate potential beneficial effect of combined therapy in treatment of SCH.
Querying XML Views of Relational Data

XML has emerged as the standard data exchange format for Internet-based business applications. This has created the need to publish existing business data, stored in relational databases, as XML. A general way to publish relational data as XML is to provide XML views over relational data, and allow business partners to query these views using an XML query language. In this paper, we address the problem of evaluating XML queries over XML views of relational data. This paper makes two main contributions. The first is a general framework for processing arbitrarily complex queries specified using the XQuery query language. The second is a technique for efficiently evaluating XML queries by pushing most of the query computation down to the relational engine.
Allocation of channels in wireless tree topologies

Judiciously assigning channels to a wireless mesh network can substantially enhance capacity of the network. One particular flavor of mesh network is that with a tree topology, which has the property that all traffic passes through one central point. Usually the allocation problem is linked to problems of routing, load, and measurements of interference. In this paper we take advantage of the restrictions on the routing, and on potential load imposed by the topology, to present an allocation algorithm that is tailored for wireless trees. Using the connection characteristics of the topology, we define a conflict graph over which a coloring heuristic can provide better performance by not having to focus on connectivity/routing/interference. We show that the algorithm has a low complexity (quadratic in the number of nodes), and in simulation shows significant gains in performance when compared to simpler solutions.
Enhanced Fingerprinting and Trajectory Prediction for IoT Localization in Smart Buildings

Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian's locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist.
Small Business Responsiveness and Organizational Culture in the Context of a Developing Economy

The organization’s responsiveness represents an important concept related to both performance and market environment. This empirical study analyzes the relationship between responsiveness and organizational culture in the context of Romanian small and medium-sized enterprises (SMEs). The country offers a business context with many changes over the last two decades, a challenge and an opportunity for researchers. Results show that the combination of entrepreneurial cultural characteristics and planning and goal oriented managerial styles suits best successful companies. Dimensions of responsiveness are identified and their relationships with different types of organizational culture are analyzed.
DBuffer: A state machine oriented control system for DASH

The popularity of Internet video streaming services has dramatically increased, leveraged by the emerging Dynamic Adaptive Streaming over HTTP (DASH) standard. DASH also favors the growth of video content access by mobile devices. However, due to fluctuating bandwidth and high latency, mobile networks, e.g. 3G, remain a challenging environment. The user's Quality of Experience (QoE) in this scenario constantly needs to improve. One way to achieve this is through the development of a better adaptation logic that reduces rebuffering events and delivers higher and more stable video qualities. In this paper, we present DBuffer, a control system developed for improving the QoE during a streaming session through mobile networks. DBuffer estimates the ideal video quality to request, during a 3G streaming session, through client-side buffer analysis. The main component of the control system is a buffer-based state machine, which manages the adaptation logic. Our experimental results show that DBuffer outperforms some of the existing advanced adaptation techniques.
An Automated Approach for Goal-driven, Specification-based Testing

This paper presents a specification-based approach that addresses several known challenges including false positives and domain knowledge errors. Our approach begins with a goal graph and plans. Source code is annotated with goals and events and precompiled to emit those at run time. Plans are automatically translated into a rule-based recognizer. An oracle is produced from the pre- and postconditions associated with the plan's goals. When the program is executed, goals and events are emitted and automatically tested against plans and oracles. The concept is demonstrated on a small example and a larger publicly available case study
Level crossing rate and average fade duration of the select-and-hold policy of selection diversity combining in a Rayleigh fading channel

The select-and-hold policy (SAH) is a general form of selection diversity combining (SDC). In this letter, we provide closed-form expressions for average level crossing rate and average fade duration for Nth-order SAH-SDC. The diversity branches are assumed to be independent and identical Rayleigh fading channels. A useful guideline for choice of the holding time is also obtained.
A 35 /spl mu/W 1.1 V gate array 8/spl times/8 IDCT processor for video-telephony

