Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate Analysis of Transverse Slit in Microstrip Transmission Lines using a Simple and Novel Techni ue

Applying an improved version of rigorously coupled multi-strip method (RCMS), presented in the work of Collin (1996), for a transverse slit in microstrip lines enables us to find more accurate s-parameters of the structure than those obtained using traditional quasi-TEM methods. This method also can be used to get to the longitudinal current distribution across the strip width, which can not be obtained using traditional quasi-TEM methods. In this approach the strip has been divided into longitudinally uniform and nonuniform narrower strips. To calculate the s-parameters of the two-port network, the slit is regarded as a two asymmetrical steps cascaded by an ordinary transmission line which it's length is equal to the width of the slit. The method has been validated through both full wave and the equivalent circuit of transverse slit given in the literature (Pozar, 1990)
Parity Prediction of S-Box for AES

In this paper, we present the parity prediction approach of the S-Box for designing high performance and fault detection structures of the AES. Unlike the traditional scheme which is based on using look-up tables, we use the logical gates implementation based on the composite fields for fault detection of S-Box in AES. We find closed formulations for the output parity bits of S-Box considering the composite-field transformation matrix and its inverse in GF(28) as well as the affine transformation. To the best of our knowledge, no closed formulations for parity prediction of the S-Box have been proposed in the open literature
A Maximum Entropy Model of Phonotactics and Phonotactic Learning

The study of phonotactics is a central topic in phonology. We propose a theory of phonotactic grammars and a learning algorithm that constructs such grammars from positive evidence. Our grammars consist of constraints that are assigned numerical weights according to the principle of maximum entropy. The grammars assess possible words on the basis of the weighted sum of their constraint violations. The learning algorithm yields grammars that can capture both categorical and gradient phonotactic patterns. The algorithm is not provided with constraints in advance, but uses its own resources to form constraints and weight them. A baseline model, in which Universal Grammar is reduced to a feature set and an SPE-style constraint format, suffices to learn many phonotactic phenomena. In order for the model to learn nonlocal phenomena such as stress and vowel harmony, it must be augmented with autosegmental tiers and metrical grids. Our results thus offer novel, learning-theoretic support for such representations. We apply the model in a variety of learning simulations, showing that the learned grammars capture the distributional generalizations of these languages and accurately predict the findings of a phonotactic experiment.
Iterative dynamic programming: an approach to minimum energy trajectory planning for robotic manipulators

This paper proposes a technique of iterative dynamic programming to plan minimum energy consumption trajectories for robotic manipulators. The dynamic programming method is modified to perform a series of dynamic programming passes over a small reconfigurable grid covering only a portion of the solution space at any one pass. Although strictly no longer a global optimization process, this iterative approach retains the ability to avoid some poor local minima while avoiding the curse of dimensionality associated with a pure dynamic programming approach. The algorithm has an inherent parallel structure, allowing for reduced computation time on parallel architecture computers. No limiting assumptions are made about the performance index, or function to be optimized. As such, extremely complex functions and constraints are easily handled. Joint actuator and time constraints are considered in this work. The modified dynamic programming approach is verified experimentally by planning and executing a minimum energy consumption path for a Reis V15 industrial manipulator.
The Surface Layer Homology Domain-Containing Proteins of Alkaliphilic Bacillus pseudofirmus OF4 Play an Important Role in Alkaline Adaptation via Peptidoglycan Synthesis

It is well known that the Na+ cycle and the cell wall are essential for alkaline adaptation of Na+-dependent alkaliphilic Bacillus species. In Bacillus pseudofirmus OF4, surface layer protein A (SlpA), the most abundant protein in the surface layer (S-layer) of the cell wall, is involved in alkaline adaptation, especially under low Na+ concentrations. The presence of a large number of genes that encode S-layer homology (SLH) domain-containing proteins has been suggested from the genome sequence of B. pseudofirmus OF4. However, other than SlpA, the functions of SLH domain-containing proteins are not well known. Therefore, a deletion mutant of the csaB gene, required for the retention of SLH domain-containing proteins on the cell wall, was constructed to investigate its physiological properties. The csaB mutant strain of B. pseudofirmus OF4 had a chained morphology and alkaline sensitivity even under a 230 mM Na+ concentration at which there is no growth difference between the parental strain and the slpA mutant strain. Ultra-thin section transmission electron microscopy showed that a csaB mutant strain lacked an S-layer part, and its peptidoglycan (PG) layer was disturbed. The slpA mutant strain also lacked an S-layer part, although its PG layer was not disturbed. These results suggested that the surface layer homology domain-containing proteins of B. pseudofirmus OF4 play an important role in alkaline adaptation via peptidoglycan synthesis.
Three-dimensional surface reconstruction within noncontact diffuse optical tomography using structured light

