Miracle-dz/UniFAR-FTUs huggingface dataset:
Ieee Transactions on Wireless Communications on the Transmission Rate Strategies in Cognitive Radios

—We investigate instantaneous transmission rate strategies for secondary users in cognitive radio networks by analyzing their effective capacity performance in different signal-to-noise ratio regimes with different quality-of-service constraints and transmission block sizes. Describing a channel model with one secondary transmitter and one secondary receiver with the potential presence of primary users, we present an interference power constraint that limits the transmission power of secondary users not only when a channel is sensed as busy but also when a channel is sensed as idle. Calling the existing transmission rate strategy Optimistic Policy, we introduce two other strategies , particularly Conservative Policy and Greedy Policy. Secondary users in Optimistic Policy set the instantaneous transmission rate to the instantaneous mutual information assuming the correct-ness of channel sensing results, whereas they set the instantaneous transmission rate to the instantaneous mutual information regarding possible transmission outages in Conservative Policy and disregarding possible transmission outages in Greedy Policy. We construct a state transition diagram and formulate the effective capacity employing these policies. We calculate the minimum energy-per-bit requirements and the high signal-to-noise ratio slope in order to explore performance variations in low and high signal-to-noise ratio regimes, respectively. Correspondingly, we show that Optimistic Policy is, in general, more favorable in secondary users when the quality-of-service constraints are loose, the transmission blocks are shorter, and the signal-to-noise ratio is low. On the other hand, Conservative Policy is better when the quality-of-service constraints are strict, the transmission blocks are longer, and the signal-to-noise ratio is high.
Reduced penetration force through ultrasound activation of a standard needle: An experimental and computational study

Needle insertion in soft tissue has attracted considerable attention in recent years because of its applications in minimally invasive percutaneous procedures such as cancer biopsy and regional anaesthesia. Tissue deformation and needle deflection contingent on needle penetration force are common reasons behind needle placement errors, leading to missampling in biopsy, incomplete anaesthesia, and possible post-operative morbidity. In this paper, we report the design of an ultrasonic device, based on a piezoelectric transducer with a sandwich structure, to reduce the standard needle's penetration force and deflection. Experimental trials were carried out on brain-mimicking phantom and ex vivo porcine tissue. The results show that a standard needle with ultrasound actuation can achieve force reduction by 34.5% and deflection reduction by 38.3%. In addition, numerical simulation of static and dynamic forces was conducted. A comparison between experimental and computational results demonstrates very close agreement. All the experimental and computational results suggest success in the proposed ultrasonic device's ability to perform accurate biopsy and regional anaesthesia.
Prognostic value of chromosomal abnormalities in follicular lymphoma.

The t(14;18)(q32;q21) chromosomal translocation is observed in more than 75% of cases of follicular lymphoma. Several additional chromosomal abnormalities, which might contribute to tumor progression, have also been described. However, prognostic implications of cytogenetic features in follicular lymphoma have not been clearly established. In an attempt to correlate cytogenetic findings with clinical outcome, we have studied survival and risk of transformation into a more aggressive lymphoma in 66 follicular lymphoma patients from whom a lymph node had been karyotyped at the time of diagnosis. A t(14;18) was the most common abnormality, having been observed in 51 patients (77%), but this showed no correlation with clinical outcome. Seventeen other recurrent numerical or structural abnormalities were identified in more than 10% of the patients. A high percentage of cells (> or = 90%) with abnormal metaphases and a number of chromosomal breaks higher than 6 were associated with a poor survival (P > .01 each). Patients with an abnormality of chromosome region 1p21-22 (P < .01), of 6q23-26 (P < .001), or of the short arm of chromosome 17 (P < .001) had a significantly shorter survival in univariate analysis. Multivariate analysis identified a break at 6q23-26 (P = .01) and 17p (P = .01) as independent prognostic factors in this population. The risk of transformation into a diffuse large-cell lymphoma was significantly higher in patients with either a 6q23-26 (P < .001) or a 17p (P < .01) abnormality. Chromosomal analysis of follicular lymphoma at the time of diagnosis can thus provide important information about the risk of transformation and survival. (ABSTRACT TRUNCATED AT 250 WORDS)
A Cloud Automation Platform for Flexibility in Applications and Resources Provisioning

Cloud technologies are still characterized by critical issues, which pose specific challenges for application developers and operators. In particular cloud application-level and infrastructure-level are completely decoupled both in the development and runtime phases leading in poor QoS cloud services. Main issues related to the optimize the use of the hardware resources are partially solve with virtualization technologies but innovative methodology in the automatic management of resources, applications provisioning and deployment are urgently needed. This paper presents ALM Automation Platform over CHEF framework in context of services virtualization for Public Administration where typically a large number of technologies heterogeneous resources, applications are deployed and managed.
V-buffer: visible volume rendering

