The hyperbolic lattice point problem in conjugacy classes

For Γ a cocompact or cofinite Fuchsian group, we study the hyperbolic lattice point problem in conjugacy classes, which is a modification of the classical hyperbolic lattice point problem. We use large sieve inequalities for the Riemann surfaces Γ∖ℍ to obtain average results for the error term, which are conjecturally optimal. We give a new proof of the error bound O(X2/3), due to Good. For SL2(ℤ) we interpret our results in terms of indefinite quadratic forms

Audio-visual object localization and separation using low-rank and sparsity

The ability to localize visual objects that are associated with an audio source and at the same time seperate the audio signal is a corner stone in several audio-visual signal processing applications. Past efforts usually focused on localizing only the visual objects, without audio separation abilities. Besides, they often rely computational expensive pre-processing steps to segment images pixels into object regions before applying localization approaches. We aim to address the problem of audio-visual source localization and separation in an unsupervised manner. The proposed approach employs low-rank in order to model the background visual and audio information and sparsity in order to extract the sparsely correlated components between the audio and visual modalities. In particular, this model decomposes each dataset into a sum of two terms: the low-rank matrices capturing the background uncorrelated information, while the sparse correlated components modelling the sound source in visual modality and the associated sound in audio modality. To this end a novel optimization problem, involving the minimization of nuclear norms and matrix ℓ1-norms is solved. We evaluated the proposed method in 1) visual localization and audio separation and 2) visual-assisted audio denoising. The experimental results demonstrate the effectiveness of the proposed method

An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks

A Network Intrusion Detection System is a critical component of every internet-connected system due to likely attacks from both external and internal sources. Such Security systems are used to detect network born attacks such as flooding, denial of service attacks, malware, and twin-evil intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in 5G and IoT network. We evaluated the algorithm with the benchmark Aegean Wi-Fi Intrusion dataset. Our results showed an excellent performance with an overall detection accuracy of 99.9% for Flooding, Impersonation and Injection type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm

Online attention for interpretable conflict estimation in political debates

Conflict arises naturally in dyadic interactions when involved individuals act on incompatible goals, interests, or actions. In this paper, the problem of conflict intensity estimation from audiovisual recordings is addressed. To this end, we propose an online attention-based neural network in order to learn a mapping from a sequence of audiovisual features to time-series describing conflict intensity. The proposed method is evaluated by conducting experiments in conflict intensity estimation by employing the CONFER dataset. Experimental results indicate the superiority of the proposed model compared to the state of the art. Furthermore, we demonstrate that by incorporating sparsity in the model, the origin of conflict can be traced back to specific key frames facilitating the interpretation of conflict escalation

Blind audio-visual localization and separation via low-rank and sparsity

The ability to localize visual objects that are associated with an audio source and at the same time to separate the audio signal is a cornerstone in audio-visual signal-processing applications. However, available methods mainly focus on localizing only the visual objects, without audio separation abilities. Besides that, these methods often rely on either laborious preprocessing steps to segment video frames into semantic regions, or additional supervisions to guide their localization. In this paper, we aim to address the problem of visual source localization and audio separation in an unsupervised manner and avoid all preprocessing or post-processing steps. To this end, we devise a novel structured matrix decomposition method that decomposes the data matrix of each modality as a superposition of three terms: 1) a low-rank matrix capturing the background information; 2) a sparse matrix capturing the correlated components among the two modalities and, hence, uncovering the sound source in visual modality and the associated sound in audio modality; and 3) a third sparse matrix accounting for uncorrelated components, such as distracting objects in visual modality and irrelevant sound in audio modality. The generality of the proposed method is demonstrated by applying it onto three applications, namely: 1) visual localization of a sound source; 2) visually assisted audio separation; and 3) active speaker detection. Experimental results indicate the effectiveness of the proposed method on these application domains

Can one hear the shape of the Universe?

It is shown that the recent observations of NASA's explorer mission "Wilkinson Microwave Anisotropy Probe" (WMAP) hint that our Universe may possess a non-trivial topology. As an example we discuss the Picard space which is stretched out into an infinitely long horn but with finite volume.Comment: 4 page

Mode-hop-free tuning over 135 GHz of external cavity diode lasers without anti-reflection coating

We report an external cavity diode laser (ECDL), using a diode whose front facet is not antireflection (AR) coated, that has a mode-hop-free (MHF) tuning range greater than 135 GHz. We achieved this using a short external cavity and by simultaneously tuning the internal and external modes of the laser. We find that the precise location of the pivot point of the grating in our laser is less critical than commonly believed. The general applicability of the method, combined with the compact portable mechanical and electronic design, makes it well suited for both research and industrial applications.Comment: 5 pages, 5 figure

A branch and efficiency algorithm for the optimal design of supply chain networks

Supply chain operations directly affect service levels. Decision on amendment of facilities is generally decided based on overall cost, leaving out the efficiency of each unit. Decomposing the supply chain superstructure, efficiency analysis of the facilities (warehouses or distribution centers) that serve customers can be easily implemented. With the proposed algorithm, the selection of a facility is based on service level maximization and not just cost minimization as this analysis filters all the feasible solutions utilizing Data Envelopment Analysis (DEA) technique. Through multiple iterations, solutions are filtered via DEA and only the efficient ones are selected leading to cost minimization. In this work, the problem of optimal supply chain networks design is addressed based on a DEA based algorithm. A Branch and Efficiency (B&E) algorithm is deployed for the solution of this problem. Based on this DEA approach, each solution (potentially installed warehouse, plant etc) is treated as a Decision Making Unit, thus is characterized by inputs and outputs. The algorithm through additional constraints named “efficiency cuts”, selects only efficient solutions providing better objective function values. The applicability of the proposed algorithm is demonstrated through illustrative examples

Transition Radiation Spectra of Electrons from 1 to 10 GeV/c in Regular and Irregular Radiators

We present measurements of the spectral distribution of transition radiation generated by electrons of momentum 1 to 10 GeV/c in different radiator types. We investigate periodic foil radiators and irregular foam and fiber materials. The transition radiation photons are detected by prototypes of the drift chambers to be used in the Transition Radiation Detector (TRD) of the ALICE experiment at CERN, which are filled with a Xe, CO2 (15 %) mixture. The measurements are compared to simulations in order to enhance the quantitative understanding of transition radiation production, in particular the momentum dependence of the transition radiation yield.Comment: 18 pages, 15 figures, submitted to Nucl. Instr. Meth. Phys. Res.