All the details on Uber's biggest announcements as well as updates on the controversial company's trials and tribulations

The Immigration Department announced today that the operation of all nine Smart Identity Card Replacement Centres will be fully resumed on May 11 in light of the more stabilised epidemic situation.

The department earlier suspended the replacement of Hong Kong identity cards at the centres to avoid the increased risk of spreading COVID-19.

To arrange for people affected by the service suspension to replace their identity cards in an orderly manner, the Secretary for Security has made an amendment order to revise the replacement period for people born in 1957 to 1963 and 1970 to 1976 and the arrangement for members of the sixth term of District Councils.

Click here for the arrangements.

If the replacement of identity cards needs to be suspended again in the future to cope with a sudden turn of the epidemic situation, the amendment order also provides that if all the centres are not in service for a period of 21 working days or more from May 11 to July 27 for public health reasons, the specified period for the above people will be further extended or amended.

The amendment order will be tabled at the Legislative Council on May 13 for negative vetting.

To reduce crowd gatherings, applicants who have not made appointments previously should do so via the Internet, the department’s mobile application or the 24-hour hotline at 2121 1234.

The department also appealed to applicants to pre-fill the application form when making appointments through the Internet or mobile application.

For details click here or call 2824 6111.

There is no special announcement (19:45 HKT on 03.05.2020)

Gregory McMichael and his son, Travis, were charged with murder and aggravated assault in relation to the shooting death of Ahmaud Arbery in February.

This week it is all about Apple as the company announces its eagerly awaited iPhone X and iPhone 8/8 Plus smartphones, a new Apple watch (yawn) and Apple TV news. Macworld UK editor Karen Haslam is our host this week, as she chats to a distinctly unconvinced Dominic Preston about the latest iPhone, followed by David Price on the Apple Watch Series 3, which he likes but not as much as its predecessor. Then everyone piles in together to discuss the quite-good iPhone 8, the quite-late Apple TV 4K, and all the rest of the evening's festivities.  

See acast.com/privacy for privacy and opt-out information.

When Yandy Diaz arrived at Rays camp on Sunday, he quickly established himself as the most muscular player inside the clubhouse. During Spring Training, his focus will be to establish himself as an everyday player.

5 November 2019

This ethnographic study is the first of its kind to analyse energy access and resilience strategies deployed in two refugee camps in Kenya and Burkina Faso. It highlights the need for new methodological approaches to expand the evidence base for humanitarian energy interventions and policies.

The Trump administration has significantly cranked up the immigration enforcement machinery in the U.S. interior. Yet even as arrests and deportations are up in the early Trump months, they remain less than half their peaks. This report demonstrates how pushback from California and other "sanctuary" locations makes it quite unlikely that ICE will be able to match record enforcement levels.

Discussion at this event focused on findings from MPI's report examining the interior immigration enforcement system in the United States, including ICE data on deportations and arrests, and the responses of state and local governments, civil society, and consulates.  

Testimony of Marc R. Rosenblum, Deputy Director, U.S. Immigration Program, before the House Judiciary Committee on the efficacy of immigration enforcement at the U.S. border and in the interior.

Marking the release of an MPI report, this briefing explores the effects of recent revisions in DHS immigration enforcement priorities on deportations within the U.S. interior.

Marking the release of an MPI report, this discussion focuses on the dynamics in low-wage workplaces and immigration law that have contributed to systematic violations of labor standards and explores new and effective enforcement strategies that state and local governments across the United States are utilizing.

This discussion, featuring California's Labor Commissioner and the head of the Tennessee Bureau of Workers' Compensation, launched a report that examines state innovations in labor standards enforcement in low-wage, immigrant-dense industries. With wage underpayment, payroll fraud, and other violations widespread in industries such as construction and car-washing, the discussion focused on how targeted enforcement can deter practices that hurt native-born and immigrant workers alike, cost state tax revenue, and disadvantage law-abiding employers. 

Marking the release of an MPI study, this discussion examines the operation of today’s interior enforcement system and how state and local governments, civil society, and consulates are responding.    

Nearly 6 million Afghans fled after violence erupted in the late 1970s, primarily to Iran and Pakistan. While millions returned after the collapse of the Taliban in 2001, the security situation has since deteriorated and the government struggles to meet the needs of vulnerable populations, particularly the internally displaced. This country profile explores Afghanistan’s complex migration and displacement history as well as ongoing challenges.

Marking the release of an MPI report, speakers on this webinar present an overview of regional immigration enforcement trends, including U.S. and Mexican apprehensions and deportations of both children and adults, along with a demographic, socioeconomic, and criminal profile of child and adult deportees.

This webinar includes an overview of regional immigration enforcement trends, including U.S. and Mexican apprehensions and deportations of Central American migrants, along with a demographic, socioeconomic, and criminal profile of child and adult deportees.

OBJECTIVE

In patients with type 1 diabetes and end-stage renal disease, it is controversial whether a simultaneous pancreas-kidney (SPK) transplantation improves survival compared with kidney transplantation alone. We compared long-term survival in SPK and living- or deceased-donor kidney transplant recipients.

How did the U.S. border enforcement picture go in the span of two years from the lowest levels of illegal immigration since 1971 to a spiraling border security and humanitarian crisis? This report draws on enforcement and other data as well as analysis of changing migration trends and policies to tell this story. The authors outline key elements for a new strategy that can succeed over the long term.