We have designed and fabricated a low power IC to perform the inverse 8/spl times/8 DCT transform according to the CCITT precision specifications, suitable for portable video communication devices. Several design techniques have been used to reduce the power, such as a fast algorithm, an architecture that can exploit input signal correlation, and large amount of parallelism. The chip is fabricated in a triple metal 0.5 /spl mu/m gate array CMOS technology. The maximum throughput is 400 Kpix/s at 1.1 V, and 27 Mpix/s at 3.3 V. The measured power consumption is 35 /spl mu/W for typical image sequences in color QCIF format at 10 frames/sec with a 1.1 V power supply, making this device ideal for low power portable applications.
Dynamical Properties of the North Atlantic Atmospheric Circulation in the Past 150 Years in CMIP5 Models and the 20CRv2c Reanalysis

It is of fundamental importance to evaluate the ability of climate models to capture the large-scale atmospheric circulation patterns and, in the context of a rapidly increasing greenhouse forcing, the robustness of the changes simulated in these patterns over time. Here we approach this problem from an innovative point of view based on dynamical systems theory. We characterize the atmospheric circulation over the North Atlantic in the CMIP5 historical simulations (1851–2000) in terms of two instantaneous metrics: local dimension of the attractor and stability of phase-space trajectories. We then use these metrics to compare the models to the Twentieth Century Reanalysis version 2c (20CRv2c) over the same historical period. The comparison suggests that (i) most models capture to some degree the median attractor properties, and models with finer grids generally perform better; (ii) in most models the extremes in the dynamical systems metrics match large-scale patterns similar to those found in the reanalysis; (iii) changes in the attractor properties observed for the ensemble-mean 20CRv2c are artifacts resulting from inhomogeneities in the standard deviation of the ensemble over time; and (iv) the long-term trends in local dimension observed among the 56 members of the 20CR ensemble have the same sign as those observed in the CMIP5 multimodel mean, although the multimodel trend is much weaker.
Dictionary learning with equiprobable matching pursuit

Sparse signal representations based on linear combinations of learned atoms have been used to obtain state-of-the-art results in several practical signal processing applications. Approximation methods are needed to process high-dimensional signals in this way because the problem to calculate optimal atoms for sparse coding is NP-hard. Here we study greedy algorithms for unsupervised learning of dictionaries of shift-invariant atoms and propose a new method where each atom is selected with the same probability on average, which corresponds to the homeostatic regulation of a recurrent convolutional neural network. Equiprobable selection can be used with several greedy algorithms for dictionary learning to ensure that all atoms adapt during training and that no particular atom is more likely to take part in the linear combination on average. We demonstrate via simulation experiments that dictionary learning with equiprobable selection results in higher entropy of the sparse representation and lower reconstruction and denoising errors, both in the case of ordinary matching pursuit and orthogonal matching pursuit with shift-invariant dictionaries. Furthermore, we show that the computational costs of the matching pursuits are lower with equiprobable selection, leading to faster and more accurate dictionary learning algorithms.
BLAST++: BLASTing queries in batches

UNLABELLED BLAST++ is a tool that is integrated with NCBI BLAST, allowing multiple, say K, queries to be searched against a database concurrently. The results obtained by BLAST++ are identical to that obtained by executing BLAST on each of the K queries, but BLAST++ completes the processing in a much shorter time. AVAILABILITY http://xena1.ddns. comp.nus. edu.sg/~genesis/blast++ SUPPLEMENTARY INFORMATION http://xena1.ddns. comp.nus. edu.sg/~genesis/blast++
Credibility Assessment Using Wikipedia for Messages on Social Network Services

We propose methods for calculating credibility values of messages in Social Network Services (SNSs), such as Linked In and Face book. Many users post messages on SNSs, however, not all of these messages are credible. Our method is based on two assumptions: an SNS message is credible (1) if the SNS message is similar to information from other resources and (2) if the information is confirmed as credible. For assumption (1), we developed a method to retrieve similar descriptions from Wikipedia articles. For assumption (2), we developed a method for assessing Wikipedia articles using the edit history. Using these two methods, we can calculate accurate credibility values for SNS messages. In an experiment, we confirmed that our method can calculate appropriate credibility values for SNS messages if Wikipedia has credible articles related to the SNS messages.
Anti-inflammatory Effects of Heme Oxygenase-1 Depend on Adenosine A2A- and A2B-Receptor Signaling in Acute Pulmonary Inflammation

