Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Detector Randomization in Cognitive Radio Systems in the Presence of Imperfect Sensing Decisions

In this study, optimal detector randomization is developed for secondary users in a cognitive radio system in the presence of imperfect spectrum sensing decisions. It is shown that the minimum average probability of error can be achieved by employing no more than four maximum a-posteriori probability (MAP) detectors at the secondary receiver. Optimal MAP detectors and generic expressions for their average probability of error are derived in the presence of possible sensing errors. Also, sufficient conditions are presented related to improvements due to optimal detector randomization.
MLi-2, a Potent, Selective, and Centrally Active Compound for Exploring the Therapeutic Potential and Safety of LRRK2 Kinase Inhibition

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the most common genetic cause of familial and sporadic Parkinson’s disease (PD). That the most prevalent mutation, G2019S, leads to increased kinase activity has led to a concerted effort to identify LRRK2 kinase inhibitors as a potential disease-modifying therapy for PD. An internal medicinal chemistry effort identified several potent and highly selective compounds with favorable drug-like properties. Here, we characterize the pharmacological properties of cis-2,6-dimethyl-4-(6-(5-(1-methylcyclopropoxy)-1H-indazol-3-yl)pyrimidin-4-yl)morpholine (MLi-2), a structurally novel, highly potent, and selective LRRK2 kinase inhibitor with central nervous system activity. MLi-2 exhibits exceptional potency in a purified LRRK2 kinase assay in vitro (IC50 = 0.76 nM), a cellular assay monitoring dephosphorylation of LRRK2 pSer935 LRRK2 (IC50 = 1.4 nM), and a radioligand competition binding assay (IC50 = 3.4 nM). MLi-2 has greater than 295-fold selectivity for over 300 kinases in addition to a diverse panel of receptors and ion channels. Acute oral and subchronic dosing in MLi-2 mice resulted in dose-dependent central and peripheral target inhibition over a 24-hour period as measured by dephosphorylation of pSer935 LRRK2. Treatment of MitoPark mice with MLi-2 was well tolerated over a 15-week period at brain and plasma exposures >100× the in vivo plasma IC50 for LRRK2 kinase inhibition as measured by pSer935 dephosphorylation. Morphologic changes in the lung, consistent with enlarged type II pneumocytes, were observed in MLi-2-treated MitoPark mice. These data demonstrate the suitability of MLi-2 as a compound to explore LRRK2 biology in cellular and animal models.
Using genetic algorithms to select architecture of a feedforward arti cial neural network

This paper proposes a model selection methodology for feedforward network models based on the genetic algorithms and makes a number of distinct but inter-related contributions to the model selection literature for the feedforward networks. First, we construct a genetic algorithm which can search for the global optimum of an arbitrary function as the output of a feedforward network model. Second, we allow the genetic algorithm to evolve the type of inputs, the number of hidden units and the connection structure between the inputs and the output layers. Third, we study how introduction of a local elitist procedure which we call the election operator a ects the algorithm’s performance. We conduct a Monte Carlo simulation to study the sensitiveness of the global approximation properties of the studied genetic algorithm. Finally, we apply the proposed methodology to the daily foreign exchange returns. c © 2001 Published by Elsevier Science B.V. All rights reserved.
Progressive defective recognition of familiar people

Abstract This paper describes a right-handed patient (Maria), with a mild cognitive deterioration, who demonstrated the neuropsychological deficits of progressive bi-temporal atrophy. Temporal atrophy was ascertained by repeated MRI scanning. The patient was first assessed 54 years from clinical onset. The first 2/½ years were hallmarked by a worsening impairment of recognizing and identifying familiar people, which at our last assessment (13 months after the first) were very severe. Extra-facial information provided via conversation, gestures or clothes failed to counter the recognition defect. When given the names of the unrecognized familiar people, she had no difficulty retrieving the relevant biographical information. By contrast, hearing the name did not appear to help her to retrieve the corresponding visuo-perceptual characteristics. Two years post-onset, a milder, multisensorial defect of object recognition began to develop. During the last 6 months of observation, increasing lexico-semantic and .
An improved method for speech/speaker recognition

