Miracle-dz/UniFAR-FTUs huggingface dataset:
WBWTP + : A Packet Scheduling Algorithm for Achieving Proportional Delay Differentiation in IP Networks

(WBWTP+), the proposed algorithm, is an evolution of the WBWTP algorithm that adjusts dynamically the relative weights of transmitted and waiting for transmission packets according to the current state of the system. The WBWTP+ delay estimator makes possible to accelerate or to delay the transmission of backlogged packets. Simulations performed to asses the performance of the WBWTP+ show that it presents significant improvement in the attendance of the PDM objective in relation to WBWTP in most scenarios, excepted when the link utilization rate is 100%. Even in that case the performance of WBWTP+ was better than that of WTP and PAD algorithms.
Alkaline Phosphatase, Serum Phosphate, and Incident Cardiovascular Disease and Total Mortality in Older Men

Objective—We have examined the association between serum phosphate and alkaline phosphatase (ALP) with incident cardiovascular disease (CVD) outcomes and total mortality in older men. Approach and Results—A prospective study of 3381 men, aged 60 to 79 years, without a history of myocardial infarction or stroke followed up for an average 11 years during which there were 605 major CVD events (fatal coronary heart disease and nonfatal myocardial infarction, stroke, and CVD death) and 984 total deaths. ALP but not serum phosphate was associated with increased risk of coronary heart disease and overall CVD events which persisted after adjustment for CVD risk factors and markers of inflammation and after exclusion of men with chronic kidney disease (adjusted hazard ratio per SD, 1.19 [1.05, 1.34]; P=0.007 and 1.10 [1.01, 1.21]; P=0.04). In contrast, serum phosphate was only associated with increased CVD mortality owing to noncoronary heart disease or stroke causes (adjusted hazard ratio per SD, 1.35 [1.01, 1.83]; P=0.04). Both raised phosphate and ALP were associated with significantly increased total mortality after full adjustment and exclusion of men with chronic kidney disease. Conclusions—ALP but not serum phosphate is associated with coronary heart disease risk in elderly men. High levels of ALP and serum phosphate are both associated with increased total mortality.
Speaker recognition based on transformed line spectral frequencies

Line spectral frequencies (LSF) and five types of transformed LSF are studied for robust text-independent speaker identification. Transformations are constructed by considering physical aspects of the vocal tract. These aspects are: location of formants/nulls; bandwidth of formants/nulls; bandwidth and location of formants; bandwidth and location of nulls; interval of adjacent formant and null locations. Identification tests using the TIMIT database verify that all features are useful for speaker recognition; the bandwidth and location of formants, especially, show the best performance. Simulation results also show that LSF and some of the transformed LSF give better performance than Mel-frequency cepstral coefficient (MFCC).
iSeS: Intelligent semantic search framework

With vast amounts of data being produced, present world is overwhelmed with information and searching for appropriate content has turned out to be harder than ever before. Semantics, which typically focuses on the relationship between signifiers, such as words, phrases, signs and symbols, and what they stand for is now being used more and more in search engines to provide the user with more meaningful content. Further it is no more the case that users are interested in search results that the majority of users would agree to, but are more interested in results being personalized to them. In this research paper we present iSeS: Intelligent Semantic Search Framework, which is a search framework, a custom web site or an application can adapt. We focus on using underlying semantics of the content being indexed in providing more meaningful search results personalized to each user. We look into both latent semantic indexing and metadata extraction based methods for providing semantically rich search results. Collaborative filtering and how it is used to personalize search results is also explored in this paper.
Paradoxical effects of streptozotocin-induced diabetes on endothelial dysfunction in stroke-prone spontaneously hypertensive rats

Non‐Technical Summary Elevated blood glucose is generally regarded as one of the risk factors that lead to coronary heart disease in patients with type 2 diabetes. However, our studies show that after inducing short‐term damage, high blood glucose subsequently provides paradoxical protection for vessel function of animals with high blood pressure. Vessels can adapt to sustained high blood glucose and produce different stress proteins to counteract, to some extent, the damage brought about by hypertension. The results help us understand part of the basis for vessel adaptation in diabetes. The implication for treatment of diabetes is that if the patients have long‐standing diabetes and established cardiovascular disease, the target of blood glucose lowering should be less stringent and reached gradually to avoid abrupt cancellation of the pre‐existing adaptations.
Does Legality Matter? The Case of Tax Avoidance and Evasion

