Weakly and Partially Supervised Learning Frameworks for Anomaly Detection

The automatic detection of abnormal events in surveillance footage is still a concern of the research community. Since protection is the primary purpose of installing video surveillance systems, the monitoring capability to keep public safety, and its rapid response to satisfy this purpose, is a significant challenge even for humans. Nowadays, human capacity has not kept pace with the increased use of surveillance systems, requiring much supervision to identify unusual events that could put any person or company at risk, without ignoring the fact that there is a substantial waste of labor and time due to the extremely low likelihood of occurring anomalous events compared to normal ones. Consequently, the need for an automatic detection algorithm of abnormal events has become crucial in video surveillance. Even being in the scope of various research works published in the last decade, the state-of-the-art performance is still unsatisfactory and far below the required for an effective deployment of this kind of technology in fully unconstrained scenarios. Nevertheless, despite all the research done in this area, the automatic detection of abnormal events remains a challenge for many reasons. Starting by environmental diversity, the complexity of movements resemblance in different actions, crowded scenarios, and taking into account all possible standard patterns to define a normal action is undoubtedly difficult or impossible. Despite the difficulty of solving these problems, the substantive problem lies in obtaining sufficient amounts of labeled abnormal samples, which concerning computer vision algorithms, is fundamental. More importantly, obtaining an extensive set of different videos that satisfy the previously mentioned conditions is not a simple task. In addition to its effort and time-consuming, defining the boundary between normal and abnormal actions is usually unclear. Henceforward, in this work, the main objective is to provide several solutions to the problems mentioned above, by focusing on analyzing previous state-of-the-art methods and presenting an extensive overview to clarify the concepts employed on capturing normal and abnormal patterns. Also, by exploring different strategies, we were able to develop new approaches that consistently advance the state-of-the-art performance. Moreover, we announce the availability of a new large-scale first of its kind dataset fully annotated at the frame level, concerning a specific anomaly detection event with a wide diversity in fighting scenarios, that can be freely used by the research community. Along with this document with the purpose of requiring minimal supervision, two different proposals are described; the first method employs the recent technique of self-supervised learning to avoid the laborious task of annotation, where the training set is autonomously labeled using an iterative learning framework composed of two independent experts that feed data to each other through a Bayesian framework. The second proposal explores a new method to learn an anomaly ranking model in the multiple instance learning paradigm by leveraging weakly labeled videos, where the training labels are done at the video-level. The experiments were conducted in several well-known datasets, and our solutions solidly outperform the state-of-the-art. Additionally, as a proof-of-concept system, we also present the results of collected real-world simulations in different environments to perform a field test of our learned models.A detecção automática de eventos anómalos em imagens de videovigilância permanece uma inquietação por parte da comunidade científica. Sendo a proteção o principal propósito da instalação de sistemas de vigilância, a capacidade de monitorização da segurança pública, e a sua rápida resposta para satisfazer essa finalidade, é uma adversidade até para o ser humano. Nos dias de hoje, com o aumento do uso de sistemas de videovigilância, a capacidade humana não tem alcançado a cadência necessária, exigindo uma supervisão exorbitante para a identificação de acontecimentos invulgares que coloquem uma identidade ou sociedade em risco. O facto da probabilidade de se suceder um incidente ser extremamente reduzida comparada a eventualidades normais, existe um gasto substancial de tempo de ofício. Consequentemente, a necessidade para um algorítmo de detecção automática de incidentes tem vindo a ser crucial em videovigilância. Mesmo sendo alvo de vários trabalhos científicos publicados na última década, o desempenho do estado-da-arte continua insatisfatório e abaixo do requisitado para uma implementação eficiente deste tipo de tecnologias em ambientes e cenários totalmente espontâneos e incontinentes. Porém, apesar de toda a investigação realizada nesta área, a automatização de detecção de incidentes é um desafio que perdura por várias razões. Começando pela diversidade ambiental, a complexidade da semalhança entre movimentos de ações distintas, cenários de multidões, e ter em conta todos os padrões para definir uma ação normal, é indiscutivelmente difícil ou impossível. Não obstante a dificuldade de resolução destes problemas, o obstáculo fundamental consiste na obtenção de um número suficiente de instâncias classificadas anormais, considerando algoritmos de visão computacional é essencial. Mais importante ainda, obter um vasto conjunto de diferentes vídeos capazes de satisfazer as condições previamente mencionadas, não é uma tarefa simples. Em adição ao esforço e tempo despendido, estabelecer um limite entre ações normais e anormais é frequentemente indistinto. Tendo estes aspetos em consideração, neste trabalho, o principal objetivo é providenciar diversas soluções para os problemas previamente mencionados, concentrando na análise de métodos do estado-da-arte e apresentando uma visão abrangente dos mesmos para clarificar os conceitos aplicados na captura de padrões normais e anormais. Inclusive, a exploração de diferentes estratégias habilitou-nos a desenvolver novas abordagens que aprimoram consistentemente o desempenho do estado-da-arte. Por último, anunciamos a disponibilidade de um novo conjunto de dados, em grande escala, totalmente anotado ao nível da frame em relação à detecção de anomalias em um evento específico com uma vasta diversidade em cenários de luta, podendo ser livremente utilizado pela comunidade científica. Neste documento, com o propósito de requerer o mínimo de supervisão, são descritas duas propostas diferentes; O primeiro método põe em prática a recente técnica de aprendizagem auto-supervisionada para evitar a árdua tarefa de anotação, onde o conjunto de treino é classificado autonomamente usando uma estrutura de aprendizagem iterativa composta por duas redes neuronais independentes que fornecem dados entre si através de uma estrutura Bayesiana. A segunda proposta explora um novo método para aprender um modelo de classificação de anomalias no paradigma multiple-instance learning manuseando vídeos fracamente anotados, onde a classificação do conjunto de treino é feita ao nível do vídeo. As experiências foram concebidas em vários conjuntos de dados, e as nossas soluções superam consolidamente o estado-da-arte. Adicionalmente, como sistema de prova de conceito, apresentamos os resultados da execução do nosso modelo em simulações reais em diferentes ambientes

