Separating dijet resonances using the color discriminant variable

Color-singlet and color-octet vector bosons predicted in theories beyond the Standard Model have the potential to be discovered as dijet resonances at the LHC. A color-singlet resonance that has leptophobic couplings needs further investigation to be distinguished from a color-octet one. In previous work, we introduced a method for discriminating between the two kinds of resonances when their couplings are flavor-universal, using measurements of the dijet resonance mass, total decay width and production cross-section. Here, we describe two extensions of that work. First, we broaden the method to the case where the vector resonances have flavor non-universal couplings, by incorporating measurements of the heavy-flavor decays of the resonance. Second, we apply the method to separating vector bosons from color-octet scalars and excited quarks

Signal transducer and activator of transcription-1 localizes to the mitochondria and modulates mitophagy

The signal transducer and activator of transcription (STAT) proteins are latent transcription factors that have been shown to be involved in cell proliferation, development, apoptosis, and autophagy. STAT proteins undergo activation by phosphorylation at tyrosine 701 and serine 727 where they translocate to the nucleus to regulate gene expression. STAT1 has been shown to be involved in promoting apoptotic cell death in response to cardiac ischemia/reperfusion and has recently been shown by our laboratory to be involved in negatively regulating autophagy. These processes are thought to promote cell death and restrict cell survival leading to the generation of an infarct. Here we present data that shows STAT1 localizes to the mitochondria and co-immunoprecipitates with LC3. Furthermore, electron microscopy studies also reveal mitochondria from ex vivo I/R treated hearts of STAT1KO mice contained within a double membrane autophagosome indicating that STAT1 may be involved in negatively regulating mitophagy. This is the first description of STAT1 being localized to the mitochondria and also having a role in mitophagy

Significant familial differences in the frequency of abortion and Toxoplasma gondii infection within a flock of Charollais sheep

A study was carried out to investigate the frequencies of abortion and congenital Toxoplasma gondii infection within 27 families (765 individuals) of a pedigree Charollais sheep flock maintained on a working farm in Worcestershire, UK, since 1992. Pedigree lambing records were analysed to establish the frequency of abortion for each family. The frequency of congenital infection was determined for each family by PCR analysis of tissue samples taken from newborn lambs. Atotal of 155 lambs were tested for congenital T. gondii infection, which were all born during the study period 2000–2003. Significant differences in the frequency of abortion between sheep families within this flock were observed with frequencies ranging between 0% and 48% (P<0.01). Significantly different infection frequencies with T. gondii were also observed for different families and ranged between 0% and 100% (P<0.01). Although the actual cause of each abortion was not verified, a highly significant positive correlation was found to exist between the frequency of abortion and the frequency of T. gondii infection in the same families (P<0.01). The data presented here raise further questions regarding the significance of congenital transmission of T. gondii within sheep populations, the possible successive vertical transmission of T. gondii within families of sheep, and the potential role of inherited genetic susceptibility to abortion with respect to T. gondii infection. This work invites further study into the epidemiology of ovine toxoplasmosis and may have implications for sheep husbandry methods in the future. Key words: Toxoplasma gondii, ovine, toxoplasmosis, congenital, transmission, pedigree, sheep

High levels of congenital transmission of toxoplasma gondii in longitudinal and cross-sectional studies on sheep farms provides evidence of vertical transmission in ovine hosts

Recent research suggests that vertical transmission may play an important role in sustaining Toxoplasma gondii infection in some species. We report here that congenital transmission occurs at consistently high levels in pedigree Charollais and outbred sheep flocks sampled over a 3-year period. Overall rates of transmission per pregnancy determined by PCR based diagnosis, were consistent over time in a commercial sheep flock (69%) and in sympatric (60%) and allopatric (41%) populations of Charollais sheep. The result of this was that 53·7% of lambs were acquiring an infection prior to birth: 46·4% of live lambs and 90·0% of dead lambs (in agreement with the association made between T. gondii and abortion). No significant differences were observed between lamb sexes. Although we cannot distinguish between congenital transmission occurring due to primary infection at pregnancy or reactivation of chronic infection during pregnancy, our observations of consistently high levels of congenital transmission over successive lambings favour the latter

