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During the Permian-Triassic transition, the insect faunas from all over the world suffered changes related to their diversity and abundance, as well as to their morphometric patterns. The data were obtained from the original descriptions of each species. The total size of wing was considered, whereas the small fragments without estimative total size were not taken into account. The morphometric analysis of the recorded species of the Carboniferous- Permian-Triassic entomofauna from Argentina and Brazil shows a morphologic change that reflects a general trend of body size reduction. The groups that showed an inverse trend were Mecopteroidea and Coleoptera. The statistical analysis demonstrated a decreasing trend of the wing size of Paleoptera, Orthopteroidea, Blattopteromorpha and Hemipteroidea from Paleozoic to Mesozoic. Decreasing or increasing trends could be directly related with climate changes occurred during Permian-Triassic times. These changes could have caused ecologic nanism or gigantism, connected to food availability or other synergetic factors. A high gradient of temperature provides ideal conditions to a big rate of insect proliferation, supporting its diversity, while the extinctions are associated to environmental catastrophic events. At first sight, it could be interpreted that the trend of decreasing insect dimensions reflects the high levels of environmental stress already documented in the literature. However, alochronic speciation, as a result of ecologic nanism or gigantism, should also be considered when the real diversity of Permian-Triassic boundary insects is analysed. Simposio III: Ecosistemas triásicos, su paleobiología y el contexto de recuperación de la gran extinción Facultad de Ciencias Naturales y Museo

Tourism information services are evolving rapidly. With Internet, tourists organize their trips by managing information before arriving at their destination. Nature is the main tourist attraction in Argentina. However, the information tools as field guides, have had few improvements in their digital version compared to printed ones. This work compares machine learning, deep learning, artificial intelligence and image recognition services, to evaluate the app development for mobile phones that offers the recognition in real time of flora species in natural areas with low or no internet connectivity. Recognition of three Nothofagus tree species were evaluated in the Tierra del Fuego National Park, using IBM Watson and Microsoft Azure, with good results in general. A next iteration of this work expects to use assisted learning to improve the efficiency of the neural network obtained to know the adaptation capacities for each evaluated service. Red de Universidades con Carreras en Informática (RedUNCI) Track Gobierno Digital y Ciudades Inteligentes

The parallel computing model used in this paper, the Collective Computing Model (CCM), is a variant of the well-known Bulk Synchronous Parallel (BSP) model. The synchronicity imposed by the BSP model restricts the set of available algorithms and prevents the overlapping of computation and communication. Other models, like the LogP model, allow asynchronous computing and overlapping but depend on the use of specific libraries. The CCM describes a system exploited through a standard software platform providing facilities for group creation, collective operations and remote memory operations. Based in the BSP model, two kinds of supersteps are considered: division supersteps and normal supersteps. To illustrate these concepts, the Fast Fourier Transform Algorithm is used. Computational results prove the accuracy of the model in four different parallel computers: a Parsytec Power PC, a Cray T3E, a Silicon Graphics Origin 2000 and a Digital Alpha Server. Red de Universidades con Carreras en Informática (RedUNCI) Eje: Disribución y tiempo real

VHDL is a versatile high level language for the specification and simulation of hardware components. Here a functional VHDL model is presented for performing fast convolution based on Mersenne's number theoretic transform. For filtering a rather long input sequence xn() we can decomposed it into a number of short segments, each of which can be processed individually. The output yn()then becomes a combination of partial convolutions. The superposition principle for linear operators is used here. Each partial convolution can be solved using the Discrete Fourier Transform (DFT) implementing a fast FFT (Fast Fourier Transform) algorithm. This DFT approach is the most popular. In this paper we use the Mersenne Number Transform (MNT) as an alternative for the DFT in the framework of a register transfer level (RTL) implementation of the filter operation. Even when the MNT does not have a fast algorithm it can be see that RTL in the natural level of abstraction for the implementation of the MNT. This work is conceived as part of an academic exercise in the use of VHDL for modeling a DSP algorithm all the way from the mathematical specification to the circuit implementation. Eje: Procesamiento distribuido y paralelo. Tratamiento de señales Red de Universidades con Carreras en Informática (RedUNCI)

Abstract: Spike-and-wave discharge (SWD) pattern detection in electroencephalography (EEG) is a crucial signal processing problem in epilepsy applications. It is particularly important for overcoming time-consuming, difficult, and error-prone manual analysis of long-term EEG recordings. This paper presents a new method to detect SWD, with a low computational complexity making it easily trained with data from standard medical protocols. Precisely, EEG signals are divided into time segments for which the continuous Morlet 1-D wavelet decomposition is computed. The generalized Gaussian distribution (GGD) is fitted to the resulting coefficients and their variance and median are calculated. Next, a k-nearest neighbors (k-NN) classifier is trained to detect the spike-and-wave patterns, using the scale parameter of the GGD in addition to the variance and the median. Experiments were conducted using EEG signals from six human patients. Precisely, 106 spike-and-wave and 106 non-spike-and-wave signals were used for training, and 96 other segments for testing. The proposed SWD classification method achieved 95% sensitivity (True positive rate), 87% specificity (True Negative Rate), and 92% accuracy. These promising results set the path for new research to study the causes underlying the so-called absence epilepsy in long-term EEG recordings.

