One dimensional Fokker-Planck reduced dynamics of decision making models in Computational Neuroscience

We study a Fokker-Planck equation modelling the firing rates of two interacting populations of neurons. This model arises in computational neuroscience when considering, for example, bistable visual perception problems and is based on a stochastic Wilson-Cowan system of differential equations. In a previous work, the slow-fast behavior of the solution of the Fokker-Planck equation has been highlighted. Our aim is to demonstrate that the complexity of the model can be drastically reduced using this slow-fast structure. In fact, we can derive a one-dimensional Fokker-Planck equation that describes the evolution of the solution along the so-called slow manifold. This permits to have a direct efficient determination of the equilibrium state and its effective potential, and thus to investigate its dependencies with respect to various parameters of the model. It also allows to obtain information about the time escaping behavior. The results obtained for the reduced 1D equation are validated with those of the original 2D equation both for equilibrium and transient behavior

No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track a User's Position

News reports of the last few years indicated that several intelligence agencies are able to monitor large networks or entire portions of the Internet backbone. Such a powerful adversary has only recently been considered by the academic literature. In this paper, we propose a new adversary model for Location Based Services (LBSs). The model takes into account an unauthorized third party, different from the LBS provider itself, that wants to infer the location and monitor the movements of a LBS user. We show that such an adversary can extrapolate the position of a target user by just analyzing the size and the timing of the encrypted traffic exchanged between that user and the LBS provider. We performed a thorough analysis of a widely deployed location based app that comes pre-installed with many Android devices: GoogleNow. The results are encouraging and highlight the importance of devising more effective countermeasures against powerful adversaries to preserve the privacy of LBS users.Comment: 14 pages, 9th International Conference on Network and System Security (NSS 2015

Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience. Computers learn how to recognize patterns, make unintended decisions, or react to a dynamic environment. Certain trained machines may be more effective than others because they are based on more suitable ML algorithms or because they were trained through superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. While much research has been performed about the privacy of the elements of training sets, in this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. This kind of information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights

Association between attention and heart rate fluctuations in pathological worriers

Recent data suggests that several psychopathological conditions are associated with alterations in the variability of behavioral and physiological responses. Pathological worry, defined as the cognitive representation of a potential threat, has been associated with reduced variability of heart beat oscillations (i.e., decreased heart rate variability; HRV) and lapses of attention indexed by reaction times (RTs). Clinical populations with attention deficit show RTs oscillation around 0.05 and 0.01 Hz when performing a sustained attention task. We tested the hypothesis that people who are prone to worry do it in a predictable oscillating pattern revealed through recurrent lapses in attention and concomitant oscillating HRV. Sixty healthy young adults (50% women) were recruited: 30 exceeded the clinical cut-off on the Penn State Worry Questionnaire (PSWQ; High-Worry, HW); the remaining 30 constituted the Low-Worry (LW) group. After a diagnostic assessment, participants performed two 15-min sustained attention tasks, interspersed by a standardized worry-induction procedure. RTs, HRV and moods were assessed. The analyses of the frequency spectrum showed that the HW group presents a significant higher and constant peak of RTs oscillation around 0.01 Hz (period 100 s) after the induction of worry, in comparison with their baseline and with the LW group that was not responsive to the induction procedure. Physiologically, the induction significantly reduced high-frequency HRV and such reduction was associated with levels of self-reported worry. Results are coherent with the oscillatory nature of the default mode network (DMN) and further confirm an association between cognitive rigidity and autonomic nervous system inflexibility

The histone H3K4 demethylase JARID1A directly interacts with haematopoietic transcription factor GATA1 in erythroid cells through its second PHD domain

Chromatin remodelling and transcription factors play important roles in lineage commitment and development through control of gene expression. Activation of selected lineage-specific genes and repression of alternative lineage-affiliated genes results in tightly regulated cell differentiation transcriptional programmes. However, the complex functional and physical interplay between transcription factors and chromatin modifying enzymes remains elusive. Recent evidence has implicated histone demethylases in normal haematopoietic differentiation as well as in malignant haematopoiesis. Here we report an interaction between H3K4 demethylase JARID1A and the haematopoietic-specific master transcription proteins SCL and GATA1 in red blood cells. Specifically, we observe a direct physical contact between GATA1 and the second PHD domain of JARID1A. This interaction has potential implications for normal and malignant haematopoiesis

Household’s Consumer Behaviour: Economic Recession and Quality of Institution. The case of Italy

The recent crisis saw the Italian household cutting consumption spending reshaping expenses behaviours. In this respect the role of macroeconomic fac- tors like institutions has received poor theoretical treatment and is scarcely proven. Based on the Istat Household Budgetary Survey, this paper focuses on the effects of crisis on selected consumption items (energy; healthcare; leisure; travels; eating out) controlling for micro and macro factors, such the Institutional-Quality-Index (IQI) and the regional GDP. IQI emerge as cru- cial in determining household healthcare expenses before the recession: where the local endowment of institutional quality is higher, the private expenses for medical/dental care, pharmaceuticals and diagnostic tests, significantly decrease. The higher the quality of institutional quality, and then of pub- lic health services, the lower the private expenditure. The recession resets the impact of IQI and increases the positive correlation with strictly microe- conomic variables such as income, wealth and the number of household’s earners

Automated detection of impulsive movements in HCI

This paper introduces an algorithm for automatically measuring impulsivity. This can be used as a major expressive movement feature in the development of systems for realtime analysis of emotion expression from human full-body movement, a research area which has received increased attention in the affective computing community. In particular, our algorithm is developed in the framework of the EUH2020- ICT Project DANCE aiming at investigating techniques for sensory substitution in blind people, in order to enable perception of and participation in non-verbal, artistic whole-body experiences. The algorithm was tested by applying it to a reference archive of short dance performances. The archive includes a collection of both impulsive and fluid movements. Results show that our algorithm can reliably distinguish impulsive vs. sudden performances

Social retrieval of music content in multi-user performance

An emerging trend in interactive music performance consists of the audience directly participating in the performance by means of mobile devices. This is a step forward with respect to concepts like active listening and collaborative music making: non-expert members of an audience are enabled to directly participate in a creative activity such as the performance. This requires the availability of technologies for capturing and analysing in real-time the natural behaviour of the users/performers, with particular reference to non- verbal expressive and social behaviour. This paper presents a prototype of a non-verbal expressive and social search engine and active listening system, enabling two teams of non-expert users to act as performers. The performance consists of real-time sonic manipulation and mixing of music pieces selected according to features characterising performers\u2019 movements captured by mobile devices. The system is described with specific reference to the SIEMPRE Podium Performance, a non-verbal socio-mobile music performance presented at the Art & ICT Exhibition that took place in Vilnius (LI) in November 2013