Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective Detection in an Iterative Soft-Interference Cancellation Receiver

A simple approach to achieve a computationally efficient iterative-soft-interference-cancellation receiver is proposed. The receiver consists of two major stages, a soft-input soft-output (SISO) multiuser detector and a bank of single-user SISO decoders. Similar to turbo decoding, the soft output of each stage is used as a priori information in the next stage. However, decisions are made based on the reliability of the extrinsic information before they are being fed back to the detector. From the decision, only unreliable information are re-detected in the next iteration step to reduce complexity. The conventional iterative-soft-interference-cancellation receiver is briefly outlined, and then the reduced computation model is proposed. The simulation result shows that it significantly reduces the complexity, while achieving a comparable bit-error-rate (BER) performance
Distance control between multiple drones for stable communication

Unmanned Aerial Vehicles (UAVs) are flying objects which can be controlled either manually or by autonomous control software. We have designed and implemented a system called SQCUAIR for controlling the suitable wireless communication distance between two UAVs and evaluated the basic performance using AR. Drones. SQCUAIR is assumed to be utilized when we control the distance between two UAVs. This paper describes the design and implementation of distance control algorithms between two AR. Drones and shows results of experiments.
Payoff Information and Self-Confirming Equilibrium

In a self-confirming equilibrium, each player correctly forecasts the actions that opponents will take along the equilibrium path, but may be mistaken about the way that opponents would respond to deviations. This models a steady state of a learning process in which players observe actions played by their opponents, rather than a complete specification of their strategies. Consequently, players need not receive evidence that their forecasts of off-path play are incorrect. In practice, players understand that opponents are rational and have some information about their opponents payoffs. This paper develops a refinement of self-confirming equilibrium that incorporates the effects of such information. We show that this concept is robust. We also discuss its relationship to other concepts. In particular, we show that it is closely connected to assuming almost common certainty of payoffs in an epistemic model with independent beliefs.
Chemical Composition and Biological Activity of the Essential Oil and Solvent Extracts of

Forty five components were identified in the oil, among them, germacrene D (18.1±0.1%), bicyclogermacrene (1.4±0.0%), and spathunelnol (2.7±0.0%) were the major sesquiterpenoids, n-tetradecanol (4.9±0.0%) was the most abundant fatty alcohol and diisobuthyl phthalate (43.9±0.2%) was the most abundant constituent. The extracts of aerial parts of Sc. nodosa were prepared using different solvents; dichloromethane (DCM), methanol (MeOH) and MeOH: H2O (80:20). Antibacterial activity of the oil and each solvent extract was tested against three Gram-positive and three Gram-negative bacteria by nutrient broth micro dilution method. Among the tested microorganisms, Staphylococcus epidermidis (IC50 1.25-5mg/ml), Bacillus subtilis (IC50 1.25mg/ml) and Escherichia coli (IC50 1.25mg/ml) were the most susceptible to the applied oils and the extracts. The antioxidant potential and total phenol contents of the extracts were assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay and FolinCiocalteu reagent, respectively. Methanol and 80% methanol extracts of the plant showed relatively weak DPPH radical scavenging activity and low amounts of total phenol contents.
Transcriptional response of dermal fibroblasts in direct current electric fields

