The brain is overall bilaterally symmetrical, but also exhibits considerable asymmetry. While symmetry may endow neural networks with robustness and resilience, asymmetry may enable parallel information processing and functional specialization. How is this tradeoff between symmetrical and asymmetrical brain architecture balanced? To address this, we focused on the Caenorhabditis elegans connectome, comprising 99 classes of bilaterally symmetrical neuron pairs. We found symmetry in the number of synaptic partners between neuron class members, but pronounced asymmetry in the identity of these synapses. We applied graph theoretical metrics for evaluating Redundancy, the selective reinforcement of specific neural paths by multiple alternative synaptic connections, and Reachability, the extent and diversity of synaptic connectivity of each neuron class. We found Redundancy and Reachability to be stochastically tunable by the level of network asymmetry, driving the C. elegans connectome to favor Redundancy over Reachability. These results elucidate fundamental relations between lateralized neural connectivity and function.
What is the impact of uncommon but notable violent acts on conflict dynamics? We analyze the impact of the murder of a Palestinian child on the broader dynamics of Israeli-Palestinian violence in Jerusalem. By using novel micro-level event data and utilizing Discrete Furrier Transform and Bayesian Poisson Change Point Analysis, we compare the impact of the murder to that of two other lethal but more typical Israeli-Palestinian events. We demonstrate that the murder had a large and durable effect that altered the broader conflict dynamics, whereas the other events caused smaller, short-term effects. We demonstrate that scholars should devote more attention to the analysis of atypical violent acts and present a set of tools for conducting such analysis.
Sensory neurons specialize in detecting and signaling the presence of diverse environmental stimuli. Neuronal injury or disease may undermine such signaling, diminishing the availability of crucial information. Can animals distinguish between a stimulus not being present and the inability to sense that stimulus in the first place? To address this question, we studied Caenorhabditis elegans nematode worms that lack gentle body touch sensation due to genetic mechanoreceptor dysfunction. We previously showed that worms can compensate for the loss of touch by enhancing their sense of smell, via an FLP-20 neuropeptide pathway. Here, we find that touch-deficient worms exhibit, in addition to sensory compensation, also cautious-like behavior, as if preemptively avoiding potential undetectable hazards. Intriguingly, these behavioral adjustments are abolished when the touch neurons are removed, suggesting that touch neurons are required for signaling the unavailability of touch information, in addition to their conventional role of signaling touch stimulation. Furthermore, we found that the ASE taste neurons, which similarly to the touch neurons, express the FLP-20 neuropeptide, exhibit altered FLP-20 expression levels in a touch-dependent manner, thus cooperating with the touch circuit. These results imply a novel form of neuronal signaling that enables C. elegans to distinguish between lack of touch stimulation and loss of touch sensation, producing adaptive behavioral adjustments that could overcome the inability to detect potential threats.
Imaging inside scattering media at optical resolution is a longstanding challenge affecting multiple fields, from bio-medicine to astronomy. In recent years, several groundbreaking techniques for imaging inside scattering media, in particular scattering-matrix-based approaches, have shown great promise. However, due to their reliance on the optical “memory-effect,” these techniques usually suffer from a restricted field of view. Here, we demonstrate that diffraction-limited imaging beyond the optical memory-effect can be robustly achieved by combining acousto-optic spatial-gating with state-of-the-art matrix-based imaging techniques. In particular, we show that this can be achieved by computational processing of scattered light fields captured under scanned acousto-optic modulation. The approach can be directly utilized whenever the ultrasound focus size is of the order of the memory-effect range, independently of the scattering angle.
How does living on property taken from others affect voting behavior? Recent studies argued that benefiting from historical violence leads to support for the far right. We extend this literature with new theoretical insights and data from Israel, using case-specific variation in the nature of displacement to uncover heterogeneous treatment effects. Exploiting the coercion during the settlement of Jewish migrants on rural lands following the 1948 war, we show that living on lands taken from Palestinians consistently led to hawkish right-wing voting in the following 70 years. We also show that exposure to the ruins of the displaced villages increased right-wing voting and that the impact of intergroup contact is divergent: it decreased intolerant voting in most villages but increased it among Jewish communities that reside on violently taken land. Our results are robust when matching is used to account for several controls and spatiotemporal dependencies.
