Activity detection and classification are very important for autonomous monitoring of humans for applications, including assistive living, rehabilitation, and surveillance. Wearable sensors have ...found wide-spread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, such as accelerometer, gyroscope, and camera, it has become more feasible to develop activity monitoring algorithms employing one or more of these sensors with increased accessibility. We provide a complete and comprehensive survey on activity classification with wearable sensors, covering a variety of sensing modalities, including accelerometer, gyroscope, pressure sensors, and camera- and depth-based systems. We discuss differences in activity types tackled by this breadth of sensing modalities. For example, accelerometer, gyroscope, and magnetometer systems have a history of addressing whole body motion or global type activities, whereas camera systems provide the context necessary to classify local interactions, or interactions of individuals with objects. We also found that these single sensing modalities laid the foundation for hybrid works that tackle a mix of global and local interaction-type activities. In addition to the type of sensors and type of activities classified, we provide details on each wearable system that include on-body sensor location, employed learning approach, and extent of experimental setup. We further discuss where the processing is performed, i.e., local versus remote processing, for different systems. This is one of the first surveys to provide such breadth of coverage across different wearable sensor systems for activity classification.
Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, ...offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes.
When we recall a past experience, accessing what is known as an ‘episodic memory’, it usually does not appear as a still image or a snapshot of what occurred. Instead, our memories tend to be dynamic: we remember how a sequence of events unfolded, and when we do this, we often re-experience at least part of that same sequence. If the memory includes physical movement, the sequence combines space and time to remember a trajectory. For example, a mouse might remember how it went down a hole and found cheese there.
However, mathematical models of how past experiences are stored in our brains and retrieved when we remember them have so far focused on snapshot memories. ‘Attractor network models’ are one type of mathematical model that neuroscientists use to represent how neurons communicate with each other to store memories. These models can provide insights into how circuits of neurons, for example those in the hippocampus (a part of the brain crucial for memory), may have evolved to remember the past, but so far they have only focused on how single moments, rather than sequences of events, are represented by populations of neurons.
Spalla et al. found a way to extend these models, so they could analyse how networks of neurons can store and retrieve dynamic memories. These memories are represented in the brain as ‘continuous attractors’, which can be thought of as arrows that attract mental trajectories first to the arrow itself, and once on the arrow, to the arrowhead. Each recalled event elicits the next one on the arrow, as the mental trajectory advances towards the arrowhead. Spalla et al. determined that memory networks in the hippocampus of mammals can store large numbers of these ‘arrows’, up to the same amount of ‘snapshot’ memories predicted to be stored with similar models.
Spalla et al.’s results may allow researchers to better understand memory storage and recall, since they allow for the modelling of complex and realistic aspects of episodic memories. This could provide insights into processes such as why our minds wander, as well as having implications for the study of how neurons physically interact with each other to transmit information.
Da che mondo è mondo, gli adulti, fisiologicamente, si scordano di essere stati adolescenti; a volte alcuni di essi sviluppano addirittura una sorta di ostilità mista a repulsione verso le nuove ...generazioni, che è causa e allo stesso tempo effetto di un"incomprensione profonda. …
Object tracking from videos is still a challenging task due to various changes throughout a video sequence including occlusions, motion blur, scale and other deformation changes. In this paper, we ...propose a selective parts-based approach, using correlation filters, that makes choices based on a consensus of the parts and global tracking. Moreover, we further enhance our parts-based approach by introducing a segmentation-assisted parts initialization. In addition, we present a genetic algorithm-based method to autonomously select various parameters of the tracking algorithm, as opposed to the common practice of manually tuning those parameters. In contrast to existing part-based methods, the proposed method does not dilute accurate tracking by averaging results over multiple parts at every frame. Instead, we take a selective approach based on the relative weight of the responses across parts. Moreover, we only make location corrections when a part diverges, and rely on these location corrections to maintain an accurate appearance model. In the case of occlusions, which are among the main reasons for using a parts-based approach, our proposed approach consistently achieves the best performance. It is due to the ability to handle occlusion and not dilute decisions with incorrect parts, that our proposed approach enables state-of-the-art performance. The proposed approach was evaluated on videos from three different challenging benchmark datasets. Our approach has resulted in better overall precision and success rates for three different base tracking approaches.
Gli studenti di un liceo bolognese, reperiti sia testimonianze dirette memoriali, sia testi letterari pertinenti, hanno indagato il valore dell'esperienza e della memoria, individuale e collettiva; ...l'incidenza della guerra sull'alimentazione e sui comportamenti sociali; la capacità dei “classici” di fare della rappresentazione del reale lo strumento interpretativo della società e dell’animo umano. In corso altri approfondimenti sull’identità alimentare nella documentazione storico-letteraria.
