Miracle-dz/UniFAR-FTUs huggingface dataset:
Web workload generation challenges – an empirical investigation

Workload generators are widely used for testing the performance of Web‐based systems. Typically, these tools are also used to collect measurements such as throughput and end‐user response times that are often used to characterize the QoS provided by a system to its users. However, our study finds that Web workload generation is more difficult than it seems. In examining the popular RUBiS client generator, we found that reported response times could be grossly inaccurate, and that the generated workloads were less realistic than expected, causing server scalability to be incorrectly estimated. Using experimentation, we demonstrate how the Java virtual machine and the Java network library are the root causes of these issues. Our work serves as an example of how to verify the behavior of a Web workload generator. Copyright © 2011 John Wiley & Sons, Ltd.
Vesicular stomatitis virus in Drosophila melanogaster cells: lack of leader RNA transport into the nuclei and frequent abortion of the replication step

In cultured Drosophila melanogaster cells, vesicular stomatitis virus (VSV) establishes a persistent, noncytopathic infection. No inhibition of host macromolecular synthesis occurs. We studied the synthesis of VSV plus-strand leader RNA, which may be directly involved in vertebrate host synthesis shut-off. Leader RNA accumulated in Drosophila cell cytoplasm, but in low amounts, it was either free or associated to structures larger than the leader RNA-N protein complexes found in vertebrate cells. Only a few leader RNA copies migrated into the cell nucleus; no increase of this transport was observed at any time during the virus cycle. Viral RNAs complementary to the 3' end of the genome and ranging in size from the leader to several hundred nucleotides were found to accumulate in Drosophila cell cytoplasm. Their synthesis was inhibited in the presence of cycloheximide, which blocks all protein synthesis and VSV replication. Correlation between the absence of VSV cytopathogenicity in Drosophila cells and the lack of leader RNA transport into their nuclei is discussed, as well as the possible relationship between the restriction of viral synthesis and the frequent initiation of an abortive replication step.
Gray and white matter changes linking cerebral small vessel disease to gait disturbances

Objective: To investigate the topographic changes of white matter (WM) integrity and cortical thickness related to gait disturbances and determine whether these neural correlates mediate the association between cerebral small vessel disease (CSVD) and gait disturbances. Methods: A total of 129 patients with subcortical vascular cognitive impairment were included. CSVD severity was quantified as global and regional WM hyperintensities (WMH) volume and lacune and microbleed numbers. Amyloid burdens were assessed using Pittsburgh compound B (PiB)–PET scanning. Gait score was measured using a standardized scale. WM integrity was assessed by applying tract-based spatial statistics. Cortical thickness was measured using surface-based methods. Path analysis for gait score was performed using regional CSVD markers as predictors and fractional anisotropy (FA) and cortical thickness as mediators. Results: Periventricular WMH (PWMH) volume was associated with gait score, regardless of other CSVD. PiB retention ratio was not associated with gait score. Gait score was correlated with FA in the frontal and parietal WM and bilateral corpus callosum and with cortical thinning in the bilateral frontal and lateral temporo-parieto-occipital regions. Path analysis for gait score showed that PWMH contributed to gait disturbances with the mediation of mean FA or cortical thickness. Conclusions: Our findings suggest that WMH-related cortical thinning as well as disrupted integrity of periventricular WM is linked to gait disturbances.
Perceived security and privacy of cloud computing applications used in educational ecosystem

When employed in educational settings, cloud computing applications enable users to create, store, organize, and share divergent artefacts with their peers. As an outcome, they have a large number of users worldwide which makes them vulnerable to a variety of security and privacy related threats. With an aim to examine the extent of the perceived security and privacy in the context of cloud computing applications that are most commonly used for educational purposes, an empirical study was carried out. Participants in the study were students from two Croatian higher education institutions. Data was gathered by means of the post-use questionnaire. Study findings uncovered pros and cons of examined cloud computing applications with respect to the manner they are addressing security and privacy concerns of their users.
Analysis of Android Device-Based Solutions for Fall Detection

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.
Properties of shunt-protected tunneling devices for the Electron Counting Capacitance Standard (ECCS) experiment at PTB

We report on the progress in the Electron-Counting-Capacitance-Standard (ECCS) experiment. In particular, we address electrostatic breakdown in metallic single-electron tunneling (SET) structures fabricated on high-quality insulating substrates, An approach was developed via on-chip silicon shunts protecting the high-ohmic nanoscale tunnel junctions against damage.
Slipped capital femoral epiphysis update

