Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal location of transmitters for micro-cellular radio communication system design

This paper is concerned with the mathematical modeling and analysis of a radio communication system design problem that seeks an optimal location of a single transmitter, or that of multiple transmitters, in order to serve a specified distribution of receivers. The problem is modeled by discretizing the radio coverage region into a grid of receiver locations and by specifying a function that estimates the path-loss or signal attenuation for each receiver location, given a particular location for a transmitter that communicates with it. The resulting model is a nonlinear programming problem having an implicitly defined objective function of minimizing a measure of weighted path-losses. Specializations of three nonlinear optimization algorithms, namely, the Hooke and Jeeves' method, the quasi-Newton, and conjugate gradient search procedures are investigated for solving this problem. The technique described here is intended to interact with various propagation prediction models and may be used in a CAD system for radio communication system design.
Investigation on the Worst Read Scenario of a ReRAM Crossbar Array

This paper disproves the worst read scenario of a ReRAM crossbar array. If the previously believed worst read scenario is not the worst one, the read margin evaluated based on the scenario can be incorrect. We explored for read scenario worse than the previously believed worst scenario by wisely sampling scenarios and iteratively searching for the worse one. In experiment, our algorithm successfully found the scenario worse than the previously believed one, disproving the previously believed worst read scenario. Our results show that the sensing window estimated by the incorrect previously believed worst scenario is 14 times as large as the estimation by the worst scenario found by our algorithm.
In vitro infection of primary and retrovirus-infected human leukocytes by human foamy virus

The infectivity of human foamy virus (HFV) was examined in primary and cultured human leukocytes. Cell-free infectious viral stocks of HFV were prepared from the human kidney cell line 293 transfected with an infectious molecular clone of HFV. HFV productively infects a variety of human myeloid and lymphoid cell lines. In addition, primary cell cultures enriched for human CD4+, monocytes and brain-derived microglial cells, were readily infected by HFV. Interestingly, while infected primary CD4+ lymphocytes and microglial cells showed marked cytopathology characteristic of foamy virus, HFV-infected monocyte-derived macrophages failed to show any cytopathology. In addition, marked cytotoxicity due to HFV infection was seen in both human T-cell leukemia virus type 1- and human immunodeficiency virus type 1-infected T-cell lines and in human immunodeficiency virus type 1-infected monocytoid cell lines. Thus, HFV infection produces differential cytopathology in a wide host range of primary human leukocytes and hematopoietic cell lines.
The 380 kb pCMU01 Plasmid Encodes Chloromethane Utilization Genes and Redundant Genes for Vitamin B12- and Tetrahydrofolate-Dependent Chloromethane Metabolism in Methylobacterium extorquens CM4: A Proteomic and Bioinformatics Study

Chloromethane (CH3Cl) is the most abundant volatile halocarbon in the atmosphere and contributes to the destruction of stratospheric ozone. The only known pathway for bacterial chloromethane utilization (cmu) was characterized in Methylobacterium extorquens CM4, a methylotrophic bacterium able to utilize compounds without carbon-carbon bonds such as methanol and chloromethane as the sole carbon source for growth. Previous work demonstrated that tetrahydrofolate and vitamin B12 are essential cofactors of cmuA- and cmuB-encoded methyltransferases of chloromethane dehalogenase, and that the pathway for chloromethane utilization is distinct from that for methanol. This work reports genomic and proteomic data demonstrating that cognate cmu genes are located on the 380 kb pCMU01 plasmid, which drives the previously defined pathway for tetrahydrofolate-mediated chloromethane dehalogenation. Comparison of complete genome sequences of strain CM4 and that of four other M. extorquens strains unable to grow with chloromethane showed that plasmid pCMU01 harbors unique genes without homologs in the compared genomes (bluB2, btuB, cobA, cbiD), as well as 13 duplicated genes with homologs of chromosome-borne genes involved in vitamin B12-associated biosynthesis and transport, or in tetrahydrofolate-dependent metabolism (folC2). In addition, the presence of both chromosomal and plasmid-borne genes for corrinoid salvaging pathways may ensure corrinoid coenzyme supply in challenging environments. Proteomes of M. extorquens CM4 grown with one-carbon substrates chloromethane and methanol were compared. Of the 49 proteins with differential abundance identified, only five (CmuA, CmuB, PurU, CobH2 and a PaaE-like uncharacterized putative oxidoreductase) are encoded by the pCMU01 plasmid. The mainly chromosome-encoded response to chloromethane involves gene clusters associated with oxidative stress, production of reducing equivalents (PntAA, Nuo complex), conversion of tetrahydrofolate-bound one-carbon units, and central metabolism. The mosaic organization of plasmid pCMU01 and the clustering of genes coding for dehalogenase enzymes and for biosynthesis of associated cofactors suggests a history of gene acquisition related to chloromethane utilization.
Identification of candidates for observation

