Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Internet Trading Based on Optimized Physics Models of Markets

We describe a real-time, internet-based S&P futures trading system, including a description of general aspects of internet-mediated interactions with electronic exchanges. Inner-shell stochastic nonlinear dynamic models are developed, and Canonical Momenta Indicators (CMI) are derived from a fitted Lagrangian used by outer-shell trading models dependent on these indicators. Recursive and adaptive optimization using Adaptive Simulated Annealing (ASA) is used for fitting parameters shared across these shells of dynamic and trading models.
A Radial Basis Function Neural Network with Adaptive Structure via Particle Swarm Optimization

Radial Basis Function neural network (RBFNN) is a combination of learning vector quantizer LVQ-I and gradient descent. RBFNN is first proposed by (Broomhead & Lowe, 1988), and their interpolation and generalization properties are thoroughly investigated in (Lowe, 1989), (Freeman & Saad, 1995). Since the mid-1980s, RBFNN has been used to apply on many applications, such as pattern classification, system identification, nonlinear function approximation, adaptive control, speech recognition, and time-series prediction, and so on. In contrast to the well-known Multilayer Perceptron (MLP) Networks, the RBF network utilizes a radial construction mechanism. MLP were trained by the error Back Propagation (BP) algorithm, since the RBFNN has a faster training procedure substantially and adopts typical two-stage training scheme, it can avoid solution to fall into local optima. A key point of RBFNN is to decide a proper number of hidden nodes. If the hidden node number of RBFNN is too small, the generated output vectors may be in low accuracy. On the contrary, it with too large number of hidden nodes may cause over-fitting for the input data, and influences global generalization performance. In conventional RBF training approach, the number of hidden node is usually decided according to the statistic properties of input data, then determine the centers and spread width for each hidden nodes by means of k-means clustering algorithm (Moddy & Darken, 1989). The drawback of this approach is that the network performance is depended on the pre-selected number of hidden nodes. If an unsuitable number is chosen, RBFNN may present a poor global generalization capability, as slow training speed, and requirement for large memory space. To solve this problem, the self-growing RBF techniques were proposed in (Karayiannis & Mi, 1997), (Zheng et al, 1999). However, the predefined parameters and local searching on solution space cause the inaccuracy of approximation from a sub-solution. Evolutionary computation is a globally optimization technique, where the aim is to improve the ability of individual to survive. Among that, Genetic Algorithm (GA) is a parallel searching technique that mimics natural genetics and the evolutionary process. In (Back et al, 1997), they employed GA to determine the RBFNN structure so the optimal number and distribution of RBF hidden nodes can be obtained automatically. A common approach is applied GA to search for the optimal network structure among several candidates
Investigation of Rotor Performance and Loads of a UH-60A Individual Blade Control System

Wind tunnel measurements of performance, loads, and vibration of a full-scale UH-60A Black Hawk main rotor with an individual blade control (IBC) system are compared with calculations obtained using the comprehensive helicopter analysis CAMRAD II and a coupled CAMRAD II/OVERFLOW 2 analysis. Measured data show a 5.1% rotor power reduction (8.6% rotor lift to effective-drag ratio increase) using 2/rev IBC actuation with 2.0 ◦ amplitude at μ = 0.4. At the optimum IBC phase for rotor performance, IBC actuator force (pitch link force) decreased, and neither flap nor chord bending moments changed significantly. CAMRAD II predicts the rotor power variations with the IBC phase reasonably well at μ = 0.35. However, the correlation degrades at μ = 0.4. Coupled CAMRAD II/OVERFLOW 2 shows excellent correlation with the measured rotor power variations with the IBC phase at both μ = 0.35 and μ = 0.4. Maximum reduction of IBC actuator force is better predicted with CAMRAD II, but general trends are better captured with the coupled analysis. The correlation of vibratory hub loads is generally poor by both methods, although the coupled analysis somewhat captures general trends.
Boron Nitride Nanomesh: A template for Nano-ice

Using variable temperature scanning tunneling microscopy and dI/dz barrier height spectroscopy, the structure of water on h-BN/Rh(111) nanomesh has been investigated. Below its desorption temperature, two distinct phases of water self-assemble within the 3.2 nm unit cell of the nanomesh. In the 2 nm holes, an ordered phase of nano-ice crystals with about 40 molecules is found. The ice crystals arrange in a bilayer honeycomb lattice, where the hydrogen atoms of the lower layer point to the substrate. The phase on the 1 nm wires, is a low density gas phase, which is characterized by contrast modulations and streaky noise in the STM images. Tunneling barrier measurements infer the proton positions in the nano-ice clusters. August 6, 2009 †Physik-Institut, Universität Zürich ‡Physikalisch-Chemisches Institut, Universität Zürich 1 ar X iv :0 90 8. 08 75 v1 [ co nd -m at . m tr lsc i] 6 A ug 2 00 9 Haifeng Ma et al. Boron Nitride Nanomesh: A template .
Flestolol: An Ultra‐short‐Acting Beta‐Adrenergic Blocking Agent