During the course of normal wound healing, fibroblasts at the wound edge are exposed to electric fields (EFs) ranging from 40 to 200 mV/mm. Various forms of EFs influence fibroblast migration, proliferation, and protein synthesis. Thus, EFs may contribute to fibroblast activation during wound repair. To elucidate the role of EFs during the normal progression of healing, this study compares gene expression in normal adult dermal fibroblasts exposed to a 100 mV/mm EF for 1 h to non‐stimulated controls. Significantly increased expression of 162 transcripts and decreased expression of 302 transcripts was detected using microarrays, with 126 transcripts above the level of 1.4‐fold increases or decreases compared to the controls. Above the level of twofold, only 11 genes were significantly increased or decreased compared to controls. Many of these significantly regulated genes are associated with wound repair through the processes of matrix production, cellular signaling, and growth. Activity within specific cellular signaling pathways is noted, including TGF‐β, G‐proteins, and inhibition of apoptosis. In addition, RT‐PCR analysis of the expression of KLF6, FN1, RGS2, and JMJD1C over continued stimulation and at different field strengths suggests that there are specific windows of field characteristics for maximum induction of these genes. EFs thus appear to have an important role in controlling fibroblast activity in the process of wound healing. Bioelectromagnetics 29:394–405, 2008. © 2008 Wiley‐Liss, Inc.
Throughput Analysis for Downlink Resource Reusing D2D Communications in Cellular Networks

In this paper, the performance of device-to-device (D2D) communications in cellular networks is studied when the downlink communication resources are reused. To guarantee the successful coexistence of multiple D2D pairs reusing the identical radio resource, the Power Emission Density (PED)-based interference modeling method is adopted to explore proper network settings. With the constraint of the Signal-to-Interference Ratio (SIR) requirements for both the macro-cell and D2D communications, an exclusive region-based analytical model is proposed to obtain the guard distances from a D2D user to the transmitting base station, to the receiving cellular user, and to other communicating D2D pairs, respectively. With these guard distances, the bounds of the throughput improvement provided by D2D communications are then derived for different cases. Finally, extensive simulations are conducted to verify our analytical results. The new results obtained in this work can provide useful guidelines for the deployment of future cellular networks with underlaying D2D communications.
Predicting MicroRNA-Disease Associations Using Network Topological Similarity Based on DeepWalk

Recently, increasing experimental studies have shown that microRNAs (miRNAs) involved in multiple physiological processes are connected with several complex human diseases. Identifying human disease-related miRNAs will be useful in uncovering novel prognostic markers for cancer. Currently, several computational approaches have been developed for miRNA-disease association prediction based on the integration of additional biological information of diseases and miRNAs, such as disease semantic similarity and miRNA functional similarity. However, these methods do not work well when this information is unavailable. In this paper, we present a similarity-based miRNA-disease prediction method that enhances the existing association discovery methods through a topology-based similarity measure. DeepWalk, a deep learning method, is utilized in this paper to calculate similarities within a miRNA-disease association network. It shows superior predictive performance for 22 complex diseases, with area under the ROC curve scores ranging from 0.805 to 0.937 by using five-fold cross-validation. In addition, case studies on breast cancer, lung cancer, and prostatic cancer further justify the use of our method to discover latent miRNA-disease pairs.
Equalization for OFDMA Systems Exploiting Unused Subchannels

In orthogonal frequency division multiple access (OFDMA) systems, the cyclic prefix (CP) length needs to be no less than the longest channel delay spread of many users, reducing bandwidth efficiency more significantly than in single user OFDM systems. We propose a novel receiver for OFDMA systems with an insufficient CP, exploiting the unused subchannels for a user. With the aid of computer simulations, we demonstrate that the proposed equalization method is superior to the conventional equalization method using a time domain channel shortening filter in terms of the achievable SNR and the error performance
In Situ Spectroscopy of Calcium Fluoride Anchored Metal–Organic Framework Thin Films during Gas Sorption

Abstract Surface‐mounted metal–organic frameworks (SURMOFs) show promising behavior for a manifold of applications. As MOF thin films are often unsuitable for conventional characterization techniques, understanding their advantageous properties over their bulk counterparts presents a great analytical challenge. In this work, we demonstrate that MOFs can be grown on calcium fluoride (CaF2) windows after proper functionalization. As CaF2 is optically (in the IR and UV/Vis range of the spectrum) transparent, this makes it possible to study SURMOFs using conventional spectroscopic tools typically used during catalysis or gas sorption. Hence, we have measured HKUST‐1 during the adsorption of CO and NO. We show that no copper oxide impurities are observed and also confirm that SURMOFs grown by a layer‐by‐layer (LbL) approach possess Cu+ species in paddlewheel confirmation, but 1.9 times less than in bulk HKUST‐1. The developed methodology paves the way for studying the interaction of any adsorbed gases with thin films, not limited to MOFs, low temperatures, or these specific probe molecules, pushing the boundaries of our current understanding of functional porous materials.
Robust gait recognition based on partitioning and canonical correlation analysis

