A Critical Review of the US State Department's 2015 Progress Report on Haiti

This review, published jointly by the Center for Economic and Policy Research and the Haiti Advocacy Working Group, looks at the US State Department's annual reports on US assistance to Haiti mandated under the 2014 Assessing Progress in Haiti Act. The review analyzes the various components of the reports and identifies significant omissions and deficiencies, including incomplete data, a failure to link projects and outcomes, and a failure to adequately identify mistakes and lessons learned.In addition, the review shares feedback from Haitian civil society groups and makes recommendations on how the US Agency for International Development and the State Department can improve future progress reports

Time-Contrastive Networks: Self-Supervised Learning from Video

We propose a self-supervised approach for learning representations and robotic behaviors entirely from unlabeled videos recorded from multiple viewpoints, and study how this representation can be used in two robotic imitation settings: imitating object interactions from videos of humans, and imitating human poses. Imitation of human behavior requires a viewpoint-invariant representation that captures the relationships between end-effectors (hands or robot grippers) and the environment, object attributes, and body pose. We train our representations using a metric learning loss, where multiple simultaneous viewpoints of the same observation are attracted in the embedding space, while being repelled from temporal neighbors which are often visually similar but functionally different. In other words, the model simultaneously learns to recognize what is common between different-looking images, and what is different between similar-looking images. This signal causes our model to discover attributes that do not change across viewpoint, but do change across time, while ignoring nuisance variables such as occlusions, motion blur, lighting and background. We demonstrate that this representation can be used by a robot to directly mimic human poses without an explicit correspondence, and that it can be used as a reward function within a reinforcement learning algorithm. While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human. Reward functions obtained by following the human demonstrations under the learned representation enable efficient reinforcement learning that is practical for real-world robotic systems. Video results, open-source code and dataset are available at https://sermanet.github.io/imitat

Pressure-Induced Effects on the Structure of the FeSe Superconductor

A polycrystalline sample of FeSe, which adopts the tetragonal PbO-type structure (P4/nmm) at room temperature, has been prepared using solid state reaction. We have investigated pressure-induced structural changes in tetragonal FeSe at varying hydrostatic pressures up to 0.6 GPa in the orthorhombic (T = 50 K) and tetragonal (T = 190 K) phases using high resolution neutron powder diffraction. We report that the structure is quite compressible with a Bulk modulus around 31 GPa to 33 GPa and that the pressure response is anisotropic with a larger compressibility along the c-axis. Key bond angles of the SeFe4 pyramids and FeSe4 tetrahedra are also determined as a function of pressure

Synthesis and asymmetric hydrogenation of (3E)-1-benzyl-3-[(2-oxopyridin-1(2H)-yl)methylidene]piperidine-2,6-dione

The synthesis of (3E)-1-benzyl-3-[(2-oxopyridin-1(2H)-yl)methylidene]piperidine-2,6-dione 4 from N-benzylglutarimide was achieved in three steps. The asymmetric hydrogenation of 4 gave either the product of partial reduction (10) or full reduction (13), depending on the catalyst which was employed, in high ee in each case. Attempts at asymmetric transfer hydrogenation (ATH) of 4 resulted in formation of a racemic product