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Based on recent perturbative and non-perturbative lattice calculations with almost quark flavors and the thermal contributions from photons, neutrinos, leptons, electroweak particles, and scalar Higgs bosons, various thermodynamic quantities, at vanishing net-baryon densities, such as pressure, energy density, bulk viscosity, relaxation time, and temperature have been calculated up to the TeV-scale, i.e., covering hadron, QGP, and electroweak (EW) phases in the early Universe. This remarkable progress motivated the present study to determine the possible influence of the bulk viscosity in the early Universe and to understand how this would vary from epoch to epoch. We have taken into consideration first- (Eckart) and second-order (Israel–Stewart) theories for the relativistic cosmic fluid and integrated viscous equations of state in Friedmann equations. Nonlinear nonhomogeneous differential equations are obtained as analytical solutions. For Israel–Stewart, the differential equations are very sophisticated to be solved. They are outlined here as road-maps for future studies. For Eckart theory, the only possible solution is the functionality, H(a(t)), where H(t) is the Hubble parameter and a(t) is the scale factor, but none of them so far could to be directly expressed in terms of either proper or cosmic time t. For Eckart-type viscous background, especially at finite cosmological constant, non-singular H(t) and a(t) are obtained, where H(t) diverges for QCD/EW and asymptotic EoS. For non-viscous background, the dependence of H(a(t)) is monotonic. The same conclusion can be drawn for an ideal EoS. We also conclude that the rate of decreasing H(a(t)) with increasing a(t) varies from epoch to epoch, at vanishing and finite cosmological constant. These results obviously help in improving our understanding of the nucleosynthesis and the cosmological large-scale structure.
Recurrent cortical networks provide reservoirs of states that are thought to play a crucial role for sequential information processing in the brain. However, classical reservoir computing requires manual adjustments of global network parameters, particularly of the spectral radius of the recurrent synaptic weight matrix. It is hence not clear if the spectral radius is accessible to biological neural networks. Using random matrix theory, we show that the spectral radius is related to local properties of the neuronal dynamics whenever the overall dynamical state is only weakly correlated. This result allows us to introduce two local homeostatic synaptic scaling mechanisms, termed flow control and variance control, that implicitly drive the spectral radius toward the desired value. For both mechanisms the spectral radius is autonomously adapted while the network receives and processes inputs under working conditions. We demonstrate the effectiveness of the two adaptation mechanisms under different external input protocols. Moreover, we evaluated the network performance after adaptation by training the network to perform a time-delayed XOR operation on binary sequences. As our main result, we found that flow control reliably regulates the spectral radius for different types of input statistics. Precise tuning is however negatively affected when interneural correlations are substantial. Furthermore, we found a consistent task performance over a wide range of input strengths/variances. Variance control did however not yield the desired spectral radii with the same precision, being less consistent across different input strengths. Given the effectiveness and remarkably simple mathematical form of flow control, we conclude that self-consistent local control of the spectral radius via an implicit adaptation scheme is an interesting and biological plausible alternative to conventional methods using set point homeostatic feedback controls of neural firing.
Scanning Hall probe microscopy is attractive for minimally invasive characterization of magnetic thin films and nanostructures by measurement of the emanating magnetic stray field. Established sensor probes operating at room temperature employ highly miniaturized spin-valve elements or semimetals, such as Bi. As the sensor layer structures are fabricated by patterning of planar thin films, their adaption to custom-made sensor probe geometries is highly challenging or impossible. Here we show how nanogranular ferromagnetic Hall devices fabricated by the direct-write method of focused electron beam induced deposition (FEBID) can be tailor-made for any given probe geometry. Furthermore, we demonstrate how the magnetic stray field sensitivity can be optimized in situ directly after direct-write nanofabrication of the sensor element. First proof-of-principle results on the use of this novel scanning Hall sensor are shown.