Abstract. A main field in biomedical optics research is diffuse optical tomography, where intensity variations of the transmitted light traversing through tissue are detected. Mathematical models and reconstruction algorithms based on finite element methods and Monte Carlo simulations describe the light transport inside the tissue and determine differences in absorption and scattering coefficients. Precise knowledge of the sample’s surface shape and orientation is required to provide boundary conditions for these techniques. We propose an integrated method based on structured light three-dimensional (3-D) scanning that provides detailed surface information of the object, which is usable for volume mesh creation and allows the normalization of the intensity dispersion between surface and camera. The experimental setup is complemented by polarization difference imaging to avoid overlaying byproducts caused by inter-reflections and multiple scattering in semitransparent tissue.
Religiosity and Internalized Homonegativity: The Effect of Religious Conservatism on Depression in Sexual Minorities

Sexual minorities report higher rates of mental illnesses than the general population, most notably depression. This study focuses on the relationship between religious commitment in sexual minorities, internalized homonegativity, and depression. In order to do this, 265 participants answered a survey that consisted of inventories to test for sexual orientation, religious commitment, political and religious views, and internalized homonegativity. Participants came from Abilene Christian University and LGBT groups from the surrounding Abilene, Texas area. Of the 265 participants, 33 self-identified as a sexual minority. The data from those that identified as a sexual minority was analyzed to find correlations between religious commitment, political views, internalized homonegativity, and depression. The results from the data analysis show that there is a significant correlation between religious commitment and internalized homonegativity and a significant correlation between internalized homonegativity and depression. Data analysis was also performed on religious commitment and depression and no significant correlation was found. Religiosity and Internalized Homonegativity: The Effect of Religious Conservatism on Depression in Sexual Minorities A Thesis Presented to The Faculty of the Graduate School Abilene Christian University In Partial Fulfillment Of the Requirements for the Degree Masters of Science Psychology By Matthew S. Thaxton
SmartDPSS: Cost-Minimizing Multi-source Power Supply for Datacenters with Arbitrary Demand

To tackle soaring power costs, significant carbon emission and unexpected power outage, Cloud Service Providers (CSPs) typically equip their Datacenters with a Power Supply System (DPSS) nurtured by multiple sources: (1) smart grid with time-varying electricity prices, (2) uninterrupted power supply (UPS), and (3) renewable energy with intermittent and uncertain supply. It remains a significant challenge how to operate among multiple power supply sources in a complementary manner, to deliver reliable energy to datacenter users with arbitrary demand over time, while minimizing a CSP's operation cost over the long run. This paper proposes an efficient, online control algorithm for DPSS, SmartDPSS, based on the two-timescale Lyapunov optimization techniques. Without requiring a priori knowledge of system statistics, SmartDPSS allows CSPs to make online decisions on how much power demand, including delay-sensitive demand and delay-tolerant demand, to serve at each time, the amount of power to purchase from the long-term-ahead and realtime grid markets, and charging and discharging of UPS over time, in order to fully leverage the available renewable energy and time-varying prices from the grid markets, for minimum operational cost. We thoroughly analyze the performance of our online control algorithm with rigorous theoretical analysis. We also demonstrate its optimality in terms of operational cost, demand service delay, datacenter availability, system robustness and scalability, using extensive simulations based on one-month worth of traces from live power systems.
Hardware-Supported Pointer Detection for Common Garbage Collections

Many mobile systems have to achieve both high performance and low memory usage, and the total performance of the wide range of platforms now can be affected by the effectiveness of Garbage Collection (GC). GC algorithms have been actively studied and improved, but they still have not reached any fundamental solution. In this paper, we focus on the point that the objects on the call stack should be traced in many GC algorithms, and propose a hardware support technique for speed up of this trace. To trace objects, it is needed to find pointers on the call stack. Hence, we install tables for managing all pointers on the call stack. By referring these tables, the GC routine can detect pointers immediately. The result of the simulation experiment shows the proposed method leads to low GC latency.
A Parallel Corpus of Music and Lyrics Annotated with Emotions

In this paper, we introduce a novel parallel corpus of music and lyrics, annotated with emotions at line level. We first describe the corpus, consisting of 100 popular songs, each of them including a music component, provided in the MIDI format, as well as a lyrics component, made available as raw text. We then describe our work on enhancing this corpus with emotion annotations using crowdsourcing. We also present some initial experiments on emotion classification using the music and the lyrics representations of the songs, which lead to encouraging results, thus demonstrating the promise of using joint music-lyric models for song processing.
Performance of network and computing resource sharing in federated cloud systems

The increasing demand of computing, storage and communication resources by cloud-based applications is fostering new forms of infrastructure sharing such as cloud federations, which can take advantage of virtualization technologies and, in particular, of virtual machine live migration techniques. Such a scenario requires a quantitative characterization of the performance of the inter-data center communication considering possible limitations in both network and computing resource availability. This paper provides an analytical model for joint dimensioning of shared network and data center capacity in a federate cloud.
SINR based routing for Mobile Ad-hoc Networks

