Fast, invariant representation for human action in the visual system

Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition, however, the underlying neural computations remain poorly understood. We use magnetoencephalography (MEG) decoding and a dataset of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discounts changes in 3D viewpoint relative to when it initially discriminates between actions. We measure the latency difference between invariant and non-invariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. Our results show no difference in decoding latency or temporal profile between invariant and non-invariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time, and that both form and motion information are crucial for fast, invariant action recognition

The good and bad of ERBB receptors in breast - quanno viniti mi s’allarga lu cori, ma quanno vinni iti puru

The mammary gland is a dynamic organ displaying structural changes throughout the female reproductive cycle. The gland differentiation follows defined stages (embryonic, prepubertal and pubertal stages, pregnancy, lactation and involution) connected to sexual development and reproduction. Complex two-way interactions between mammary epithelial cells and the surrounding stroma direct proliferation, duct formation, branching and terminal differentiation during these stages. The members of the ERBB family of receptor tyrosine kinases (RTK) are involved in each of these processes and play distinct and complementary roles. Altered ERBB signaling, mostly due to over-expression and/or, to a minor extent, mutation of one or more of these receptors, results in aberrant cellular responses leading to breast cancers. Thus, the phenotype induced by altered ERBB modulation in breast cancer may highlight relevant aspects of the molecular mechanisms underlying normal breast development. In the last 15 years, in collaboration with other groups, we have studied the molecular basis of RTK modulation, and contributed to the definition of relevant molecular events and organelle interactions underlying ERBB1 (EGFR) and ERBB2 internalization and trafficking (1-9). These studies brought us to approach the role of these events (10-18) in cancer pathogenesis and progression, and led to the identification of a key druggable molecular target to revert the resistance to Trastuzumab (Herceptin®), a humanized antibody to ERBB2, representing the front line treatment in ERBB2 over-expressing breast cancer (19). In this lecture I will review the current knowledge on the role of ERBB receptors in normal breast development, their role in breast cancer onset and progression, and our recent results in the field

GURLS: a Toolbox for Regularized Least Squares Learning

We present GURLS, a toolbox for supervised learning based on the regularized least squares algorithm. The toolbox takes advantage of all the favorable properties of least squares and is tailored to deal in particular with multi-category/multi-label problems. One of the main advantages of GURLS is that it allows training and tuning a multi-category classifier at essentially the same cost of one single binary classifier. The toolbox provides a set of basic functionalities including different training strategies and routines to handle computations with very large matrices by means of both memory-mapped storage and distributed task execution. The system is modular and can serve as a basis for easily prototyping new algorithms. The toolbox is available for download, easy to set-up and use

The role of monoclonal antibodies in smoldering and newly diagnosed transplant-eligible multiple myeloma

The recent introduction of monoclonal antibodies (MoAbs), with several cellular targets, such as CD-38 (daratumumab and isatuximab) and SLAM F7 (elotuzumab), differently combined with other classes of agents, has significantly extended the outcomes of patients with multiple myeloma (MM) in different phases of the disease. Initially used in advanced/refractory patients, different MoAbs combination have been introduced in the treatment of newly diagnosed transplant eligible patients (NDTEMM), showing a significant improvement in the depth of the response and in survival outcomes, without a significant price in terms of toxicity. In smoldering MM, MoAbs have been applied, either alone or in combination with other drugs, with the goal of delaying the progression to active MM and restoring the immune system. In this review, we will focus on the main results achieved so far and on the main on-going trials using MoAbs in SMM and NDTEMM

The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).

This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream -- from V1, V2, V4 and to IT -- is to discount image transformations, after learning them during development

El desafío de interconectar sistemas en terminales portuarias en Latinoamérica

Mundialmente el TOS (Terminal Operating System) más utilizado es Navis N41, un producto extensible, en el cual se configuran y controlan todos los procesos core en las terminales portuarias, por lo que la adquisición de TOS es de vital importancia en la operatoria. Se presentan extensiones orientadas a mercados de Latinoamérica, con diferentes regulaciones y cambios, propios de la región.Director profesional: Santiago UrrizolaFacultad de Informátic

La realidad africana y el extremismo violento

La realidad africana es múltiple: víctima de conflictos regionales, golpes de Estado, enfermedades endémicas, analfabetismo, hambre, corrupción, terrorismo y desestructuración social, incapaz de apostar por el desarrollo y de asegurar un futuro mejor para las generaciones futuras. África es diversa: política, económica y religiosamente. Tras décadas de ocupación colonial, trata de esclavos, explotación y sometimiento de las metrópolis europeas a partir del siglo XV, es recién en la década de los sesenta cuando numerosos territorios adquieren la categoría de Estados independientes, tras la intervención esencial del Comité de Descolonización. El fin de la Segunda Guerra Mundial, el contexto internacional resultante y la creación de la Organización de las Naciones Unidas, conllevó al inicio de la descolonización en África. Este proceso de colonización permitió a las grandes potencias, a partir de la explotación simultánea de recursos y la mano de obra barata, la elaboración de productos para el mercado internacional, quitándole a la sociedad tradicional africana su autonomía y toda perspectiva de evolución. Con muy pocas excepciones (Madagascar, Lesoto, Etiopía y Suazilandia), los Estados actuales son herencia de una formación social colonial, careciendo de soberanía nacional. Aún hoy, simbolizan una prolongación del Estado colonial, incapaz de controlar a la población que vive en su territorio. Lo cierto es que estos Estados, caracterizados como "frágiles", se enfrentan día a día a diferentes amenazas tales como guerras civiles, atrocidades a gran escala y emergencias humanitarias. A través de los medios de comunicación, sólo somos espectadores de una serie de testimonios relacionados con el extremismo violento en África. No obstante, sus alcances y consecuencias destructivas se encuentran entre los principales desafíos de paz que la comunidad internacional enfrenta en la actualidad.Departamento de Seguridad Internacional y Defensa.Instituto de Relaciones Internacionale

A Neural Architecture for Designing Truthful and Efficient Auctions

Auctions are protocols to allocate goods to buyers who have preferences over them, and collect payments in return. Economists have invested significant effort in designing auction rules that result in allocations of the goods that are desirable for the group as a whole. However, for settings where participants' valuations of the items on sale are their private information, the rules of the auction must deter buyers from misreporting their preferences, so as to maximize their own utility, since misreported preferences hinder the ability for the auctioneer to allocate goods to those who want them most. Manual auction design has yielded excellent mechanisms for specific settings, but requires significant effort when tackling new domains. We propose a deep learning based approach to automatically design auctions in a wide variety of domains, shifting the design work from human to machine. We assume that participants' valuations for the items for sale are independently sampled from an unknown but fixed distribution. Our system receives a data-set consisting of such valuation samples, and outputs an auction rule encoding the desired incentive structure. We focus on producing truthful and efficient auctions that minimize the economic burden on participants. We evaluate the auctions designed by our framework on well-studied domains, such as multi-unit and combinatorial auctions, showing that they outperform known auction designs in terms of the economic burden placed on participants