Purpose of review The clinical value of active surveillance may still be limited due to acceptance and considerable misclassification rates, and inadequate follow-up criteria. This review focuses on the most recent developments in the use of active surveillance and patient-specific factors that may be used to identify patients suitable for this strategy. Recent findings The number of patients diagnosed with low-risk prostate cancer has risen. Active surveillance acceptance rates are increasing, but still limited and varying importantly (2–49%). Misclassification is inevitable in all currently used protocols, although most of these patients still have relatively favorable-risk prostate cancer. African-American race, obese, and older-aged patients show more unfavorable intermediate results in an active surveillance situation. These are unlikely to be explained by the small differences in preoperative characteristics only. Psychological profiling may also be added to the selection process. Most studies report intermediate endpoints only. Summary Patient-specific factors may be incorporated when identifying patients for active surveillance. This does not imply that active surveillance is not justified in specific groups, but may suggest the need for an intensified and personalized selection, instead of a one-size-fits-all approach.
Positive affect: phenotypic and etiologic associations with prosocial behaviors and internalizing problems in toddlers

Despite evidence for the associations of positive affect to prosocial behaviors and internalizing problems, relatively little is known about the underlying etiology. The sample comprised over 300 twin pairs at age 3. Positive affect, prosocial behaviors, and internalizing problems were assessed using the Toddler Behavior Assessment Questionnaire (Goldsmith, 1996), the Revised Rutter Parent Scale for Preschool Children (Hogg et al., 1997), and the Child Behavior Checklist for ages 1.5–5 (Achenbach, 1991), respectively. Positive affect correlated positively with prosocial behaviors, and negatively with internalizing problems. Prosocial behaviors were negatively associated with internalizing problems. The relations of positive affect to prosocial behaviors and internalizing problems were due to environmental effects (shared and non-shared). In contrast, the link between prosocial behaviors and internalizing problems was entirely explained by genetic effects. The current study has moved beyond prior emphasis on negative affect and elucidated the less understood etiology underlying the associations between positive affect, prosocial behaviors, and internalizing problems. This study could guide the development of programs for promoting prosocial behaviors and alleviating internalizing problems in children.
Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization

One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization. One of the most widely-used methods is the Kalman filter, which is both extremely simple and general. However, Kalman filters require a motion model and measurement model to be specified a priori, which burdens the modeler and simultaneously demands that we use explicit models that are often only crude approximations of reality. For example, in the pose-estimation tasks mentioned above, it is common to use motion models that assume constant velocity or constant acceleration, and we believe that these simplified representations are severely inhibitive. In this work, we propose to instead learn rich, dynamic representations of the motion and noise models. In particular, we propose learning these models from data using long shortterm memory, which allows representations that depend on all previous observations and all previous states. We evaluate our method using three of the most popular pose estimation tasks in computer vision, and in all cases we obtain state-of-the-art performance.
An Empirical Study of an Abstract Interpretation of Scheme Programs

