An LP-Based Approach for Goal Recognition as Planning

Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent. In this paper, we develop a method based on the operator-counting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recognition tasks. Our method reasons explicitly about both partial and noisy observations: estimating uncertainty for the former, and satisfying observations given the unreliability of the sensor for the latter. We evaluate our approach empirically over a large data set, analyzing its components on how each can impact the quality of the solutions. In general, our approach is superior to previous methods in terms of agreement ratio, accuracy, and spread. Finally, our approach paves the way for new research on combinatorial optimization to solve goal recognition tasks.Comment: 8 pages, 4 tables, 3 figures. Published in AAAI 2021. Updated final authorship and tex

Correlation amplitude and entanglement entropy in random spin chains

Using strong-disorder renormalization group, numerical exact diagonalization, and quantum Monte Carlo methods, we revisit the random antiferromagnetic XXZ spin-1/2 chain focusing on the long-length and ground-state behavior of the average time-independent spin-spin correlation function C(l)=\upsilon l^{-\eta}. In addition to the well-known universal (disorder-independent) power-law exponent \eta=2, we find interesting universal features displayed by the prefactor \upsilon=\upsilon_o/3, if l is odd, and \upsilon=\upsilon_e/3, otherwise. Although \upsilon_o and \upsilon_e are nonuniversal (disorder dependent) and distinct in magnitude, the combination \upsilon_o + \upsilon_e = -1/4 is universal if C is computed along the symmetric (longitudinal) axis. The origin of the nonuniversalities of the prefactors is discussed in the renormalization-group framework where a solvable toy model is considered. Moreover, we relate the average correlation function with the average entanglement entropy, whose amplitude has been recently shown to be universal. The nonuniversalities of the prefactors are shown to contribute only to surface terms of the entropy. Finally, we discuss the experimental relevance of our results by computing the structure factor whose scaling properties, interestingly, depend on the correlation prefactors.Comment: v1: 16 pages, 15 figures; v2: 17 pages, improved discussions and statistics, references added, published versio

Can Heart Rate Variability Predict the Second Metabolic Threshold in Young Soccer Players?

International Journal of Exercise Science 11(2): 1105-1111, 2018. Heart rate variability (HRV) is an effective method to assess the influence of the autonomic nervous system, which may be directly linked to metabolic demand. The aim of the study was to determine if the second metabolic threshold can be identified by HRV. Thirteen athletes were assessed in cardiopulmonary exercise test with concomitant gas analysis. The RR intervals (RRi) were plotted in a spreadsheet for graphics analysis and the point at which there was a shift in the RRi curve was determined as RRiT2. The second ventilatory threshold (VT2) was used as the gold standard technique. A positive correlation was found in the test time (r = 0.84), heart rate (r = 0.97) and VO2 (r = 0.97) between the VT2 and HRV second threshold (RRiT2). All parameters identified by RRiT2 were lower than predicted by VT2 (p \u3c 0.05)

Cerebral venous thrombosis: retrospective analysis of 49 cases

Introduction: Cerebral Venous Thrombosis (CVT) is a rare and potentially life-threatening disease, accounting for about 0.5% of stroke cases. However, it is believed to be an underdiagnosed condition. Early diagnosis requires a high degree of suspicion and appropriate use of imaging modalities. Objectives: Imagiological and clinical characterization of CVT cases diagnosed at our hospital from 2004 to 2007. Methods: This study was a retrospective, cross-sectional analysis from 2004 to 2007, using our institution database. We reviewed hospital discharge data to assess the incidence of CVT. The study population consisted of 49 patients. Retrospective review of the clinical data and imaging studies of these patients was then performed. Results: Of the 49 patients with confirmed CVT, 38 were female. Patient age varied between 16 and 75 years, with an average of 42.6 years. Thrombotic risk factors were found in 43 patients; the most frequent was dyslipidemia (n = 22) followed by oral contraceptive use (n = 18). Initial head Computerized Tomography (CT) was normal in six cases. Diagnosis was made by Magnetic Resonance (MR) in 38 cases, Cerebral CT-Venography in 10 cases and Digital Subtraction Angiography in one case. Average time from onset of symptoms to diagnosis was nine days; this was not significantly different when comparing the group diagnosed by MR with the group diagnosed by CT-Venography. Right transverse sinus was the most frequent location of thrombosis (n = 36). Only in four cases thrombosis did not involve the lateral sinuses. Conclusions: Lateral sinus thrombosis is a frequent variety of CVT, accounting for 91.8% of our cases. A negative Head CT scan does not exclude the presence of cerebral venous thrombosis; therefore appropriate imaging study should be performed whenever there's a high degree of clinical suspicion. Cerebral CT-Venography seems to be a good alternative to MR for the diagnosis of CVT

Molecular properties via a subsystem density functional theory formulation: A common framework for electronic embedding

