NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition

Long-tailed visual recognition has received increasing attention in recent years. Due to the extremely imbalanced data distribution in long-tailed learning, the learning process shows great uncertainties. For example, the predictions of different experts on the same image vary remarkably despite the same training settings. To alleviate the uncertainty, we propose a Nested Collaborative Learning (NCL++) which tackles the long-tailed learning problem by a collaborative learning. To be specific, the collaborative learning consists of two folds, namely inter-expert collaborative learning (InterCL) and intra-expert collaborative learning (IntraCL). In-terCL learns multiple experts collaboratively and concurrently, aiming to transfer the knowledge among different experts. IntraCL is similar to InterCL, but it aims to conduct the collaborative learning on multiple augmented copies of the same image within the single expert. To achieve the collaborative learning in long-tailed learning, the balanced online distillation is proposed to force the consistent predictions among different experts and augmented copies, which reduces the learning uncertainties. Moreover, in order to improve the meticulous distinguishing ability on the confusing categories, we further propose a Hard Category Mining (HCM), which selects the negative categories with high predicted scores as the hard categories. Then, the collaborative learning is formulated in a nested way, in which the learning is conducted on not just all categories from a full perspective but some hard categories from a partial perspective. Extensive experiments manifest the superiority of our method with outperforming the state-of-the-art whether with using a single model or an ensemble. The code will be publicly released.Comment: arXiv admin note: text overlap with arXiv:2203.1535

Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baseline

Existing audio-visual event localization (AVE) handles manually trimmed videos with only a single instance in each of them. However, this setting is unrealistic as natural videos often contain numerous audio-visual events with different categories. To better adapt to real-life applications, in this paper we focus on the task of dense-localizing audio-visual events, which aims to jointly localize and recognize all audio-visual events occurring in an untrimmed video. The problem is challenging as it requires fine-grained audio-visual scene and context understanding. To tackle this problem, we introduce the first Untrimmed Audio-Visual (UnAV-100) dataset, which contains 10K untrimmed videos with over 30K audio-visual events. Each video has 2.8 audio-visual events on average, and the events are usually related to each other and might co-occur as in real-life scenes. Next, we formulate the task using a new learning-based framework, which is capable of fully integrating audio and visual modalities to localize audio-visual events with various lengths and capture dependencies between them in a single pass. Extensive experiments demonstrate the effectiveness of our method as well as the significance of multi-scale cross-modal perception and dependency modeling for this task.Comment: Accepted by CVPR202

Novel support vector machines for diverse learning paradigms

This dissertation introduces novel support vector machines (SVM) for the following traditional and non-traditional learning paradigms: Online classification, Multi-Target Regression, Multiple-Instance classification, and Data Stream classification. Three multi-target support vector regression (SVR) models are first presented. The first involves building independent, single-target SVR models for each target. The second builds an ensemble of randomly chained models using the first single-target method as a base model. The third calculates the targets\u27 correlations and forms a maximum correlation chain, which is used to build a single chained SVR model, improving the model\u27s prediction performance, while reducing computational complexity. Under the multi-instance paradigm, a novel SVM multiple-instance formulation and an algorithm with a bag-representative selector, named Multi-Instance Representative SVM (MIRSVM), are presented. The contribution trains the SVM based on bag-level information and is able to identify instances that highly impact classification, i.e. bag-representatives, for both positive and negative bags, while finding the optimal class separation hyperplane. Unlike other multi-instance SVM methods, this approach eliminates possible class imbalance issues by allowing both positive and negative bags to have at most one representative, which constitute as the most contributing instances to the model. Due to the shortcomings of current popular SVM solvers, especially in the context of large-scale learning, the third contribution presents a novel stochastic, i.e. online, learning algorithm for solving the L1-SVM problem in the primal domain, dubbed OnLine Learning Algorithm using Worst-Violators (OLLAWV). This algorithm, unlike other stochastic methods, provides a novel stopping criteria and eliminates the need for using a regularization term. It instead uses early stopping. Because of these characteristics, OLLAWV was proven to efficiently produce sparse models, while maintaining a competitive accuracy. OLLAWV\u27s online nature and success for traditional classification inspired its implementation, as well as its predecessor named OnLine Learning Algorithm - List 2 (OLLA-L2), under the batch data stream classification setting. Unlike other existing methods, these two algorithms were chosen because their properties are a natural remedy for the time and memory constraints that arise from the data stream problem. OLLA-L2\u27s low spacial complexity deals with memory constraints imposed by the data stream setting, and OLLAWV\u27s fast run time, early self-stopping capability, as well as the ability to produce sparse models, agrees with both memory and time constraints. The preliminary results for OLLAWV showed a superior performance to its predecessor and was chosen to be used in the final set of experiments against current popular data stream methods. Rigorous experimental studies and statistical analyses over various metrics and datasets were conducted in order to comprehensively compare the proposed solutions against modern, widely-used methods from all paradigms. The experimental studies and analyses confirm that the proposals achieve better performances and more scalable solutions than the methods compared, making them competitive in their respected fields