Human societies are characterized by three constituent features, besides others. (A) Options, as for jobs and societal positions, differ with respect to their associated monetary and non-monetary payoffs. (B) Competition leads to reduced payoffs when individuals compete for the same option as others. (C) People care about how they are doing relatively to others. The latter trait –the propensity to compare one’s own success with that of others– expresses itself as envy. It is shown that the combination of (A)–(C) leads to spontaneous class stratification. Societies of agents split endogenously into two social classes, an upper and a lower class, when envy becomes relevant. A comprehensive analysis of the Nash equilibria characterizing a basic reference game is presented. Class separation is due to the condensation of the strategies of lower-class agents, which play an identical mixed strategy. Upper-class agents do not condense, following individualist pure strategies. The model and results are size-consistent, holding for arbitrary large numbers of agents and options. Analytic results are confirmed by extensive numerical simulations. An analogy to interacting confined classical particles is discussed.
This work presents, to our knowledge, the first completely passive imaging with human-body-emitted radiation in the lower THz frequency range using a broadband uncooled detector. The sensor consists of a Si CMOS field-effect transistor with an integrated log-spiral THz antenna. This THz sensor was measured to exhibit a rather flat responsivity over the 0.1–1.5-THz frequency range, with values of the optical responsivity and noise-equivalent power of around 40 mA/W and 42 pW/√Hz, respectively. These values are in good agreement with simulations which suggest an even broader flat responsivity range exceeding 2.0 THz. The successful imaging demonstratestheimpressivethermalsensitivitywhichcanbeachievedwithsuchasensor. Recording of a 2.3×7.5-cm2-sized image of the fingers of a hand with a pixel size of 1 mm2 at a scanning speed of 1 mm/s leads to a signal-to-noise ratio of 2 and a noise-equivalent temperature difference of 4.4 K. This approach shows a new sensing approach with field-effect transistors as THz detectors which are usually used for active THz detection.
Radar technology in the millimeter-wave frequency band offers many interesting features for wind park surveillance, such as structural monitoring of rotor blades or the detection of bats and birds in the vicinity of wind turbines (WTs). Currently, the majority of WTs are affected by shutdown algorithms to minimize animal fatalities via direct collision with the rotor blades or barotrauma effects. The presence of rain is an important parameter in the definition of those algorithms together with wind speed, temperature, time of the day, and season of the year. A Ka-band frequency-modulated continuous-wave radar (33.4-36.0 GHz) installed at the tower of a 2-MW WT was used during a field study. We have observed characteristic rain-induced patterns, based on the range-Doppler algorithm. To better understand those signatures, we have developed a laboratory experiment and implemented a numerical modeling framework. Experimental and numerical results for rain detection and classification are presented and discussed here. Based on this article, a bat- and bird-friendly adaptive WT control can be developed for improved WT efficiency in periods of rain and, at the same time, reduced animal mortality.
Envy, the inclination to compare rewards, can be expected to unfold when inequalities in terms of pay-off differences are generated in competitive societies. It is shown that increasing levels of envy lead inevitably to a self-induced separation into a lower and an upper class. Class stratification is Nash stable and strict, with members of the same class receiving identical rewards. Upper-class agents play exclusively pure strategies, all lower-class agents the same mixed strategy. The fraction of upper-class agents decreases progressively with larger levels of envy, until a single upper-class agent is left. Numerical simulations and a complete analytic treatment of a basic reference model, the shopping trouble model, are presented. The properties of the class-stratified society are universal and only indirectly controllable through the underlying utility function, which implies that class-stratified societies are intrinsically resistant to political control. Implications for human societies are discussed. It is pointed out that the repercussions of envy are amplified when societies become increasingly competitive.
Surface plasmon polaritons on (silver) nanowires are promising components for future photonic technologies. Here, we study near-field patterns on silver nanowires with a scattering-type scanning near-field optical microscope that enables the direct mapping of surface waves. We analyze the spatial pattern of the plasmon signatures for different excitation geometries and polarization and observe a plasmon wave pattern that is canted relative to the nanowire axis, which we show is due to a superposition of two different plasmon modes, as supported by electromagnetic simulations including the influence of the substrate. These findings yield new insights into the excitation and propagation of plasmon polaritons for applications in nanoplasmonic devices.
