Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction. In the High Dimension Low Sample Size (HDLSS) setting, one may prefer modified principal components, with penalized loadings, and automated penalty selection by implementing model selection among these different models with varying penalties. The earlier work [1, 2] has proposed penalized PCA, indicating the feasibility of model selection in $L_2$- penalized PCA through the solution path of Ridge regression, however, it is extremely time-consuming because of the intensive calculation of matrix inverse. In this paper, we propose a fast model selection method for penalized PCA, named Approximated Gradient Flow (AgFlow), which lowers the computation complexity through incorporating the implicit regularization effect introduced by (stochastic) gradient flow [3, 4] and obtains the complete solution path of $L_2$-penalized PCA under varying $L_2$-regularization. We perform extensive experiments on real-world datasets. AgFlow outperforms existing methods (Oja [5], Power [6], and Shamir [7] and the vanilla Ridge estimators) in terms of computation costs.

What is learning? 20$^{st}$ century formalizations of learning theory -- which precipitated revolutions in artificial intelligence -- focus primarily on $\mathit{in-distribution}$ learning, that is, learning under the assumption that the training data are sampled from the same distribution as the evaluation distribution. This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning). We therefore make a small change to existing formal definitions of learnability by relaxing that assumption. We then introduce $\mathbf{learning\ efficiency}$ (LE) to quantify the amount a learner is able to leverage data for a given problem, regardless of whether it is an in- or out-of-distribution problem. We then define and prove the relationship between generalized notions of learnability, and show how this framework is sufficiently general to characterize transfer, multitask, meta, continual, and lifelong learning. We hope this unification helps bridge the gap between empirical practice and theoretical guidance in real world problems. Finally, because biological learning continues to outperform machine learning algorithms on certain OOD challenges, we discuss the limitations of this framework vis-\'a-vis its ability to formalize biological learning, suggesting multiple avenues for future research.

In high-income Asian nations, such as Japan and Singapore, babies born today can expect to live into their ninth decade or beyond. Advances in medical science and technology promise to lengthen our lifespans even further. In fact, Asia's'silver generation' – the cohort of people aged over 60 – is due to triple between 2010 and 2050, reaching close to 1.3 billion people. But as more of us enter our silver years, many will encounter new challenges. Aging-related chronic diseases, like diabetes, heart disease, and some types of cancer, are impacting lives across Asia and putting enormous pressure on our already under-resourced healthcare sectors.

Alphabet subsidiary Wing has launched a pilot program that will have its drones fly products from the rooftops of shopping centers. In fact, it has already started the program in its biggest market, Logan, Australia. The subsidiary has teamed up with Australian retail property group, Vicinity Centres, to test the new model at Logan's Grand Plaza, where Wing's drones have been flying orders to customers from businesses directly below their launching pad. Wing has been operating in Logan over the past two years, but up until now, businesses have had to co-locate their products at the company's delivery facility. This is the first time the subsidiary is conducting deliveries from participating merchants' existing location instead.

The last words that Liviana Sulzer spoke, 18 months ago, were very much in character: "Now it's time for a song." This was often how she felt, living as she did inside a toddler movie-musical, where even just a spilled cup of milk could get her up onto a chair, twirling with her arms out wide and singing as loud as she could manage: We just spilled our milk … It was messy on the table, and then we cleaned it up … And noooow it's aaaaall cleeeaaaned up! When the song was over, she'd bend toward her brothers, ages 6 and 1, in a deep and gracious bow. It was May 2020--a week before Livie's fourth birthday--and the kids were playing in the yard. Throughout the Sulzers' quiet neighborhood in Austin, Texas, the Persian silk trees had begun to bloom in pink-tipped puffs. There were flowers in their backyard, too. Livie had a favorite one, purple and about as tall as she was. Iris and, trapped at home by the COVID-19 shutdown, she'd made a game of scooting over to it in her push-car and spilling all her problems. But the loneliest phase of the pandemic, with its makeshift games and spotty child care, was nearly over. Things were getting back to normal. A nanny had started just over a week before, and Livie's mother, Lindsay--a bioengineer and expert in regenerative medicine--was headed to the office for her first day back at work, at a local cell-therapy start-up. Livie's father, James, an assistant professor at the University of Texas at Austin who specializes in rehabilitation robotics, was grading papers in the walk-in closet that he'd turned into a home office. He'd asked his graduate students to propose studies or devices that might one day help a patient recover from a nervous-system injury. The sky was clear and calm and sunny. Livie stood near the center of the yard, 30 feet below the overhanging branches of a pecan tree. Her two brothers were nearby.