Purpose of review Slipped capital femoral epiphysis (SCFE) is the most common adolescent hip condition. Its importance lies in its high morbidity if not diagnosed and treated in its early stages, not only in childhood but also as a cause of osteoarthritis in adulthood. This article highlights key diagnostic tools and optimal treatment plans for SCFE. Recent findings SCFE involves displacement between the proximal femoral neck and the femoral head at the level of the open physis, with biomechanical and biochemical factors implicated. Acute major trauma is rarely involved; a gradual onset of symptoms and deformity is more common. Patients with unstable SCFE are in severe pain and unable to bear weight. SCFE occasionally is associated with endocrine or metabolic abnormality (hypothyroidism, panhypopituitarism and renal rickets). On physical examination, limited internal rotation of the affected hip is usual; obligatory external rotation of hip in flexion is classic. Diagnosis is confirmed on anteroposterior and frog-leg lateral radiographs of both hips. Treatment is surgical, with stabilization across the physis by in-situ pinning being the gold standard. Summary Prompt diagnosis and timely surgical treatment usually lead to excellent long-term results with minimal morbidity. It is crucial to recognize that groin pulls are very rare in adolescents. Children with suggestive groin symptoms should have hip anteroposterior and frog-leg lateral radiographs to rule out the much more common SCFE.
Scheduling Stochastic Real-Time D2D Communications

To meet the real-time requirements of device-to-device (D2D) communications, we study the scheduling problems of real-time D2D communications to handle the challenges introduced by the stochastic packet arrivals, packet losses, and heterogeneous deadlines. In this paper, we first model the scheduling problem by the Markov Decision Process (MDP), and then propose an <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>-optimal scheduling algorithm based on this model. Considering the curse of dimensionality of the MDP-based model, we further model the scheduling problem by the restless multi-armed bandit model. Based on the model, we prove the indexability of the scheduling problem and derive the closed-form Whittle's index. According to the closed-form index, we thus propose an index-based scheduling algorithm that has very low time complexity. Finally, numerous simulations demonstrate the effectiveness of the proposed algorithms. The <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>-optimal scheduling algorithm converges very fast to optimal solutions with small-sized networks and small <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>. The index-based scheduling algorithm outperforms the compared algorithms in terms of the total discounted reward.
Difficulty Guided Image Retrieval Using Linear Multiple Feature Embedding

Existing image retrieval systems suffer from a performance variance for different queries. Severe performance variance may greatly degrade the effectiveness of the subsequent query-dependent ranking optimization algorithms, especially those that utilize the information mined from the initial search results. In this paper, we tackle this problem by proposing a query difficulty guided image retrieval system, which can predict the queries' ranking performance in terms of their difficulties and adaptively apply ranking optimization approaches. We estimate the query difficulty by comprehensively exploring the information residing in the query image, the retrieval results, and the target database. To handle the high-dimensional and multi-model image features in the large-scale image retrieval setting, we propose a linear multiple feature embedding algorithm which learns a linear transformation from a small set of data by integrating a joint subspace in which the neighborhood information is preserved. The transformation can be effectively and efficiently used to infer the subspace features of the newly observed data in the online setting. We prove the significance of query difficulty to image retrieval by applying it to guide the conduction of three retrieval refinement applications, i.e., reranking, federated search, and query suggestion. Thorough empirical studies on three datasets suggest the effectiveness and scalability of the proposed image query difficulty estimation algorithm, as well as the promising of the image difficulty guided retrieval system.
Circularly Polarized Beam-Steering Antenna Array With Butler Matrix Network

A circularly polarized switched-beam patch antenna array with butler matrix is designed and experimentally demonstrated in this letter. With suitable rotation technique, circular polarized radiation can be improved for beam steering. Also, this technique enhances the axial-ratio (AR) bandwidth within its scanning angle. Measured results showed this array has an impedance and AR bandwidth over a frequency range of 5.75-6.2 GHz when the main beam of array is scanned to different directions. Good agreement is observed between simulation and measurement.
Acute mechanical complications of coronary arterial disease. Surgical correction.