Analisis Pola dan Struktur Inflasi Kota Medan

Adapun tujuan yang akan dicapai dalam penelitian ini adalah untuk melakukan identifikasi tentang pola inflasi yang terjadi di kota Medan, mengetahui kelompok barang yang berkontribusi besar pada inflasi di kota Medan dan untuk mengetahui faktor-faktor yang mempengaruhi inflasi di kota Medan. jenis data yang digunakan adalah data kuantitatif dengan jenis rasio dan kualitatif. Sedangkan berdasarkan dimensi waktu, maka data yang digunakan adalah data runtun waktu (time serries). Metode analisa data yang digunakan oleh penulis dalam penelitian ini adalah dengan menghitung pertumbuhan ekonomi untuk melihat pola inflasi, dengan menggunakan analisis kuantitatif dengan menggunakan analisa korelasi dan regresi dan dengan menggunakan analisis kualitatif untuk melihat jenis kelompok barang yang mendominasi inflasi di kota Medan.Hasil pembahasan yang diperoleh dari analisa data bahwa laju inflasi di kota Medan dalam kurun waktu tahun 2000-2011 relatif sangat fluktuatif, dengan rata-rata 8,48%. Kemudian dapat kesimpulan bahwa ada hubungan antara konsumsi masyarakat (C), Investasi (I), dan konsumsi pemerintah (G) sebesar 92,4% dengan laju inflasi di kota Medan. Tingkat konsumsi (C) berpengaruh secara positif dan signifikan terhadap laju inflasi, investasi (I) berpengaruh secara positif dan signifikan terhadap laju inflasi dan pengeluaran pemerintah pemerintah kota Medan (G) berpengaruh secara positif dan signifikan terhadap laju inflasi

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial Intelligence XXXV, 201

Hi-Val: Iterative Learning of Hierarchical Value Functions for Policy Generation

Task decomposition is effective in manifold applications where the global complexity of a problem makes planning and decision-making too demanding. This is true, for example, in high-dimensional robotics domains, where (1) unpredictabilities and modeling limitations typically prevent the manual specification of robust behaviors, and (2) learning an action policy is challenging due to the curse of dimensionality. In this work, we borrow the concept of Hierarchical Task Networks (HTNs) to decompose the learning procedure, and we exploit Upper Confidence Tree (UCT) search to introduce HOP, a novel iterative algorithm for hierarchical optimistic planning with learned value functions. To obtain better generalization and generate policies, HOP simultaneously learns and uses action values. These are used to formalize constraints within the search space and to reduce the dimensionality of the problem. We evaluate our algorithm both on a fetching task using a simulated 7-DOF KUKA light weight arm and, on a pick and delivery task with a Pioneer robot

ContextVP: Fully Context-Aware Video Prediction

Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. We identify an important contributing factor for imprecise predictions that has not been studied adequately in the literature: blind spots, i.e., lack of access to all relevant past information for accurately predicting the future. To address this issue, we introduce a fully context-aware architecture that captures the entire available past context for each pixel using Parallel Multi-Dimensional LSTM units and aggregates it using blending units. Our model outperforms a strong baseline network of 20 recurrent convolutional layers and yields state-of-the-art performance for next step prediction on three challenging real-world video datasets: Human 3.6M, Caltech Pedestrian, and UCF-101. Moreover, it does so with fewer parameters than several recently proposed models, and does not rely on deep convolutional networks, multi-scale architectures, separation of background and foreground modeling, motion flow learning, or adversarial training. These results highlight that full awareness of past context is of crucial importance for video prediction.Comment: 19 pages. ECCV 2018 oral presentation. Project webpage is at https://wonmin-byeon.github.io/publication/2018-ecc