Observational Bounds on Modified Gravity Models

Modified gravity provides a possible explanation for the currently observed cosmic accelaration. In this paper, we study general classes of modified gravity models. The Einstein-Hilbert action is modified by using general functions of the Ricci and the Gauss-Bonnet scalars, both in the metric and in the Palatini formalisms. We do not use an explicit form for the functions, but a general form with a valid Taylor expansion up to second order about redshift zero in the Riemann-scalars. The coefficients of this expansion are then reconstructed via the cosmic expansion history measured using current cosmological observations. These are the quantities of interest for theoretical considerations relating to ghosts and instabilities. We find that current data provide interesting constraints on the coefficients. The next-generation dark energy surveys should shrink the allowed parameter space for modifed gravity models quite dramatically.Comment: 23 pages, 5 figures, uses RevTe

Object Contour and Edge Detection with RefineContourNet

A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Keeping our focus in mind, we fuse the high, mid and low-level features in that specific order, which differs from many other approaches. It uses the tensor with the highest-levelled features as the starting point to combine it layer-by-layer with features of a lower abstraction level until it reaches the lowest level. We train this network on a modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a refined PASCAL-val dataset reaching an excellent performance and an Optimal Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500 dataset we reach state-of-the-art results for edge-detection with an ODS of 0.824.Comment: Keywords: Object Contour Detection, Edge Detection, Multi-Path Refinement CN

Uncorrelated Measurements of the Cosmic Expansion History and Dark Energy from Supernovae

We present a method for measuring the cosmic expansion history H(z) in uncorrelated redshift bins, and apply it to current and simulated type Ia supernova data assuming spatial flatness. If the matter density parameter Omega_m can be accurately measured from other data, then the dark energy density history X(z)=rho_X(z)/rho_X(0) can trivially be derived from this expansion history H(z). In contrast to customary ``black box'' parameter fitting, our method is transparent and easy to interpret: the measurement of H(z)^{-1} in a redshift bin is simply a linear combination of the measured comoving distances for supernovae in that bin, making it obvious how systematic errors propagate from input to output. We find the Riess et al. (2004) ``gold'' sample to be consistent with the ``vanilla'' concordance model where the dark energy is a cosmological constant. We compare two mission concepts for the NASA/DOE Joint Dark Energy Mission (JDEM), the Joint Efficient Dark-energy Investigation (JEDI), and the Supernova Accelaration Probe (SNAP), using simulated data including the effect of weak lensing (based on numerical simulations) and a systematic bias from K-corrections. Estimating H(z) in seven uncorrelated redshift bins, we find that both provide dramatic improvements over current data: JEDI can measure H(z) to about 10% accuracy and SNAP to 30-40% accuracy.Comment: 7 pages, 4 color figures. Expanded and revised version; PRD in pres

Trauma-related psychological disorders among Palestinian children and adults in Gaza and West Bank, 2005-2008

BACKGROUND: Trauma from war and violence has led to psychological disorders in individuals living in the Gaza strip and West Bank. Few reports are available on the psychiatric disorders seen in children and adolescents or the treatment of affected populations. This study was conducted in order to describe the occurrence and treatment of psychiatric disorders in the Palestinian populations of the Gaza strip and Nablus district in the West Bank. METHODS: From 2005 to 2008, 1369 patients aged more than 1 year were identified through a local mental health and counseling health network. All were clinically assessed using a semi-structured interview based on the DSM-IV-TR criteria. RESULTS: Among 1254 patients, 23.2% reported post-traumatic stress disorder [PTSD], 17.3% anxiety disorder (other than PTSD or acute stress disorder), and 15.3% depression. PTSD was more frequently identified in children < or = 15 years old, while depression was the main symptom observed in adults. Among children < or = 15 years old, factors significantly associated with PTSD included being witness to murder or physical abuse, receiving threats, and property destruction or loss (p < 0.03). Psychological care, primarily in the form of individual, short-term psychotherapy, was provided to 65.1% of patients, with about 30.6% required psychotropic medication. Duration of therapy sessions was higher for children < or = 15 years old compared with adults (p = 0.05). Following psychotherapy, 79.0% had improved symptoms, and this improvement was significantly higher in children < or = 15 years old (82.8%) compared with adults (75.3%; p = 0.001). CONCLUSION: These observations suggest that short-term psychotherapy could be an effective treatment for specific psychiatric disorders occurring in vulnerable populations, including children, living in violent conflict zones, such as in Gaza strip and the West Bank