Acute pulmonary inflammation is still a frightening complication in intensive care units. In our previous study, we determined that heme oxygenase (HO)-1 had anti-inflammatory effects in pulmonary inflammation. Recent literature has emphasized a link between HO-1 and the nucleotide adenosine. Since adenosine A2A- and A2B-receptors play a pivotal role in pulmonary inflammation, we investigated their link to the enzyme HO-1. In a murine model of pulmonary inflammation, the activation of HO-1 by hemin significantly decreased polymorphonuclear leukocyte (PMN) migration into the lung. This anti-inflammatory reduction of PMN migration was abolished in A2A- and A2B-knockout mice. Administration of hemin significantly reduced chemokine levels in the BAL of wild-type animals but had no effects in A2A-/- and A2B-/- mice. Microvascular permeability was significantly attenuated in HO-1-stimulated wild-type mice, but not in A2A-/- and A2B-/- mice. The activity of HO-1 rose after LPS inhalation in wild-type animals and, surprisingly, also in A2A-/- and A2B-/- mice after the additional administration of hemin. Immunofluorescence images of animals revealed alveolar macrophages to be the major source of HO-1 activity in both knockout strains—in contrast to wild-type animals, where HO-1 was also significantly augmented in the lung tissue. In vitro studies on PMN migration further confirmed our in vivo findings. In conclusion, we linked the anti-inflammatory effects of HO-1 to functional A2A/A2B-receptor signaling under conditions of pulmonary inflammation. Our findings may explain why targeting HO-1 in acute pulmonary inflammation has failed to prove effective in some patients, since septic patients have altered adenosine receptor expression.
Ethyl 3-amino-5-bromo-1-benzofuran-2-carboxylate

The title compound, C11H10BrNO3, is close to planar with the benzofuran unit and the ester group subtending a dihedral angle of 5.25 (2)°. The molecular structure features an intramolecular N—H⋯O interaction. In the crystal, N—H⋯O hydrogen bonds involving carboxyl O-atom acceptors generate a chain extending along [201].
Alex - A simple and efficient scanner generator

Alex is a scanner generator which translates the lexical description of a programming language into a scanner for that language. The scanner description language is easy to use, as it is intentionally small and simple. Alex as well as the generated scanners are written in Modula-2 and implemented on several microcomputers. The scanner generator may be used in conjunction with a compiler-compiler. As an example we show how to describe the lexical structure of Modula-2 with our tool. The scanner generated from that description is only about 20 percent slower than a hand-coded scanner.
Fluoride Intake through Consumption of Tap Water and Bottled Water in Belgium

There is a tendency to align higher levels of fluoride in natural mineral water with the existing higher levels in tap water. Treatment of natural mineral waters could harm the preservation of their natural character. In this study fluoride intake through bottled and tap water consumption in the Belgian adult population was assessed, taking into account regional differences. A deterministic approach was used whereby consumption quantities of tap water and different brands of bottled water were linked with their respective fluoride concentrations. Data from the national food consumption survey (2004) were used and the Nusser methodology was applied to obtain usual intake estimates. Mean intake of fluoride through total water consumption in Flanders was 1.4±0.7 mg/day (97.5th percentile: 3.1 mg/day), while in the Walloon region it was on average 0.9±0.6 mg/day (97.5th percentile: 2.4 mg/day). The probability of exceeding the UL of 7 mg per day via a normal diet was estimated to be low. Consequently, there is no need to revise the existing norms, but higher fluoride concentrations should be more clearly indicated on the labels. Reliable data about total dietary fluoride intake in children, including intake of fluoride via tooth paste and food supplements, are needed.
Identifying epochs in text archives

