Miracle-dz/UniFAR-FTUs huggingface dataset:
Amphetamine sensitisation and memory in healthy human volunteers: A functional magnetic resonance imaging study

Amphetamine sensitisation (AS) is an established animal model of the hypersensitivity to psychostimulants seen in patients with schizophrenia. AS also models the dysregulation of mesolimbic dopamine signalling which has been implicated in the development of psychotic symptoms. Recent data suggest that the enhanced excitability of mesolimbic dopamine neurons in AS is driven by a hyperactivity of hippocampal (subiculum) neurons, consistent with a strong association between hippocampal dysfunction and schizophrenia. While AS can be modelled in human volunteers, its functional consequences on dopaminoceptive brain regions (i.e. striatum and hippocampus) remains unclear. Here we describe the effects of a sensitising dosage pattern of dextroamphetamine on the neural correlates of motor sequence learning in healthy volunteers, within a randomised, double-blind, parallel-groups design. Behaviourally, sensitisation was characterised by enhanced subjective responses to amphetamine but did not change performance (i.e. learning rate) during an explicit sequence learning task. In contrast, functional magnetic resonance imaging (fMRI) measurements showed that repeated intermittent amphetamine exposure was associated with increased blood-oxygen-level dependent (BOLD) signal within the medial temporal lobe (MTL) (subiculum/entorhinal cortex) and midbrain, in the vicinity of the substantia nigra/ventral tegmental area (SN/VTA) during sequence encoding. Importantly, MTL hyperactivity correlated with the sensitisation of amphetamine-induced attentiveness. The MTL-midbrain hyperactivity reported here mirrors observations in sensitised rodents and is consistent with contemporary models of schizophrenia and behavioural sensitisation. These findings of meso-hippocampal hyperactivity during AS thus link pathophysiological concepts of dopamine dysregulation to cognitive models of psychosis.
A geometric model for 3-D confocal image analysis

The authors use partial-differential-equation-based filtering as a preprocessing and post processing strategy for computer-aided cytology. They wish to accurately extract and classify the shapes of nuclei from confocal microscopy images, which is a prerequisite to an accurate quantitative intranuclear (genotypic and phenotypic) and internuclear (tissue structure) analysis of tissue and cultured specimens. First, the authors study the use of a geometry-driven edge-preserving image smoothing mechanism before nuclear segmentation. They show how this filter outperforms other widely-used filters in that it provides higher edge fidelity. Then they apply the same filter with a different initial condition, to smooth nuclear surfaces and obtain sub-pixel accuracy. Finally the authors use another instance of the geometrical filter to correct for misinterpretations of the nuclear surface by the segmentation algorithm. Their prefiltering and post filtering nicely complements their initial segmentation strategy, in that it provides substantial and measurable improvement in the definition of the nuclear surfaces.
From state-based to event-based contextual security policies

In this paper, we present a formal contextual security model for pervasive computing applications. Main features of the model are: support of authorization and obligation policies, monitoring and dynamic revocation of access rights, support of personalized security rule contexts, and support of collaborative applications. The model is also logic-based. Therefore, it enables the use of formal policy conflict and dynamic system analysis techniques.
Design and Software Validation of Coded Communication Schemes using Multidimensional Signal Sets without Constellation Expansion Penalty in Band-Limited Gaussian and Fading Channels