Mobile Adhoc Networks (MANETs) are self-organizing and self-configuring multi-hop wireless networks capable of adaptive re-configuration as effected by node mobility. In Such wireless networks, there can be different sources of noise which can be interpreted falsely as a signal which may lead to several undesired consequences. It is imperative if such noise and interferences are separated from actual signal and thereby making the communication system more efficient. In this work, we propose a system which eliminates the noisy signals received at the physical layer by comparing the signal to interference noise ratio with the signal to noise threshold value. Interference and noise signal along with actual signal strength parameters are measured at physical layer of OSI reference architecture. Ratio of these two parameters (SINR) is calculated and is cross layered to the routing layer as a cross layer parameter. In routing layer, we maintain a signal to interference noise ratio threshold value (SINRT). SINR is compared with SINRT to select or discard the corresponding received message. If the received message has SINR which is less than the threshold value, such messages are discarded. If message has higher SINR when compared with SINRT, then it is selected for further processing. We implemented this by making changes to AODV protocol (CLAODV) using NS2 simulator. The resulting system makes links in CLAODV reliable and thereby increasing the throughput, packet delivery ratio and reducing the delay and overhead.
On Scatternet Formation in Bluetooth Networks using Fuzzy Logic

Bluetooth is a prominent technology to construct a wireless personal area network. Recently, many scatternet formation schemes have been proposed to connect piconets and form a scatternet. Past studies have pointed out that appropriate role selection can improve the network performance and reduce the communication delay. However, previous schemes rarely address the node mobility issue. In this paper, we propose a scatternet formation scheme based on fuzzy logic called FLSF with considerations on two properties of Bluetooth devices, the remaining power and the mobility, to build a scatternet with high stability. The simulation results show that the FLSF scatternet has a longer lifetime and is comparatively more stable. We believe that FLSF can be practiced in real Bluetooth applications to construct a large-scale WPAN.
A Combination of Fuzzy Theory and Genetic-Neural Network Algorithm

Nowadays, the BP network algorithm has achieved a great success and many nonlinear problems can be solved well. However, standard BP network algorithm has some Shortcomings. Such as local minimum, low convergence and oscillation effects etc. GA has a strong macro-search capability. It has some advantages. Such as simple and universal, robust, parallel computing features, so use it to complete the pre-search can overcome the shortcomings of BP. Fuzzy system is good at express people's experiential knowledge. It can deal with vague information. It can solve the intelligent questions better. Fuzzy clustering methods have been used widely in pattern recognition. Combine fuzzy systems with genetic-neural Network Algorithm not only make the algorithm more efficient, but also to address the intelligent questions better. It has become a hot research.
Disruption of Endocannabinoid Release and Striatal Long-Term Depression by Postsynaptic Blockade of Endocannabinoid Membrane Transport

Activation of the CB1 cannabinoid receptor inhibits neurotransmission at numerous synapses in the brain. Indeed, CB1 is essential for certain types of both short- and long-term synaptic depression. It was demonstrated recently that CB1 is critical for activity-dependent long-term depression (LTD) at glutamatergic corticostriatal synapses in acute brain slice preparations. Here, we show that CB1 activation is necessary, but not solely sufficient, for induction of LTD and that the requisite signaling by endocannabinoids (eCBs) occurs during a time window limited to the first few minutes after high-frequency stimulation delivery. In addition, we have applied intracellularly anandamide membrane transporter inhibitors to provide novel evidence that postsynaptic transport mechanisms are responsible for the release of eCBs from striatal medium spiny neurons. These findings shed new light on the mechanisms by which transient eCB formation participates in the induction of long-lasting changes in synaptic efficacy that could contribute to brain information storage.
Subspace selection in high-dimensional big data using genetic algorithm in apache spark

In high-dimensional space with large amounts of data, distances between data points tend to become relatively uniform. The notion of the nearest neighbours of a data point thus becomes meaningless, a phenomenon known as "curse of dimensionality. " Identifying outliers (data points with statistical characteristics significantly different than the majority of the data) in such a high-dimensional space can be a significant challenge. Mining for outliers in subspaces with relevant attributes is one of approaches for this problem, and identifying these attributes is the main objective of this work. In this paper, we scale a grid-based solution to search for subspaces that are candidates for outlier detection with regard to the subset of features in the subspace. We specify a population and a fitness function for a distributed genetic algorithm to heuristically search the subspaces within the high dimensional data, and find the subspace with maximal sparsity. We designed and implemented our proposed subspace selection algorithm in Apache Spark, a fast in-memory engine for large-scale data processing. The initial experimental results on a large dataset (77,000 records and 1,379 attributes) confirm that our proposed method can identify the most relevant subspaces for outlier detection.
Two classes of (r, t)-locally repairable codes