A new improved method for speech/speaker recognition is presented in this paper using combination of discrete wavelet transform (DWT) and Relative Spectra Perceptual Linear Prediction (RASTA-PLP) for feature extraction. A graphical processing unit (GPU) is used for increasing the speed of computations of the neural network (NN) that acts as a classifier. Wavelet transform is applied to the speech signal. Then RASTA-PLP coefficients of the wavelet channels are calculated. Two new sets of features are generated by concatenating the two extracted features and taking the average of the concatenated vector. The obtained feature vector is then fed to NN for classification where parallel computation for NN is introduced using GPU. The objective of the proposed method is to enhance the performance by introducing more features from the signal and applying parallel computations technique leading to an improvement in both the recognition rate and the computational speed whether for a clean or a noisy speech signal for the two proposed methods in comparison to using DWT and Mel-Frequency Cepstral Coefficients (MFCCs).
On the Learnability of Rich Function Classes

The probably approximately correct (PAC) model of learning and its extension to real-valued function classes sets a rigorous framework based upon which the complexity of learning a target from a function class using a finite sample can be computed. There is one main restriction, however, that the function class have a finite VC-dimension or scale-sensitive pseudo-dimension. In this paper we present an extension of the PAC framework with which rich function classes with possibly infinite pseudo-dimension may be learned with a finite number of examples and a finite amount of partial information. As an example we consider learning a family of infinite dimensional Sobolev classes.
Untangling dopamine-adenosine receptor-receptor assembly in experimental parkinsonism in rats

Parkinson’s disease (PD) is a dopaminergic-related pathology in which functioning of the basal ganglia is altered. It has been postulated that a direct receptor-receptor interaction – i.e. of dopamine D2 receptor (D2R) with adenosine A2A receptor (A2AR) (forming D2R-A2AR oligomers) – finely regulates this brain area. Accordingly, elucidating whether the pathology prompts changes to these complexes could provide valuable information for the design of new PD therapies. Here, we first resolved a long-standing question concerning whether D2R-A2AR assembly occurs in native tissue: by means of different complementary experimental approaches (i.e. immunoelectron microscopy, proximity ligation assay and TR-FRET), we unambiguously identified native D2R-A2AR oligomers in rat striatum. Subsequently, we determined that, under pathological conditions (i.e. in a rat PD model), D2R-A2AR interaction was impaired. Collectively, these results provide definitive evidence for alteration of native D2R-A2AR oligomers in experimental parkinsonism, thus conferring the rationale for appropriate oligomer-based PD treatments.
Safety and Efficacy of Ultrasound-Enhanced Thrombolysis: A Comprehensive Review and Meta-Analysis of Randomized and Nonrandomized Studies