Mechanical complications that cause heart failure after acute myocardial infarction may be corrected early by surgery. The development of effective cardiac assistance by intra-aortic balloon pumping (IABP) has proved invaluable in resuscitating patients in over-whelming cardiac failure, maintaining them during diagnostic evaluation, and affording long-term (up to two weeks) postoperative support. Four of five patients with acutely expanding ventricular aneurysms, two of six patients with acute mitral regurgitation, and one of two patients with acute ventricular septal defects are living and well. Ten of these 13 patients required IABP. Seven of the group, which was totally refractory to medical therapy, are long-term survivors as a result of this combined supportive, diagnostic, and surgical approach. Usually the heart can be repaired successfully. Patients are lost primarily from the ravages, prior to surgery, of over-whelming cardiac failure on other organs.
Road recognition based on multi-scale convolutional network with multi-level feature fusion

Road recognition from optical remote sensing images is important for many applications like intelligent transportation system. Currently, convolutional neural networks (CNNs) methods are widely utilized in road recognition. However, many CNN methods hardly get good recognition performance when they process high-resolution images with large road width variance and complex background. For this problem, we develop a road recognition method based on a multi-scale convolutional network (MCN) with multi-level feature fusion. MCN comprises several CNNs with different scales of inputs and thus can extract multi-scale features. Each CNN fuses low-level geometrical features, middle-level features and high-level semantic features respectively from shallow, middle and deep layers. The multi-scale scheme and multi-level feature fusion make the MCN capable to handle large road width variance and complex background. Our method is validated on a manually labeled visible remote sensing image dataset. Moreover, our method is compared with CNNs without multi-scale or multi-level feature fusion and a fully convolutional network (FCN). The experimental results show that our method can well deal with complex visible remote sensing images with large road width variance.
Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure

Boundary features are widely used in traditional Chinese Word Segmentation (CWS) methods as they can utilize unlabeled data to help improve the Out-of-Vocabulary (OOV) word recognition performance. Although various neural network methods for CWS have achieved performance competitive with state-of-the-art systems, these methods, constrained by the domain and size of the training corpus, do not work well in domain adaptation. In this paper, we propose a novel BLSTM-based neural network model which incorporates a global recurrent structure designed for modeling boundary features dynamically. Experiments show that the proposed structure can effectively boost the performance of Chinese Word Segmentation, especially OOV-Recall, which brings benefits to domain adaptation. We achieved state-of-the-art results on 6 domains of CNKI articles, and competitive results to the best reported on the 4 domains of SIGHAN Bakeoff 2010 data.
MANet: A Modal Attention Network for Describing Videos

Exploiting multimodal features has become a standard approach towards many video applications, including the video captioning task. One problem with the existing work is that it models the relevance of each type of features evenly, which neutralizes the impact of each individual modality to the word to be generated. In this paper, we propose a novel Modal Attention Network (MANet) to account for this issue. Our MANet extends the standard encoder-decoder network by adapting the attention mechanism to video modalities. As a result, MANet emphasizes the impact of each modality with respect to the word to be generated. Experimental results show that our MANet effectively utilizes multimodal features to generate better video descriptions. Especially, our MANet system was ranked among the top three systems at the 2nd Video to Language Challenge in both automatic metrics and human evaluations.
IT Industry Development and the Knowledge Economy: A Four Country Study

It is generally accepted that knowledge has become a third major factor of production, in addition to the traditional factors â€” labor and capital. Information technology production is a significant factor in the knowledge economy both because it is a major enabler of that economy and because it is itself highly knowledge intensive. Many countries around the world are looking for ways to promote the development of the knowledge economy, and information technology industries in particular. An important question is to what extent â€” and how â€” small developed countries might succeed in this endeavor. This study suggests a modified and more comprehensive version of the Ein-Dor et al. (1997) model of IT (information technology) industry success in small developed countries. Whereas the earlier model of IT industry success was based solely on the macro-economic theory of Grossman and Helpman (1991), the revised model suggested here incorporates Romerâ€™s (1990) work in New Growth economics. A significant advance over earlier work in this area is the use of both longitudinal and time slice data. This article provides an in-depth analysis of the IT industry in four countries over a five-year period: Finland, Israel, New Zealand and Singapore. It analyses some changes that occurred over the period 1994 through 1998 and thus provides a reasonably comprehensive picture of the factors affecting the production of IT in these small developed countries. Our study reveals that four of the five endogenous variables studied have a close relationship to the development of IT industries in small developed countries. These variables are research and development, technological infrastructure, firm strategies, and capital availability. On the other hand, domestic IT use does not seem to be a major factor in IT industry development. Our analysis thus largely supports the more comprehensive model of IT industry success. These findings should be of interest to both researchers and policy makers seeking to develop the knowledge economy and information technology industries in particular.
Comparison of fast measurement methods for short-term negative bias temperature stress and relaxation