Quark mass density- and temperature- dependent model for strange quark matter

It is found that the radius of a stable strangelet decreases as the temperature increases in a quark mass density-dependent model. To overcome this difficulty, we extend this model to a quark mass density- and temperature- dependent model in which the vacuum energy density at zero baryon density limit B depends on temperature. An ansatz is introduced and the regions for the best choice of the parameters are studied.Comment: 5 pages, 4 figure

High-order volterra model predictive control and its application to a nonlinear polymerisation process

Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but the existing design and implementation methods are restricted to linear process models. A chemical process involves, however, severe nonlinearity which cannot be ignored in practice. This paper aims to solve this nonlinear control problem by extending MPC to nonlinear models. It develops an analytical framework for nonlinear model predictive control (NMPC), and also offers a third-order Volterra series based nonparametric nonlinear modelling technique for NMPC design which relieves practising engineers from the need for first deriving a physical-principles based model. An on-line realisation technique for implementing the NMPC is also developed. The NMPC is then applied to a Mitsubishi Chemicals polymerisation reaction process. The results show that this nonlinear MPC technique is feasible and very effective. It considerably outperforms linear and low-order Volterra model based methods. The advantages of the approach developed lie not only in control performance superior to existing NMPC methods, but also in relieving practising engineers from the need for deriving an analytical model and then converting it to a Volterra model through which the model can only be obtained up to the second order

Peningkatan Keterampilan Menyimak Cerita Rakyat Melalui Media Audio Pada Siswa Kelas V SDN No. I PancaMukti

Penelitian ini adalah penelitian tindakan kelas yang dilaksanakan di SDN No. I Panca Mukti, melibatkan 26 orang siswa terdiri atas 16 orang laki-laki dan 10 orang perempuan yang terdaftar pada tahun ajaran 2013/2014. Penelitian ini menggunakan desain penelitian model Kemmis dan Mc Taggart yang terdiri atas dua siklus. Di mana pada setiap siklus dilaksanakan dua kali pertemuan di kelas dan setiap siklus terdiri empat tahap yaitu perencanaan, pelaksanaan, observasi, dan refleksi. Hasil penelitian menunjukkan bahwa pada pra tindakan diperoleh ketuntasan klasikal 27,27% dan daya serap klasikal 55,45%. Pada tindakan siklus I diperoleh ketuntasan klasikal 69,23% dan daya serap klasikal 72,11% sedangkan pada tindakan siklus II diperoleh ketuntasan klasikal 98,8% dan daya serap klasikal 76,51%. Hal ini berarti pembelajaran pada siklus II telah memenuhi indikator keberhasilan dengan nilai daya serap klasikal minimal 70% dan ketuntasan belajar klasikal minimal 80%. Berdasarkan nilai rata-rata daya serap klasikal dan ketuntasan belajar klasikal pada kegiatan pembelajaran siklus II, maka dapat disimpulkan bahwa pembelajaran dengan menggunakan media audio dapat meningkatkan hasil belajar siswa kelas V pada pembelajaran di SDN No. I Panca Mukti

Local Algorithms for Block Models with Side Information

There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs. Gamarnik and Sudan (2014) showed that local algorithms are weaker than global algorithms for finding large independent sets in sparse random regular graphs. Montanari (2015) showed that local algorithms are suboptimal for finding a community with high connectivity in the sparse Erd\H{o}s-R\'enyi random graphs. For the symmetric planted partition problem (also named community detection for the block models) on sparse graphs, a simple observation is that local algorithms cannot have non-trivial performance. In this work we consider the effect of side information on local algorithms for community detection under the binary symmetric stochastic block model. In the block model with side information each of the $n$ vertices is labeled $+$ or $-$ independently and uniformly at random; each pair of vertices is connected independently with probability $a/n$ if both of them have the same label or $b/n$ otherwise. The goal is to estimate the underlying vertex labeling given 1) the graph structure and 2) side information in the form of a vertex labeling positively correlated with the true one. Assuming that the ratio between in and out degree $a/b$ is $\Theta(1)$ and the average degree $(a+b) / 2 = n^{o(1)}$, we characterize three different regimes under which a local algorithm, namely, belief propagation run on the local neighborhoods, maximizes the expected fraction of vertices labeled correctly. Thus, in contrast to the case of symmetric block models without side information, we show that local algorithms can achieve optimal performance for the block model with side information.Comment: Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract here is shorter than that in the PDF fil