A randomized, phase II study of afatinib versus cetuximab in metastatic or recurrent squamous cell carcinoma of the head and neck.

BackgroundAfatinib is an oral, irreversible ErbB family blocker that has shown activity in epidermal growth factor receptor (EGFR)-mutated lung cancer. We hypothesized that the agent would have greater antitumor activity compared with cetuximab in recurrent or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) patients, whose disease has progressed after platinum-containing therapy.Patients and methodsAn open-label, randomized, phase II trial was conducted in 43 centers; 124 patients were randomized (1 : 1) to either afatinib (50 mg/day) or cetuximab (250 mg/m(2)/week) until disease progression or intolerable adverse events (AEs) (stage I), with optional crossover (stage II). The primary end point was tumor shrinkage before crossover assessed by investigator (IR) and independent central review (ICR).ResultsA total of 121 patients were treated (61 afatinib, 60 cetuximab) and 68 crossed over to stage II (32 and 36 respectively). In stage I, mean tumor shrinkage by IR/ICR was 10.4%/16.6% with afatinib and 5.4%/10.1% with cetuximab (P = 0.46/0.30). Objective response rate was 16.1%/8.1% with afatinib and 6.5%/9.7% with cetuximab (IR/ICR). Comparable disease control rates were observed with afatinib (50%) and cetuximab (56.5%) by IR; similar results were seen by ICR. Most common grade ≥3 drug-related AEs (DRAEs) were rash/acne (18% versus 8.3%), diarrhea (14.8% versus 0%), and stomatitis/mucositis (11.5% versus 0%) with afatinib and cetuximab, respectively. Patients with DRAEs leading to treatment discontinuation were 23% with afatinib and 5% with cetuximab. In stage II, disease control rate (IR/ICR) was 38.9%/33.3% with afatinib and 18.8%/18.8% with cetuximab.ConclusionAfatinib showed antitumor activity comparable to cetuximab in R/M HNSCC in this exploratory phase II trial, although more patients on afatinib discontinued treatment due to AEs. Sequential EGFR/ErbB treatment with afatinib and cetuximab provided sustained clinical benefit in patients after crossover, suggesting a lack of cross-resistance

OFDM PLC transmission for aircraft flight control system

International audienc

The Effect of loads on insertion gain and capacity of SISO Power Line Communication

Advanced communication technologies have allowed power-line-communication (PLC) channel to be a transmission medium that enables the transfer of high-speed digital data over the classical indoor electrical wires. The development of PLC systems for Internet, voice, and data services requires measurement-based models of the transfer characteristics of the mains network suitable for performance analysis by simulation. This paper presents the impact of load impedances connected on the electrical network on insertion gain and capacity of the PLC link

Compromising Radiated Emission from a Power Line Communication Cable

This contribution presents a preliminary investigation on the possibility of eavesdropping, i.e., of extracting information by exploiting the electromagnetic field radiated in the vicinity of a power line communication (PLC) network. This kind of problem is usually known in the electromagnetic compatibility area under the codename TEMPEST. Electromagnetic field measurements were carried out in a laboratory environment, both inside and outside a building, and the main statistical characteristics of the compromising channel are presented. A software tool simulating a PLC communication has been developed and used to draw apreliminary conclusion on whether the radiated emissions can be exploited or not

A new concurrent chemotherapy with vinorelbine and mitomycin C in combination with radiotherapy in patients with locally advanced squamous cell carcinoma of the head and neck

Objective: The purpose of this pilot study was to evaluate the feasibility and toxicity of concurrent chemotherapy with vinorelbine and mitomycin C in combination with accelerated radiotherapy (RT) in patients with locally advanced cancer of the head and neck. Patients and Methods: Between January 2003 and March 2004, 15 patients with T4/N2-3 squamous cell carcinoma (12/15) and with N3 cervical lymph node metastases of carcinoma of unknown primary (3/15) were treated with chemotherapy and simultaneous accelerated RT. Results: 11 patients completed therapy without interruption or dose reduction. Grade 3 - 4 acute mucosal toxicity was observed in 9/15 patients, grade 4 hematologic toxicity in 6/15 patients. At a median follow-up of 7.5 months, 2 patients have died of intercurrent disease, 2 patients have experienced local relapse; 5 patients are alive with no evidence of disease at the primary tumor site. Discussion: The described regimen is highly effective, but led to remarkable side effects