Aim
Monitoring the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) immunization in patients with autoimmune diseases is of particular concern to understand their response to ...the infection and to the vaccine. In fact, the immunological disorder and the immunosuppressive therapies could affect the serological response. SARS-CoV2 serological tests potentially provide this information, although they were rapidly commercialized with internal verifications. Here, we analysed the seroprevalence to SARS-CoV2 in a cohort of patients with liver autoimmune diseases.
Methods
From May to December 2020, a cohort of patients affected by primary biliary cholangitis (PBC), autoimmune hepatitis (AIH) and PBC/AIH overlap syndrome were screened with (reverse transcription-polymerase chain reaction) RT-PCR of nasopharyngeal swabs, rapid antigenic test and chemiluminescent serological test during routine follow-up.
Results
The analysis of 42 patients was carried out: 18 (42.85%) PBC, 12 (28.57%) AIH and 12 (28.57%) PBC/AIH overlap syndromes. Only 2 patients (4.76%) resulted positive to the RNA, antigen and antibody detection tests, hence affected by SARS-CoV2 infection. 14 subjects out of 40 negative cases presented a positive serology for SARS-CoV2 antibodies, hence with a false positivity in the 35% of cases without infection. Among these, 6 (42.86%) patients presented only immunoglobulin (Ig)M positivity, 6 (42.86%) patients presented positivity for only IgG and 2 (14.28%) patients were positive to both IgM and IgG.
Notably, the presence of autoantibodies did not correlate with the serological false positivity, highlighting that there is no cross-reactivity with autoantibodies. The presence of polyclonal hypergammaglobulinemia did not interfere with the serological test as well.
Interestingly, the patients with false positive serology showed higher levels of gamma-glutamyltransferase (GGT) and C-reactive protein (CRP).
Conclusions
Patients with liver autoimmune diseases present a high rate of false positive SARS-CoV2 serology. Therefore, new strategies are needed to study the serological response in this patient category.
Autonomous navigation and obstacle avoidance systems are critically relevant and important for visually impaired people, assisted driving applications, and autonomous robots. Even though there has ...been significant amount of work on obstacle detection and avoidance using LiDAR and camera data, there has not been much effort focusing on providing a lightweight, cost conscious, energy efficient, reliable, and portable solution for the visually impaired. We propose a new method for autonomous obstacle detection and classification, which incorporates a different and novel type of sensor, namely, patterned light field, with camera. The proposed device is small in size, easily carried, as well as low cost. The grid, projected by the patterned light source, is apparent and differentiable as the sensing system is hand carried in natural indoor and outdoor environments over and toward different types of obstacles. Our proposed approach exploits these patterns, without calibration, by employing deep learning techniques, including a convolutional neural network-based classification on individual frames. We further refine our approach by smoothing the frame-based classifications over multiple frames using long short-term memory units. The proposed method provides very promising results with overall detection and classification accuracies of 98.37% for the binary case as well as 95.97% and 92.62% for two different multi-class scenarios. These results represent the average number of sequences correctly detected and classified and were obtained on a sequence-based analysis of over 120 sequences from four different users.
Deep convolutional neural networks have recently demonstrated incredible capabilities in areas such as image classification and object detection, but they require large datasets of quality ...pre-labeled data to achieve high levels of performance. Almost all data is not properly labeled when it is captured, and the process of manually labeling large enough datasets for effective learning is impractical in many real-world applications. New studies have shown that synthetic data, generated from a simulated environment, can be effective training data for DCNNs. However, synthetic data is only as effective as the simulation from which it is gathered, and there is often a significant trade-off between designing a simulation that properly models real-world conditions and simply gathering better real-world data. Using generative network architectures, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), it is possible to produce new synthetic samples based on the features of real-world data. This data can be used to augment small datasets to increase DCNN performance, similar to traditional augmentation methods such as scaling, translation, rotation, and adding noise. In this paper, we compare the advantages of synthetic data from GANs and VAEs to traditional data augmentation techniques. Initial results are promising, indicating that using synthetic data for augmentation can improve the accuracy of DCNN classifiers.
In the representation of the ‘hero’ who, in the contemporary Italian dystopian novels Pinocchio. 2112 by P. Donà, Something, out there by B. Arpaia, The doctrine of evil by A. Berselli and 3012. The ...year of the Prophet by S. Vassalli, is confronted with different types of catastrophes, reviving the topos of descensus ad inferos, there are several ways of combining the relationship between authentic and inauthentic, a fundamental element of the established figural repertoire of the dystopian genre. We focus in particular on the narratological mechanisms of the points of view by which the reader is confronted with the traditional pedagogical function of the dystopian apologue. Thus, we can see the originality of the structure of Vassalli’s 3012, a novel which – although in line with the author’s conception, which can be found in the comparison with the rest of his narrative production – proposes a figure of ‘funny’, within the forms of Menipp’s satire and parody.
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