Gait recognition would be greatly affected by some covariate factors including clothing type and carrying objects. Finding an approach robust to these covariate factors is the most challenging problem. In this paper, we propose a method based on canonical correlation analysis (CCA) to model the correlation between gait sequences under two different walking conditions. Correlation strength is used in KNN classifier as similarity measure. GEIs are partitioned into several parts and vast majority voting is employed among these parts to reduce the effect of the covariate factors. Experiment results show that our proposed method outperforms other classical methods over all views.
Ventilatory response to nitrogen multiple‐breath washout in infants

Nitrogen multiple‐breath washout (N2MBW) using 100% oxygen (O2) has regained interest to assess efficiency of tracer gas clearance in, for example, children with Cystic Fibrosis (CF). However, the influence of hyperoxia on the infants' respiratory control is unclear. We assessed safety and impact on breathing pattern from hyperoxia, and if exposure to 40% O2 first induces tolerance to subsequent 100% O2 for N2MBW.
Cleaning Relations Using Knowledge Bases

We study the data cleaning problem of detecting and repairing wrong relational data, as well as marking correct data, using well curated knowledge bases (KBs). We propose detective rules (DRs), a new type of data cleaning rules that can make actionable decisions on relational data, by building connections between a relation and a KB. The main invention is that, a DR simultaneously models two opposite semantics of a relation using types and relationships in a KB: the positive semantics that explains how attribute values are linked to each other in correct tuples, and the negative semantics that indicates how wrong attribute values are connected to other correct attribute values within the same tuples. Naturally, a DR can mark correct values in a tuple if it matches the positive semantics. Meanwhile, a DR can detect/repair an error if it matches the negative semantics. We study fundamental problems associated with DRs, e.g., rule generation and rule consistency. We present efficient algorithms to apply DRs to clean a relation, based on rule order selection and inverted indexes. Extensive experiments, using both real-world and synthetic datasets, verify the effectiveness and efficiency of applying DRs in practice.
Entropies as measures of software information

This paper investigates the use of entropies as measures of software information content. Several entropies, including the well-known Shannon entropy, are characterized by their mathematical properties. Based on these characterizations, the entropies, which are suitable for measuring software systems, are rigorously chosen. By treating a software system as an information source, the function calls in procedural systems or method invocations in object-oriented systems resemble the emission of symbols from an information source. Thus, the probabilities, required for computing the entropies, are obtained using an empirical distribution of function calls or method invocations. Application of the suggested measures on procedural and object-oriented programs is further explained using two small examples. Because a. rigorous definition of information measures does not guarantee their usefulness in practice, an evaluation case study is performed In particular, the aim of this study is to practically evaluate the intuitiveness and scalability of the measures on a real software system totaling about 460000 lines of code. In addition to being intuitive and meaningful, the case study results highlight differences between the information measures. Thus, the family of measures presented can satisfy different measurement requirements.
American Heart Association Scientific Sessions