Previous research argues that law expresses social values and could, therefore, influence individual behavior independently of enforcement and penalization. Using three laboratory experiments on tax avoidance and evasion, we study how legality affects individuals’ decisions. We find that, without any risk of negative financial consequences, the qualification of tax minimization as illegal versus legal reduces tax minimization considerably. Legislators can thus, in principle, affect subjects’ decisions by defining the borderline between legality and illegality. However, once we introduce potential negative financial consequences, legality does not affect tax minimization. Only if we use moral priming to increase subjects’ moral cost do we again find a legality effect on tax minimization. Overall, this demonstrates the limitations of the expressive function of law. Legality appears to be an important determinant of behavior only if we consider activities with no or low risk of negative financial consequences or if subjects are morally primed.
On Fault Tolerance for Distributed Iterative Dataflow Processing

Large-scale graph and machine learning analytics widely employ distributed iterative processing. Typically, these analytics are a part of a comprehensive workflow, which includes data preparation, model building, and model evaluation. General-purpose distributed dataflow frameworks execute all steps of such workflows holistically. This holistic view enables these systems to reason about and automatically optimize the entire pipeline. Here, graph and machine learning analytics are known to incur a long runtime since they require multiple passes over the data until convergence is reached. Thus, fault tolerance and a fast-recovery from any intermittent failure is critical for efficient analysis. In this paper, we propose novel fault-tolerant mechanisms for graph and machine learning analytics that run on distributed dataflow systems. We seek to reduce checkpointing costs and shorten failure recovery times. For graph processing, rather than writing checkpoints that block downstream operators, our mechanism writes checkpoints in an unblocking manner that does not break pipelined tasks. In contrast to the conventional approach for unblocking checkpointing (e.g., that manage checkpoints independently for immutable datasets), we inject the checkpoints of mutable datasets into the iterative dataflow itself. Hence, our mechanism is iteration-aware by design. This simplifies the system architecture and facilitates coordinating checkpoint creation during iterative graph processing. Moreover, we are able to rapidly rebound, via confined recovery, by exploiting the fact that log files exist locally on healthy nodes and managing to avoid a complete recomputation from scratch. In addition, we propose replica recovery for machine learning algorithms, whereby we employ a broadcast variable that enables us to quickly recover without having to introduce any checkpoints. In order to evaluate our fault tolerance strategies, we conduct both a theoretical study and experimental analyses using Apache Flink and discover that they outperform blocking checkpointing and complete recovery.
Application of the Frequency Spectrum to Spectral Similarity Measures

Several frequency-based spectral similarity measures, derived from commonly-used ones, are developed for hyperspectral image classification based on the frequency domain. Since the frequency spectrum (magnitude spectrum) of the original signature for each pixel from hyperspectral data can clearly reflect the spectral features of different types of land covers, we replace the original spectral signature with its frequency spectrum for calculating the existing spectral similarity measure. The frequency spectrum is symmetrical around the direct current (DC) component; thus, we take one-half of the frequency spectrum from the DC component to the highest frequency component as the input signature. Furthermore, considering the fact that the low frequencies include most of the frequency energy, we can optimize the classification result by choosing the ratio of the frequency spectrum (from the DC component to the highest frequency component) involved in the calculation. In our paper, the frequency-based measures based on the spectral gradient angle (SAM), spectral information divergence (SID), spectral correlation mapper (SCM), Euclidean distance (ED), normalized Euclidean distance (NED) and SID × sin(SAM) (SsS) measures are called the F-SAM, F-SID, F-SCM, F-ED, F-NED and F-SsS, respectively. In the experiment, three commonly-used hyperspectral remote sensing images are employed as test data. The frequency-based measures proposed here are compared to the corresponding existing ones in terms of classification accuracy. The classification results by parameter optimization are also analyzed. The results show that, although not all frequency-based spectral similarity measures are better than the original ones, some frequency-based measures, such as the F-SsS and F-SID, exhibit a relatively better performance and have more robust applications than the other spectral similarity measures.
Ancient DNA Reveals Matrilineal Continuity in Present-Day Poland over the Last Two Millennia

While numerous ancient human DNA datasets from across Europe have been published till date, modern-day Poland in particular, remains uninvestigated. Besides application in the reconstruction of continent-wide human history, data from this region would also contribute towards our understanding of the history of the Slavs, whose origin is hypothesized to be in East or Central Europe. Here, we present the first population-scale ancient human DNA study from the region of modern-day Poland by establishing mitochondrial DNA profiles for 23 samples dated to 200 BC – 500 AD (Roman Iron Age) and for 20 samples dated to 1000–1400 AD (Medieval Age). Our results show that mitochondrial DNA sequences from both periods belong to haplogroups that are characteristic of contemporary West Eurasia. Haplotype sharing analysis indicates that majority of the ancient haplotypes are widespread in some modern Europeans, including Poles. Notably, the Roman Iron Age samples share more rare haplotypes with Central and Northeast Europeans, whereas the Medieval Age samples share more rare haplotypes with East-Central and South-East Europeans, primarily Slavic populations. Our data demonstrates genetic continuity of certain matrilineages (H5a1 and N1a1a2) in the area of present-day Poland from at least the Roman Iron Age until present. As such, the maternal gene pool of present-day Poles, Czechs and Slovaks, categorized as Western Slavs, is likely to have descended from inhabitants of East-Central Europe during the Roman Iron Age.
Plasmid-encoded regulation of colicin E1 gene expression