This paper develops an automated approach to the ‘distant reading’ of textual archives in order to classify epochs in the use of language and examine their particular characteristic. It classifies epochs by applying a series of standardised dictionaries to map the semantics of government documents, using the changing frequency of terms in these dictionaries to identify moments of rupture in language. It then tests a variety of techniques to chart the relationship between the changing shape of individual linguistic elements and aggregate patterns, particularly topic models and word2vec word embeddings. The result are a set of largely automated tools for understanding the structure of digital textual archives.
Memetic evolutionary training for recurrent neural networks: an application to time‐series prediction

Abstract: Artificial neural networks are bio‐inspired mathematical models that have been widely used to solve complex problems. The training of a neural network is an important issue to deal with, since traditional gradient‐based algorithms become easily trapped in local optimal solutions, therefore increasing the time taken in the experimental step. This problem is greater in recurrent neural networks, where the gradient propagation across the recurrence makes the training difficult for long‐term dependences. On the other hand, evolutionary algorithms are search and optimization techniques which have been proved to solve many problems effectively. In the case of recurrent neural networks, the training using evolutionary algorithms has provided promising results. In this work, we propose two hybrid evolutionary algorithms as an alternative to improve the training of dynamic recurrent neural networks. The experimental section makes a comparative study of the algorithms proposed, to train Elman recurrent neural networks in time‐series prediction problems.
Paying Attention to Attention in Recognition Memory

Reliance on remembered facts or events requires memory for their sources, that is, the contexts in which those facts or events were embedded. Understanding of source retrieval has been stymied by the fact that uncontrolled fluctuations of attention during encoding can cloud results of key importance to theoretical development. To address this issue, we combined electrophysiology (high-density electroencephalogram, EEG, recordings) with computational modeling of behavioral results. We manipulated subjects’ attention to an auditory attribute, whether the source of individual study words was a male or female speaker. Posterior alpha-band (8–14 Hz) power in subjects’ EEG increased after a cue to ignore the voice of the person who was about to speak. Receiver-operating-characteristic analysis validated our interpretation of oscillatory dynamics as a marker of attention to source information. With attention under experimental control, computational modeling showed unequivocally that memory for source (male or female speaker) reflected a continuous signal detection process rather than a threshold recollection process.
Identification of causal effects on binary outcomes using structural mean models

Structural mean models (SMMs) were originally formulated to estimate causal effects among those selecting treatment in randomized controlled trials affected by nonignorable noncompliance. It has already been established that SMMs can identify these causal effects in randomized placebo-controlled trials under fairly weak assumptions. SMMs are now being used to analyze other types of study where identification depends on a no effect modification assumption. We highlight how this assumption depends crucially on the unknown causal model that generated the data, and so is difficult to justify. However, we also highlight that, if treatment selection is monotonic, additive and multiplicative SMMs do identify local (or complier) causal effects, but that the double-logistic SMM estimator does not without further assumptions. We clarify the proper interpretation of inferences from SMMs by means of an application and a simulation study.
Object categories specific brain activity classification with simultaneous EEG-fMRI

Any kind of visual information is encoded in terms of patterns of neural activity occurring inside the brain. Decoding neural patterns or its classification is a challenging task. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are non-invasive neuroimaging modalities to capture the brain activity pattern in term of images and electric potential respectively. To get higher spatiotemporal resolution of human brain from these two complementary neuroimaging modalities, simultaneous EEG-fMRI can be helpful. In this paper, we proposed a framework for classifying the brain activity patterns with simultaneous EEG-fMRI. We have acquired five human participants' data with simultaneous EEG-fMRI by showing different object categories. Further, combined analysis of EEG and fMRI data was carried out. Extracted information through combine analysis is passed to support vector machine (SVM) classifier for classification purpose. We have achieved better classification accuracy using simultaneous EEG-fMRI i.e., 81.8% as compared to fMRI data standalone. This shows that multimodal neuroimaging can improve the classification accuracy of brain activity patterns as compared to individual modalities reported in literature.