Background and Purpose— Ultrasound-enhanced thrombolysis is a promising new approach to facilitate reperfusion therapies for acute ischemic stroke. So far, 3 different ultrasound technologies were used to increase the thrombolytic activity of tissue plasminogen activator (tPA), including transcranial Doppler (TCD), transcranial color-coded duplex (TCCD), and low-frequency ultrasound. We performed a meta-analysis to evaluate the safety and efficacy of ultrasound-enhanced thrombolysis compared to the current standard of care (intravenous tPA). Subjects and Methods— Through Medline, Embase, and Cochrane database search, we identified and abstracted all studies of ultrasound-enhanced thrombolysis in acute cerebral ischemia. Principal investigators were contacted if data not available through peer-reviewed publication were needed. Symptomatic intracerebral hemorrhage (sICH) and recanalization rates were compared between tPA, tPA+TCD±microspheres (&mgr;S), tPA+TCCD±&mgr;S, and tPA+low-frequency ultrasound. Results— A total of 6 randomized (n=224) and 3 nonrandomized (n=192) studies were identified. The rates of symptomatic intracerebral hemorrhage in randomized studies were as follows: tPA+TCD, 3.8% (95% CI, 0%–11.2%); tPA+TCCD, 11.1% (95% CI, 0%–28.9%); tPA+low-frequency ultrasound, 35.7% (95% CI, 16.2%– 61.4%); and tPA alone, 2.9% (95% CI, 0%–8.4%). Complete recanalization rates were higher in patients receiving combination of TCD with tPA 37.2% (95% CI, 26.5%– 47.9%) compared with patients treated with tPA alone 17.2% (95% CI, 9.5%–24.9%). In 8 trials of high-frequency (TCD/TCCD) ultrasound-enhanced thrombolysis, tPA+TCD/TCCD±&mgr;S was associated with a higher likelihood of complete recanalization (pooled OR, 2.99; 95% CI, 1.70–5.25; P=0.0001) when compared to tPA alone. High-frequency ultrasound-enhanced thrombolysis was not associated with an increased risk of symptomatic intracerebral hemorrhage (pooled OR, 1.26; 95% CI, 0.44–3.60; P=0.67). Conclusions— The present safety and signal-of-efficacy data of high-frequency ultrasound-enhanced thrombolysis should be taken into account in the design of future randomized controlled trials.
Recent trends in classification of remote sensing data: active and semisupervised machine learning paradigms

This paper addresses the recent trends in machine learning methods for the automatic classification of remote sensing (RS) images. In particular, we focus on two new paradigms: semisupervised and active learning. These two paradigms allow one to address classification problems in the critical conditions where the available labeled training samples are limited. These operational conditions are very usual in RS problems, due to the high cost and time associated with the collection of labeled samples. Semisupervised and active learning techniques allow one to enrich the initial training set information and to improve classification accuracy by exploiting unlabeled samples or requiring additional labeling phases from the user, respectively. The two aforementioned strategies are theoretically and experimentally analyzed considering SVM-based techniques in order to highlight advantages and disadvantages of both strategies.
Characterizing the Network of Drugs and Their Affected Metabolic Subpathways

A fundamental issue in biology and medicine is illustration of the overall drug impact which is always the consequence of changes in local regions of metabolic pathways (subpathways). To gain insights into the global relationship between drugs and their affected metabolic subpathways, we constructed a drug–metabolic subpathway network (DRSN). This network included 3925 significant drug–metabolic subpathway associations representing drug dual effects. Through analyses based on network biology, we found that if drugs were linked to the same subpathways in the DRSN, they tended to share the same indications and side effects. Furthermore, if drugs shared more subpathways, they tended to share more side effects. We then calculated the association score by integrating drug-affected subpathways and disease-related subpathways to quantify the extent of the associations between each drug class and disease class. The results showed some close drug–disease associations such as sex hormone drugs and cancer suggesting drug dual effects. Surprisingly, most drugs displayed close associations with their side effects rather than their indications. To further investigate the mechanism of drug dual effects, we classified all the subpathways in the DRSN into therapeutic and non-therapeutic subpathways representing drug therapeutic effects and side effects. Compared to drug side effects, the therapeutic effects tended to work through tissue-specific genes and these genes tend to be expressed in the adrenal gland, liver and kidney; while drug side effects always occurred in the liver, bone marrow and trachea. Taken together, the DRSN could provide great insights into understanding the global relationship between drugs and metabolic subpathways.
Flow and neuronal density in tissue surrounding chronic infarction.

