Artificial Intelligence

Rumor spreading and conductance

In this article, we study the completion time of the PUSH-PULL variant of rumor spreading, also known as randomized broadcast.We show that if a network has n nodes and conductance φ then, with high probability, PUSH-PULL will deliver themessage to all nodes in the graph within O(logn/φ) many communication rounds. This bound is best possible. We also give an alternative proof that the completion time of PUSH-PULL is bounded by a polynomial in logn/φ, based on graph sparsification.

Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation

Mobile biometrics technologies are nowadays the new frontier for secure use of data and services, and are considered particularly important due to the massive use of handheld devices in the entire world. Among the biometric traits with potential to be used in mobile settings, the iris/ocular region is a natural candidate, even considering that further advances in the technology are required to meet the operational requirements of such ambitious environments. Aiming at promoting these advances, we organized the Mobile Iris Challenge Evaluation (MICHE)-I contest.

Biopen–Fusing password choice and biometric interaction at presentation level

The paper presents experiments with the home-made, low-cost prototype of a sensor-equipped pen for handwriting-based biometric authentication. The pen allows to capture the dynamics of user writing on normal paper, while producing a kind of password (passphrase) chosen in advance. The use of a word of any length instead of the user's signature makes the approach more robust to spoofing, since there is no repetitive pattern to steal. Moreover, if the template gets violated, this is much less harmful than signature catch.

Results from MICHE II - Mobile Iris CHallenge Evaluation II

Mobile biometrics represent the new frontier of authentication. The most appealing feature of mobile devices is the wide availability and the presence of more and more reliable sensors for capturing biometric traits, e.g., cameras and accelerometers. Moreover, they more and more often store personal and sensitive data, that need to be protected. Doing this on the same device using biometrics to enforce security seems a natural solution. This makes this research topic attracting and generally promising.

Algorithms for ?p Low Rank Approximation

We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entry-wise ?p-approximation error, for any P ? 1; the case p = 2 is the classical SVD problem. We obtain the first provably good approximation algorithms for this version of low-rank approximation that work for every value of p ? 1, including p = ?. Our algorithms are simple, easy to implement, work well in practice, and illustrate interesting tradeoffs between the approximation quality, the running time, and the rank of the approximating matrix.

Automatic decoding of input sinusoidal signal in a neuron model. Improved SNR spectrum by low-pass homomorphic filtering

The principles on how neurons encode and process information from low-level stimuli are still open questions in neuroscience. Neuron models represent useful tools to answer this question but a sensitive method is needed to decode the input information embedded in the neuron spike sequence. In this work, we developed an automatic decoding procedure based on the SNR spectrum improved by low-pass homomorphic filtering. The procedure was applied to a stochastic Hodgkin Huxley neuron model forced by a low-level sinusoidal signal in the range 50 Hz-300 Hz.

Encoding and Simulating the Past. A Machine Learning Approach to the Archaeological Information

The encoding of the spatial-temporal archeological, historical and anthropological records can be considered an ideal-typical representation of the human reasoning and thus also an artificial membrane interposed between the researcher and the past. These membranes are here considered artificial networks and can undergo interrogation processes through the most advanced analytical tools for learning and modeling complex configurations.

Pooling or sampling: Collective dynamics for electrical flow estimation

The computation of electrical flows is a crucial primitive for many recently proposed optimization algorithms on weighted networks. While typically implemented as a centralized subroutine, the ability to perform this task in a fully decentralized way is implicit in a number of biological systems. Thus, a natural question is whether this task can provably be accomplished in an efficient way by a network of agents executing a simple protocol.

SapienzaAI

Italiano

Dual NVIDIA DGX-1 AI System providing a 2 petaFLOPS computing power optimised for Deep Learning. The two machines are connected with IB EDR high performance network.
System specs:
2 x NVIDIA DGX-1 systems, each one cnsisting in 8 NVIDIA® Tesla V100 GPUs with 256 GB total GPU-RAM, 40960 CUDA cores, 5120 Tenosr cores, 512 GB system memory, 8 TB storage, 4 IB EDR

Improving the integration between BIMs and Agent-Based Simulations. The Swarm Building Modeling – SBM

The disruptive factor of the current industrial sectors is the constant spreading of ICT technology into produc-tion cycles. Following this trend, several companies improved their productive factors towards the optimiza-tion of processes through the digitalization of themselves, also thanks to public and private funds.

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