This paper presents rendering techniques that use volumes as the basic geometric primitives. It defines data structures composed of numerous subvolumes, in excess of 100,000. Over each subvolume, a scalar field describes the variation of some physical quantity. The two rendering methods described herein assume a trilinear variation of this scalar field within each volume element, unlike voxel-based techniques that assume a constant value for each subvolume. The result is a higher order approximation of the structures within the volume. In addition, solid texture mapping, atmospheric attenuation, and transfer functions relating the dynamic range of the scalar field to color and opacity are used to isolate important data features. The result is a new method for the visualization of three-dimensional data resulting from numerical simulations and observations of natural phenomena. This method continuously covers the gap between surface-based and voxel-based techniques.
The power of localization for efficiently learning linear separators with noise

We introduce a new approach for designing computationally efficient and noise tolerant algorithms for learning linear separators. We consider the malicious noise model of Valiant [41, 32] and the adversarial label noise model of Kearns, Schapire, and Sellie [34]. For malicious noise, where the adversary can corrupt an η of fraction both the label part and the feature part, we provide a polynomial-time algorithm for learning linear separators in Rd under the uniform distribution with nearly information-theoretically optimal noise tolerance of η = Ω(ε), improving on the Ω(&epsilon/d1/4) noise-tolerance of [31] and the Ω(ε2/log(d/ε) of [35]. For the adversarial label noise model, where the distribution over the feature vectors is unchanged, and the overall probability of a noisy label is constrained to be at most η, we give a polynomial-time algorithm for learning linear separators in Rd under the uniform distribution that can also handle a noise rate of η = Ω(ε). This improves over the results of [31] which either required runtime super-exponential in 1/ε (ours is polynomial in 1/ε) or tolerated less noise. In the case that the distribution is isotropic log-concave, we present a polynomial-time algorithm for the malicious noise model that tolerates Ω(ε/log2(1/ε)) noise, and a polynomial-time algorithm for the adversarial label noise model that also handles Ω(ε/log2(1/ε)) noise. Both of these also improve on results from [35]. In particular, in the case of malicious noise, unlike previous results, our noise tolerance has no dependence on the dimension d of the space. Our algorithms are also efficient in the active learning setting, where learning algorithms only receive the classifications of examples when they ask for them. We show that, in this model, our algorithms achieve a label complexity whose dependence on the error parameter ε is polylogarithmic (and thus exponentially better than that of any passive algorithm). This provides the first polynomial time active learning algorithm for learning linear separators in the presence of malicious noise or adversarial label noise.
Hard Satisfiable Clause Sets for Benchmarking Equivalence Reasoning Techniques

A family of satisable benchmark instances in conjunctive normal form is introduced. The instances are constructed by transforming a random regular graph into a system of linear equations followed by clausication. Schemes for introducing nonlinearity to the instances are developed, making the instances suitable for benchmarking solvers with equivalence reasoning techniques. An extensive experimental evaluation shows that state-ofthe-art solvers scale exponentially in the instance size. Compared with other well-known families of satisable benchmark instances, the present instances are among the hardest.
Multiple description image transmission for diversity systems using block-based DC separation

Multiple description coding (MDC) and reconstruction of images has recently received considerable attention in the signal processing community because of its interesting and excellent properties over unreliable communication networks such as the Internet, ATM networks, packet-switched networks, wireless communication networks over fading multipath channel, and so on. In this paper, an efficient and reliable MDC scheme over unreliable communication networks is developed. A general framework of multiple description robust communication system is presented with block-based DC separation method. The advantage of this system is that, if all the channels work, a high quality, possibly lossless, reconstruction can be achieved from all received descriptions; while a lower but still acceptable quality can be achieved if some of the channels do not work.
Modeling and Simulation of Custom Power Devices to Mitigate Power Quality Problems

A Power quality problem is an occurrence manifested as a nonstandard voltage, current or frequency that results in a failure or a misoperation of end use Equipments. Utility distribution networks, sensitive industrial loads, and critical commercial operations all suffer from various types of outages and service interruptions which can cost significant financial 1oss per incident based on process down-time, lost production, idle work forces, and other factors. With the restructuring of Power Systems and with shifting trend towards distributed and dispersed Generation, the issue of Power Quality is going to take newer dimensions. The aim therefore, in this work, is to identify the prominent concerns in the area and thereby to recommend measures that can enhance the quality of the power, keeping in mind their economic viability and technical repercussions. In this paper ectromagnetic transient studies are presented for the following two custom power controllers: the distribution static compensator (D-STATCOM), and the dynamic voltage restorer (DVR). Comprehensive results are presented to assess the performance of each device to mitigate the power quality problems.
Traffic flow forecasting based on hybrid deep learning framework