A coherent perfect absorber exploits the interferometric nature of light to deposit all of a light field’s incident energy into an otherwise weakly absorbing sample. The downside of this concept is that the necessary destructive interference in coherent perfect absorbers gets easily destroyed both by spectrally or spatially detuning the incoming light field. Each of these two limitations has recently been overcome by insights from exceptional-point physics and by using a degenerate cavity, respectively. Here, we show how these two concepts can be combined into a new type of cavity design, which allows broadband exceptional-point absorption of arbitrary wavefronts. We present two possible implementations of such a massively degenerate exceptional-point absorber and compare analytical results with numerical simulations.
We introduce a physics-based computational reconstruction framework for non-invasive photoacoustic tomography through a thick aberrating layer. Our wave-based approach leverages an analytic formulation of diffraction to beamform a photoacoustic image, when the aberrating layer profile is known. When the profile of the aberrating layer is unknown, the same analytical formulation serves as the basis for an automatic-differentiation regularized optimization algorithm that simultaneously reconstructs both the profile of the aberrating layer and the optically absorbing targets. Results from numerical studies and proof-of-concept experiments show promise for fast beamforming that takes into account diffraction effect occurring in the propagation through thick, highly-aberrating layers.
Stochastic orbital techniques offer reduced computational scaling and memory requirements to describe ground and excited states at the cost of introducing controlled statistical errors. Such techniques often rely on two basic operations, stochastic trace estimation and stochastic resolution of identity, both of which lead to statistical errors that scale with the number of stochastic realizations (\$N\_\\textbackslashxi\\$) as \$\textbackslashsqrt\N\_\\textbackslashxi\ˆ\-1\\\$. Reducing the statistical errors without significantly increasing \$N\_\\textbackslashxi\\$ has been challenging and is central to the development of efficient and accurate stochastic algorithms. In this work, we build upon recent progress made to improve stochastic trace estimation based on the ubiquitous Hutchinson's algorithm and propose a two-step approach for the stochastic resolution of identity, in the spirit of the Hutch++ method. Our approach is based on employing a randomized low-rank approximation followed by a residual calculation, resulting in statistical errors that scale much better than \$\textbackslashsqrt\N\_\\textbackslashxi\ˆ\-1\\\$. We implement the approach within the second-order Born approximation for the self-energy in the computation of neutral excitations and discuss three different low-rank approximations for the two-body Coulomb integrals. Tests on a series of hydrogen dimer chains with varying lengths demonstrate that the Hutch++-like approximations are computationally more efficient than both deterministic and purely stochastic (Hutchinson) approaches for low error thresholds and intermediate system sizes. Notably, for arbitrarily large systems, the Hutchinson-like approximation outperforms both deterministic and Hutch++-like methods.
We investigate the depletion contributions to the self-assembly of microcolloids on solid substrates. The assembly is driven by the exclusion of nanoparticles and nonadsorbing polymers from the depletion zone between the microcolloids in the liquid and the underlying substrate. The model system consists of 1 μm polystyrene particles that we deposit on a flat glass slab in an electrolyte solution. Using polystyrene nanoparticles and poly(acrylic acid) polymers as depleting agents, we demonstrate in our experiments that nanoparticle concentrations of 0.5% (w/v) support well-ordered packing of microcolloids on glass, while the presence of polymers leads to irregular aggregate deposition structures. A mixture of nanoparticles and polymers enhances the formation of colloidal aggregate and particulate surface coverage compared to using the polymers alone as a depletion agent. Moreover, tuning the polymer ionization state from pH 4 to 9 modifies the polymer conformational state and radius of gyration, which in turn alters the microcolloid deposition from compact multilayers to flocculated structures. Our study provides entropic strategies for manipulating particulate assembly on substrates from dispersed to continuous coatings.