An (r, t)-locally repairable code satisfies a property that the value at each coordinate can be recovered from t disjoint repair sets each containing at most r other coordinates. This property is extremely useful in distributed storage systems for hot data. In this paper, we propose two constructions of (r, t)-LRCs. The first one is a cyclic code of which the parity check polynomial is closely related to the trace function over finite fields. This code can achieve high availability and large minimum distance. The second one is based on the inclusion matrix of linear subspaces in Fmq . For some specific parameters, we prove that its information rate is always higher than r/r+t which was conjectured to be near to the optimal rate for (r, t)-LRCs (A. Wang and Z. Zhang, ISIT 2015). By shortening this code in a specially designed way, we obtain (r, t)-LRCs with more desirable locality r at a slight expense of information rate.
Endothelial Cell and Platelet Bioenergetics: Effect of Glucose and Nutrient Composition

It has been suggested that cells that are independent of insulin for glucose uptake, when exposed to high glucose or other nutrient concentrations, manifest enhanced mitochondrial substrate oxidation with consequent enhanced potential and generation of reactive oxygen species (ROS); a paradigm that could predispose to vascular complications of diabetes. Here we exposed bovine aortic endothelial (BAE) cells and human platelets to variable glucose and fatty acid concentrations. We then examined oxygen consumption and acidification rates using recently available technology in the form of an extracellular oxygen and proton flux analyzer. Acute or overnight exposure of confluent BAE cells to glucose concentrations from 5.5 to 25 mM did not enhance or change the rate of oxygen consumption (OCR) under basal conditions, during ATP synthesis, or under uncoupled conditions. Glucose also did not alter OCR in sub-confluent cells, in cells exposed to low serum, or in cells treated with added pyruvate. Likewise, overnight exposure to fatty acids of varying saturation had no such effects. Overnight exposure of BAE cells to low glucose concentration decreased maximal uncoupled respiration, but not basal or ATP related oxygen consumption. Labeled glucose oxidation to CO2 increased, but only marginally after high glucose exposure while oleate oxidation to CO2 decreased. Overnight exposure to linolenic acid, but not oleic or linoleic acid increased extracellular acidification consistent with enhanced glycolytic metabolism. We were unable to detect an increase in production of reactive oxygen species (ROS) from BAE cells exposed to high medium glucose. Like BAE cells, exposure of human platelets to glucose did not increase oxygen consumption. As opposed to BAE cells, platelet mitochondria demonstrate less respiratory reserve capacity (beyond that needed for basal metabolism). Our data do not support the concept that exposure to high glucose or fatty acids accelerates mitochondrial oxidative metabolism in endothelial cells or platelets.
Normalization Issues in Granular Evidence-Based Adaptive Network

Granular evidence-based adaptive network ANGIE (adaptive network for granular information processing) is considered in this paper. The network described can be used in fuzzy-evidence-based decision support systems. The architecture and learning algorithm underlying ANGIE is presented. The proposed learning procedure helps in solving a task that can be related to sensitivity analysis in decision aid models. However, normalization procedure should be applied to network parameters to avoid inconsistency. This paper considers different approaches towards normalization. The effectiveness and accuracy of different approaches are compared.
Incidence of Acute Kidney Injury among Patients Treated with Piperacillin-Tazobactam or Meropenem in Combination with Vancomycin

ABSTRACT Acute kidney injury (AKI) increases during empirical antimicrobial therapy with the combination of piperacillin-tazobactam (TZP) and vancomycin (VAN) compared to the number of incidences with monotherapy or the combination of cefepime and VAN. Limited data regarding the impact of meropenem (MEM) combined with VAN exist. This study examined the AKI incidence among patients treated with MEM plus VAN (MEM+VAN) or TZP+VAN. Data were collected from the University of Kentucky Center for Clinical and Translational Science Enterprise Data Trust from September 2007 through October 2015. Adults without previous renal disease who received MEM+VAN or TZP+VAN for at least 2 days were included. AKI was assessed using risk, injury, failure, loss, and end-stage (RIFLE) criteria. Inverse probability of treatment weighting was utilized to control for differences between groups. In total, 10,236 patients met inclusion criteria, with 9,898 receiving TZP+VAN and 338 receiving MEM+VAN. AKI occurred in 15.4% of MEM+VAN patients and in 27.4% of TZP+VAN patients (P < 0.001). TZP+VAN was associated with increased AKI compared to the level with MEM+VAN (odds ratio [OR], 2.53; 95% confidence interval [CI], 1.82 to 3.52), after controlling for confounders. Use of MEM+VAN should be considered an appropriate alternative therapy to TZP+VAN if nephrotoxicity is a major concern. The results of this study demonstrate that judicial use of TZP+VAN for empirical coverage of infection is needed.