A plasmid-encoded factor that regulates the expression of the colicin E1 gene was found in molecular cloning experiments. The 2,294-base-pair AvaII fragment of the colicin E1 plasmid (ColE1) carrying the colicin E1 structural gene and the promoter-operator region had the same information with respect to the repressibility and inducibility of colicin E1 synthesis as the original ColE1 plasmid. An operon fusion was constructed between the 204-bp fragment containing the colicin E1 promoter-operator and xylE, the structural gene for catechol 2,3-dioxygenase encoded on the TOL plasmid of Pseudomonas putida. The synthesis of the dioxygenase from the resulting plasmid occurred in recA+, but not in recA- cells and was derepressed in the recA lexA(Def) double mutant. These results indicate that the ColE1 plasmid has no repressor gene for colicin E1 synthesis and that the lexA protein functions as a repressor. Colicin E1 gene expression was adenosine 3',5'-phosphate (cAMP) dependent. Upon the removal of two PvuII fragments (2,000 bp in length) from the ColE1 plasmid, the induced synthesis of colicin E1 occurred in the adenylate-cyclase mutant even without cAMP. The 3,100-bp Tth111I fragment of the ColE1 plasmid cloned on pACYC177 restored the cAMP dependency of the deleted ColE1 plasmid. Since the deleted fragments correspond to the mobility region of ColE1, the cAMP dependency of the gene expression should be somehow related to the plasmid mobilization function.
A Study of Concept Extraction Across Different Types of Clinical Notes

Our research investigates methods for creating effective concept extractors for specialty clinical notes. First, we present three new "specialty area" datasets consisting of Cardiology, Neurology, and Orthopedics clinical notes manually annotated with medical concepts. We analyze the medical concepts in each dataset and compare with the widely used i2b2 2010 corpus. Second, we create several types of concept extraction models and examine the effects of training supervised learners with specialty area data versus i2b2 data. We find substantial differences in performance across the datasets, and obtain the best results for all three specialty areas by training with both i2b2 and specialty data. Third, we explore strategies to improve concept extraction on specialty notes with ensemble methods. We compare two types of ensemble methods (Voting/Stacking) and a domain adaptation model, and show that a Stacked ensemble of classifiers trained with i2b2 and specialty data yields the best performance.
Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET

The norepinephrine transporter (NET) transports norepinephrine from the synapse into presynaptic neurons, where norepinephrine regulates signaling pathways associated with cardiovascular effects and behavioral traits via binding to various receptors (e.g., β2-adrenergic receptor). NET is a known target for a variety of prescription drugs, including antidepressants and psychostimulants, and may mediate off-target effects of other prescription drugs. Here, we identify prescription drugs that bind NET, using virtual ligand screening followed by experimental validation of predicted ligands. We began by constructing a comparative structural model of NET based on its alignment to the atomic structure of a prokaryotic NET homolog, the leucine transporter LeuT. The modeled binding site was validated by confirming that known NET ligands can be docked favorably compared to nonbinding molecules. We then computationally screened 6,436 drugs from the Kyoto Encyclopedia of Genes and Genomes (KEGG DRUG) against the NET model. Ten of the 18 high-scoring drugs tested experimentally were found to be NET inhibitors; five of these were chemically novel ligands of NET. These results may rationalize the efficacy of several sympathetic (tuaminoheptane) and antidepressant (tranylcypromine) drugs, as well as side effects of diabetes (phenformin) and Alzheimer’s (talsaclidine) drugs. The observations highlight the utility of virtual screening against a comparative model, even when the target shares less than 30% sequence identity with its template structure and no known ligands in the primary binding site.
A novel technique for non-overlapping image mosaicing based on pyramid method

Image mosaicing has been used in various arenas of image engineering. The main purpose of this research work is to develop a novel blending algorithm for mosaicing of two non-overlapping images with minimum distortion and no visible seam. In the proposed method, images are mosaiced using the pyramid technique and Savitzky-Golay filter is used for smoothing the seam between the images in the generated mosaic. Savitzky-Golay filter, is based on the least square fitting of polynomial functions of image intensities. It effectively reduces the noise and preserves the image information content resulting in a visually pleasing mosaiced image. The results and comparative analysis show the effectiveness of the proposed method.
Extracorporal blood purification: more than diffusion and convection. Does this help?