It has been well reported that the use of multidimensional constellation signals can help to reduce the bit error rate in Additive Gaussian channels by using the hyperspace geometry more efficiently. Similarly, in fading channels, dimensionality provides an inherent signal space diversity (distinct components between two constellations points), so the amplitude degradation of the signal are combated significantly better. Moreover, the set of Ndimensional signals also provides great compatibility with various Trellis Coded modulation schemes: N-dimensional signaling along with a convolutional encoder uses fewer redundant bits for each 2D signaling interval, and increases intra-subset minimum squared Euclidean distance (MSED) to approach the ultimate capacity limit predicted by Shannon’s theory. The multidimensional signals perform better for the same complexity than two-dimensional schemes. The inherent constellation expansion penalty factor paid for using classical mapping structures can be decreased by enlarging the constellation’s dimension. In this thesis, a multidimensional signal set construction paradigm that completely eliminates the constellation expansion penalty is used in standard AWGN band-limited channels and in fading channels. Theoretical work on performance analysis and computer simulations for QuadratureQuadrature Phase Shift Keying (QPSK), Constant Envelope (CE) QPSK, and trellis-coded 16D CEQPSK in ideal band-limited channels of various bandwidths is presented along with a novel discussion on visualization techniques for 4D Quadrature-Quadrature Phase Shift Keying (QPSK), Saha’s Constant Envelope (CE) QPSK, and Cartwright’s CEQPSK in ideal band-limited channels. Furthermore, a metric designed to be used in fading channels, with Hamming Distance (HD) as a primary concern and Euclidean distance (ED) as secondary is also introduced. Simulation results show that the 16D TCM CEQPSK system performs well in channels with AWGN and fading, even with the simplest convolutional encoder tested; achievable coding gains using 16-D CEQPSK Expanded TCM schemes under various conditions are reported.
Lehrstuhl Für Informatik 10 (systemsimulation) Unknowns the Largest Finite Element System That Can Be Solved Today?

The hierarchical hybrid Grids (HHG) framework attempts to remove limitations on the size of problem that can be solved using a finite element discretization of a partial differential equation (PDE) by using a process of regular refinement, of an unstructured input grid, to generate a nested hierarchy of patch-wise structured grids that is suitable for use with geometric multigrid. The regularity of the resulting grids may be exploited in such a way that it is no longer necessary to explicitly assemble the global discretization matrix. In particular, given an appropriate input grid, the discretization matrix may be defined implicitly using stencils that are constant for each structured patch. This drastically reduces the amount of memory required for the discretization, thus allowing for a much larger problem to be solved. Here we present a brief description of the HHG framework, detailing the principles that led to solving a finite element system with 1.7 × 10 10 unknowns, on an SGI Altix supercomputer, using 1024 nodes, with an overall performance of 0.96 TFLOP/s, on a logically unstructured grid, using geometric multigrid as a solver.
An 8b 20-Msample / s Pipelined A / D Converter in 0 . 5-μ m CMOS with 7 . 8 ENOB

This paper presents an 8-b 20-Msample/s pipelined analog-to-digital converter (ADC) designed in 0.5-μm CMOS technology. On first silicon this converter achieved 7.8 effective-number-of-bits (ENOB), a peak differential-non-linearity (DNL) of -0.36LSB and integralnon-linearity (INL) of 0.35LSB.
A Semiblind Channel Estimation Algorithm for MIMO Systems

in this paper, a very efficient semi blind algorithm for orthogonally coded multiple-input multiple-output (MIMO) systems is proposed. Our main idea is to establish a mathematical model that is based on orthogonal space-time codes, and then to estimate each channel impulse response with this model. To speed up the convergence rate and the elimination of fuzzy channel,we insert a small amount of pilot signal at the transmitter. Simulation results prove that the algorithm effectively reduce the estimation error, no special requirements on the transmitted signal, and no increasing the transmission bandwidth.
Toward Autonomous Learning of an Ontology of Tool Affordances by a Robot

complex environments, however, it is impossible to know in advance the exact nature and number of possible environmental outcomes that the robot can induce through its behaviors. In addition, the changes that the robot can detect are often high-dimensional, making it difcult to use standard machine learning algorithms. This work addresses this problem by proposing a framework in which the robot learns a taxonomy for the types of perceivable changes produced by its own behaviors. The proposed method also allows the robot to incrementally update the taxonomy and to conceptualize new types of observed outcomes. In addition, the robot solves a hierarchical classication task by learning a model that predicts the future outcome of its behaviors in relation to the learned taxonomy. Thus, the robot builds an affordance ontology consisting of an outcome class taxonomy and a predictive model grounded in the robot’s perceptual and behavioral repertoire.
Constraining Λ Using Cluster Quadrupoles