Flestolol (ACC‐9089) is a nonselective, competitive, ultra‐short‐acting beta‐adrenergic blocking agent, without any intrinsic sympathomimetic activity. Flestolol is metabolized by plasma esterases and has an elimination half‐life of approximately 6.5 minutes. This agent was well tolerated in healthy volunteers at doses up to 100 μg/kg/min. In long‐term infusion studies, flestolol was well tolerated at the effective beta‐blocking dose (5 μg/kg/min) for up to seven days. Flestolol blood concentrations increased linearly with increasing dose and good correlation exists between blood concentrations of flestolol and beta‐adrenergic blockade. Flestolol produced a dose‐dependent attenuation of isoproterenol‐induced tachycardia. Electrophysiologic and hemodynamic effects of flestolol are similar to those of other beta blockers. In contrast with other beta blockers, flestolol‐induced effects reverse rapidly (within 30 minutes) following discontinuation because of its short half‐life. Flestolol effectively reduced heart rate in patients with supraventricular tachyarrhythmia. In patients with unstable angina, flestolol infusion was found to be safe and effective in controlling chest pain. It is concluded that flestolol is a potent, well‐tolerated, ultra‐short‐acting beta‐adrenergic blocking agent. Use of flestolol in the critical care setting is currently undergoing investigation.
An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images

In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semiparametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov random field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semiparametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach.
Towards Better Understanding the Clothing Fashion Styles: A Multimodal Deep Learning Approach

In this paper, we aim to better understand the clothing fashion styles. There remain two challenges for us: 1) how to quantitatively describe the fashion styles of various clothing, 2) how to model the subtle relationship between visual features and fashion styles, especially considering the clothing collocations. Using the words that people usually use to describe clothing fashion styles on shopping websites, we build a Fashion Semantic Space (FSS) based on Kobayashi's aesthetics theory to describe clothing fashion styles quantitatively and universally. Then we propose a novel fashion-oriented multimodal deep learning based model, Bimodal Correlative Deep Autoencoder (BCDA), to capture the internal correlation in clothing collocations. Employing the benchmark dataset we build with 32133 full-body fashion show images, we use BCDA to map the visual features to the FSS. The experiment results indicate that our model outperforms (+13% in terms of MSE) several alternative baselines, confirming that our model can better understand the clothing fashion styles. To further demonstrate the advantages of our model, we conduct some interesting case studies, including fashion trends analyses of brands, clothing collocation recommendation, etc.
Morphological decomposition in second language derivational processing

In order to detect the morphological decomposition in L2 processing of polymorphemic words, the present study has used one behavioral task-the masked priming to monitor the online L2 derivational processing. Experiment results showed great priming effects when the derived words were presented before their stems (e.g., speaker-speak), indicating the morphological decomposition in L2 processing. It was also discovered that, the decomposition takes place for all the derived words, regardless of participants' familiarity with them. Based on these results, it was proposed that, the morphological decomposition is an automatic processing mechanism for polymorphemic words. As a universal mechanism, it can be used by both L1 and L2 speakers. Based on these findings, it is suggested that, the natural language processing and artificial language decoding research should stress the discovery and application of universal language patterns.
Executive Functioning and Positive Psychological Characteristics: A Replication and Extension

Positive psychological characteristics and executive function are correlated with gratitude, satisfaction with life, and forgiveness. The goal of this study was to replicate these findings while examining two additional constructs, namely, hope and optimism. 113 students (25 men, 88 women) between the ages of 17 to 24 years (M = 19.4, SD = 1.5) volunteered to participate. Positive correlations between executive function and gratitude were found but mixed results were obtained for forgiveness and satisfaction with life. Hope and optimism correlated positively with executive function and hierarchical multiple regression analysis indicated that they contributed significantly to the explanation of executive functioning. Further investigation into relationships between executive functioning, its neurobiological substrates, and positive psychological attributes is recommended.
CIQ, a positive allosteric modulator of GluN2C/D‐containing N‐methyl‐d‐aspartate receptors, rescues striatal synaptic plasticity deficit in a mouse model of Parkinson's disease

To investigate if CIQ, a positive allosteric modulator of N‐methyl‐d‐aspartate receptors (NMDARs) containing GluN2C/D subunits, rescues the loss of long‐term potentiation (LTP) and forelimb‐use asymmetry in a mouse model of Parkinson's disease (PD).
[ Proposing a low-density

