In the physical design of integrated circuits, global and detailed routing are critical stages involving the determination of the interconnected paths of each net on a circuit while satisfying the design constraints. Existing actual routers as well as routability predictors either have to resort to expensive approaches that lead to high computational times, or use heuristics that do not generalize well. Even though new, learning-based routing methods have been proposed to address this need, requirements on labelled data and difficulties in addressing complex design rule constraints have limited their adoption in advanced technology node physical design problems. In this work, we propose a new router: attention router, which is the first attempt to solve the track-assignment detailed routing problem using reinforcement learning. Complex design rule constraints are encoded into the routing algorithm and an attention-model-based REINFORCE algorithm is applied to solve the most critical step in routing: sequencing device pairs to be routed. The attention router and its baseline genetic router are applied to solve different commercial advanced technologies analog circuits problem sets. The attention router demonstrates generalization ability to unseen problems and is also able to achieve more than 100 times acceleration over the genetic router without significantly compromising the routing solution quality. We also discover a similarity between the attention router and the baseline genetic router in terms of positive correlations in cost and routing patterns, which demonstrate the attention router's ability to be utilized not only as a detailed router but also as a predictor for routability and congestion.

We work on dynamic problems with collected data $\{\mathsf{x}_i\}$ that distributed on a manifold $\mathcal{M}\subset\mathbb{R}^p$. Through the diffusion map, we first learn the reaction coordinates $\{\mathsf{y}_i\}\subset \mathcal{N}$ where $\mathcal{N}$ is a manifold isometrically embedded into an Euclidean space $\mathbb{R}^\ell$ for $\ell \ll p$. The reaction coordinates enable us to obtain an efficient approximation for the dynamics described by a Fokker-Planck equation on the manifold $\mathcal{N}$. By using the reaction coordinates, we propose an implementable, unconditionally stable, data-driven upwind scheme which automatically incorporates the manifold structure of $\mathcal{N}$. Furthermore, we provide a weighted $L^2$ convergence analysis of the upwind scheme to the Fokker-Planck equation. The proposed upwind scheme leads to a Markov chain with transition probability between the nearest neighbor points. We can benefit from such property to directly conduct manifold-related computations such as finding the optimal coarse-grained network and the minimal energy path that represents chemical reactions or conformational changes. To establish the Fokker-Planck equation, we need to acquire information about the equilibrium potential of the physical system on $\mathcal{N}$. Hence, we apply a Gaussian Process regression algorithm to generate equilibrium potential for a new physical system with new parameters. Combining with the proposed upwind scheme, we can calculate the trajectory of the Fokker-Planck equation on $\mathcal{N}$ based on the generated equilibrium potential. Finally, we develop an algorithm to pullback the trajectory to the original high dimensional space as a generative data for the new physical system.

Building rich machine learning datasets in a scalable manner often necessitates a crowd-sourced data collection pipeline. In this work, we use human studies to investigate the consequences of employing such a pipeline, focusing on the popular ImageNet dataset. We study how specific design choices in the ImageNet creation process impact the fidelity of the resulting dataset---including the introduction of biases that state-of-the-art models exploit. Our analysis pinpoints how a noisy data collection pipeline can lead to a systematic misalignment between the resulting benchmark and the real-world task it serves as a proxy for. Finally, our findings emphasize the need to augment our current model training and evaluation toolkit to take such misalignments into account. To facilitate further research, we release our refined ImageNet annotations at https://github.com/MadryLab/ImageNetMultiLabel.

This paper studies a high-dimensional inference problem involving the matrix tensor product of random matrices. This problem generalizes a number of contemporary data science problems including the spiked matrix models used in sparse principal component analysis and covariance estimation. It is shown that the information-theoretic limits can be described succinctly by formulas involving low-dimensional quantities. On the technical side, this paper introduces some new techniques for the analysis of high-dimensional matrix-valued signals. Specific contributions include a novel extension of the adaptive interpolation method that uses order-preserving positive semidefinite interpolation paths and a variance inequality based on continuous-time I-MMSE relations.

In this paper, novel gradient based online learning algorithms are developed to investigate an important environmental application: real-time river pollution source identification, which aims at estimating the released mass, the location and the released time of a river pollution source based on downstream sensor data monitoring the pollution concentration. The problem can be formulated as a non-convex loss minimization problem in statistical learning, and our online algorithms have vectorized and adaptive step-sizes to ensure high estimation accuracy on dimensions having different magnitudes. In order to avoid gradient-based method sticking into the saddle points of non-convex loss, the "escaping from saddle points" module and multi-start version of algorithms are derived to further improve the estimation accuracy by searching for the global minimimals of the loss functions. It can be shown theoretically and experimentally $O(N)$ local regret of the algorithms, and the high probability cumulative regret bound $O(N)$ under particular error bound condition on loss functions. A real-life river pollution source identification example shows superior performance of our algorithms than the existing methods in terms of estimating accuracy. The managerial insights for decision maker to use the algorithm in reality are also provided.