We examine how the statistics of the quadrupoles of (projected) cluster masses can discriminate between flat cold dark matter (ΛCDM) universes with or without a cosmological constant term. Even in the era of high-precision cosmology that cosmic microwave background experiments should soon open, it is important to devise self-consistent tests of cosmogonic theories tuned at the matter radiation decoupling epoch using data from the nonlinear evolved universe. We build cluster catalogs from two large-volume simulations of a "tilted" cold dark matter (TCDM) model and a ΛCDM model with cosmic density parameter Ωm = 0.35 and cosmological constant contribution ΩΛ = 0.65. From the projected mass distribution of the clusters we work out the quadrupoles Q and examine their dependence on cluster mass and the cosmological model. We find that TCDM clusters have systematically larger quadrupoles than their ΛCDM counterpart. The effect is mass dependent: massive clusters (M ≳ 1015 h-1 M☉) have quadrupoles differing by more than 30% in the two models, while for M ≲ 4 × 1014 h-1 M☉ the difference rapidly drops to ~1%. Performing a Kolmogorov-Smirnov D-statistic (K-S) test of the Q distributions, we estimate that using just the 15 most massive clusters in the simulation volume (360 h-1 Mpc a side), we can discriminate between TCDM and ΛCDM at a confidence level better than 99.9%. In the volume probed by existing observations, there are potentially several hundred clusters with masses above the threshold for which the differences in the quadrupoles become relevant. Should weak lensing data become available for this whole set, a quadrupole analysis may be expected to discriminate among different values of Λ.
Generalized-ensemble algorithms for molecular simulations of biopolymers.

In complex systems with many degrees of freedom such as peptides and proteins, there exists a huge number of local-minimum-energy states. Conventional simulations in the canonical ensemble are of little use, because they tend to get trapped in states of these energy local minima. A simulation in generalized ensemble performs a random walk in potential energy space and can overcome this difficulty. From only one simulation run, one can obtain canonical-ensemble averages of physical quantities as functions of temperature by the single-histogram and/or multiple-histogram reweighting techniques. In this article we review uses of the generalized-ensemble algorithms in biomolecular systems. Three well-known methods, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described first. Both Monte Carlo and molecular dynamics versions of the algorithms are given. We then present three new generalized-ensemble algorithms that combine the merits of the above methods. The effectiveness of the methods for molecular simulations in the protein folding problem is tested with short peptide systems.
Linear-Time FPT Algorithms via Half-Integral Non-returning A-path Packing

A recent trend in the design of FPT algorithms is exploiting half-integrality of LP relaxations. That is, starting with a half-integral optimal solution to an LP relaxation, we assign integral values to variables one by one by branch and bound. This technique is general and the resulting time complexity has a small dependency on the parameter. However, the time complexity often becomes a large polynomial in the input size because we need to compute half-integral optimal LP solutions. In this paper, we address this issue by observing that, for many problems, the LP relaxation can be seen as the dual of the LP relaxation of a non-returning $A$-path packing problem. Then, we provide an $O(km)$-time algorithm that computes a half-integral optimal solution to the latter problem, where $k$ is the optimal value and $m$ is the number of edges. This improves the computational time even for a special case of the non-returning $A$-path packing, called internally-disjoint $A$-path packing. As a corollary, we obtain FPT algorithms for various problems, including Group Feedback Vertex Set, Subset Feedback Vertex Set, Node Multiway Cut, Node Unique Label Cover, and Non-Monochromatic Cycle Transversal. For each of these problems, the obtained running time is linear in the input size and has the current smallest dependency on the parameter. In particular, these are the first linear-time FPT algorithms for Group Feedback Vertex Set and Non-Monochromatic Cycle Transversal.
Sizing Router Buffers for Large-Scale TCP/IP Networks

