The Computational Power of Optimization in Online Learning

We consider the fundamental problem of prediction with expert advice where the experts are "optimizable": there is a black-box optimization oracle that can be used to compute, in constant time, the leading expert in retrospect at any point in time. In this setting, we give a novel online algorithm that attains vanishing regret with respect to $N$ experts in total $\widetilde{O}(\sqrt{N})$ computation time. We also give a lower bound showing that this running time cannot be improved (up to log factors) in the oracle model, thereby exhibiting a quadratic speedup as compared to the standard, oracle-free setting where the required time for vanishing regret is $\widetilde{\Theta}(N)$. These results demonstrate an exponential gap between the power of optimization in online learning and its power in statistical learning: in the latter, an optimization oracle---i.e., an efficient empirical risk minimizer---allows to learn a finite hypothesis class of size $N$ in time $O(\log{N})$. We also study the implications of our results to learning in repeated zero-sum games, in a setting where the players have access to oracles that compute, in constant time, their best-response to any mixed strategy of their opponent. We show that the runtime required for approximating the minimax value of the game in this setting is $\widetilde{\Theta}(\sqrt{N})$, yielding again a quadratic improvement upon the oracle-free setting, where $\widetilde{\Theta}(N)$ is known to be tight

A Deep Dive into Adversarial Robustness in Zero-Shot Learning

Machine learning (ML) systems have introduced significant advances in various fields, due to the introduction of highly complex models. Despite their success, it has been shown multiple times that machine learning models are prone to imperceptible perturbations that can severely degrade their accuracy. So far, existing studies have primarily focused on models where supervision across all classes were available. In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes. In this paper, we present a study aimed on evaluating the adversarial robustness of ZSL and GZSL models. We leverage the well-established label embedding model and subject it to a set of established adversarial attacks and defenses across multiple datasets. In addition to creating possibly the first benchmark on adversarial robustness of ZSL models, we also present analyses on important points that require attention for better interpretation of ZSL robustness results. We hope these points, along with the benchmark, will help researchers establish a better understanding what challenges lie ahead and help guide their work.Comment: To appear in ECCV 2020, Workshop on Adversarial Robustness in the Real Worl

Inapproximability Results for Approximate Nash Equilibria.

We study the problem of finding approximate Nash equilibria that satisfy certain conditions, such as providing good social welfare. In particular, we study the problem $\epsilon$-NE $\delta$-SW: find an $\epsilon$-approximate Nash equilibrium ($\epsilon$-NE) that is within $\delta$ of the best social welfare achievable by an $\epsilon$-NE. Our main result is that, if the exponential-time hypothesis (ETH) is true, then solving $\left(\frac{1}{8} - \mathrm{O}(\delta)\right)$-NE $\mathrm{O}(\delta)$-SW for an $n\times n$ bimatrix game requires $n^{\mathrm{\widetilde \Omega}(\log n)}$ time. Building on this result, we show similar conditional running time lower bounds on a number of decision problems for approximate Nash equilibria that do not involve social welfare, including maximizing or minimizing a certain player's payoff, or finding approximate equilibria contained in a given pair of supports. We show quasi-polynomial lower bounds for these problems assuming that ETH holds, where these lower bounds apply to $\epsilon$-Nash equilibria for all $\epsilon < \frac{1}{8}$. The hardness of these other decision problems has so far only been studied in the context of exact equilibria.Comment: A short (14-page) version of this paper appeared at WINE 2016. Compared to that conference version, this new version improves the conditional lower bounds, which now rely on ETH rather than RETH (Randomized ETH

The complex TIE between macrophages and angiogenesis

Macrophages are primarily known as phagocytic immune cells, but they also play a role in diverse processes, such as morphogenesis, homeostasis and regeneration. In this review, we discuss the influence of macrophages on angiogenesis, the process of new blood vessel formation from the pre-existing vasculature. Macrophages play crucial roles at each step of the angiogenic cascade, starting from new blood vessel sprouting to the remodelling of the vascular plexus and vessel maturation. Macrophages form promising targets for both pro- and anti-angiogenic treatments. However, to target macrophages, we will first need to understand the mechanisms that control the functional plasticity of macrophages during each of the steps of the angiogenic cascade. Here, we review recent insights in this topic. Special attention will be given to the TIE2-expressing macrophage (TEM), which is a subtype of highly angiogenic macrophages that is able to influence angiogenesis via the angiopoietin-TIE pathway