Focused electron and ion beam-induced deposition (FEBID/FIBID) are direct-write techniques with particular advantages in three-dimensional (3D) fabrication of ferromagnetic or superconducting nanostructures. Recently, two novel precursors, HCo 3 Fe(CO) 12 and Nb(NMe 3 ) 2 (N-t-Bu), were introduced, resulting in fully metallic CoFe ferromagnetic alloys by FEBID and superconducting NbC by FIBID, respectively. In order to properly define the writing strategy for the fabrication of 3D structures using these precursors, their temperature-dependent average residence time on the substrate and growing deposit needs to be known. This is a prerequisite for employing the simulation-guided 3D computer aided design (CAD) approach to FEBID/FIBID, which was introduced recently. We fabricated a series of rectangular-shaped deposits by FEBID at different substrate temperatures between 5 ∘ C and 24 ∘ C using the precursors and extracted the activation energy for precursor desorption and the pre-exponential factor from the measured heights of the deposits using the continuum growth model of FEBID based on the reaction-diffusion equation for the adsorbed precursor.
Holographic imaging techniques, which exploit the coherence properties of light, enable the reconstruction of the 3D scenery being viewed. While the standard approaches for the recording of holographic images require the superposition of scattered light with a reference field, heterodyne detection techniques enable direct measurement of the amplitude and relative phase of the electric light field. Here, we explore heterodyne Fourier imaging and its capabilities using active illumination with continuous-wave radiation at 300 GHz and a raster-scanned antenna-coupled field-effect transistor (TeraFET) for phase-sensitive detection. We demonstrate that the numerical reconstruction of the scenery provides access to depth resolution together with the capability to numerically refocus the image and the capability to detect an object obscured by another object in the beam path. In addition, the digital refocusing capability allows us to employ Fourier imaging also in the case of small lens-object distances (virtual imaging regime), thus allowing high spatial frequencies to pass through the lens, which results in enhanced lateral resolution.
The influence of temperature is regarded as particularly important for a structural health monitoring system based on ultrasonic guided waves. Since the temperature effect causes stronger signal changes than a typical defect, the former must be addressed and compensated for reliable damage assessment. Development of new temperature compensation techniques as well as the comparison of existing algorithms require high-quality benchmark measurements. This paper investigates a carbon fiber reinforced plastic (CFRP) plate that was fully characterized in previous research in terms of stiffness tensor and guided wave propagation. The same CFRP plate is used here for the analysis of the temperature effect for a wide range of ultrasound frequencies and temperatures. The measurement data are a contribution to the Open Guided Waves (OGW) platform: http://www.open-guided-waves.de. The technical validation includes initial results on the analysis of phase velocity variations with temperature and exemplary damage detection results using state-of-the-art signal processing methods that aim to suppress the temperature effect.
Light-matter interaction in the strong coupling regime is of profound interest for fundamental quantum optics, information processing and the realization of ultrahigh-resolution sensors. Here, we report a new way to realize strong light-matter interaction, by coupling metamaterial plasmonic "quasi-particles" with photons in a photonic cavity, in the terahertz frequency range. The resultant cavity polaritons exhibit a splitting which can reach the ultra-strong coupling regime, even with the comparatively low density of quasi-particles, and inherit the high Q-factor of the cavity despite the relatively broad resonances of the Swiss-cross and split-ring-resonator metamaterials used. We also demonstrate nonlocal collective interaction of spatially separated metamaterial layers mediated by the cavity photons. By applying the quantum electrodynamic formalism to the density dependence of the polariton splitting, we can deduce the intrinsic transition dipole moment for single-quantum excitation of the metamaterial quasi-particles, which is orders of magnitude larger than those of natural atoms. These findings are of interest for the investigation of fundamental strong-coupling phenomena, but also for applications such as ultra-low-threshold terahertz polariton lasing, voltage-controlled modulators and frequency filters, and ultra-sensitive chemical and biological sensing.