Novel Graphene Electrode for Retinal Implants: An in vivo Biocompatibility Study

Evaluating biocompatibility is a core essential step to introducing a new material as a candidate for brain-machine interfaces. Foreign body reactions often result in glial scars that can impede the performance of the interface. Having a high conductivity and large electrochemical window, graphene is a candidate material for electrical stimulation with retinal prosthesis. In this study, non-functional devices consisting of chemical vapor deposition (CVD) graphene embedded onto polyimide/SU-8 substrates were fabricated for a biocompatibility study. The devices were implanted beneath the retina of blind P23H rats. Implants were monitored by optical coherence tomography (OCT) and eye fundus which indicated a high stability in vivo up to 3 months before histology studies were done. Microglial reconstruction through confocal imaging illustrates that the presence of graphene on polyimide reduced the number of microglial cells in the retina compared to polyimide alone, thereby indicating a high biocompatibility. This study highlights an interesting approach to assess material biocompatibility in a tissue model of central nervous system, the retina, which is easily accessed optically and surgically.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 785219 (GrapheneCore2) and No. 881603 (GrapheneCore3). DN has received funding from the doctoral school of Cerveau, cognition, comportement (3C) of Sorbonne Université. SP was also supported by the French state funds managed by the Agence Nationale de la Recherche within the Programme Investissements d’Avenir, LABEX LIFESENSES (ANR-10-LABX-65) and IHU FOReSIGHT (ANR-18-IAHU-0001). This work has made use of the Spanish ICTS Network MICRONANOFABS partially supported by MICINN and the ICTS ‘NANBIOSIS,’ more specifically by the Micro-NanoTechnology Unit of the CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) at the IMB-CNM

Melanocortin-1 receptor (MC1R) genotypes do not correlate with size in two cohorts of medium-to-giant congenital melanocytic nevi

Congenital melanocytic nevi (CMN) are cutaneous malformations whose prevalence is inversely correlated with projected adult size. CMN are caused by somatic mutations, but epidemiological studies suggest that germline genetic factors may influence CMN development. In CMN patients from the U.K., genetic variants in MC1R, such as p.V92M and loss-of-function variants, have been previously associated with larger CMN. We analyzed the association of MC1R variants with CMN characteristics in two distinct cohorts of medium-to-giant CMN patients from Spain (N = 113) and from France, Norway, Canada, and the United States (N = 53), similar at the clinical and phenotypical level except for the number of nevi per patient. We found that the p.V92M or loss-of-function MC1R variants either alone or in combination did not correlate with CMN size, in contrast to the U.K. CMN patients. An additional case-control analysis with 259 unaffected Spanish individuals showed a higher frequency of MC1R compound heterozygous or homozygous variant genotypes in Spanish CMN patients compared to the control population (15.9% vs. 9.3%; p = .075). Altogether, this study suggests that MC1R variants are not associated with CMN size in these non-UK cohorts. Additional studies are required to define the potential role of MC1R as a risk factor in CMN development.© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

Randomised, phase II trial comparing oral capecitabine (Xeloda®) with paclitaxel in patients with metastatic/advanced breast cancer pretreated with anthracyclines

Capecitabine, an oral fluoropyrimidine carbamate, was designed to generate 5-fluorouracil preferentially at the tumour site. This randomised, phase II trial evaluated the efficacy and safety of capecitabine or paclitaxel in patients with anthracycline-pretreated metastatic breast cancer. Outpatients with locally advanced and/or metastatic breast cancer whose disease was unresponsive or resistant to anthracycline therapy were randomised to 3-week cycles of intermittent oral capecitabine (1255 mg m−2 twice daily, days 1–14, (22 patients)) or a reference arm of i.v. paclitaxel (175 mg m−2, (20 patients)). Two additional patients were initially randomised to continuous capecitabine 666 mg m−2 twice daily, but this arm was closed following selection of the intermittent schedule for further development. Overall response rate was 36% (95% CI 17–59%) with capecitabine (including three complete responses) and 26% (95% CI 9–51%) with paclitaxel (no complete responses). Median time to disease progression was similar in the two treatment groups (3.0 months with capecitabine, 3.1 months with paclitaxel), as was overall survival (7.6 and 9.4 months, respectively). Paclitaxel was associated with more alopecia, peripheral neuropathy, myalgia and neutropenia, whereas typical capecitabine-related adverse events were diarrhoea, vomiting and hand–foot syndrome. Twenty-three per cent of capecitabine-treated patients and 16% of paclitaxel-treated patients achieved a ⩾10% improvement in Karnofsky Performance Status. Oral capecitabine is active in anthracycline-pretreated advanced/metastatic breast cancer and has a favourable safety profile. Furthermore, capecitabine provides a convenient, patient-orientated therapy