We investigate the validity of reducing router buffer size in a large-scale network that includes both core networks and edge networks. We first devise a novel mathematical analysis method of estimating the average behavior of TCP connections in a network with 100/1,000/10,000 routers/endhosts/links and 100,000 concurrent TCP connections. By applying our analysis method to the Abilene-inspired network, we demonstrate the influence of small buffer on link utilization, packet loss ratio and the performance of TCP connections passing through the router. One important result is that, especially when the edge network becomes faster, decreasing buffer size at core routers causes unfairness between TCP connections that traverse the core network and those do not traverse the core network.
A Unified Framework for Utility Based Measures for Mining Itemsets

A pattern is of utility to a person if its use by that per- son contributes to reaching a goal. Utility based measures use the utilities of the patterns to re∞ect the user's goals. In this paper, we flrst review utility based measures for itemset mining. Then, we present a unifled framework for incorpo- rating several utility based measures into the data mining process by deflning a unifled utility function. Next, within this framework, we summary the mathematical properties of utility based measures that will allow the time and space costs of the itemset mining algorithm to be reduced.
Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory

In regular statistical models, the leave-one-out cross-validation is asymptotically equivalent to the Akaike information criterion. However, since many learning machines are singular statistical models, the asymptotic behavior of the cross-validation remains unknown. In previous studies, we established the singular learning theory and proposed a widely applicable information criterion, the expectation value of which is asymptotically equal to the average Bayes generalization loss. In the present paper, we theoretically compare the Bayes cross-validation loss and the widely applicable information criterion and prove two theorems. First, the Bayes cross-validation loss is asymptotically equivalent to the widely applicable information criterion as a random variable. Therefore, model selection and hyperparameter optimization using these two values are asymptotically equivalent. Second, the sum of the Bayes generalization error and the Bayes cross-validation error is asymptotically equal to 2λ/n, where λ is the real log canonical threshold and n is the number of training samples. Therefore the relation between the cross-validation error and the generalization error is determined by the algebraic geometrical structure of a learning machine. We also clarify that the deviance information criteria are different from the Bayes cross-validation and the widely applicable information criterion.
Does Cascading Schmitt-Trigger Stages Improve the Metastable Behavior?

Schmitt-Trigger stages are the method of choice for robust discretization of input voltages with excessive transition times or significant noise. However, they may suffer from metastability. Based on the experience that the cascading of flip-flop stages yields a dramatic improvement of their overall metastability hardness, in this paper we elaborate on the question whether the cascading of Schmitt-Trigger stages can obtain a similar gain. We perform a theoretic analysis that is backed up by an existing metastability model for a single Schmitt-Trigger stage and elaborate some claims about the behavior of a Schmitt-Trigger cascade. These claims suggest that the occurrence of metastability is indeed reduced from the first stage to the second which suggests an improvement. On the downside, however, it becomes clear that metastability can still not be completely ruled out, and in some cases the behavior of the cascade may be less beneficial for a given application, e.g. by introducing seemingly acausal transitions. We validate our findings by extensive HSPICE simulations in which we directly cover our most important claims.
Globular clusters and dwarf spheroidal galaxies

Traditionally, globular clusters and dwarf spheroidal galaxies have been distinguished by using one or more of the following criteria: (i) mass, (ii) luminosity, (iii) size, (iv) mass-to-light ratio and (v) spread in metallicity. However, a few recently discovered objects show some overlap between the domains in parameter space that are occupied by galaxies and clusters. In this Letter, it is shown that ellipticity can, in some cases, be used to help distinguish between globular clusters and dwarf spheroidal galaxies.
Game-Theoretic Energy Management for Residential Users with Dischargeable Plug-in Electric Vehicles