Five decades of US, UK, German and Dutch music charts show that cultural processes are accelerating
(2019)
Analysing the timeline of US, UK, German and Dutch music charts, we find that the evolution of album lifetimes and of the size of weekly rank changes provide evidence for an acceleration of cultural processes. For most of the past five decades, number one albums needed more than a month to climb to the top, nowadays an album is in contrast top ranked either from the start, or not at all. Over the last three decades, the number of top-listed albums increased as a consequence from roughly a dozen per year, to about 40. The distribution of album lifetimes evolved during the last decades from a log-normal distribution to a power law, a profound change. Presenting an information–theoretical approach to human activities, we suggest that the fading relevance of personal time horizons may be causing this phenomenon. Furthermore, we find that sales and airplay- based charts differ statistically and that the inclusion of streaming affects chart diversity adversely. We point out in addition that opinion dynamics may accelerate not only in cultural domains, as found here, but also in other settings, in particular in politics, where it could have far reaching consequences.
Behavior is characterized by sequences of goal oriented conducts, such as food uptake, socializing and resting. Classically, one would define for each task a corresponding satisfaction level, with the agent engaging, at a given time, in the activity having the lowest satisfaction level. Alternatively, one may consider that the agent follows the overarching objective to generate sequences of distinct activities. To achieve a balanced distribution of activities would then be the primary goal, and not to master a specific task. In this setting the agent would show two types of behaviors, task-oriented and task-searching phases, with the latter interseeding the former. We study the emergence of autonomous task switching for the case of a simulated robot arm. Grasping one of several moving objects corresponds in this setting to a specific activity. Overall, the arm should follow a given object temporarily and then move away, in order to search for a new target and reengage. We show that this behavior can be generated robustly when modeling the arm as an adaptive dynamical system. The dissipation function is in this approach time dependent. The arm is in a dissipative state when searching for a nearby object, dissipating energy on approach. Once close, the dissipation function starts to increase, with the eventual sign change implying that the arm will take up energy and wander off. The resulting explorative state ends when the dissipation function becomes again negative and the arm selects a new target. We believe that our approach may be generalized to generate self-organized sequences of activities in general.
We report on the observation of coherent terahertz (THz) emission from the quasi-one-dimensional charge-density wave (CDW) system, blue bronze (K0.3MoO3), upon photo-excitation with ultrashort near-infrared optical pulses. The emission contains a broadband, low-frequency component due to the photo-Dember effect, which is present over the whole temperature range studied (30–300 K), as well as a narrow-band doublet centered at 1.5 THz, which is only observed in the CDW state and results from the generation of coherent transverse-optical phonons polarized perpendicular to the incommensurate CDW b-axis. As K0.3MoO3 is centrosymmetric, the lowest-order generation mechanism which can account for the polarization dependence of the phonon emission involves either a static surface field or quadrupolar terms due to the optical field gradients at the surface. This phonon signature is also present in the ground-state conductivity, and decays in strength with increasing temperature to vanish above $T\sim 100\,{\rm{K}}$, i.e. significantly below the CDW transition temperature. The temporal behavior of the phonon emission can be well described by a simple model with two coupled modes, which initially oscillate with opposite polarity.
Self-organized robots may develop attracting states within the sensorimotor loop, that is within the phase space of neural activity, body and environmental variables. Fixpoints, limit cycles and chaotic attractors correspond in this setting to a non-moving robot, to directed, and to irregular locomotion respectively. Short higher-order control commands may hence be used to kick the system from one self-organized attractor robustly into the basin of attraction of a different attractor, a concept termed here as kick control. The individual sensorimotor states serve in this context as highly compliant motor primitives. We study different implementations of kick control for the case of simulated and real-world wheeled robots, for which the dynamics of the distinct wheels is generated independently by local feedback loops. The feedback loops are mediated by rate-encoding neurons disposing exclusively of propriosensoric inputs in terms of projections of the actual rotational angle of the wheel. The changes of the neural activity are then transmitted into a rotational motion by a simulated transmission rod akin to the transmission rods used for steam locomotives. We find that the self-organized attractor landscape may be morphed both by higher-level control signals, in the spirit of kick control, and by interacting with the environment. Bumping against a wall destroys the limit cycle corresponding to forward motion, with the consequence that the dynamical variables are then attracted in phase space by the limit cycle corresponding to backward moving. The robot, which does not dispose of any distance or contact sensors, hence reverses direction autonomously.