T he objectives of this article are two-fold: First, to present the problem of joint source and channel (JSC) coding from a graphical model perspective and second, to propose a structure that uses a new graphical model for jointly encoding and decoding a redundant source. In the first part of the article, relevant contributions to JSC coding, ranging from the Slepian-Wolf problem to joint decoding of variable length codes with state-of-the-art source codes, are reviewed and summarized. In the second part, a double low-density parity-check (LDPC) code for JSC coding is proposed. The double LDPC code can be decoded as a single bipartite graph using standard belief propagation (BP) and its limiting performance is analyzed by using extrinsic information transfer (EXIT) chart approximations.
On the Power Laws of Language: Word Frequency Distributions

About eight decades ago, Zipf postulated that the word frequency distribution of languages is a power law, i.e., it is a straight line on a log-log plot. Over the years, this phenomenon has been documented and studied extensively. For many corpora, however, the empirical distribution barely resembles a power law: when plotted on a log-log scale, the distribution is concave and appears to be composed of two differently sloped straight lines joined by a smooth curve. A simple generative model is proposed to capture this phenomenon. The word frequency distributions produced by this model are shown to match the observations both analytically and empirically.
Quantifying the biological significance of gene ontology biological processes - implications for the analysis of systems-wide data

MOTIVATION Gene Ontology (GO), the de facto standard for representing protein functional aspects, is being used beyond the primary goal for which it is designed: protein functional annotation. It is increasingly used to evaluate large sets of relationships between proteins, e.g. protein-protein interactions or mRNA co-expression, under the assumption that related proteins tend to have the same or similar GO terms. Nevertheless, this assumption only holds for terms representing functional groups with biological significance ('classes'), and not for the ones representing human-imposed aggregations or conceptualizations lacking a biological rationale ('categories'). RESULTS Using a data-driven approach based on a set of high-quality functional associations, we quantify the functional coherence of GO biological process (GO:BP) terms as well as their explicit and implicit relationships, trying to distinguish classes and categories. We show that the quantification used is in agreement with the distinction one would intuitively make between these two concepts. As not all GO:BP terms and relationships are equally supported by current functional associations, any detailed validation of new experimental data using GO:BP, beyond whole-system statistics, should take such unbalance into account. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Partial-Volume Effect Correction Improves Quantitative Analysis of 18F-Florbetaben β-Amyloid PET Scans

Neocortical atrophy reduces PET signal intensity, potentially affecting the diagnostic efficacy of β-amyloid (Aβ) brain PET imaging. This study investigated whether partial-volume effect correction (PVEC), adjusting for this atrophy bias, improves the accuracy of 18F-florbetaben Aβ PET. Methods: We analyzed 18F-florbetaben PET and MRI data obtained from 3 cohorts. The first was 10 patients with probable Alzheimer disease (AD) and 10 age-matched healthy controls (HCs), the second was 31 subjects who underwent in vivo imaging and postmortem histopathology for Aβ plaques, and the third was 5 subjects who underwent PET and MRI at baseline and 1 y later. The imaging data were coregistered and segmented. PVEC was performed using the voxel-based modified Müller-Gärtner method (PVELab, SPM8). From the PET data, regional and composite SUV ratios (SUVRs) with and without PVEC were obtained. In the MRI data, mesial temporal lobe atrophy was determined by the Scheltens mesial temporal atrophy scale and gray matter volumes by voxel-based morphometry. Results: In cohort 1, PVEC increased the effect on AD-versus-HC discrimination from a Cohen d value of 1.68 to 2.0 for composite SUVRs and from 0.04 to 1.04 for mesial temporal cortex SUVRs. The PVEC-related increase in mesial temporal cortex SUVR correlated with the Scheltens score (r = 0.84, P < 0.001), and that of composite SUVR correlated with the composite gray matter volume (r = −0.75, P < 0.001). In cohort 2, PVEC increased the correlation coefficient between mesial temporal cortex SUVR and histopathology score for Aβ plaque load from 0.28 (P = 0.09) to 0.37 (P = 0.03). In cohort 3, PVEC did not affect the composite SUVR dynamics over time for the Aβ-negative subject. This finding was in contrast to the 4 Aβ-positive subjects, in 2 of whom PVEC changed the composite SUVR dynamics. Conclusion: The influence of PVEC on 18F-florbetaben PET data is associated with the degree of brain atrophy. Thus, PVEC increases the ability of 18F-florbetaben PET to discriminate between AD patients and HCs, to detect Aβ plaques in the atrophic mesial temporal cortex, and potentially to evaluate changes in brain Aβ load over time. As such, the use of PVEC should be considered for quantitative 18F-florbetaben PET scans, especially in assessing patients with brain atrophy.
Truncation of the C Terminus of the Rat Brain Na+-Ca2+ Exchanger RBE-1 (NCX1.4) Impairs Surface Expression of the Protein*

