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Renal Cell Carcinoma is typically asymptomatic at the early stages for many patients. This leads to a late diagnosis of the tumor, where the curability likelihood is lower, and makes the mortality rate of Renal Cell Carcinoma high, with respect to its incidence rate. To increase the survival chance, a fast and correct categorization of the tumor subtype is paramount. Nowadays, computerized methods, based on artificial intelligence, represent an interesting opportunity to improve the productivity and the objectivity of the microscopy-based Renal Cell Carcinoma diagnosis. Nonetheless, much of their exploitation is hampered by the paucity of annotated dataset, essential for a proficient training of supervised machine learning technologies. This study sets out to investigate a novel self supervised training strategy for machine learning diagnostic tools, based on the multi-resolution nature of the histological samples. We aim at reducing the need of annotated dataset, without significantly reducing the accuracy of the tool. We demonstrate the classification capability of our tool on a whole slide imaging dataset for Renal Cancer subtyping, and we compare our solution with several state-of-the-art classification counterparts.

Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on variational inference, which is best suited for real-time execution on the edge. The proposed approach was validated on a robotic arm, part of a pilot production line, and compared with several state-of-the-art algorithms, obtaining the best trade-off between anomaly detection accuracy, power consumption and inference frequency on two different edge platforms.

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before., Comment: Accepted at the 26th Forum on specification and Design Languages (FDL 2023)

Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper we present GAN-DL, a Discriminator Learner based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images. We show that Wasserstein Generative Adversarial Networks combined with linear Support Vector Machines enable high-throughput compound screening based on raw images. We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines. In contrast to previous methods, our deep learning based approach does not require any annotation besides the one that is normally collected during the sample preparation process. We test our technique on the RxRx19a Sars-CoV-2 image collection. The dataset consists of fluorescent images that were generated to assess the ability of regulatory-approved or in late-stage clinical trials compound to modulate the in vitro infection from SARS-CoV-2 in both VERO and HRCE cell lines. We show that our technique can be exploited not only for classification tasks, but also to effectively derive a dose response curve for the tested treatments, in a self-supervised manner. Lastly, we demonstrate its generalization capabilities by successfully addressing a zero-shot learning task, consisting in the categorization of four different cell types of the RxRx1 fluorescent images collection.

The investigation of the energy frontier in physics requires novel concepts for future colliders. The idea of a muon collider is very appealing since it would allow to study particle collisions at up to tens of TeV energy, while offering a cleaner experimental environment with respect to hadronic colliders. One key element in the muon collider design is the low-emittance muon production. Recently,the Low EMittance Muon Accelerator (LEMMA) collaboration has explored the muon pair production close to its kinematic threshold by annihilating 45 GeV positrons with electrons in a low Z material target. In this configuration, muons are emerging from the target with a naturally low-emittance. In this paper we describe the performance of a system, to study this production mechanism, that consists in several segmented absorbers with alternating active layers composed of fast Cherenkov detectors together with a muon identification technique based on this detector. Passive layers were made of tungsten. We collected data corresponding to muon and electron beams produced at the H2 line in the North Area of the European Organization for Nuclear Research (CERN) in September 2018.

Convolutional Neural Networks (CNNs) are supposed to be fed with only high-quality annotated datasets. Nonetheless, in many real-world scenarios, such high quality is very hard to obtain, and datasets may be affected by any sort of image degradation and mislabelling issues. This negatively impacts the performance of standard CNNs, both during the training and the inference phase. To address this issue we propose Wise2WipedNet (W2WNet), a new two-module Convolutional Neural Network, where a Wise module exploits Bayesian inference to identify and discard spurious images during the training, and a Wiped module takes care of the final classification while broadcasting information on the prediction confidence at inference time. The goodness of our solution is demonstrated on a number of public benchmarks addressing different image classification tasks, as well as on a real-world case study on histological image analysis. Overall, our experiments demonstrate that W2WNet is able to identify image degradation and mislabelling issues both at training and at inference time, with a positive impact on the final classification accuracy.