The Royal Bahamas Police Force (RBPF) could be on the verge of making another leap in the fight against crime in The Bahamas, with high-level meetings taking place this week with potential vendors and stakeholders in public safety artificial intelligence technology. During a welcome and oath-swearing ceremony for nearly 100 new police recruits, Commissioner of Police Paul Rolle told the men and women that his executive team was present with the exceptions of two assistant commissioner's, including Assistant Commissioner Zhavargo Dames, "who's representing me in another meeting trying to get some technology, additional technology for the Royal Bahamas Police Force". When contacted for specifics, Rolle told Eyewitness News: "We are doing some exploration with artificial intelligence." He did not expound on what area the AI technology could be used in or what it could potentially achieve in its use in The Bahamas. The use of AI in law enforcement is not uncommon in other jurisdictions with significant advances in recent years.

Tissue awareness has a great demand to improve surgical accuracy in minimally invasive procedures. In arthroscopy, it is one of the challenging tasks due to surgical sites exhibit limited features and textures. Moreover, arthroscopic surgical video shows high intra-class variations. Arthroscopic videos are recorded with endoscope known as arthroscope which records tissue structures at proximity, therefore, frames contain minimal joint structure. As consequences, fully conventional network-based segmentation model suffers from long- and short- term dependency problems. In this study, we present a densely connected shape aware multi-scale segmentation model which captures multi-scale features and integrates shape features to achieve tissue-tool segmentations. The model has been evaluated with three distinct datasets. Moreover, with the publicly available polyp dataset our proposed model achieved 5.09 % accuracy improvement.

Prior AI successes in complex games have largely focused on settings with at most hundreds of actions at each decision point. In contrast, Diplomacy is a game with more than 10^20 possible actions per turn. Previous attempts to address games with large branching factors, such as Diplomacy, StarCraft, and Dota, used human data to bootstrap the policy or used handcrafted reward shaping. In this paper, we describe an algorithm for action exploration and equilibrium approximation in games with combinatorial action spaces. This algorithm simultaneously performs value iteration while learning a policy proposal network. A double oracle step is used to explore additional actions to add to the policy proposals. At each state, the target state value and policy for the model training are computed via an equilibrium search procedure. Using this algorithm, we train an agent, DORA, completely from scratch for a popular two-player variant of Diplomacy and show that it achieves superhuman performance. Additionally, we extend our methods to full-scale no-press Diplomacy and for the first time train an agent from scratch with no human data. We present evidence that this agent plays a strategy that is incompatible with human-data bootstrapped agents. This presents the first strong evidence of multiple equilibria in Diplomacy and suggests that self play alone may be insufficient for achieving superhuman performance in Diplomacy.

Deep neural networks (DNN) can achieve high performance when applied to In-Distribution (ID) data which come from the same distribution as the training set. When presented with anomaly inputs not from the ID, the outputs of a DNN should be regarded as meaningless. However, modern DNN often predict anomaly inputs as an ID class with high confidence, which is dangerous and misleading. In this work, we consider three classes of anomaly inputs, (1) natural inputs from a different distribution than the DNN is trained for, known as Out-of-Distribution (OOD) samples, (2) crafted inputs generated from ID by attackers, often known as adversarial (AD) samples, and (3) noise (NS) samples generated from meaningless data. We propose a framework that aims to detect all these anomalies for a pre-trained DNN. Unlike some of the existing works, our method does not require preprocessing of input data, nor is it dependent to any known OOD set or adversarial attack algorithm. Through extensive experiments over a variety of DNN models for the detection of aforementioned anomalies, we show that in most cases our method outperforms state-of-the-art anomaly detection methods in identifying all three classes of anomalies.

A "bigger is better" explosion in the number of parameters in deep neural networks has made it increasingly challenging to make state-of-the-art networks accessible in compute-restricted environments. Compression techniques have taken on renewed importance as a way to bridge the gap. However, evaluation of the trade-offs incurred by popular compression techniques has been centered on high-resource datasets. In this work, we instead consider the impact of compression in a data-limited regime. We introduce the term low-resource double bind to refer to the co-occurrence of data limitations and compute resource constraints. This is a common setting for NLP for low-resource languages, yet the trade-offs in performance are poorly studied. Our work offers surprising insights into the relationship between capacity and generalization in data-limited regimes for the task of machine translation. Our experiments on magnitude pruning for translations from English into Yoruba, Hausa, Igbo and German show that in low-resource regimes, sparsity preserves performance on frequent sentences but has a disparate impact on infrequent ones. However, it improves robustness to out-of-distribution shifts, especially for datasets that are very distinct from the training distribution. Our findings suggest that sparsity can play a beneficial role at curbing memorization of low frequency attributes, and therefore offers a promising solution to the low-resource double bind.