The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different cell types in motor cortex due to transcranial magnetic stimulation. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict detailed neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to predict activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also predicts differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of corctial pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.
This paper presents an imaging radar system for structural health monitoring (SHM) of wind turbine blades. The imaging radar system developed here is based on two frequency modulated continuous wave (FMCW) radar sensors with a high output power of 30 dBm. They have been developed for the frequency bands of 24,05 GHz-24,25 GHz and 33.4 GHz-36.0 GHz, respectively. Following the successful proof of damage detection and localization in laboratory conditions, we present here the installation of the sensor system at the tower of a 2 MW wind energy plant at 95 m above ground. The realization of the SHM-system will be introduced including the sensor system, the data acquisition framework and the signal processing procedures. We have achieved an imaging of the rotor blades using inverse synthetic aperture radar techniques under changing environmental and operational condition. On top of that, it was demonstrated that the front wall and back wall radar echo can be extracted from the measured signals demonstrating the full penetration of wind turbine blades during operation.
Far outside the surface of slabs, the exact exchange (EXX) potential vx falls off as −1/z , if z denotes the direction perpendicular to the surface and the slab is localized around z=0 . Similarly, the EXX energy density ex behaves as −n/(2z) , where n is the electron density. Here, an alternative proof of these relations is given, in which the Coulomb singularity in the EXX energy is treated in a particularly careful fashion. This new approach allows the derivation of the next-to-leading order contributions to the asymptotic vx and ex . It turns out that in both cases, the corrections are proportional to 1/z2 in general.
An empirical study of the per capita yield of science Nobel prizes : is the US era coming to an end?
(2018)
We point out that the Nobel prize production of the USA, the UK, Germany and France has been in numbers that are large enough to allow for a reliable analysis of the long-term historical developments. Nobel prizes are often split, such that up to three awardees receive a corresponding fractional prize. The historical trends for the fractional number of Nobelists per population are surprisingly robust, indicating in particular that the maximum Nobel productivity peaked in the 1970s for the USA and around 1900 for both France and Germany. The yearly success rates of these three countries are to date of the order of 0.2–0.3 physics, chemistry and medicine laureates per 100 million inhabitants, with the US value being a factor of 2.4 down from the maximum attained in the 1970s. The UK in contrast managed to retain during most of the last century a rate of 0.9–1.0 science Nobel prizes per year and per 100 million inhabitants. For the USA, one finds that the entire history of science Noble prizes is described on a per capita basis to an astonishing accuracy by a single large productivity boost decaying at a continuously accelerating rate since its peak in 1972.
By the fabrication of periodically arranged nanomagnetic systems it is possible to engineer novel physical properties by realizing artificial lattice geometries that are not accessible via natural crystallization or chemical synthesis. This has been accomplished with great success in two dimensions in the fields of artificial spin ice and magnetic logic devices, to name just two. Although first proposals have been made to advance into three dimensions (3D), established nanofabrication pathways based on electron beam lithography have not been adapted to obtain free-form 3D nanostructures. Here we demonstrate the direct-write fabrication of freestanding ferromagnetic 3D nano-architectures. By employing micro-Hall sensing, we have determined the magnetic stray field generated by our free-form structures in an externally applied magnetic field and we have performed micromagnetic and macro-spin simulations to deduce the spatial magnetization profiles in the structures and analyze their switching behavior. Furthermore we show that the magnetic 3D elements can be combined with other 3D elements of different chemical composition and intrinsic material properties.