Abstract Interpretation, a powerful and general framework for performing global program analysis, is being applied to problems whose difficulty far surpasses the traditional "bit-vector'''' dataflow problems for which many of the high-speed abstract interpretation algorithms worked so well. Our experience has been that current methods of large scale abstract interpretation are unacceptably expensive. We studied a typical large-scale abstract interpretation problem: computing the control flow of a higher order program. Researchers have proposed various solutions that are designed primarily to improve the accuracy of the analysis. The cost of the analyses, and its relationship to accuracy, is addressed only cursorily in the literature. Somewhat paradoxically, one can view these strategies as attempts to simultaneously improve the accuracy and reduce the cost. The less accurate strategies explore many spurious control paths because many flowgraph paths represent illegal execution paths. For example, the less accurate strategies violate the LIFO constraints on procedure call and return. More accurate analyses investigate fewer control paths, and therefore may be more efficient despite their increased overhead. We empirically studied this accuracy versus efficiency tradeoff. We implemented two fixpoint algorithms, and four semantics (baseline, baseline + stack reasoning, baseline + contour reasoning, baseline + stack reasoning + contour reasoning) for a total of eight control flow analyzers. Our benchmarks test various programming constructs in isolation --- hence, if a certain algorithm exhibits poor performance, the experiment also yields insight into what kind of program behavior results in that poor performance. The results suggest that strategies that increase accuracy in order to eliminate spurious paths often generate unacceptable overhead in the parts of the analysis that do not benefit from the increased accuracy. Furthermore, we found little evidence that the extra effort significantly improves the accuracy of the final result. This suggests that increasing the accuracy of the analysis globally is not a good idea, and that future research shouldEinvestigate adaptive algorithms that use different amounts of precision on different parts of the problem.
Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images

The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density. To ensure nonnegativity of the solution, a penalty function is used to convert the problem to one of unconstrained optimization. Preconditioners are used to enhance convergence rates. These methods generally achieve effective convergence in 15-25 iterations. Reconstructions are presented of an (18)FDG whole body scan from data collected using a Siemens/CTI ECAT931 whole body system. These results indicate significant improvements in emission image quality using the Bayesian approach, in comparison to filtered backprojection, particularly when reprojections of the MAP transmission image are used in place of the standard attenuation correction factors.
Particle flow for sequential Monte Carlo implementation of probability hypothesis density

Target tracking is a challenging task and generally no analytical solution is available, especially for the multi-target tracking systems. To address this problem, probability hypothesis density (PHD) filter is used by propagating the PHD instead of the full multi-target posterior. Recently, the particle flow filter based on the log homotopy provides a new way for state estimation. In this paper, we propose a novel sequential Monte Carlo (SMC) implementation for the PHD filter assisted by the particle flow (PF), which is called PF-SMC-PHD filter. Experimental results show that our proposed filter has higher accuracy than the SMC-PHD filter and is computationally cheaper than the Gaussian mixture PHD (GM-PHD) filter.
Young's Modulus and Loss Factor Estimation of Sandwich Beam with Three Optimization Methods by FEM

physical properties identification of sandwich beams by measured flexural resonance frequencies. The physical parameters are the Young‟s modulus and the loss factor. They are estimated for each one of materials that constitute the structure of the sandwich beam which is made with the association of Hot-rolled steel, Polyurethane Rigid Foam and High Impact Polystyrene. This kind of the sandwich beam are widely used for the assembly of household refrigerators and food freezers. The solutions are obtained with parametric optimization of physical parameters of the materials that forming the sandwich beam with three methods: Genetic Algorithms (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Furthermore, this work intend verify the quality of the solutions obtained with parametric optimization. The parameters are estimated using measured and numeric frequency response functions (FRFs). The mathematical model to verify numeric FRF is obtained using the Finite Element Method and the two-dimensional elasticity theory coupled to three optimization methods. The results of the optimizations show that it is possible to determine effectively the physical parameters of a sandwich beam with this methodology.
Submitted to the Annals of Statistics ESTIMATION OF A K − MONOTONE DENSITY , PART 4 : LIMIT DISTRIBUTION THEORY AND THE SPLINE CONNECTION By