In this article, we present a consistent derivation of a density functional theory (DFT) based embedding method which encompasses wave-function theory-in-DFT (WFT-in-DFT) and the DFT-based subsystem formulation of response theory (DFT-in-DFT) by Neugebauer [J. Neugebauer, J. Chem. Phys. 131, 084104 (2009)10.1063/1.3212883] as special cases. This formulation, which is based on the time-averaged quasi-energy formalism, makes use of the variation Lagrangian techniques to allow the use of non-variational (in particular: coupled cluster) wave-function-based methods. We show how, in the time-independent limit, we naturally obtain expressions for the ground-state DFT-in-DFT and WFT-in-DFT embedding via a local potential. We furthermore provide working equations for the special case in which coupled cluster theory is used to obtain the density and excitation energies of the active subsystem. A sample application is given to demonstrate the method. © 2012 American Institute of Physics

Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

In the current golden age of multimedia, human visualization is no longer the single main target, with the final consumer often being a machine which performs some processing or computer vision tasks. In both cases, deep learning plays a undamental role in extracting features from the multimedia representation data, usually producing a compressed representation referred to as latent representation. The increasing development and adoption of deep learning-based solutions in a wide area of multimedia applications have opened an exciting new vision where a common compressed multimedia representation is used for both man and machine. The main benefits of this vision are two-fold: i) improved performance for the computer vision tasks, since the effects of coding artifacts are mitigated; and ii) reduced computational complexity, since prior decoding is not required. This paper proposes the first taxonomy for designing compressed domain computer vision solutions driven by the architecture and weights compatibility with an available spatio-temporal computer vision processor. The potential of the proposed taxonomy is demonstrated for the specific case of point cloud classification by designing novel compressed domain processors using the JPEG Pleno Point Cloud Coding standard under development and adaptations of the PointGrid classifier. Experimental results show that the designed compressed domain point cloud classification solutions can significantly outperform the spatial-temporal domain classification benchmarks when applied to the decompressed data, containing coding artifacts, and even surpass their performance when applied to the original uncompressed data

Deep Learning-Based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

info:eu-repo/semantics/publishedVersio

HOT WIRE METHOD FOR THE THERMAL CHARACTERIZATION OF MATERIALS: INVERSE PROBLEM APPLICATION

An experimental set-up of the hot wire method is presented. The present design allows the measurement of the temperatures at two different points on the heating wire with an acquisition system that performs measurements at a frequency of 1kHz with a 12 bit numerical converter. An analytical solution for the direct model for the time dependent problem of heat transfer is employed. It is based on the quadrupole method which basically consists in a transfer matrix approach which is possible through the use of Laplace transforms. It extends the electrical analogy of heat transfer problems using the notion of impedance, and allows to take into account the thermal behavior of the wire, as well as contact resistance and heat loss effects in a very simple straightforward way. In the identification process carried on the temperature experimental data relies on a sampling of the data that is logarithmically spaced in time. The initial guesses for the thermal conductivity values are obtained applying the well known and ideal model of the linear temperature evolution versus the logarithm of the time. These values are used to start up the algorithms that are applied in the minimization of the cost functional of the squared residues between measured and calculated temperatures. The precision of the estimates is assessed with the calculated confidence bounds obtained by the variance-covariance matrix at the converged solution

Chimpanzee lip-smacks confirm primate continuity for speech-rhythm evolution

Speech is a human hallmark, but its evolutionary origins continue to defy scientific explanation. Recently, the open-close mouth rhythm of 2-7 Hz (cycles/second) characteristic of all spoken languages has been identified in the orofacial signals of several nonhuman primate genera, including orangutans, but evidence from any of the African apes remained missing. Evolutionary continuity for the emergence of speech is, thus, still inconclusive. To address this empirical gap, we investigated the rhythm of chimpanzee lip-smacks across four populations (two captive and two wild). We found that lip-smacks exhibit a speech-like rhythm at approximately 4 Hz, closing a gap in the evidence for the evolution of speech-rhythm within the primate order. We observed sizeable rhythmic variation within and between chimpanzee populations, with differences of over 2 Hz at each level. This variation did not result, however, in systematic group differences within our sample. To further explore the phylogenetic and evolutionary perspective on this variability, inter-individual and inter-population analyses will be necessary across primate species producing mouth signals at speech-like rhythm. Our findings support the hypothesis that speech recruited ancient primate rhythmic signals and suggest that multi-site studies may still reveal new windows of understanding about these signals' use and production along the evolutionary timeline of speech

Water regimes and bean cultivar effects on the soil porous system characteristics

Bean (Phaseolus vulgaris L.) is a crop of great economic and social impacts in Brazil. This crop is extremely appreciated by the Brazilian population and an important source of protein. Usually the small farmers are responsible by the largest production of the bean in Brazil. This work deals with the analysis of the effect of different water regimes (35, 28, 21 and 14%) on the porous system of a soil cropped with two distinct cultivars (Campos Gerais and Tuiuiú). Soil water retention curve (SWRC) and its derivative were utilized with the aim of investigating the changes in the porous system. Pore size distribution was also evaluated. The experiment was carried out at a greenhouse and the soil water content for the different water regimes was monitored by means of a TDR. Four undisturbed samples were collected from each wooden bed (eight) for the physic-hydrical characterization. Discrepancies in the SWRC were noticed for the region of small pressure heads. Differences were not observed between bean cultivars to SWRC. However, the water capacity function was sensitive to show differences in the soil porous system due to the treatments and cultivars. The lowest water regimes promoted the highest volume of fissures (big pores >250 µm) and, consequently, the highest ones had the largest volume of storage pores (<25 µm)