Community ecology and management of lianas

In this investigation, the basic pigment component, or anthocyanidin, of the ten most common spots was identified using paper and thin layer chromatographic techniques

Decoding movement kinematics from EEG using an interpretable convolutional neural network

Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural networks (DNNs) are emerging as powerful decoders, for their ability to automatically learn features from lightly pre-processed signals. However, DNNs for kinematics decoding lack in the interpretability of the learned features and are only used to realize within-subject decoders without testing other training approaches potentially beneficial for reducing calibration time, such as transfer learning. Here, we aim to overcome these limitations by using an interpretable convolutional neural network (ICNN) to decode 2-D hand kinematics (position and velocity) from EEG in a pursuit tracking task performed by 13 participants. The ICNN is trained using both within-subject and cross-subject strategies, and also testing the feasibility of transferring the knowledge learned on other subjects on a new one. Moreover, the network eases the interpretation of learned spectral and spatial EEG features. Our ICNN outperformed most of the other state-of-the-art decoders, showing the best trade-off between performance, size, and training time. Furthermore, transfer learning improved kinematics prediction in the low data regime. The network attributed the highest relevance for decoding to the delta-band across all subjects, and to higher frequencies (alpha, beta, low-gamma) for a cluster of them; contralateral central and parieto-occipital sites were the most relevant, reflecting the involvement of sensorimotor, visual and visuo-motor processing. The approach improved the quality of kinematics prediction from the EEG, at the same time allowing interpretation of the most relevant spectral and spatial features

Moregrasp: Restoration of Upper Limb Function in Individuals with High Spinal Cord Injury by Multimodal Neuroprostheses for Interaction in Daily Activities

The aim of the MoreGrasp project is to develop a noninvasive, multimodal user interface including a brain-computer interface (BCI) for intuitive control of a grasp neuroprosthesis to support individuals with high spinal cord injury (SCI) in everyday activities. We describe the current state of the project, including the EEG system, preliminary results of natural movements decoding in people with SCI, the new electrode concept for the grasp neuroprosthesis, the shared control architecture behind the system and the implementation of a user-centered design

Hole Doping Evolution of the Quasiparticle Band in Models of Strongly Correlated Electrons for the High-T_c Cuprates

Quantum Monte Carlo (QMC) and Maximum Entropy (ME) techniques are used to study the spectral function $A({\bf p},\omega)$ of the one band Hubbard model in strong coupling including a next-nearest-neighbor electronic hopping with amplitude $t'/t= -0.35$. These values of parameters are chosen to improve the comparison of the Hubbard model with angle-resolved photoemission (ARPES) data for $Sr_2 Cu O_2 Cl_2$. A narrow quasiparticle (q.p.) band is observed in the QMC analysis at the temperature of the simulation $T=t/3$, both at and away from half-filling. Such a narrow band produces a large accumulation of weight in the density of states at the top of the valence band. As the electronic density $$ decreases further away from half-filling, the chemical potential travels through this energy window with a large number of states, and by $\sim 0.70$ it has crossed it entirely. The region near momentum $(0,\pi)$ and $(\pi,0)$ in the spectral function is more sensitive to doping than momenta along the diagonal from $(0,0)$ to $(\pi,\pi)$. The evolution with hole density of the quasiparticle dispersion contains some of the features observed in recent ARPES data in the underdoped regime. For sufficiently large hole densities the ``flat'' bands at $(\pi,0)$ cross the Fermi energy, a prediction that could be tested with ARPES techniques applied to overdoped cuprates. The population of the q.p. band introduces a {\it hidden} density in the system which produces interesting consequences when the quasiparticles are assumed to interact through antiferromagnetic fluctuations and studied with the BCS gap equation formalism. In particular, a region of extended s-wave is found to compete with d-wave in the overdoped regime, i.e. when the chemical potential has almost entirely crossed the q.p.Comment: 14 pages, Revtex, with 13 embedded ps figures, submitted to Phys. Rev. B., minor modifications in the text and in figures 1b, 2b, 3b, 4b, and 6

Effects of Electronic Correlations on the Thermoelectric Power of the Cuprates

We show that important anomalous features of the normal-state thermoelectric power S of high-Tc materials can be understood as being caused by doping dependent short-range antiferromagnetic correlations. The theory is based on the fluctuation-exchange approximation applied to Hubbard model in the framework of the Kubo formalism. Firstly, the characteristic maximum of S as function of temperature can be explained by the anomalous momentum dependence of the single-particle scattering rate. Secondly, we discuss the role of the actual Fermi surface shape for the occurrence of a sign change of S as a function of temperature and doping.Comment: 4 pages, with eps figure

Charge pairing, superconducting transition and supersymmetry in high-temperature cuprate superconductors

We propose a model for high-T$_{c}$ superconductors, valid for $0\leq\delta\leq\delta_{SC}$, that includes both the spin fluctuations of the Cu$^{++}$ magnetic ions and of the O$^{--}$ doped holes. Spin-charge separation is taken into account with the charge of the doped holes being associated to quantum skyrmion excitations (holons) of the Cu$^{++}$ spin background. The holon effective interaction potential is evaluated as a function of doping, indicating that Cooper pair formation is determined by the competition between the spin fluctuations of the Cu$^{++}$ background and of spins of the O$^{--}$ doped holes (spinons). The superconducting transition occurs when the spinon fluctuations dominate, thereby reversing the sign of the interaction. At this point ($\delta = \delta_{SC}$), the theory is supersymmetric at short distances and, as a consequence, the leading order results are not modified by radiative corrections. The critical doping parameter for the onset of superconductivity at T=0 is obtained and found to be a universal constant determined by the shape of the Fermi surface. Our theoretical values for $\delta_{SC}$ are in good agreement with the experiment for both LSCO and YBCO.Comment: RevTex, 4 pages, no figure

Trade-Offs Between Carbon Stocks and Timber Recovery in Tropical Forests are Mediated by Logging Intensity

Forest degradation accounts for ~70% of total carbon losses from tropical forests. Substantial emissions are from selective logging, a land-use activity that decreases forest carbon density. To maintain carbon values in selectively logged forests, climate change mitigation policies and government agencies promote the adoption of reduced-impact logging (RIL) practices. However, whether RIL will maintain both carbon and timber values in managed tropical forests over time remains uncertain. In this study, we quantify the recovery of timber stocks and aboveground carbon at an experimental site where forests were subjected to different intensities of RIL (4, 8, and 16 trees/ha). Our census data span 20 years postlogging and 17 years after the liberation of future crop trees from competition in a tropical forest on the Guiana Shield, a globally important forest carbon reservoir. We model recovery of timber and carbon with a breakpoint regression that allowed us to capture elevated tree mortality immediately after logging. Recovery rates of timber and carbon were governed by the presence of residual trees (i.e., trees that persisted through the first harvest). The liberation treatment stimulated faster recovery of timber albeit at a carbon cost. Model results suggest a threshold logging intensity beyond which forests managed for timber and carbon derive few benefits from RIL, with recruitment and residual growth not sufficient to offset losses. Inclusion of the breakpoint at which carbon and timber gains outpaced postlogging mortality led to high predictive accuracy, including out-of-sample R2 values \u3e90%, and enabled inference on demographic changes postlogging. Our modeling framework is broadly applicable to studies that aim to quantify impacts of logging on forest recovery. Overall, we demonstrate that initial mortality drives variation in recovery rates, that the second harvest depends on old growth wood, and that timber intensification lowers carbon stocks