In 6 cats, cerebral infarction was produced by transorbital occlusion of the left middle cerebral artery (MCA). Five animals developed typical cortical infarcts. Eight weeks later, cerebral blood flow (CBF) was determined by 14C-iodoantipyrine autoradiography and the number of intact neurons was counted histologically. Two non-operated cats served as controls. Cortical blood flow in the infarcted hemisphere was reduced by 24.6-74.4% when compared to the flow in the contralateral cortex and in controls. Averaged white matter flow was decreased by 39.1%. Regional cortical flow was gradually reduced from parasagittal regions towards the infarct. In the surrounding of the infarct, cortical perfusion was decreased to 24.8 +/− 9.7 ml/100 g/min, i.e. 19.7% of contralateral flow. Although the infarcts were sharply demarcated macroscopically, the number of cortical neurons decreased gradually from the midline to the peri-infarct zone. A significant linear correlation was found between absolute CBF-values and the number of neurons in areas of the infarcted hemisphere. The homolateral gyrus lateralis had normal neuronal density but flow was reduced by 20%. These findings suggest that the blood flow reduction in tissue surrounding chronic infarcts is due to neuronal cell loss and to functional inactivation caused by damage of afferent fibers.
The 3 D Object Mediator : Handling 3 D Models on Internet

The 3D Object MEdiator (3DOME ) offers two services for handling 3D models: a modelshop and a renderfarm. These services can be consulted through the Internet. The modelshop meets the demands for brokerage of geometric descriptions of 3D models. People who create geometric models of objects can supply their models to the modelshop and 3DOME will offer them to potential customers. People who need models can browse through the model database and buy the models they need. The renderfarm, consisting of a large number of workstations and multi– processor computers, offers rendering services to generate animations and realistic images of large scenes. In general, these kinds of renderjobs cannot be done by a single workstation in an acceptable amount of time. 3DOME’s parallel rendering facilities may be very helpful for these jobs.
A Theoretic Model for Linear Geometric ICA

Geometric algorithms for linear ICA have recently received some attention due to their pictorial description and their relative ease of implementation. The geometric approach to ICA has been proposed first by Puntonet and Prieto in order to separate linear mixtures. We will reconsider geometric ICA in a solid theoretic framework showing that fixpoints of geometric ICA fulfill a so called geometric convergence condition, which the mixed images of the unit vectors satisfy, too. This leads to a conjecture claiming that in the supergaussian unimodal symmetric case there is only one stable fixpoint, thus demonstrating uniqueness of geometric ICA after convergence.
A Hierarchical Neighbor Finding Strategy for Sampling-BasedMotion Planning

Sampling-Based Motion Planning (SBMP) has been successful in planning the motion for a wide variety of robot types. An important primitive of these methods is selecting candidate neighbors and validating/invalidating the pathways between nodes of the map. These neighbors are commonly selected based on some distance metric (DM). An ideal distance metric for SBMP should identify configurations visible to each other, i.e., connectible by some local planner, such as straight-line interpolations. However, no perfect DM exists as different DMs capture different aspects of configurations. In this work, we introduce a Hierarchical Neighbor Finding Strategy that applies multiple DMs in series. There are two main benefits of this strategy. First, this strategy applies cheaper an d less accurate DMs to be used; then in succession, more expensive and accurate DMs can be applied on iteratively reduced sets of configurations, yielding high quality neighbor selection with reduced computation time. Additionally, this strategy enables the ability to use complementary DMs that handle different aspects of the geometry at once, which we show through the combination of a Knot Theory distance and a Scaled Euclidean disctance for highly articulated linkages. Our results show that this Hierarchical Neighbor Finding Strategy provides improved performance in terms of connectivity and efficiency for Probabilistic Roadmap Methods for DOF up to 1024. We also demonstrate how our method can provide improvements for roadmap-based protein folding applications.
The Dark Side of the Interconnection: Security and Privacy in the Web of Things

The Web of Things (WoT) promises to dramatically boost the potentiality of interconnecting smart and physical devices over the Internet as it not only enhances ergonomics and productivity of the Internet of Things (IoT), but it also introduces new capabilities for device interoperation and data aggregation and analysis. These advances pose the challenge of preserving data security and privacy (S&P), as well as the reliability of the overall infrastructure. Deploying existing S&P solutions and technologies in the WoT is not straightforward because of its potential vastness, its intrinsic inhomogeneity and the wide variety of involved entities and interests. In such scenario, every choice comes from a non-trivial trade-off among different aspects including security, availability and legal issues. In this paper, we investigate the nature of this trade-off, pointing out the different kinds of S&P issues and surveying some of the available solutions. In addition, we discuss the major issues raised while securing an existing WoT infrastructure.
Theoretical Perspectives on Work and the Employment Relationship