Elham Mansoori, member of Afghan Dreamers, an all-girls robotics team in Afghanistan, works on their prototype of a ventilator. In Afghanistan, a group of teenage girls are trying to build a mechanized, hand-operated ventilator for coronavirus patients, using a design from M.I.T. and parts from old Toyota Corollas. It sounds like an impossible dream, but then again, the all-girls robotics team in question is called the "Afghan Dreamers." Living a country where two-thirds of adolescent girls cannot read or write, they're used to overcoming challenges. The team of some dozen girls aged 15 to 17 was formed three years ago by Roya Mahboob, an Afghan tech entrepreneur who heads the Digital Citizen Fund, a group that runs classes for girls in STEM and robotics and oversees and funds the Afghan Dreamers.

Beijing is accelerating its bid for global leadership in key technologies, planning to pump more than a trillion dollars into the economy through the rollout of everything from wireless networks to artificial intelligence (AI). In the master plan backed by President Xi Jinping himself, China will invest an estimated $1.4 trillion over six years to 2025, calling on urban governments and private tech giants like Huawei Technologies Co. to deploy fifth generation wireless networks, install cameras and sensors and develop AI software that will underpin technologies from autonomous driving to automated factories and mass surveillance. The new infrastructure initiative is expected to drive mainly local giants, from Alibaba and Huawei to SenseTime Group Ltd., at the expense of U.S. companies. As tech-nationalism mounts, the investment drive will reduce China's dependence on foreign technology -- echoing objectives set forth previously in the Made in China 2025 program. Such initiatives have already drawn fierce criticism from the Trump administration, resulting in moves to block the rise of Chinese technology companies such as Huawei. "Nothing like this has happened before; this is China's gambit to win the global tech race," said Digital China Holdings Chief Operating Officer Maria Kwok, as she sat in a Hong Kong office surrounded by facial recognition cameras and sensors.

WASHINGTON--A U.S. national security panel has ordered the breakup of a joint venture formed between Chinese investors and a California firm that makes exoskeletons, robotic devices that can help disabled people walk but can also help soldiers carry heavy loads. In an announcement Wednesday, Ekso Bionics Holdings Inc. said that panelists on the Committee on Foreign Investment in the U.S., which reviews deals that threaten the country's national security, are requiring the company to end its joint-venture with its Chinese business...

Budget airline easyJet was aware of the data breach, which revealed personal information of nine million customers and the credit card information of over 2,200 customers, in January. News of the cyber attack broke yesterday, revealing that the attacker or attackers had access to the data of customers who booked flights from 17 October 2019 to 4 March 2020. In a statement, the airline said: "We're sorry that this has happened, and we would like to reassure customers that we take the safety and security of their information very seriously. "There is no evidence that any personal information of any nature has been misused." However, while there is no evidence the data was misused, that does not mean that it cannot be misused. Experts suggest that personal information "drives a higher price on the dark web" – the area of the internet inaccessible by mainstream search engines – and could be used for organised crime or ransomed. What does the easyJet data hack mean for you? What does the easyJet data hack mean for you? Two people with knowledge of the investigation have said that Chinese hackers are supposedly responsible for the hack based on similarities in hacking tools and techniques used in previous campaigns, but that has yet to be officially confirmed. In a statement, the Information Commissioners' Office (ICO) said: "We have a live investigation into the cyber attack involving easyJet.

Achieving the Bayes optimal binary classification rule subject to group fairness constraints is known to be reducible, in some cases, to learning a group-wise thresholding rule over the Bayes regressor. In this paper, we extend this result by proving that, in a broader setting, the Bayes optimal fair learning rule remains a group-wise thresholding rule over the Bayes regressor but with a (possible) randomization at the thresholds. This provides a stronger justification to the post-processing approach in fair classification, in which (1) a predictor is learned first, after which (2) its output is adjusted to remove bias. We show how the post-processing rule in this two-stage approach can be learned quite efficiently by solving an unconstrained optimization problem. The proposed algorithm can be applied to any black-box machine learning model, such as deep neural networks, random forests and support vector machines. In addition, it can accommodate many fairness criteria that have been previously proposed in the literature, such as equalized odds and statistical parity. We prove that the algorithm is Bayes consistent and motivate it, furthermore, via an impossibility result that quantifies the tradeoff between accuracy and fairness across multiple demographic groups. Finally, we conclude by validating the algorithm on the Adult benchmark dataset.