The Annual Scientific Sessions of the American Heart Association is the leading scientific conference in the cardiovascular field, both for basic and clinical research in cardiology and related disclipines. This report covers the outcome of major clinical trials that were presented in the ‘late-breaking’ clinical trial sessions. The Valsartan in Acute Myocardial Infarction Trial (VALIANT) investigated the angiotensin receptor blocker valsartan, the angiotensin-converting enzyme inhibitor captopril, and their combination in 14,703 survivors of an acute myocardial infarction with a reduced left ventricular ejection fraction on clinical outcome. The study demonstrated that valsartan and captopril where equally effective, whereas the combination was associated with an increased risk of side effects without further benefit. VALIANT is a landmark trial because it was the first large study that compared the combination of an angiotensin receptor blocker with an angiotensin-converting enzyme inhibitor in the setting of acute myocardial infarction. Other trials that will be summarised in this report are the Na+/H+ Exchange Inhibition to Prevent Coronary Events in Acute Cardiac Conditions Trial (EXPEDITION; cariporide in coronary artery bypass graft surgery), the Reversal of Atherosclerosis with Aggressive Lipid Lowering Trial (REVERSAL; atorvastatin and pravastatin for atherosclerosis reversal), the Stroke Prevention Using an Oral Thrombin Inhibitor in Atrial Fibrillation V (SPORTIF V; ximelagatran and warfarin for stroke prevention in atrial fibrillation), the Prophylactic Amiodarone for the Prevention of Arrhythmias that Begin Early After Revascularisation (PAPABEAR; amiodarone for the prevention of postoperative atrial fibrillation), the Acute and Chronic Therapeutic Impact of a Vasopressin 2 Antagonist in Congestive Heart Failure (ACTIV in CHF; vasopressin 2 antagonist tolvaptan in congestive heart failure) and Prevention of Renal and Vascular Endstage Disease Intervention Trial (PREVEND IT; fosinopril and pravastatin in microalbuminuric subjects without hypertension or hypercholesterolemia).
A knowledge-aided GMTI detection architecture [radar signal processing]

Space-time adaptive processing (STAP) plays an important role in ground moving target indication (GMTI). Heterogeneous clutter environments prevent STAP from achieving its theoretical performance bounds. The incorporation of a priori knowledge into the signal processing architecture holds the potential to greatly enhance detection performance by mitigating heterogeneous clutter effects. In this paper we propose one possible knowledge-aided STAP approach comprised of the following elements: a knowledge-aided prediction/estimation filter, a discrete matched filter, and a partially adaptive STAP applied to the clutter residual, assisted by knowledge-aided training. We focus our discussion on justifying the aforementioned elements and independently characterizing their performance potential. Using both measured and simulated data, we find the potential for substantial performance improvement.
Parameter estimation for equivalent electrical model of lithium-ion cell

This paper presents an estimation procedure of equivalent electric circuit parameters of a lithium-ion cell using data from a pulse charge/discharge test. An extraction process of the model parameters is based on a transient voltage response of relaxation process following a current pulse. The entire procedure is straightforward and there is no restriction in a number of parallel RC-branches in the electrical model. Convergence problem of the parameter estimation process, existing in classic solutions, is solved by application of a particle swarm optimization method (PSO). The entire parameter identification procedure is performed in automated way in order to reduce time required to obtain a sufficient set of parameter values for different states of charge. The model is verified based on experimental data.
Statistics Notes: Detecting skewness from summary information

Many statistical methods of analysis assume that the data have a normal distribution. When the data do not, they can often be changed to make them more normal. However, readers of published papers may wish to be certain that the authors have conducted a proper analysis. One can clearly see whether the distributional assumption is met when data are presented in the form of a histogram or scatter diagram. However, when only summary statistics are presented, the task becomes far more difficult. An idea of the distribution can be gleaned if the summary statistics include the range of the data. For example, a range from 7 to 41 around a mean of 15 suggests that the data are positively skewed. Belief in that assumption may be unreliable because the range is based upon the two most extreme, and atypical, values. Similar asymmetry affecting the lower and upper quartiles would better indicate a skewed distribution. it is suggested that for measurements which must be positive, if the mean is smaller than twice the standard deviation, the data are likely to be skewed. A second indicator of skewness can be used when there are data for several groups of individuals. Deviations from the normal distribution and a relation between the standard deviation and mean across groups often go together. A standard deviation which increases as the mean increases is a strong indication of positively skewed data, and specifically that a log transformation may be needed.
Atomic force microscopy study of DNA deposited on poly l‐ornithine‐coated mica

