Principles of Group Counseling and Their Applications for Deaf Clients

According to a model developed by Cavanagh (1982), counseling may be perceived as a unique relationship by which the counselor helps others learn to relate to themselves and others in growth producing ways. The effective counselor fosters growth by creating an environment and a relationship that is significantly different from any presently experienced by the client or clients. Underlying principles and goals of individual and group counseling are identified and discussed. One goal of counseling is to help individuals understand that most of their difficulties emanate from within themselves and not from external circumstances. The basic purposes of a therapeutic group are to increase people\u27s knowledge of themselves and others, to assist people in clarifying changes they want to make in their lives, and to help them develop some of the tools necessary to make the changes. Special characteristics of members of the Deaf culture are discussed, with implications for the group counseling process. The identification of ASL as the language of choice has great importance for the Deaf community and any effective counselor must accept it as a legitimate language distinct from English. The counselor must resist any attempt to pathologize deafness and needs to recognize it as an identifying characteristic of a distinct American social group. The utilization of interpreters with counselors not proficient in ASL is considered. Because the presence of a third party in the counseling process entails distancing in the counselor/client relationship, it is preferable to have a counselor skilled in ASL. Because of a shortage of such professionals, the use of an interpreter may be the only viable alternative

Evaluation of CNN-based Single-Image Depth Estimation Methods

While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited. We propose a set of novel quality criteria, allowing for a more detailed analysis by focusing on specific characteristics of depth maps. In particular, we address the preservation of edges and planar regions, depth consistency, and absolute distance accuracy. In order to employ these metrics to evaluate and compare state-of-the-art single-image depth estimation approaches, we provide a new high-quality RGB-D dataset. We used a DSLR camera together with a laser scanner to acquire high-resolution images and highly accurate depth maps. Experimental results show the validity of our proposed evaluation protocol

A Different Challenge for the ALI: Herein of Foreign Country Judgments, an International Treaty, and an American Statute

Symposium: Preparing for the Next Century-A New Restatement of Conflicts

A Different Challenge for the ALI: Herein of Foreign Country Judgments, an International Treaty, and an American Statute

Symposium: Preparing for the Next Century-A New Restatement of Conflicts

Cosmological Density and Power Spectrum from Peculiar Velocities: Nonlinear Corrections and PCA

We allow for nonlinear effects in the likelihood analysis of galaxy peculiar velocities, and obtain ~35%-lower values for the cosmological density parameter Om and the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be a flat LCDM model (h=0.65, n=1, COBE) with only Om as a free parameter. Since the likelihood is driven by the nonlinear regime, we "break" the power spectrum at k_b=0.2 h/Mpc and fit a power law at k>k_b. This allows for independent matching of the nonlinear behavior and an unbiased fit in the linear regime. The analysis assumes Gaussian fluctuations and errors, and a linear relation between velocity and density. Tests using proper mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark3 and SFI data Om_m=0.32+-0.06 and 0.37+-0.09 respectively, with sigma_8*Om^0.6 = 0.49+-0.06 and 0.63+-0.08, in agreement with constraints from other data. The quoted 90% errors include cosmic variance. The improvement in likelihood due to the nonlinear correction is very significant for Mark3 and moderately so for SFI. When allowing deviations from LCDM, we find an indication for a wiggle in the power spectrum: an excess near k=0.05 and a deficiency at k=0.1 (cold flow). This may be related to the wiggle seen in the power spectrum from redshift surveys and the second peak in the CMB anisotropy. A chi^2 test applied to modes of a Principal Component Analysis (PCA) shows that the nonlinear procedure improves the goodness of fit and reduces a spatial gradient of concern in the linear analysis. The PCA allows addressing spatial features of the data and fine-tuning the theoretical and error models. It shows that the models used are appropriate for the cosmological parameter estimation performed. We address the potential for optimal data compression using PCA.Comment: 18 pages, LaTex, uses emulateapj.sty, ApJ in press (August 10, 2001), improvements to text and figures, updated reference

Complete and Prolonged Response to Immune Checkpoint Blockade in POLE-Mutated Colorectal Cancer

Enid Robertson Logan Faculty Fellowship Fund6 month embargo; published online: 21 June 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Some open questions in "wave chaos"

The subject area referred to as "wave chaos", "quantum chaos" or "quantum chaology" has been investigated mostly by the theoretical physics community in the last 30 years. The questions it raises have more recently also attracted the attention of mathematicians and mathematical physicists, due to connections with number theory, graph theory, Riemannian, hyperbolic or complex geometry, classical dynamical systems, probability etc. After giving a rough account on "what is quantum chaos?", I intend to list some pending questions, some of them having been raised a long time ago, some others more recent

3D Scene Reconstruction from a Single Viewport

We present a novel approach to infer volumetric reconstructions from a single viewport, based only on an RGB image and a reconstructed normal image. To overcome the problem of reconstructing regions in 3D that are occluded in the 2D image, we propose to learn this information from synthetically generated high-resolution data. To do this, we introduce a deep network architecture that is specifically designed for volumetric TSDF data by featuring a specific tree net architecture. Our framework can handle a 3D resolution of 512³ by introducing a dedicated compression technique based on a modified autoencoder. Furthermore, we introduce a novel loss shaping technique for 3D data that guides the learning process towards regions where free and occupied space are close to each other. As we show in experiments on synthetic and realistic benchmark data, this leads to very good reconstruction results, both visually and in terms of quantitative measures