The SELFI (Scattering Assisted Localization Fluorescence Imaging) project takes off!
Financed from IIT and Crest Optics, SELFI aims to to achieve super resolution fluorescence imaging in opaque biological tissue with the aid of wavefront shaping techniques. In SELFI, I will play the role of the Principal Investigator, leading the a team of researchers and technicians to realize a proof of principle optical microscope.
A Post Doc position in “Optical Super-Resolution imaging by Wavefront Shaping in tissue” is available here (deadline 10 of Sept) . The candidate will take part of a larger team, working on fast wavefront-shaping experiment for the realization the device.
Contact me (marco dot leonetti at iit dot it) for further details.
Hyperuniformity is a state of matter between order and disorder. Hyperuniform structures are today object of intense study because may be employed to trap waves (and in particular light) in a “user-defined” fashion.
To design an hyperuniform structure is challenging: requires complex algorithms and large computational power because individual “atoms” of the structure have to be positioned satisfying rigid constraints. In a recently published paper, we demonstrate that laser speckle patterns may be tailored in a way in which intensity maxima satisfy the hyperuniformity condition i.e. it is possible to design an hyperuniform structure just by looking at maxima position of a designed speckle pattern. This result paves the way to a novel and faster strategy to design these structures, enabling direct fabrication through laser writing techniques.
The brain regions devoted to memory storage show an enhanced dilution, i.e. a reduced number of connections with respect to the maximum achievable (the fully connected configuration). Why evolution should have favored such an organization? Or in other words, why diluted networks are better than fully connected ones?
By resorting to numerical simulation performed with a simplified model, we show in a recent paper that the average number of attractors <C> (which are the dynamic neural activation structures responsible for the storage of memory) hosted by a diluted network, increases while increasing the network dilution ( \rho in the picture below ) reaching an optimum value for a dilution of 95%. This value of dilution is close to the one found in neocortex and hippocampus, thus possibly providing some hints to understand the mammalian brain functioning.
Anderson localization of light is traditionally described in analogy to electrons in a random potential. Within this description, the random potential depends on the wavelength of the incident light. For transverse Anderson localization, this leads to the prediction that the distribution of localization lengths—and, hence, its average—strongly depends on the wavelength.
In an alternative description, in terms of a spatially fluctuating electric modulus, this is not the case. Here, we report on an experimentum crucis in order to investigate the validity of the two conflicting theories using optical samples exhibiting transverse Anderson localization. We do not find any dependence of the observed average localization radii on the light wavelength. We conclude that the modulus-type description is the correct one and not the potential-type one.
The rich get richer or success breeds success effect, also called Matthew’s principle from the parable of the Talents in Matthew 25:14-30, has been invoked many times in the sociology of science to justify highly skewed distributions of bibliometric indicators measuring the scientific production of scholars. The basic underlying idea it is that if you have more, it’s easier to gain more. This is a consequence of “the process of allocation of rewards to scientists for their contributions” (recognition) “which in turn affects the flow of ideas and findings through the communication networks of science” generating a reputational effect.
Here, we propose a general explanation of the observed evidence by developing a straightforward model based on the following simple assumptions: (1) the materialist principle of the natural equality of human intelligence, (2) the success breeds success effect, which can be traced back to the Gospel parables about the Talents (Matthew) and Minas (Luke), and, (3) the recognition and reputation mechanism.
Finally, we are “feet on the ground” to build a new experimental setup in the CNR-Nanotec center in Lecce, following the road map of the LOCALITS project financed by the BrainsToSouth grant.
All the pieces are here… let’s start to build up everything!
The quantitative evaluation of Social Science and Humanities (SSH) and the investigation of the existing similarities between SSH and Life and Hard Sciences (LHS) represent the forefront of scientometrics research. We analyze the scientific production of the universe of Italian academic scholars, over a 10-year period across 2002–2012, from a national database built by the Italian National Agency for the Evaluation of Universities and Research Institutes. Here we demonstrate that all Italian scholars of SSH and LHS are equals, as far as their publishing habits. They share the same general law, which is lognormal. At the same time, however, they are different, because we measured their scientific production with different indicators required by the Italian law; we eliminated the “silent” scholars and obtained different scaling values—proxy of their productivity rates. Our findings may be useful to further develop indirect quali–quantitative comparative analysis across heterogeneous disciplines and, more broadly, to investigate the generative mechanisms behind the observed empirical regularities.
Anderson localization is a sophisticated phenomenon which is still not completely understood. In a recent paper, we propose a comprehensive mean field theory, (a combination of an effective-medium theory for transverse disorder with the self-consistent localization theory), which covers the relevant aspects of the phenomenon in the transverse case, that is in the case of light propagating in a system invariant along propagation direction and with the disorder on the transverse plane.
In particular, we explain the focusing mechanism leading to the establishment of narrow transparent channels along a disordered optical fiber.
Disorder is usually connected with opacity: the impossibility to transmit information due to the randomization of light paths. On the other hand, disorder is also responsible for a peculiar phenomenon which is the Anderson localization, trapping light into standing waves occupying a tiny space.
By exploiting light localization into an optical fiber, we demonstrated (in a recent journal paper) that disorder may be used to host single-modes: the purest form of light transmission allowing for light traveling without interfering with other optical paths. In this case, the disorder is responsible for a purer form of transparency and not for opacity.
The result may found applications in endoscopy and telecommunications.
A neural network (NN), an ensemble of interconnected neurons, is able to store memories in the so-called “fixed points”, steady activity patterns, which are the solutions of the neural dynamics and associated to the individual memory items. In past years, several works have been devoted to determine the maximum storage capacity of NN, especially for the case of the Hopfield network, the most popular kind of NN. Analyzing the thermodynamic limit of the statistical properties of the Hamiltonian corresponding to the Hopfield neural network, it has been shown in the literature that the retrieval errors diverge when the number of stored memory patterns (P) exceeds a fraction (≈ 14%) of the network size N. In a recently published paper, we study the storage performance of a generalized Hopfield model, where the diagonal elements of the connection matrix are allowed to be different from zero. We investigate this model at finite N. We give an analytical expression for the number of retrieval errors and show that, by increasing the number of stored patterns over a certain threshold, the errors start to decrease and reach values below unit for P ≫ N. We demonstrate that the strongest trade-off between efficiency and effectiveness relies on the number of patterns (P) that are stored in the network by appropriately fixing the connection weights. When P≫N and the diagonal elements of the adjacency matrix are not forced to be zero, the optimal storage capacity is obtained with a number of stored memories much larger than previously reported. This theory paves the way to the design of NN with high storage capacity and able to retrieve the desired pattern without distortions.