The plug-in electric vehicle (PEV) has attracted more and more attention because of the energy crisis and environmental pollution, which is also the main shiftable load of the residential users’ demand side management (DSM) system in the future smart grid (SG). In this paper, we employ game theory to provide an autonomous energy management system among residential users considering selling energy back to the utility company by discharging the PEV’s battery. By assuming all users are equipped with smart meters to execute automatic energy consumption scheduling (ECS) and the energy company can adopt adequate pricing tariffs relating to time and level of energy usage, we formulate an energy management game, where the players are the residential users and the strategies are their daily schedules of household appliance use. We will show that the Nash equilibrium of the formulated energy management game can guarantee the global optimization in terms of minimizing the energy costs, where the depreciation cost of PEV’s battery because of discharging and selling energy back is also considered. Simulation results verify that the proposed game-theoretic approach can reduce the total energy cost and individual daily electricity payment. Moreover, since plug-in electric bicycles (PEBs) are currently widely used in China, simulation results of residential users owing household appliances and bidirectional energy trading of PEBs are also provided and discussed.
Independent Circadian Oscillations of Period1 in Specific Brain Areas In Vivo and In Vitro

Behavioral and physiological circadian rhythms in mammals are controlled by a master pacemaker in the hypothalamic suprachiasmatic nuclei (SCN). Recently, circadian oscillations of hormone secretion, clock gene expression, and electrical activity have been demonstrated in explants of other brain regions. This suggests that some extra-SCN brain regions contain a functional, SCN-independent circadian clock, but in vivo evidence for intrinsic pacemaking is still lacking. We developed a novel method to image bioluminescence in vivo from the main olfactory bulbs (OB) of intact and SCN-lesioned (SCNX) Period1::luciferase rats for 2 d in constant darkness. The OBs expressed circadian rhythms in situ with a reliable twofold increase from day to night, similar to the phase and amplitude of ex vivo rhythms. In vivo cycling persisted for at least 1 month in the absence of the SCN. To assess indirectly in vivo rhythmicity of other brain areas, we measured the phase-dependence of their in vitro rhythms on the time of surgery. Surgery reliably reset the phase of the pineal gland and vascular organ of the lamina terminalis (VOLT) harvested from SCNX rats but had little effect on the phase of the OB. We deduce that the SCN and OB contain self-sustained circadian oscillators, whereas the pineal gland and VOLT are weak oscillators that require input from the SCN to show coordinated circadian rhythms. We conclude that the mammalian brain comprises a diverse set of SCN-dependent and SCN-independent circadian oscillators.
Development of Data leakage Detection Using Data Allocation Strategies

-A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). If the data distributed to third parties is found in a public/private domain then finding the guilty party is a nontrivial task to distributor. Traditionally, this leakage of data is handled by water marking technique which requires modification of data. If the watermarked copy is found at some unauthorized site then distributor can claim his ownership. To overcome the disadvantages of using watermark [2], data allocation strategies are used to improve the probability of identifying guilty third parties. In this project, we implement and analyze a guilt model that detects the agents using allocation strategies without modifying the original data. The guilty agent is one who leaks a portion of distributed data. The idea is to distribute the data intelligently to agents based on sample data request and explicit data request in order to improve the chance of detecting the guilty agents. The algorithms implemented using fake objects will improve the distributor chance of detecting guilty agents. It is observed that by minimizing the sum objective the chance of detecting guilty agents will increase. We also developed a framework for generating fake objects.
SmartFetch: Efficient Support for Selective Queries

The paper proposes SmartFetch, a storage strategy that relies on a combination of techniques aimed at efficiently supporting selective jobs that are only concerned with a subset of the entire dataset in systems such as Hadoop and Spark. We combine the use of an appropriate data-layout with data indexing tools to improve the data access speed and significantly shorten total job execution time. An extensive experimental evaluation of SmartFetch shows that, by avoiding reading irrelevant blocks, it can provide significant speedups when compared to the basic Hadoop and Spark implementations. Further, our system also outperforms other implementations that use several variants of the techniques we have embedded in SmartFetch.