The initial degradation during negative bias stress is often assumed to be due to hole trapping, while the generation of interface states may dominate at longer stress times. We conduct a thorough study of short-time negative bias temperature stress and relaxation using ultra-fast measurement techniques in the micro-seconds regime to clarify the physical mechanisms behind the responsible hole trapping phenomenon. We observe that the extracted degradation of the draincurrent ΔID or the threshold-voltage ΔVTH can be well fitted by a logarithmic time dependence. Only for stronger stresses, that is, higher temperatures and/or voltages, the data shows a detectable deviation from the logarithmic behavior, allowing for a power-law fit. The exponent of this power-law is about 0.04 and thus considerably smaller than the typically reported long-term exponents of about 0.12 to 0.15. We finally observe a strong field- and temperature-dependence of the initial degradation, which is incompatible with the frequently assumed elastic hole trapping mechanism but favors a thermally activated hole trapping process.
BE R M IN : A Model Selection Algorithm for Reinforcement Learning Problems

Most reinforcement learning algorithms rely on the use of some function approximation method. In general, their performance will be largely influenced by what function approximation method is used and how it is configured. Current practice is that the user of the algorithm decides about both the method and its configuration, such as the number and nature of basis functions for a linear function approximation method, and tune them in an ad hoc manner. Although good rules of thumb may exist of how to tune a particular method, or which method to use in a particular situation, there is no guarantee that a rule of thumb will give good results on the problem that the user wants to solve. A superior approach, however, is to choose and configure the method based on the data. We show that strong theoretical results can be proven if an appropriate selection procedure is used.1
GABAA receptor‐expressing astrocytes in the supraoptic nucleus lack glutamate uptake and receptor currents

An important function of astrocytes is the clearance of excess extracellular glutamate via specific carriers whose expression has become an astrocytic marker. In the present study, we found that a large population of astrocytes in the supraoptic nucleus (SON) of the rat hypothalamus lacks glutamate uptake currents and receptor responses but expresses GABAA receptors. Patch clamp recordings in acute hypothalamic slices that included the SON showed typical astrocytic membrane currents and demonstrated that GABA, via GABAA receptor activation, triggered a conductance increase with the reversal potential close to the Cl− equilibrium potential and a decrease in resting K+ conductance. Intracellular labeling with Lucifer Yellow revealed that these cells had a radial glia‐like morphology, with cell bodies lined up along the base of the brain and long processes traversing the nucleus; they were not dye‐coupled. Parallel immunocytochemical labelings showed that they expressed strong GABAA receptor and glial fibrillary acidic protein (GFAP) immunoreactivities. In addition, our electrophysiological and morphological analyses revealed another population of astrocytes in this nucleus, located next to the subarachnoid space. They were less numerous than the radial type, had a round morphology and few processes, and were dye‐coupled. Unlike the radial astrocytes, they showed little immunoreactivity for GABAA receptor or GFAP. Moreover, they did not respond to GABA but to glutamate, a response that was partially mimicked by aspartate, indicating glutamate transporter expression. Taken together, our observations add to growing evidence illustrating heterogeneity of astrocytes in the adult brain, a heterogeneity that reflects striking differences in form and function of astrocytic populations in regions as discrete as the SON of the hypothalamus. © 2003 Wiley‐Liss, Inc.
Frequency-domain approach to calculate steady-state of a stand-alone photovoltaic system

This paper presents a frequency-domain (FD) approach to calculate the steady-state of a stand-alone photovoltaic (PV) system. The PV array is represented as a Thévenin equivalent for stationary conditions, simplifying its nonlinear performance. The boost converter (BC), which acts as voltage regulator and performs maximum power point tracking (MPPT), is modeled as a FD equivalent based on the matrix representation of the discrete Fourier transform (DFT). Switching actions of the BC are accounted for by partitioning the DFT transformation matrix according to the switching-on and switching-off times. MPPT is performed via an iterative process using the duty ratio of the BC as the feedback variable. The proposed approach achieves fast computation of the periodic steady-state compared to traditional time domain modeling methods. An illustrative example is presented and validated using the PSCAD software.