Traffic flow forecasting is a key problem in the field of intelligent traffic management. In this work, we propose a hybrid deep learning framework for short-term traffic flow forecasting. It is built by the multi-layer integration deep learning architecture and jointly learns the spatial-temporal features. According to the highly nonlinear and non-stationary characteristics of traffic flow data, the framework consists of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). The former is to capture long temporal dependencies by using Long Short-Term Memory (LSTM) units and the latter is to capture the local trend features. The proposed framework is compared with other traditional shallow and deep learning models for traffic flow forecasting on PeMS datasets. The experimental results indicate that the hybrid framework is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness.
Simplified simulation models for indoor MANET evaluation are not robust

We evaluate the robustness of simplified mobility and radio propagation models for indoor MANET simulations. A robust simplification allows researchers to extrapolate simulation results and reach reliable conclusions about the expected performance of protocols in real life. We show that common simplified mobility and radio propagation models are not robust. Experiments with DSR and DSDV, two representative MANET routing protocols, show that the simplifications affect the two protocols in very different manners. Even for a single protocol, the effects on perceived performance can vary erratically as parameters change. These results cast doubt on the soundness of evaluations of MANET routing protocols based on simplified mobility and radio propagation models, and expose the urgent need for more research on realistic MANET simulation.
Multidate MESMA for monitoring vegetation growth forms in southern California shrublands

ABSTRACT Identifying habitats that should be protected from further disturbance or conversion and isolating high-risk areas is a focus of community habitat plans in southern California shrublands. Larger wildfires are occurring at shorter intervals in recent decades, contributing to degradation and conversion of shrubland vegetation. Multitemporal remote-sensing approaches can bridge the gap between vegetation mapping and field sampling in habitats where frequent quantification and mapping of vegetation growth forms over large extents is required. The objective of this study is to examine the reliability and stability of a multiple endmember spectral mixture analysis (MESMA) approach with moderate spatial resolution imagery for monitoring changes in growth form fractional cover in shrubland habitats. Estimates from visual interpretation of high spatial resolution image were used as reference data for validating MESMA-derived maps and as basis for providing complementary monitoring protocols that may be accurate and cost-effective across multiple scales. Growth form proportions modelled in burned and unburned management areas compare well with expected fractional cover in mature and regenerating shrublands. In the management areas recovering from fire, herbaceous cover fraction exceeded 0.40 for all three study dates, suggesting that large portions of those management areas may already be invaded. From 2008 to 2011 overall herbaceous cover fraction in shrubland area increased by 2%. Herbaceous cover fraction was modelled with an overall mean absolute error (MAE) of 0.08, a smaller percentage than the percentage of herbaceous cover change recorded in areas recovering from fire (increase in herbaceous cover fraction from 0.09 to 0.13). This MESMA approach would be effective for quantifying changes in fractional cover that exceed 0.10, providing a way to delineate and quantify herbaceous invasions and expansions following disturbance or succession.
Navigating blind people with a smart walker

Navigation in complex and unknown environments is a major challenge for blind people. The most popular, conventional navigation aids such as white canes and guide dogs, however, provide limited assistance in such settings as they are constrained to interpret the local environment only. At the same time, they can hardly be combined with a walker required by elderly people with walking disabilities. Technologies developed in the field of robotics have the potential to assist blind people in complex navigation tasks as they can provide information about obstacles and reason on both global and local environment models. The contribution of this paper is a smart walker that enables blind users to safely navigate. It includes an innovative vibro-tactile user interface and a controller that takes into account human characteristics based on a user study. The walker has been designed to deal with the fact that humans can only sense and interpret a limited number of commands and have a delayed response. Our experiments validate our claim that the technique outlined in this paper guides a user to the desired goal in less time and with shorter traveled distance compared to a standard robotic controller.
Relation between Mild to Moderate Chronic Kidney Disease and Coronary Artery Disease Determined with Coronary CT Angiography