Fluctuation spectroscopy measurements of quasi-two-dimensional organic charge-transfer salts (BEDT-TTF) 2 X are reviewed. In the past decade, the method has served as a new approach for studying the low-frequency dynamics of strongly correlated charge carriers in these materials. We review some basic aspects of electronic fluctuations in solids, and give an overview of selected problems where the analysis of 1/f -type fluctuations and the corresponding slow dynamics provide a better understanding of the underlying physics. These examples are related to (1) an inhomogeneous current distribution due to phase separation and/or a percolative transition; (2) slow dynamics due to a glassy freezing either of structural degrees of freedom coupling to the electronic properties or (3) of the electrons themselves, e.g., when residing on a highly-frustrated crystal lattice, where slow and heterogeneous dynamics are key experimental properties for the vitrification process of a supercooled charge-liquid. Another example is (4), the near divergence and critical slowing down of charge carrier fluctuations at the finite-temperature critical endpoint of the Mott metal-insulator transition. Here also indications for a glassy freezing and temporal and spatial correlated dynamics are found. Mapping out the region of ergodicity breaking and understanding the influence of disorder on the temporal and spatial correlated fluctuations will be an important realm of future studies, as well as the fluctuation properties deep in the Mott or charge-ordered insulating states providing a connection to relaxor or ordered ferroelectric states studied by dielectric spectroscopy.
Spontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron’s input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active.
The quasi-two-dimensional organic charge-transfer salt κ -(BEDT-TTF) 2 Cu 2 (CN) 3 is one of the prime candidates for a quantum spin-liquid due the strong spin frustration of its anisotropic triangular lattice in combination with its proximity to the Mott transition. Despite intensive investigations of the material’s low-temperature properties, several important questions remain to be answered. Particularly puzzling are the 6 K anomaly and the enigmatic effects observed in magnetic fields. Here we report on low-temperature measurements of lattice effects which were shown to be particularly strongly pronounced in this material (R. S. Manna et al., Phys. Rev. Lett. 2010, 104, 016403)). A special focus of our study lies on sample-to-sample variations of these effects and their implications on the interpretation of experimental data. By investigating overall nine single crystals from two different batches, we can state that there are considerable differences in the size of the second-order phase transition anomaly around 6 K, varying within a factor of 3. In addition, we find field-induced anomalies giving rise to pronounced features in the sample length for two out of these nine crystals for temperatures T< 9 K. We tentatively assign the latter effects to B-induced magnetic clusters suspected to nucleate around crystal imperfections. These B-induced effects are absent for the crystals where the 6 K anomaly is most strongly pronounced. The large lattice effects observed at 6 K are consistent with proposed pairing instabilities of fermionic excitations breaking the lattice symmetry. The strong sample-to-sample variation in the size of the phase transition anomaly suggests that the conversion of the fermions to bosons at the instability is only partial and to some extent influenced by not yet identified sample-specific parameters.
We present a study of the influence of disorder on the Mott metal-insulator transition for the organic charge-transfer salt κ -(BEDT-TTF) 2 Cu[N(CN) 2 ]Cl. To this end, disorder was introduced into the system in a controlled way by exposing the single crystals to X-ray irradiation. The crystals were then fine-tuned across the Mott transition by the application of continuously controllable He-gas pressure at low temperatures. Measurements of the thermal expansion and resistance show that the first-order character of the Mott transition prevails for low irradiation doses achieved by irradiation times up to 100 h. For these crystals with a moderate degree of disorder, we find a first-order transition line which ends in a second-order critical endpoint, akin to the pristine crystals. Compared to the latter, however, we observe a significant reduction of both, the critical pressure pc and the critical temperature Tc . This result is consistent with the theoretically-predicted formation of a soft Coulomb gap in the presence of strong correlations and small disorder. Furthermore, we demonstrate, similar to the observation for the pristine sample, that the Mott transition after 50 h of irradiation is accompanied by sizable lattice effects, the critical behavior of which can be well described by mean-field theory. Our results demonstrate that the character of the Mott transition remains essentially unchanged at a low disorder level. However, after an irradiation time of 150 h, no clear signatures of a discontinuous metal-insulator transition could be revealed anymore. These results suggest that, above a certain disorder level, the metal-insulator transition becomes a smeared first-order transition with some residual hysteresis.