We study the asymptotic behavior of the Maximum Likelihood and Least Squares estimators of a k-monotone density g0 at a fixed point x0 when k > 2. In Balabdaoui and Wellner (2004a), it was proved that both estimators exist and are splines of degree k− 1 with simple knots. These knots, which are also the jump points of the (k − 1)-st derivative of the estimators, cluster around a point x0 > 0 under the assumption that g0 has a continuous k-th derivative in a neighborhood of x0 and (−1)g 0 (x0) > 0. If τ− n and τ n are two successive knots, we prove that the random “gap” τ n − τ− n is Op “ n−1/(2k+1) ” for any k > 2 if a conjecture about the upper bound on the error in a particular Hermite interpolation via odd-degree splines holds. Based on the order of the gap, the asymptotic distribution of the Maximum Likelihood and Least Squares estimators can be established. We find that the j-th derivative of the estimators at x0 converges at the rate n−(k−j)/(2k+1) for j = 0, . , k−1. The limiting distribution depends on an almost surely uniquely defined stochastic process Hk that stays above (below) the k-fold integral of Brownian motion plus a deterministic drift, when k is even (odd). The family of processes Hk is studied separately in the companion manuscript Balabdaoui and Wellner (2004c). ∗Research supported in part by National Science Foundation grant DMS-0203320 †Research supported in part by National Science Foundation grant DMS-0203320, NIAID grant 2R01 AI291968-04, and an NWO Grant to the Vrije Universiteit, Amsterdam ‡Corresponding author AMS 2000 subject classifications: Primary 62G05, 60G99; secondary 60G15, 62E20
Haemolytic onset of Wilson disease in a patient with homozygous truncation of ATP7B at Arg1319

We describe a 19‐year‐old woman with haemolytic anaemia and thrombocytopenia as the initial manifestation of Wilson disease (WD). There are two reasons for reporting such an improbable case. First, it emphasizes the importance of recognizing atypical clinical presentations of potentially lethal recessive traits for which therapy is available. Second, it shows that, even in a monogenic disorder like WD, the phenotype cannot be extrapolated from the mutated genotype in a simple fashion; this patient had a relatively late‐onset form of WD despite homozygosity for a genetic lesion leading to an apparent complete loss of function of the WD copper transporter.
Application of Non-Linear and Wavelet Based Features for the Automated Identification of Epileptic EEG signals

Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.
Anti-CD 28 Monoclonal Antibody – Stimulated Cytokines Released from Blood Suppress CYP 1 A 2 , CYP 2 B 6 , and CYP 3 A 4 in Human Hepatocytes In Vitro

Like most infections and certain inflammatory diseases, some therapeutic proteins cause a cytokine-mediated suppression of hepatic drug-metabolizing enzymes, which may lead to pharmacokinetic interactions with small-molecule drugs. We propose a new in vitro method to evaluate the whole blood–mediated effects of therapeutic proteins on drug-metabolizing enzymes in human hepatocytes cocultured with Kupffer cells. The traditional method involves treating hepatocyte cocultures with the therapeutic protein, which detects hepatocyteand macrophage-mediated suppression of cytochrome P450 (P450). The new method involves treating whole human blood with a therapeutic protein to stimulate the release of cytokines from peripheral blood mononuclear cells (PBMCs), after which plasma is prepared and added to the hepatocyte coculture to evaluate P450 enzyme expression. In this study, human blood was treated for 24 hours at 37 C with bacterial lipopolysaccharide (LPS) or ANC28.1, an antibody against human T-cell receptor CD28. Cytokines were measured in plasma by sandwich immunoassay with electrochemiluminescense detection. Treatment of human hepatocyte cocultures with LPS or with plasma from LPS-treated blood markedly reduced the expression of CYP1A2, CYP2B6, and CYP3A4. However, treatment of hepatocyte cocultures with ANC28.1 did not suppress P450 expression, but treatment with plasma from ANC28.1-treated blood suppressed CYP1A2, CYP2B6, and CYP3A4 activity and mRNA levels. The results demonstrated that applying plasma from human blood treated with a therapeutic protein to hepatocytes cocultured with Kupffer cells is a suitable method to identify those therapeutic proteins that suppress P450 expression by an indirect mechanism—namely, the release of cytokines from PBMCs.