Recent Advances in Pharmaceutical Sciences VIII

This E-book is the eighth volume of a series that compiles contributions from different areas of the multidisciplinary field of Pharmaceutical Sciences. The E-book consists of 7 chapters that cover the areas of organic chemistry, health and environmental management, plant physiology, food science, toxicology, botany, parasitology, physiology, biochemistry and molecular biology, microbiology, and pharmacology

Sovereign wealth funds : Past, present and future

Author's accepted manuscript.Available from 16/11/2021.In this article, we conduct a meta-literature review of sovereign wealth funds (SWFs), covering 184 articles from 2005 to 2019. Our meta-literature review consists of qualitative analysis of content using the NVivo software program and quantitative analyses of bibliometric citations using the HistCite and VOSviewer software programs. We identify three main research streams: (i) the overview and growth of SWFs, (ii) governance and political concerns regarding SWFs, and (iii) the investment strategies of SWFs. We identify the most influential aspects of the SWF literature, such as the leading countries, institutions, journals, authors, and articles. Finally, we propose 20 research questions based on the meta-literature review of sovereign wealth funds to set the future research agenda.acceptedVersio

Flash Memory Devices

Flash memory devices have represented a breakthrough in storage since their inception in the mid-1980s, and innovation is still ongoing. The peculiarity of such technology is an inherent flexibility in terms of performance and integration density according to the architecture devised for integration. The NOR Flash technology is still the workhorse of many code storage applications in the embedded world, ranging from microcontrollers for automotive environment to IoT smart devices. Their usage is also forecasted to be fundamental in emerging AI edge scenario. On the contrary, when massive data storage is required, NAND Flash memories are necessary to have in a system. You can find NAND Flash in USB sticks, cards, but most of all in Solid-State Drives (SSDs). Since SSDs are extremely demanding in terms of storage capacity, they fueled a new wave of innovation, namely the 3D architecture. Today “3D” means that multiple layers of memory cells are manufactured within the same piece of silicon, easily reaching a terabit capacity. So far, Flash architectures have always been based on "floating gate," where the information is stored by injecting electrons in a piece of polysilicon surrounded by oxide. On the contrary, emerging concepts are based on "charge trap" cells. In summary, flash memory devices represent the largest landscape of storage devices, and we expect more advancements in the coming years. This will require a lot of innovation in process technology, materials, circuit design, flash management algorithms, Error Correction Code and, finally, system co-design for new applications such as AI and security enforcement

Machine Learning based Anomaly Detection for Cybersecurity Monitoring of Critical Infrastructures

openManaging critical infrastructures requires to increasingly rely on Information and Communi- cation Technologies. The last past years showed an incredible increase in the sophistication of attacks. For this reason, it is necessary to develop new algorithms for monitoring these infrastructures. In this scenario, Machine Learning can represent a very useful ally. After a brief introduction on the issue of cybersecurity in Industrial Control Systems and an overview of the state of the art regarding Machine Learning based cybersecurity monitoring, the present work proposes three approaches that target different layers of the control network architecture. The first one focuses on covert channels based on the DNS protocol, which can be used to establish a command and control channel, allowing attackers to send malicious commands. The second one focuses on the field layer of electrical power systems, proposing a physics-based anomaly detection algorithm for Distributed Energy Resources. The third one proposed a first attempt to integrate physical and cyber security systems, in order to face complex threats. All these three approaches are supported by promising results, which gives hope to practical applications in the next future.openXXXIV CICLO - SCIENZE E TECNOLOGIE PER L'INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI - Elettromagnetismo, elettronica, telecomunicazioniGaggero, GIOVANNI BATTIST