Software reliability is a primary concern in the construction of software, and thus a fundamental component in the definition of software quality. Analyzing software reliability requires a specification of the intended behavior of the software under analysis, and at the source code level, such specifications typically take the form of assertions. Unfortunately, software many times lacks such specifications, or only provides them for scenario-specific behaviors, as assertions accompanying tests. This issue seriously diminishes the analyzability of software with respect to its reliability. In this paper, we tackle this problem by proposing a technique that, given a Java method, automatically produces a specification of the method's current behavior, in the form of postcondition assertions. This mechanism is based on generating executions of the method under analysis to obtain valid pre/post state pairs, mutating these pairs to obtain (allegedly) invalid ones, and then using a genetic algorithm to produce an assertion that is satisfied by the valid pre/post pairs, while leaving out the invalid ones. The technique, which targets in particular methods of reference-based class implementations, is assessed on a benchmark of open source Java projects, showing that our genetic algorithm is able to generate post-conditions that are stronger and more accurate, than those generated by related automated approaches, as evaluated by an automated oracle assessment tool. Moreover, our technique is also able to infer an important part of manually written rich postconditions in verified classes, and reproduce contracts for methods whose class implementations were automatically synthesized from specifications.

The muon collider represents one of the most promising solutions for a future machine exploring the high energy frontier, but several challenges due to the 2.2 $\mu$sec muon lifetime at rest have to be carefully considered. The LEMMA project is investigating the possibility of producing low emittance muon/antimuon pairs from the e$^+$e$^-$ annihilation process at threshold energy, resulting in small transverse emittance beams without any additional beam cooling. However most of the measurements available are performed at higher $\sqrt{s}$ values. It is therefore necessary to measure muons production in positron annihilation at threshold energy and compare the experimental results with the predictions in this specific energy regime. Apart from being a topic of physical interest by itself, these near to threshold measurements can have a sizeable impact on the estimation of the ultimate luminosity achievable in a muon collider with the LEMMA injection scheme., Comment: 13 pages, 8 figures

A full-size prototype of a Micromegas precision tracking chamber for the upgrade of the ATLAS detector at the LHC Collider has been built between October 2015 and April 2016. This paper describes in detail the procedures used in constructing the single modules of the chamber in various INFN laboratories and the final assembly at the Laboratori Nazionali di Frascati (LNF). Results of the chamber exposure to the CERN SPS/H8 beam line in June 2016 are also presented. The performances achieved in the construction and the results of the test beam are compared with the requirements, which are imposed by the severe environment during the data-taking of the LHC foreseen for the next years., Comment: Contact editors: G. Mancini, A. Kourkoumeli-Charalampidi

The skin forms an efficient barrier against the environment, and rapid cutaneous wound healing after injury is therefore essential. Healing of the uppermost layer of the skin, the epidermis, involves collective migration of keratinocytes, which requires coordinated polarization of the cells. To study this process, we developed a model that allows analysis of live-cell images of migrating keratinocytes in culture based on a small number of parameters, including the radius of the cells, their mass and their polarization. This computational approach allowed the analysis of cell migration at the front of the wound and a reliable identification and quantification of the impaired polarization and migration of keratinocytes from mice lacking fibroblast growth factors 1 and 2 an established model of impaired healing. Therefore, our modeling approach is suitable for large-scale analysis of migration phenotypes of cells with specific genetic defects or upon treatment with different pharmacological agents., Comment: 27 pages, 3 figures

The Mu2e experiment at Fermilab will search for charged lepton flavor violation via the coherent conversion process mu- N --> e- N with a sensitivity approximately four orders of magnitude better than the current world's best limits for this process. The experiment's sensitivity offers discovery potential over a wide array of new physics models and probes mass scales well beyond the reach of the LHC. We describe herein the preliminary design of the proposed Mu2e experiment. This document was created in partial fulfillment of the requirements necessary to obtain DOE CD-2 approval., Comment: compressed file, 888 pages, 621 figures, 126 tables; full resolution available at http://mu2e.fnal.gov; corrected typo in background summary, Table 3.4

Mu2e at Fermilab will search for charged lepton flavor violation via the coherent conversion process mu- N --> e- N with a sensitivity approximately four orders of magnitude better than the current world's best limits for this process. The experiment's sensitivity offers discovery potential over a wide array of new physics models and probes mass scales well beyond the reach of the LHC. We describe herein the conceptual design of the proposed Mu2e experiment. This document was created in partial fulfillment of the requirements necessary to obtain DOE CD-1 approval, which was granted July 11, 2012., Comment: 562 pages, 339 figures

