Implementation in Advised Strategies: Welfare Guarantees from Posted-Price Mechanisms When Demand Queries Are NP-Hard

State-of-the-art posted-price mechanisms for submodular bidders with $m$ items achieve approximation guarantees of $O((\log \log m)^3)$ [Assadi and Singla, 2019]. Their truthfulness, however, requires bidders to compute an NP-hard demand-query. Some computational complexity of this form is unavoidable, as it is NP-hard for truthful mechanisms to guarantee even an $m^{1/2-\varepsilon}$-approximation for any $\varepsilon > 0$ [Dobzinski and Vondr\'ak, 2016]. Together, these establish a stark distinction between computationally-efficient and communication-efficient truthful mechanisms. We show that this distinction disappears with a mild relaxation of truthfulness, which we term implementation in advised strategies, and that has been previously studied in relation to "Implementation in Undominated Strategies" [Babaioff et al, 2009]. Specifically, advice maps a tentative strategy either to that same strategy itself, or one that dominates it. We say that a player follows advice as long as they never play actions which are dominated by advice. A poly-time mechanism guarantees an $\alpha$-approximation in implementation in advised strategies if there exists poly-time advice for each player such that an $\alpha$-approximation is achieved whenever all players follow advice. Using an appropriate bicriterion notion of approximate demand queries (which can be computed in poly-time), we establish that (a slight modification of) the [Assadi and Singla, 2019] mechanism achieves the same $O((\log \log m)^3)$-approximation in implementation in advised strategies

A Petunia homeodomain-leucine zipper protein, PhHD-Zip, plays an important role in flower senescence.

Flower senescence is initiated by developmental and environmental signals, and regulated by gene transcription. A homeodomain-leucine zipper transcription factor, PhHD-Zip, is up-regulated during petunia flower senescence. Virus-induced gene silencing of PhHD-Zip extended flower life by 20% both in unpollinated and pollinated flowers. Silencing PhHD-Zip also dramatically reduced ethylene production and the abundance of transcripts of genes involved in ethylene (ACS, ACO), and ABA (NCED) biosynthesis. Abundance of transcripts of senescence-related genes (SAG12, SAG29) was also dramatically reduced in the silenced flowers. Over-expression of PhHD-Zip accelerated petunia flower senescence. Furthermore, PhHD-Zip transcript abundance in petunia flowers was increased by application of hormones (ethylene, ABA) and abiotic stresses (dehydration, NaCl and cold). Our results suggest that PhHD-Zip plays an important role in regulating petunia flower senescence

Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing

Key challenges in running a retail business include how to select products to present to consumers (the assortment problem), and how to price products (the pricing problem) to maximize revenue or profit. Instead of considering these problems in isolation, we propose a joint approach to assortment-pricing based on contextual bandits. Our model is doubly high-dimensional, in that both context vectors and actions are allowed to take values in high-dimensional spaces. In order to circumvent the curse of dimensionality, we propose a simple yet flexible model that captures the interactions between covariates and actions via a (near) low-rank representation matrix. The resulting class of models is reasonably expressive while remaining interpretable through latent factors, and includes various structured linear bandit and pricing models as particular cases. We propose a computationally tractable procedure that combines an exploration/exploitation protocol with an efficient low-rank matrix estimator, and we prove bounds on its regret. Simulation results show that this method has lower regret than state-of-the-art methods applied to various standard bandit and pricing models. Real-world case studies on the assortment-pricing problem, from an industry-leading instant noodles company to an emerging beauty start-up, underscore the gains achievable using our method. In each case, we show at least three-fold gains in revenue or profit by our bandit method, as well as the interpretability of the latent factor models that are learned

Mindfulness and Well-Being: A Mixed Methods Study of Bilingual Guided Meditation In Higher Education

This mixed-methods study investigated the acceptability and outcomes of a mindful approach to teaching a foreign language in higher education institutions. The approach included Bilingual Guided Meditation (BGM®) in the classroom to reduce students’ anxiety and foster a positive mindset. The BGM program combines bilingual positive suggestions with guided meditation and relaxing background music. Results indicated that the BGM may reduce anxiety and can improve academic performance