Texts can be characterized from their content using machine learning and natural language processing techniques. In particular, understanding their topic is useful for different tasks such as personalized message recommendation, fake news detection or public opinion monitoring. Latent Dirichlet Allocation (LDA) is an unsupervised generative model for the decomposition of topics, which seeks to represent texts as random mixtures over topics with a Dirichlet distribution, and each topic is characterized by a distribution over words. However, this method is challenging to apply when the text is short and sometimes incoherent, as is often the case with posts on social networks such as twitter. Therefore, different works have shown that tweet pooling (aggregating tweets into longer documents) improves LDA results, but its performance depends on which method was used to aggregating the texts. We propose the new method to detect topics on twitter: “Community pooling”. In this novel scheme, first we define the retweet graph where users are the nodes and retweets between them are the edges. Then, we use the Louvain method for community detection in order to uncover the communities (a group of users who mainly interact with each other but not with other groups). Finally we aggregate into a single document all the tweets authored by all users in a community. Therefore, this method drastically reduces the number of total documents and makes denser word co-occurrence matrix, which is beneficial to LDA algorithm. With the intention of evaluating our model, we created two datasets of tweets with different characteristics. A first generic dataset involving various topics such as music, health and movies and a second dataset corresponding to an event: Biden’s presidential inauguration day in the United States. We compare the performance of our model with state of the art schemes and previous pooling models in terms of document retrieval performance, cluster quality and supervised machine learning classification score. Results showed that Community pooling had a better performance on all datasets and tasks, with the only exception of the retrieval task on the event dataset. Moreover, Community polling was faster than all other aggregation techniques (less than half the running time), which is particularly useful in big data scenarios. Sociedad Argentina de Informática e Investigación Operativa

The "Blue Whale" Auditorium in Buenos Aires opened in 2015. Designed to be the headquarters of the National Symphony Orchestra of Argentina, its goal was to become the city's main space for symphonic music. The architectural program posed several challenges from an acoustic point of view, as 2,000 people had to be accommodated in a square space into which none of the usual architectural typologies fit properly. It was decided, therefore, to place in this space an "ad-hoc" hall, whose shape is far from traditional. The design centred around three main premises: to achieve an enveloping acoustic field, by generating a large number of lateral reflections within the Haas limit; to establish an adequate triple-slope reverberation decay; and to combine reflective and diffusing surfaces to attain a similar acoustic field through the entire audience area. This work details the design process of the Auditorium, during which the final shape was deduced from the established acoustic premises. Facultad de Bellas Artes

The design of intelligent agents is a key issue for many applications. Although there is no universally accepted de nition of intelligence, a notion of rational agency has been proposed as an alternative for the characterization of intelligent agency. Modeling the epistemic state of a rational agent is one of the most di cult tasks to be addressed in the design process, and its complexity is directly related to the formalism used for representing the knowledge of the agent. This paper presents the main features of Observation-based Defeasible Logic Programming (ODeLP), a formalism tailored for agents that perform defeasible reasoning in dynamic domains. Most agents must have a timely interaction with their environment. Since the cognitive process of rational agents is complex and computationally expensive, this interaction is particularly hard to achieve. To solve this issue, we propose an optimization of the inference process in ODeLP based on the use of precompiled knowledge. This optimization can be e ciently implemented using concepts from pattern matching algorithms. Red de Universidades con Carreras en Informática (RedUNCI) Eje: Sistemas inteligentes

The application of computer fluid dynamics to the estimation of a stochastic wind loading model for vibration analysis of flexible buildings is studied in this paper. Large-Eddy-Simulation with random turbulence field as inflow boundary condition is used for estimating along the wind forces, across the wind forces and torsional moments along the height of the building. The stochastic turbulence of the inlet flow is modeled using techniques proposed in the literature and variations suggested by the authors, and along the wind and along the wind forces and torsional moments applied along the building height are estimated with sampled random processes resulting from the CFD analyses. The application of this numerical technique during the design stage of a concrete-wall 36-storey building with a parallelogram-shape plan is described. This structure is prone to high floor accelerations due to wind loading, compromising occupant comfort. The construction of random loading models for this building considering time and space correlation of forces and torsional moments is discussed and the use of the random loading to the design process of supplemental damping devices for the building is described. Publicado en: Mecánica Computacional vol. XXXV, no. 12 Facultad de Ingeniería

Abstract: Canonical transient receptor potential (TRPC) channels constitute a family of cation channels that exhibit a regional and cell-specific expression pattern throughout the brain. It has been reported previously that TRPC3 channels are effectors of the brain-derived neurotrophic factor (BDNF)/trkB signaling pathway. Given the long postulated role of BDNF in epileptogenesis, TRPC3 channels may be a critical component in the underlying pathophysiology of seizure and epilepsy. In this study, we investigated the precise role of TRPC3 channels in pilocarpine-induced status epilepticus (SE).