The interaction of (quasi)particles with a periodic potential arises in various domains of science and engineering, such as solid-state physics, chemical physics, and communication theory. An attractive test ground to investigate this interaction is represented by superconductors with artificial pinning sites, where magnetic flux quanta (Abrikosov vortices) interact with the pinning potential U(r) = U(r + R) induced by a nanostructure. At a combination of microwave and dc currents, fluxons act as mobile probes of U(r): The ac component shakes the fluxons in the vicinity of their equilibrium points which are unequivocally determined by the local pinning force counterbalanced by the Lorentz force induced by the dc current, linked to the curvature of U(r) which can then be used for a successful fitting of the voltage responses. A good correlation of the deduced dependences U(r) with the cross sections of the nanostructures points to that pinning is primarily caused by vortex length reduction. Our findings pave a new route to a non-destructive evaluation of periodic pinning in superconductor thin films. The approach should also apply to a broad class of systems whose evolution in time can be described by the coherent motion of (quasi)particles in a periodic potential.
The description of quantized collective excitations stands as a landmark in the quantum theory of condensed matter. A prominent example occurs in conventional magnets, which support bosonic magnons—quantized harmonic fluctuations of the ordered spins. In striking contrast is the recent discovery that strongly spin-orbital-coupled magnets, such as α-RuCl3, may display a broad excitation continuum inconsistent with conventional magnons. Due to incomplete knowledge of the underlying interactions unraveling the nature of this continuum remains challenging. The most discussed explanation refers to a coherent continuum of fractional excitations analogous to the celebrated Kitaev spin liquid. Here, we present a more general scenario. We propose that the observed continuum represents incoherent excitations originating from strong magnetic anharmonicity that naturally occurs in such materials. This scenario fully explains the observed inelastic magnetic response of α-RuCl3 and reveals the presence of nontrivial excitations in such materials extending well beyond the Kitaev state.
The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different pyramidal cell types in motor cortex due to TMS. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to calculate activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also shows differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of cortical pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical pyramidal cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.
Motivated by recent experimental suggestions of charge-order-driven ferroelectricity in organic charge-transfer salts, such as κ-(BEDT-TTF)2Cu[N(CN)2]Cl, we investigate magnetic and charge-ordered phases that emerge in an extended two-orbital Hubbard model on the anisotropic triangular lattice at 3/4 filling. This model takes into account the presence of two organic BEDT-TTF molecules, which form a dimer on each site of the lattice, and includes short-range intramolecular and intermolecular interactions and hoppings. By using variational wave functions and quantum Monte Carlo techniques, we find two polar states with charge disproportionation inside the dimer, hinting to ferroelectricity. These charge-ordered insulating phases are stabilized in the strongly correlated limit and their actual charge pattern is determined by the relative strength of intradimer to interdimer couplings. Our results suggest that ferroelectricity is not driven by magnetism, since these polar phases can be stabilized also without antiferromagnetic order and provide a possible microscopic explanation of the experimental observations. In addition, a conventional dimer-Mott state (with uniform density and antiferromagnetic order) and a nonpolar charge-ordered state (with charge-rich and charge-poor dimers forming a checkerboard pattern) can be stabilized in the strong-coupling regime. Finally, when electron–electron interactions are weak, metallic states appear, with either uniform charge distribution or a peculiar 12-site periodicity that generates honeycomb-like charge order.
The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network’s changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network’s sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.
Overrepresentation of bidirectional connections in local cortical networks has been repeatedly reported and is a focus of the ongoing discussion of nonrandom connectivity. Here we show in a brief mathematical analysis that in a network in which connection probabilities are symmetric in pairs, Pij = Pji, the occurrences of bidirectional connections and nonrandom structures are inherently linked; an overabundance of reciprocally connected pairs emerges necessarily when some pairs of neurons are more likely to be connected than others. Our numerical results imply that such overrepresentation can also be sustained when connection probabilities are only approximately symmetric.
The detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different pyramidal cell types in motor cortex due to TMS. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to calculate activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also shows differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of cortical pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical pyramidal cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.