Components Qualification for a Possible use in the Mu2e Calorimeter Waveform Digitizers

The Mu2e experiment at Fermilab searches for the charged flavor violating conversion of a muon into an electron in the Coulomb field of a nucleus. The detector consists of a straw tube tracker and a CSI crystal electromagnetic calorimeter, both housed in a superconducting solenoid. Both the front-end and the digital electronics, located inside the cryostat, will be operated in vacuum under a 1 T magnetic field, having to sustain the high flux of neutrons and ionizing particles coming from the muons stopping target. These harsh experimental conditions make the design of the calorimeter waveform digitizer quite challenging. All the selected commercial devices must be tested individually and qualified for radiation hardness and operation in high magnetic field. At the moment the expected particles flux and spectra at the digitizers location are not completely simulated and we are using initial rough estimates to select the components for the first prototype. We are gaining experience in the qualification procedures using the selected components but the choice will be frozen only when dose and neutron flux simulations will be completed. The experimental results of the first qualification campaign are presented.Comment: TWEPP 2016 - Topical Workshop on Electronics for Particle Physics, 26-30 September 2016, Karlsruhe Institute of Technology (KIT

Magnetic force microscopy and simulations of colloidal iron nanaparticles

Colloidal iron nanoparticles with a core size of 10.6 nm were examined using magnetic force microscopy. Surprisingly, single nanoparticles were more prominently visible in magnetic force gradient images than clusters. A simple qualitative model is proposed to explain this observation, speculating that the local field produced by a cluster of particles may be sufficient to align their moments in the plane of the cluster, even though the particles are superparamagnetic. An alternative possibility of spin glass formation within clusters is also considered. Calculations performed with nanoparticles represented as single dipoles appear to match experimental data quite well

GLocalX - From Local to Global Explanations of Black Box AI Models

Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications

Opening the black box: a primer for anti-discrimination

The pervasive adoption of Artificial Intelligence (AI) models in the modern information society, requires counterbalancing the growing decision power demanded to AI models with risk assessment methodologies. In this paper, we consider the risk of discriminatory decisions and review approaches for discovering discrimination and for designing fair AI models. We highlight the tight relations between discrimination discovery and explainable AI, with the latter being a more general approach for understanding the behavior of black boxes

Visually driven analysis of movement data by progressive clustering

The paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, that is, sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, (1) is difficult to build, (2) would require much time to compute, and (3) might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of ‘cheap’ distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real data set. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories

Pre-Production and Quality Assurance of the Mu2e Calorimeter Silicon Photomultipliers

The Mu2e electromagnetic calorimeter has to provide precise information on energy, time and position for $\sim$100 MeV electrons. It is composed of 1348 un-doped CsI crystals, each coupled to two large area Silicon Photomultipliers (SiPMs). A modular and custom SiPM layout consisting of a 3$\times$2 array of 6$\times$6 mm$^2$ UV-extended monolithic SiPMs has been developed to fulfill the Mu2e calorimeter requirements and a pre-production of 150 prototypes has been procured by three international firms (Hamamatsu, SensL and Advansid). A detailed quality assurance process has been carried out on this first batch of photosensors: the breakdown voltage, the gain, the quenching time, the dark current and the Photon Detection Efficiency (PDE) have been determined for each monolithic cell of each SiPMs array. One sample for each vendor has been exposed to a neutron fluency up to $\sim$8.5~$\times$~10$^{11}$ 1 MeV (Si) eq. n/cm$^{2}$ and a linear increase of the dark current up to tens of mA has been observed. Others 5 samples for each vendor have undergone an accelerated aging in order to verify a Mean Time To Failure (MTTF) higher than $\sim$10$^{6}$ hours.Comment: NDIP 2017 - New Developments In Photodetection, 3-7 July 2017, Tours (France

Silicon microcantilever sensors to detect the reversible conformational change of a molecular switch, Spiropyan

The high sensitivity of silicon microcantilever sensors has expanded their use in areas ranging from gas sensing to bio-medical applications. Photochromic molecules also represent promising candidates for a large variety of sensing applications. In this work, the operating principles of these two sensing methods are combined in order to detect the reversible conformational change of a molecular switch, spiropyran. Thus, arrays of silicon microcantilever sensors were functionalized with spiropyran on the gold covered side and used as test microcantilevers. The microcantilever deflection response was observed, in five sequential cycles, as the transition from the spiropyran (SP) (CLOSED) to the merocyanine (MC) (OPEN) state and vice-versa when induced by UV and white light LED sources, respectively, proving the reversibility capabilities of this type of sensor. The microcantilever deflection direction was observed to be in one direction when changing to the MC state and in the opposite direction when changing back to the SP state. A tensile stress was induced in the microcantilever when the SP to MC transition took place, while a compressive stress was observed for the reverse transition. These different type of stresses are believed to be related to the spatial conformational changes induced in the photochromic molecule upon photo-isomerisation

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good. Graphical abstrac