GANGLIOSIDE GM1 AS ADJUVANT FOR ORKAMBI® THERAPY TO RESTORE PLASMA MEMBRANE STABILITY AND FUNCTION OF F508DEL-CFTR

Cystic fibrosis (CF) is the most common, fatal genetic disease in the Caucasian population caused by loss of function mutations in gene encoding for the cystic fibrosis transmembrane conductance regulator (CFTR). CFTR is expressed at the apical surface of epithelial cells of different organs, such as: lungs, pancreas, gut, and testes. For this reason even if in CF the pulmonary manifestations are the most severe, CF is considered a multi-system disease, which affects several bodily districts. The new challenge for the CF therapy is based on the development of small molecules able to rescue the function of the mutated CFTR. Many pharmacological agents have been designed to increase the surface level of mutated CFTR (correctors), as well as its plasma membrane (PM) activity (potentiators). Recently, combined therapy that includes a corrector of the CFTR folding (lumacaftor or VX-809) and a potentiator of the channel activity (ivacaftor or VX-770) called Orkambi\uae, was approved for CF patients homozygous for the deletion of phenylalanine at position 508 (F508del), the most common CF-causing mutation. Unfortunately, clinical studies revealed that the effects of Orkambi\uae on lung function were modest, due to low stability of rescued F508del-CFTR at the PM level. Indeed, many factors contribute to PM CFTR stability, including its compartmentalization in PM macromolecular complexes composed of phospholipids, sphingolipids, with particular regards for monosialoganglioside 1 (GM1), and scaffolding proteins such as ezrin and NHERF-1. Interestingly, it has been proved that in bronchial epithelial cells the lack of CFTR in the cell PM, such as in the case of the patients carrying the mutation F508del, is associated with a decreased content of GM1. By performing photolabelling experiments, I demonstrated for the first time that GM1 and CFTR at PM level reside in the same microdomain, suggesting a direct interaction between them. Then I investigated on the potential effect of the exogenous administration of ganglioside GM1 on the PM stabilization and function of F508del-CFTR rescued by Orkambi\uae treatment. In particular, I proved that in CF bronchial epithelial cells GM1 antagonizes the negative effect of VX-770, increasing F508del-CFTR maturation and its channel activity by the recruitment of the scaffolding proteins NHERF-1 and ezrin. Taken together the results obtained during my PhD project pointed out the role of GM1 as possible adjuvant to Orkambi\uae therapy to restore the function of F508del-CFTR

Early Determinants of Women in the IT Workforce: A Model of Girls’ Career Choices

Purpose – To develop a testable model for girls’ career choices in technology fields based on past research and hypotheses about the future of the information technology (IT) workforce. Design/Methodology/Approach – Review and assimilation of literature from education, psychology, sociology, computer science, IT, and business in a model that identifies factors that can potentially influence a girl’s choice towards or against IT careers. The factors are categorized into social factors (family, peers, and media), structural factors (computer use, teacher/counselor influence, same sex versus coeducational schools), and individual differences. The impact of culture on these various factors is also explored. Findings – The model indicates that parents, particularly fathers, are the key influencers of girls’ choice of IT careers. Teachers and counselors provide little or no career direction. Hypotheses propose that early access to computers may reduce intimidation with technology and that same-sex education may serve to reduce career bias against IT. Research Limitations/Implications – While the model is multidisciplinary, much of research from which it draws is five to eight years old. Patterns of career choices, availability of technology, increased independence of women and girls, offshore/nearshore outsourcings of IT jobs are just some of the factors that may be insufficiently addressed in this study. Practical Implications – A “Recommendations” section provides some practical steps to increase the involvement of girls in IT-related careers and activities at an early age. The article identifies cultural research as a limitation and ways to address this. Originality/value – The paper is an assimilation of literature from diverse fields and provides a testable model for research on gender and IT