Background Both end-stage and milder stages of chronic kidney disease (CKD) are associated with an increased risk of adverse cardiovascular events. Several studies found an association between decreasing renal function and increasing coronary artery calcification, but it remains unclear if this association is independent from traditional cardiovascular risk factors. Therefore, the aim of this study was to investigate whether mild to moderate CKD is independently associated with coronary plaque burden beyond traditional cardiovascular risk factors. Methods A total of 2,038 patients with symptoms of chest discomfort suspected for coronary artery disease underwent coronary CT-angiography. We assessed traditional risk factors, coronary calcium score and coronary plaque characteristics (morphology and degree of luminal stenosis). Patients were subdivided in three groups, based on their estimated glomerular filtration rate (eGFR) Normal renal function (eGFR ≥90 mL/min/1.73 m2); mild CKD (eGFR 60–89 mL/min/1.73 m2); and moderate CKD (eGFR 30–59 mL/min/1.73 m2). Results Coronary calcium score increased significantly with decreasing renal function (P<0.001). Coronary plaque prevalence was higher in patients with mild CKD (OR 1.83, 95%CI 1.52–2.21) and moderate CKD (OR 2.46, 95%CI 1.69–3.59), compared to patients with normal renal function (both P<0.001). Coronary plaques with >70% luminal stenosis were found significantly more often in patients with mild CKD (OR 1.67 (95%CI 1.16–2.40) and moderate CKD (OR2.36, 95%CI 1.35–4.13), compared to patients with normal renal function (both P<0.01). After adjustment for traditional cardiovascular risk factors, the association between renal function and the presence of any coronary plaque as well as the association between renal function and the presence of coronary plaques with >70% luminal stenosis becomes weaker and were no longer statistically significant. Conclusion Although decreasing renal function is associated with increasing extent and severity of coronary artery disease, mild to moderately CKD is not independently associated with coronary plaque burden after adjustment for traditional cardiovascular risk factors.
Benchmarking Named Entity Disambiguation approaches for Streaming Graphs

Named Entity Disambiaguation (NED) is a central task for applications dealing with natural language text. Assume that we have a graph based knowledge base (subsequently referred as Knowledge Graph) where nodes represent various real world entities such as people, location, organization and concepts. Given data sources such as social media streams and web pages Entity Linking is the task of mapping named entities that are extracted from the data to those present in the Knowledge Graph. This is an inherently difficult task due to several reasons. Almost all these data sources are generated without any formal ontology; the unstructured nature of the input, limited context and the ambiguity involved when multiple entities are mapped to the same name make this a hard task. This report looks at two state of the art systems employing two distinctive approaches: graph based Accurate Online Disambiguation of Entities (AIDA) and Mined Evidence Named Entity Disambiguation (MENED), which employs a statistical inference approach. We compare both approaches using the data set and queries provided by the Knowledge Base Population (KBP) track at 2011 NIST Text Analytics Conference (TAC). This report begins with an overview of the respective approaches, followed by detailed description of the experimental setup. It concludes with our findings from the benchmarking exercise.
Differential Expression of the Small-Conductance, Calcium-Activated Potassium Channel SK3 Is Critical for Pacemaker Control in Dopaminergic Midbrain Neurons

The physiological activity of dopaminergic midbrain (DA) neurons is important for movement, cognition, and reward. Altered activity of DA neurons is a key finding in schizophrenia, but the cellular mechanisms have not been identified. Recently, KCNN3, a gene that encodes a member (SK3) of the small-conductance, calcium-activated potassium (SK) channels, has been proposed as a candidate gene for schizophrenia. However, the functional role of SK3 channels in DA neurons is unclear. We combined patch-clamp recordings with single-cell RT-PCR and confocal immunohistochemistry in mouse midbrain slices to study the function of molecularly defined SK channels in DA neurons. Biophysical and pharmacological analysis, single-cell mRNA, and protein expression profiling strongly suggest that SK3 channels mediate the calcium-dependent afterhyperpolarization in DA neurons. Perforated patch recordings of DA neurons in the substantia nigra (SN) demonstrated that SK3 channels dynamically control the frequency of spontaneous firing. In addition, SK3 channel activity was essential to maintain the high precision of the intrinsic pacemaker of DA SN neurons. In contrast, in the ventral tegmental area, DA neurons displayed significantly smaller SK currents and lower SK3 protein expression. In these DA neurons, SK3 channels were not involved in pacemaker control. Accordingly, they discharged in a more irregular manner compared with DA SN neurons. Thus, our study shows that differential SK3 channel expression is a critical molecular mechanism in DA neurons to control neuronal activity. This provides a cellular framework to understand the functional consequences of altered SK3 expression, a candidate disease mechanism for schizophrenia.
The Impact of Climate Change on Water Availability and Recharge of Aquifers in the Jordan River Basin

Climate change can seriously affect the Middle East region by reduced and erratic rainfall. Formulating appropriate coping policies should account for local effects and changing flows interconnecting spatial units. We apply statistical downscaling techniques of coarse global circulation models to predict future rainfall patterns in the Yarmouk Basin, using a linear regression to extrapolate these results to the entire Jordan River Basin (JRB). Using a detailed water economy model for the JRB we predict rainfall patterns to evaluate the impact of climate change on agriculture and groundwater recharge. For the JRB, rainfall in 2050 will be around 10% lower than present precipitation, but with substantial spatial spreading. An overall reduction of net revenue from crop cultivation is estimated at 150 million USD, with major losses in Israel, Jordan, and the West Bank; Syrian revenues will slightly increase. The recharge of groundwater is affected negatively, and outflow to the Dead Sea is substantially lower, leading to further increases in salinization.