Purpose of reviewA resurgence of interest in adsorbents for medical application is now being seen. In extracorporeal blood purification techniques, the application of unselective adsorbents has been proven to increase remarkably the removal of plasma solutes accumulated as a result of renal and liver failure. Recent findingsThe present technology for appropriately modifying several intrinsic characteristics of the adsorbents – such as chemical nature, particle and pore-size distribution and the surface area offered to adsorption – has helped to fine-tune extracorporeal devices for more defined clinical applications. These properties, together with improved biocompatibility, have allowed the development of adsorptive techniques to obtain clearances and total removal rates of target compound that would be unthinkable with conventional hemodialysis or hemofiltration. SummaryDespite several adsorptive techniques already being available commercially for treatment of sepsis/septic shock and acute liver failure, controlled studies with clinical end points are still lacking. In the near future it is expected that a final answer to the question in this article's title will be provided.
A Bayesian Nonparametric Approach to Modeling Mobility Patterns

Constructing models of mobile agents can be difficult without domain-specific knowledge. Parametric models flexible enough to capture all mobility patterns that an expert believes are possible are often large, requiring a great deal of training data. In contrast, nonparametric models are extremely flexible and can generalize well with relatively little training data. We propose modeling the mobility patterns of moving agents as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual mobility pattern, while the DP assigns observed trajectories to particular mobility patterns. Both the GPs and the DP adjust the model's complexity based on available data, implicitly avoiding issues of over-fitting or under-fitting. We apply our model to a helicopter-based tracking task, where the mobility patterns of the tracked agents — cars — are learned from real data collected from taxis in the greater Boston area.
Multistage Switching Architectures for Software Routers

Software routers based on personal computer (PC) architectures are becoming an important alternative to proprietary and expensive network devices. However, software routers suffer from many limitations of the PC architecture, including, among others, limited bus and central processing unit (CPU) bandwidth, high memory access latency, limited scalability in terms of number of network interface cards, and lack of resilience mechanisms. Multistage PC-based architectures can be an interesting alternative since they permit us to i) increase the performance of single- software routers, ii) scale router size, iii) distribute packet-manipulation and control functionality, iv) recover from single-component failures, and v) incrementally upgrade router performance. We propose a specific multistage architecture, exploiting PC-based routers as switching elements, to build a high-speed, large-size, scalable, and reliable software router. A small-scale prototype of the multistage router is currently up and running in our labs, and performance evaluation is under way.
Geometrical and eigenvector features for ear recognition

Unconstrained ear biometric means an ear image that has variance in view and pose. This situation is challenging in ear recognition because one ear has various presentation. In this study, two features are considered to handle unconstrained ear image. The features called geometrical feature and eigenvector features. In eigenvector feature, the ear is extracted from six regions then the eigenvector is computed from each of those regions. Each region has capability to represent particular part of the ear image. Another feature is called geometrical feature that reflecting the shape of ear image. The widely used classifier is utilized and it trained with both features. Proposed method outcome is measured to evaluate the recognition rates among single features and fused features. The experiment is carried out on benchmark database collected by University of Science and Technology Beijing (USTB). It shows the proposed method can achieved promising result.
Content Based Image Retrieval by Online and Offline

With many potential practical applications, contentbased image retrieval (CBIR) has attracted substantial attention during the past few years. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high-level semantic concepts, and thus to improve the performance of CBIR systems. Among various RF approaches, support-vector-machine (SVM)-based RF is one of the most popular techniques in CBIR. Despite the success, directly using SVM as an RF scheme has two main drawbacks. First, it treats the positive and negative feedbacks equally, which is not appropriate since the two groups of training feedbacks have distinct properties. Second, as the size of image database increases search and retrieval become slow and it affects the performance of the system. To explore solutions to overcome these two drawbacks, CBIR system is implemented as both offline and online and which make use of the properties of images.
Indoor autonomous hovering control for a small unmanned coaxial helicopter

For the nonlinear and open-loop unstable characteristics of the small helicopter, autonomous flight control is a challenging problem. In order to reduce the complexity of the helicopter autonomous control, this paper constructs an indoor autonomous flight control platform for a small coaxial helicopter ESky LAMA4, which is convenient for data collection, data analysis and control law design. Firstly, the Vicon Mx Motion Capture System is installed to obtain the attitudes and positions of the coaxial helicopter. Secondly, a PCTx converter for the radio controller is used to convert the control commands into PPM signals which are sent into the radio controller. Hence the attitude and translational motion can be controlled. Then in the inner attitude loop and outer translational loop, several PID controllers are designed for the autonomous hovering control of the small coaxial helicopter. Finally the flight results demonstrate the feasibility of the proposed approach, which can be adopted into the study of small unmanned helicopters in the future.