The C terminus of the rat brain Na+-Ca2+ exchanger (RBE-1; NCX1.4) (amino acids 875–903) is modeled to contain the last transmembrane α helix (amino acids 875–894) and an intracellular extramembraneous tail of 9 amino acids (895–903). Truncation of the last 9 C-terminal amino acids, Glu-895 to stop, did not significantly impair functional expression in HeLa or HEK 293 cells. Truncation, however, of 10 amino acids (Leu-894 to stop; mutant C10) reduced Na+gradient-dependent Ca2+ uptake to 35–39% relative to the wild type parent exchanger, and further truncation of 13 or more amino acids resulted in expression of trace amounts of transport activity. Western analysis indicated that Na+-Ca2+ exchanger protein was produced whether transfection was carried out with functional or non-functional mutants. Immunofluorescence studies of HEK 293 cells expressing N-Flag epitope-tagged wild type and mutant Na+-Ca2+exchangers revealed that transport activity in whole cells correlated with surface expression. All cells expressing the wild type exchanger or C9 exhibited surface expression of the protein. Only 39% of the cells expressing C10 exhibited surface expression, and none was detected in cells transfected with non-functional mutants C13 and C29. Since functional and non-functional mutants were glycosylated, the C terminus is not mandatory to translocation into the endoplasmic reticulum (ER). Endoglycosidase H digestion of [35S]methionine-labeled protein derived from wild type Na+-Ca2+ exchanger and from C10 indicated that resistance to the digestion was acquired after 1 and 5 h of chase, respectively. C29 did not acquire detectable resistance to endoglycosidase H digestion even after 10 h of chase. Taken together, these results suggest that the “cellular quality control machinery” can tolerate the structural change introduced by truncation of the C terminus up to Ser-893 albeit with reduced rate of ER→Golgi transfer and reduced surface expression of the truncated protein. Further truncation of C-terminal amino acids leads to retention of the truncated protein in the ER, no transfer to the Golgi, and no surface expression.
A review of speckle filtering in the context of estimation theory

Speckle filter performance depends strongly on the speckle and scene models used as the basis for filter development. These models implicitly incorporate certain assumptions about speckle, scene, and observed signals. In this study, the multiplicative and the product speckle models, which have been used for the development of most of the well-known filters, are analyzed, and their implicit assumptions with regard to the stationarity-nonstationarity nature of speckle are discussed. This leads to the definition of two categories of speckle filters: the stationary and the nonstationary multiplicative speckle model filters. The various approximate models used for the multiplicative speckle noise model are assessed as functions of speckle and scene characteristics to derive the requirements on scene signal variations for the validity of both the stationary and nonstationary multiplicative speckle models. Speckle filtering is then studied in the context of estimation theory, so as to develop a procedure for speckle filtering. It is shown that speckle filtering can be effective only in locally stationary scenes. Regions in which the signals are not stationary have to be filtered separately using a priori scene templates for the best matching of nonstationary scene features. The use of multiresolution techniques is crucial for accurate estimation of filter parameters. Under the guidance of the speckle filtering procedure, structural-multiresolution versions of the Lee (1980) and Frost et al. (1982) filters are developed for optimum application of these filters in the context of nonstationary scene signals.
Robust High Bandwidth Discrete-Time Predictive Current Control with Predictive Internal Model—A Unified Approach for Voltage-Source PWM Converters

This paper presents a robust high bandwidth discrete-time predictive current control scheme for voltage-source pulsewidth-modulated (VS-PWM) converters. First, to achieve high bandwidth current control characteristics, a digital predictive current controller with delay compensation is adopted. The compensation method utilizes a current observer with an adaptive internal model for system uncertainties. The predictive nature of both the current observer and the internal model forces the delays elements to be equivalently placed outside the closed loop system. Second, to ensure perfect tracking of the output current in the presence of uncertainties and providing means for attenuating low- and high- frequency system disturbances, the frequency modes of the disturbances to be eliminated should be included in the stable closed loop system. Toward this, an adaptive internal model for the estimated uncertainty dynamics is proposed. To cope with the high bandwidth property of the lump of uncertainties in VS-PWM converter applications, the disturbance slowly varying assumption is relaxed in the proposed controller. The relaxation is achieved by adopting a curbing sliding-mode-based feedback gain vector within the internal model observation system. Comparative evaluation tests were carried out on a grid-connected VS-PWM converter and a direct drive permanent magnet synchronous motor (DD-PMSM) drive system to demonstrate the validity and effectiveness of the proposed control scheme at different operating conditions.