Results from the gas gain monitoring (GGM) system for the muon detector using RPC in the CMS experiment at the LHC is presented. The system is designed to provide fast and accurate determination of any shift in the working point of the chambers due to gas mixture changes., Comment: 8 pages, 7 figures, uses lnfprepCMS.sty, presented by S. Colafranceschi at IEEE 08 Dresden (Germany) November 2008

The status of the CMS RPC Gas Gain Monitoring (GGM) system developed at the Frascati Laboratory of INFN (Istituto Nazionale di Fisica Nucleare) is reported on. The GGM system is a cosmic ray telescope based on small RPC detectors operated with the same gas mixture used by the CMS RPC system. The GGM gain and efficiency are continuously monitored on-line, thus providing a fast and accurate determination of any shift in working point conditions. The construction details and the first result of GGM commissioning are described., Comment: 8 pages, 9 figures, uses lnfprepCMS.sty, presented by L. Benussi at RPC07, Mumbai, INDIA 2008

The Resistive Plate Chambers (RPC) detector of the CMS experiment at the LHC proton collider (CERN, Switzerland) will employ an online gas analysis and monitoring system of the freon-based gas mixture used. We give an overview of the CMS RPC gas system, describe the project parameters and first results on gas-chromatograph analysis. Finally, we report on preliminary results for a set of monitor RPC., Comment: 9 pages, 8 figures. Presented by Stefano Bianco (Laboratori Nazionali di Frascati dell'INFN) at the IEEE NSS, San Diego (USA), October 2006

There are several educational, social, and economic trends that converge to increase the need to understand how adolescents can best become more involved in the education of younger children. Many educators have pointed to the benefits of the rich and complex learning that occurs during cross-age tutoring. Current reforms in public school education feature an emphasis on teenage service to the community, including younger children, as an integral part of the high school program. Increased demands on parents to work outside the home have opened up new requirements for after-school day care for young children who can benefit from cross-age instruction. The literature on adolescents as educational instructors has emphasized the more limited role of "tutor" of material already presented rather than "teacher" of material new to the learner. This limitation has been described as being due to a restricted range of instructional moves, lack of sophistication in the tutor, and curricula which do not focus on the specific skills of the adolescent as an instructor. There is a need to better understand the potentialities of adolescents as science instructors for younger children. The purpose of this study done by the 4-H Youth Experiences in Science (YES) Project is to illuminate the interactions and other dynamics of adolescents as teachers in a science curriculum that was planned to use teenagers as the primary instructional source teachers rather than merely as tutors. This study found that the nature of child science (the instructional goal) was particularly well suited to the instructional strengths of teenagers. The adolescents were found to be effective at initiating exploration, play, manipulations, and testing of ideas and materials, all primary goals of child science learning. The implications of this study are important so that educators can plan curricula to take advantage of adolescents as effective instructors of child science. (Contains 29 references.) (DKM)

This report is part of a paper set which focuses on a project designated as "Applying Research to Teacher Education (ARTE)." It reviews application possibilities of teacher effectiveness research in elementary classrooms to science teaching at the secondary level. Mills College (Oakland, California) was one of the sites involved in the project. A situational analysis of its teacher preparation program provided the bases for the development, implementation, and evaluation of techniques for applying research findings in both preservice and inservice teacher education programs. Four cooperating teachers and six student teachers of elementary school mathematics participated in the study. The project suggested that skill development in assessing student learning was needed and that training in measurement of academic learning time and active teaching behaviors with student and cooperating teachers would help. Highlights from the interviews of the participating student and cooperating teachers are presented. All participants reported changes in their teaching as a result of the project ideas and the associated feedback that they received about their teaching. The project was also noted to offer collaboration opportunities between teachers and research project personnel. (ML)

Researchers evaluated community social action education programs for at-risk youths using a portfolio and panel review evaluation process. Each program assembled portfolios that an expert panel analyzed for successful program components. Panelists then participated in focus groups. Results detailed 13 features necessary to make such community action programs succeed. (SM)

Findings are summaried related to restricting minority participation in the sciences and science-related professions. Four factors found most pervasive and important were: (1) lack of role models; (2) counseling; (3) attitude; and (4) preparation. Effects of both teacher attitude and student attitude were reviewed within the attitude area. The paper also reviews several promising practices currently being implemented to upgrade minority participation, including the roles of industry and institutions of higher education as well as collaborative efforts. (Author/DS)