Analyses of individual biomolecules, like DNA, or DNA–protein complexes, via atomic force microscopy, require ‘gentle’ methods to immobilize DNA on surfaces, which allow the ensemble of molecules to adopt conformations dictated primarily by their physical characteristics, and which possibly permit the use of a wide selection of buffers. We show that poly‐l‐ornithine‐coated mica is a good substrate for fast, reliable deposition of DNA for wet or dry imaging. The surface firmly secures DNA, which retains the B‐form helical rise (0.34 nm bp−1). The conformations of DNA that result are reminiscent of three‐dimensional random coils projected on to a plane. The contrast is good, especially in solution, and buffers with physiological concentrations of salt with or without divalent cations may be used. This is important for comparison of scanning probe microscopy results with those obtained by different techniques.
Quantitative Exfoliative Cytology of Normal and Abnormal Oral Mucosal Squames: Preliminary Communication

Quantitative techniques have been applied to the exfoliative cytology of normal and abnormal human oral mucosal squames. This study is ongoing but early results indicate that a normal baseline for clinically normal oral squames, related to age and site, may be difficult to achieve. However, results obtained for the pathological smears suggest that these techniques may improve the diagnostic sensitivity of cytology in the management of oral cancer.
Fiber Optic SPR-Based Uric Acid Biosensor Using Uricase Entrapped Polyacrylamide Gel

In this letter, we report a fiber optic biosensor based on surface plasmon resonance (SPR) for the detection of uric acid in aqueous samples. The sensing probe is prepared by successive coatings of silver and silicon layers over a small unclad length of the plastic clad silica optical fiber in the middle. Thereafter, the probe is dipped in a polyacrylamide gel containing enzyme uricase to immobilize it using a gel entrapment method. The sensor is characterized using a wavelength interrogation method. The SPR spectra corresponding to different concentrations of uric acid in the range of 0-0.9 mM are recorded. A red shift in the resonance wavelength is observed with an increase in the concentration of uric acid around the probe. In addition, the sensitivity of the proposed biosensor is studied in relation to parameters, such as concentration and pH of the uric acid solution. Finally, the sensor is highly selective and possesses better limit of detection and limit of quantification.
Snow Removal and Ambient Air Temperature Effects on Forest Soil Temperatures in Northern Vermont

We measured deciduous forest soil temperatures under control (unmanipulated) and snow-free (where snow is manually removed) conditions for four winters (at three soil depths) to determine effects of a snow cover reduction such as may occur as a result of climate change on Vermont forest soils. The four winters we studied were characterized as: 'cold and snowy', 'warm with low snow', 'cold with low snow', and 'cool with low snow'. Snow-free soils were colder than controls at 5- and 15-cm depth for all years, and at all depths in the two cold winters. Soil thermal variability generally decreased with both increased snow cover and soil depth. The effect of snow cover on soil freeze-thaw events was highly dependent on both the depth of snow and the soil temperature. Snow kept the soil warm and reduced soil temperature variability, but often this caused soil to remain near 0°C, resulting in more freeze-thaw events under snow at one or more soil depths. During the 'cold snowy' winter, soils under snow had daily averages consistently >0°C, whereas snow-free soil temperatures commonly dropped below -3°C. During the 'warm' year, temperatures of soil under snow were often lower than those of snow-free soils. The warmer winter resulted in less snow cover to insulate soil from freezing in the biologically active top 30 cm. The possible consequences of increased soil freezing include more root mortality and nutrient loss, which would potentially alter ecosystem dynamics, decrease productivity of some tree species, and increase sugar maple (Acer saccharum Marshall) mortality in northern hardwood forests.