Multiplexed, High Density Electrophysiology with Nanofabricated Neural Probes

Extracellular electrode arrays can reveal the neuronal network correlates of behavior with single-cell, single-spike, and sub-millisecond resolution. However, implantable electrodes are inherently invasive, and efforts to scale up the number and density of recording sites must compromise on device size in order to connect the electrodes. Here, we report on silicon-based neural probes employing nanofabricated, high-density electrical leads. Furthermore, we address the challenge of reading out multichannel data with an application-specific integrated circuit (ASIC) performing signal amplification, band-pass filtering, and multiplexing functions. We demonstrate high spatial resolution extracellular measurements with a fully integrated, low noise 64-channel system weighing just 330 mg. The on-chip multiplexers make possible recordings with substantially fewer external wires than the number of input channels. By combining nanofabricated probes with ASICs we have implemented a system for performing large-scale, high-density electrophysiology in small, freely behaving animals that is both minimally invasive and highly scalable

Escherichia coli induces apoptosis and proliferation of mammary cells

Mammary cell apoptosis and proliferation were assessed after injection of Escherichia coli into the left mammary quarters of six cows. Bacteriological analysis of foremilk samples revealed coliform infection in the injected quarters of four cows. Milk somatic cell counts increased in these quarters and peaked at 24 h after bacterial injection. Body temperature also increased, peaking at 12 h postinjection, The number of apoptotic cells was significantly higher in the mastitic tissue than in the uninfected control. Expression of Bax and interleukin-1 beta converting enzyme increased in the mastitic tissue at 24 h and 72 h postinfection, whereas Bcl-2 expression decreased at 24 h but did not differ significantly from the control at 72 h postinfection, Induction of matrix metalloproteinase-g, stromelysin-1 and urokinase-type plasminogen activator was also observed in the mastitic tissue. Moreover, cell proliferation increased in the infected tissue, These results demonstrate that Escherichia coli-induced mastitis promotes apoptosis and cell proliferation

Measuring and Comparing Party Ideology and Heterogeneity

Estimates of party ideological positions in Western Democracies yield useful party-level information, but lack the ability to provide insight into intraparty politics. In this paper, we generate comparable measures of latent individual policy positions from elite survey data which enable analysis of elite-level party ideology and heterogeneity. This approach has advantages over both expert surveys and approaches based on behavioral data, such as roll call voting and is directly relevant to the study of party cohesion. We generate a measure of elite positions for several European countries using a common space scaling approach and demonstrate its validity as a measure of party ideology. We then apply these data to determine the sources of party heterogeneity, focusing on the role of intraparty competition in electoral systems, nomination rules, and party goals. We find that policy-seeking parties and centralized party nomination rules reduce party heterogeneity. While intraparty competition has no effect, the presence of these electoral rules conditions the effect of district magnitude

Auditory Enhancement and Second Language Experience in Spanish and English Weighting of Secondary Voicing Cues

The role of secondary cues in voicing categorization was investigated in three listener groups: Monolingual English (n=20) and Spanish speakers (n=20), and Spanish speakers with significant English experience (n=16). Results showed that, in all three groups, participants used onset f0 in making voicing decisions only in the positive voice onset time (VOT) range (short lag and long lag tokens), while there was no effect of onset f0 on voicing categorization within the negative VOT range (voicing lead tokens) for any of the participant groups. These results support an auditory enhancement view of perceptual cue weighting: Onset f0 serves as a secondary cue to voicing only in the positive VOT range where it is not overshadowed by the presence of pre-voicing. Moreover, results showed that Spanish learners of English gave a significantly greater weight to onset f0 in their voicing decisions than did listeners in either of the other two groups. This result supports the view that learners may overweight secondary cues to distinguish between non-native categories that are assimilated to the same native category on the basis of a primary cue