The U.S. Department of Labor announced April 20 that it is ending the temporary period of non-enforcement of paid leave protections under the Families First Coronavirus Response Act.

The House of Representatives passed a new coronavirus relief bill April 23 that calls for additional funding for federal loan programs to help businesses nationwide, including dental practices, recover from the economic fallout of the pandemic.

MPI has released a major study that describes and analyzes today’s immigration enforcement programs, as they have developed and grown in the 25 years since IRCA launched the current enforcement era.

Release of a major report that describes and analyzes the immigration enforcement system in the United States as it has developed and grown in the quarter century since the Immigration Reform and Control Act of 1986 launched the current era of enforcement.

An unannounced sweep of 98 convenience stores by U.S. immigration authorities—resulting in the arrest of 21 unauthorized workers—may signal a new approach to worksite enforcement under the Trump administration, moving away from a strategy of paper-based audits that resulted in higher employer fines and fewer worker arrests. This article explores worksite enforcement over recent decades.

In an era of stepped-up immigration enforcement, speakers at this event present their research on the impact of enforcement policies on children from immigrant families and U.S. public schools. 

John B. Buse
Jul 1, 2002; 20:
Feature Articles

This is the thrilling account of an extraordinary life in law enforcement. Specialising in technical surveillance, Victoria Police Inspector Andrew Atkinson dedicated himself to ridding our world of murderers, corruption, terrorists and underworld crime syndicates. Now retired, Atkinson can let us inside his days as a peacekeeper in East Timor with the United Nations, an advisor to the Australia-New Zealand Counter-Terrorism Committee and the deceptive world of police surveillance.

Roosevelt, Theodore, 1858-1919.

We develop ancestral Gumbel-Top-$k$ sampling: a generic and efficient method for sampling without replacement from discrete-valued Bayesian networks, which includes multivariate discrete distributions, Markov chains and sequence models. The method uses an extension of the Gumbel-Max trick to sample without replacement by finding the top $k$ of perturbed log-probabilities among all possible configurations of a Bayesian network. Despite the exponentially large domain, the algorithm has a complexity linear in the number of variables and sample size $k$. Our algorithm allows to set the number of parallel processors $m$, to trade off the number of iterations versus the total cost (iterations times $m$) of running the algorithm. For $m = 1$ the algorithm has minimum total cost, whereas for $m = k$ the number of iterations is minimized, and the resulting algorithm is known as Stochastic Beam Search. We provide extensions of the algorithm and discuss a number of related algorithms. We analyze the properties of ancestral Gumbel-Top-$k$ sampling and compare against alternatives on randomly generated Bayesian networks with different levels of connectivity. In the context of (deep) sequence models, we show its use as a method to generate diverse but high-quality translations and statistical estimates of translation quality and entropy.

We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates (or sums) across actions when estimating the gradient, instead of relying only on the action in the sampled trajectory. For continuous action spaces, we first derive a practical result for Gaussian policies and quadratic critics and then extend it to a universal analytical method, covering a broad class of actors and critics, including Gaussian, exponential families, and policies with bounded support. For Gaussian policies, we introduce an exploration method that uses covariance proportional to the matrix exponential of the scaled Hessian of the critic with respect to the actions. For discrete action spaces, we derive a variant of EPG based on softmax policies. We also establish a new general policy gradient theorem, of which the stochastic and deterministic policy gradient theorems are special cases. Furthermore, we prove that EPG reduces the variance of the gradient estimates without requiring deterministic policies and with little computational overhead. Finally, we provide an extensive experimental evaluation of EPG and show that it outperforms existing approaches on multiple challenging control domains.

Bastien Mallein.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 356--373.

Abstract:
Consider a supercritical branching random walk on the real line. The consistent maximal displacement is the smallest of the distances between the trajectories followed by individuals at the $n$th generation and the boundary of the process. Fang and Zeitouni, and Faraud, Hu and Shi proved that under some integrability conditions, the consistent maximal displacement grows almost surely at rate $lambda^{*}n^{1/3}$ for some explicit constant $lambda^{*}$. We obtain here a necessary and sufficient condition for this asymptotic behaviour to hold.

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this paper, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the loss of energy shortfall of the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.

Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity. However, there is a lack of approaches that combine these paradigms, and it is currently unknown whether curiosity also helps to perform the hierarchical abstraction. As a novelty and scientific contribution, we tackle this issue and develop a method that combines hierarchical reinforcement learning with curiosity. Herein, we extend a contemporary hierarchical actor-critic approach with a forward model to develop a hierarchical notion of curiosity. We demonstrate in several continuous-space environments that curiosity approximately doubles the learning performance and success rates for most of the investigated benchmarking problems.

Motion synthesis in a dynamic environment has been a long-standing problem for character animation. Methods using motion capture data tend to scale poorly in complex environments because of their larger capturing and labeling requirement. Physics-based controllers are effective in this regard, albeit less controllable. In this paper, we present CARL, a quadruped agent that can be controlled with high-level directives and react naturally to dynamic environments. Starting with an agent that can imitate individual animation clips, we use Generative Adversarial Networks to adapt high-level controls, such as speed and heading, to action distributions that correspond to the original animations. Further fine-tuning through the deep reinforcement learning enables the agent to recover from unseen external perturbations while producing smooth transitions. It then becomes straightforward to create autonomous agents in dynamic environments by adding navigation modules over the entire process. We evaluate our approach by measuring the agent's ability to follow user control and provide a visual analysis of the generated motion to show its effectiveness.