Developing a strong theoretical base for research and practice in industrial relations and human resource management has, to date, remained a largely unfulfilled challenge. This pioneering volume helps close the theory gap by presenting contributions from fifteen leading scholars that develop and extend theoretical perspectives on work and the employment relationship. Subject areas covered include theories of employment relations systems, varieties of capitalism, the labor process, new institutional economics, individual work motivation, strategic human resource management, a theory of transaction costs and employment contracts, efficiency versus equity, and comparative industrial relations.
Local Atomic Strain in ZnSe1 xTex from High Real Space Resolution Neutron Pair Distribution Function Measurements

High real-space resolution atomic pair distribution functions (PDF's) have been obtained from ZnSe{sub 1-x}Te{sub x} using neutron powder diffraction. Distinct Zn-Se and Zn-Te nearest-neighbor (nn) bonds, differing in length by {Delta}r=0.14 Aa, are resolved in the measured PDF's, allowing the evolution with composition of the individual bond lengths to be studied. The local bond lengths change much more slowly with doping than the average bond length obtained crystallographically. The nn bond-length distributions are constant with doping, but higher-neighbor pair distributions broaden significantly, indicating that most of the strain from the alloying is accommodated by bond-bending forces in the alloy. PDF's of alloys across the whole doping range are well fit using a model based on the Kirkwood potential. The resulting PDF's give excellent agreement with the measured PDF's over the entire alloy range with no adjustable parameters.
CEREBRAL METABOLISM IN HYPOXIA

The experiments to be reported were undertaken in order to study the effects of varying degrees of hypoxia on the metabolism of the brain. Since formation of lactic acid and decomposition of phosphocreatine are known to occur in cerebral tissue under conditions of oxygen lack,1it seemed pertinent to find the critical oxygen levels at which these changes begin. Gas mixtures low in oxygen were administered to dogs for periods of fifteen to sixty minutes, and chemical studies of parts of the cerebral cortex were made by the technic of freezing the brain in situ with liquid air prior to removal of specimens for analysis. The freezing suddenly stops all metabolic processes in the tissue and prevents the occurrence of postmortem changes. The resultant chemical pattern gives an indication of the metabolic conditions within the tissue at the time of freezing. The cerebral arteriovenous differences in oxygen and carbon dioxide
Gender, normality and the post‐surgical body

Abstract Body image and social identity are affected by the sudden and radical changes that occur with surgery for cancer. People who had major surgery from cancer and other serious medical conditions, resulting in profound physical changes to the body, provided personal accounts of their adjustment after surgery. I am concerned in this paper specifically with notions of ‘coping’, normality and relativity that individuals employ to adjust to embodied changes and to recast their experiences in a positive light. Both men and women speak of ‘coping’ to manage their initial responses of loss and grief, then seek to reinstate normalcy—the patterns of living and social relationships that were characteristic before surgery. Normalisation, in turn, helps them to ‘cope’ in the longer term. The meanings of coping and normalcy, however, are engendered, and gender specific scripts of the normal body, masculinity and femininity shape men's and women's adjustment post‐surgery rather differently.
Key to Network Controllability

Liu et al recently proposed a minimum number of driver nodes needed to obtain full structural controllability over a directed network. Driver nodes are unmatched nodes, from which there are directed paths to all matched nodes. Their most important assertion is that a system's controllability is to a great extent encoded by the underlying network's degree distribution, $P(k_{in}, k_{out})$. Is the controllability of a network decided almost completely by the immediate neighbourhood of a node, while, even slightly distant nodes play no role at all? Motivated by the above question, in this communication, we argue that an effective understanding of controllability in directed networks can be reached using distance based measures of closeness centrality and betweenness centrality and may not require the knowledge of local connectivity measures like in-degree and out-degree.