The Root–Unroot Algorithm for Density Estimation as Implemented via Wavelet Block Thresholding

We propose and implement a density estimation procedure which begins by turning density estimation into a nonparametric regression problem. This regression problem is created by binning the original observations into many small size bins, and by then applying a suitable form of root transformation to the binned data counts. In principle many common nonparametric regression estimators could then be applied to the transformed data. We propose use of a wavelet block thresholding estimator in this paper. Finally, the estimated regression function is un-rooted by squaring and normalizing. The density estimation procedure achieves simultaneously three objectives: computational efficiency, adaptivity, and spatial adaptivity. A numerical example and a practical data example are discussed to illustrate and explain the use of this procedure. Theoretically it is shown that the estimator simultaneously attains the optimal rate of convergence over a wide range of the Besov classes. The estimator also automatically adapts to the local smoothness of the underlying function, and attains the local adaptive minimax rate for estimating functions at a point. There are three key steps in the technical argument: Poissonization, quantile coupling, and oracle risk bound for block thresholding in the non-Gaussian setting. Some of the technical results may be of independent interest

Interaction of Mitochondrial Elongation Factor Tu With Aminoacyl-tRNA and Elongation Factor Ts

Elongation factor (EF) Tu promotes the binding of aminoacyl-tRNA (aa-tRNA) to the acceptor site of the ribosome. This process requires the formation of a ternary complex (EF-Tu·GTP·aa-tRNA). EF-Tu is released from the ribosome as an EF-Tu·GDP complex. Exchange of GDP for GTP is carried out through the formation of a complex with EF-Ts (EF-Tu·Ts). Mammalian mitochondrial EF-Tu (EF-Tumt) differs from the corresponding prokaryotic factors in having a much lower affinity for guanine nucleotides. To further understand the EF-Tumt subcycle, the dissociation constants for the release of aa-tRNA from the ternary complex (K tRNA) and for the dissociation of the EF-Tu·Tsmt complex (K Ts) were investigated. The equilibrium dissociation constant for the ternary complex was 18 ± 4 nM, which is close to that observed in the prokaryotic system. The kinetic dissociation rate constant for the ternary complex was 7.3 × 10− 4 s− 1, which is essentially equivalent to that observed for the ternary complex inEscherichia coli. The binding of EF-Tumt to EF-Tsmt is mutually exclusive with the formation of the ternary complex. K Ts was determined by quantifying the effects of increasing concentrations of EF-Tsmt on the amount of ternary complex formed with EF-Tumt. The value obtained for K Ts(5.5 ± 1.3 nM) is comparable to the value ofK tRNA

Baechi: Fast Device Placement of Machine Learning Graphs

Machine Learning graphs (or models) can be challenging or impossible to train when either devices have limited memory, or models are large. To split the model across devices, learning-based approaches are still popular. While these result in model placements that train fast on data (i.e., low step times), learning-based model-parallelism is time-consuming, taking many hours or days to create a placement plan of operators on devices. We present the Baechi system, the first to adopt an algorithmic approach to the placement problem for running machine learning training graphs on small clusters of memory-constrained devices. We integrate our implementation of Baechi into two popular open-source learning frameworks: TensorFlow and PyTorch. Our experimental results using GPUs show that: (i) Baechi generates placement plans 654 X - 206K X faster than state-of-the-art learning-based approaches, and (ii) Baechi-placed model's step (training) time is comparable to expert placements in PyTorch, and only up to 6.2% worse than expert placements in TensorFlow. We prove mathematically that our two algorithms are within a constant factor of the optimal. Our work shows that compared to learning-based approaches, algorithmic approaches can face different challenges for adaptation to Machine learning systems, but also they offer proven bounds, and significant performance benefits.Comment: Extended version of SoCC 2020 paper: https://dl.acm.org/doi/10.1145/3419111.342130