Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism

Online learning has been successfully applied to many problems in which data are revealed over time. In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities. Specifically, we propose and analyze a class of adaptive dual averaging schemes in which agents only need to accumulate gradient feedback received from the whole system, without requiring any between-agent coordination. In the single-agent case, the adaptivity of the proposed method allows us to extend a range of existing results to problems with potentially unbounded delays between playing an action and receiving the corresponding feedback. In the multi-agent case, the situation is significantly more complicated because agents may not have access to a global clock to use as a reference point; to overcome this, we focus on the information that is available for producing each prediction rather than the actual delay associated with each feedback. This allows us to derive adaptive learning strategies with optimal regret bounds, at both the agent and network levels. Finally, we also analyze an "optimistic" variant of the proposed algorithm which is capable of exploiting the predictability of problems with a slower variation and leads to improved regret bounds

Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling

Owing to their stability and convergence speed, extragradient methods have become a staple for solving large-scale saddle-point problems in machine learning. The basic premise of these algorithms is the use of an extrapolation step before performing an update; thanks to this exploration step, extra-gradient methods overcome many of the non-convergence issues that plague gradient descent/ascent schemes. On the other hand, as we show in this paper, running vanilla extragradient with stochastic gradients may jeopardize its convergence, even in simple bilinear models. To overcome this failure, we investigate a double stepsize extragradient algorithm where the exploration step evolves at a more aggressive time-scale compared to the update step. We show that this modification allows the method to converge even with stochastic gradients, and we derive sharp convergence rates under an error bound condition.Comment: In Advances in Neural Information Processing Systems 33 (NeurIPS 2020); 29 pages, 5 figure

Adaptive learning in continuous games: Optimal regret bounds and convergence to Nash equilibrium

International audienceIn game-theoretic learning, several agents are simultaneously following their individual interests, so the environment is non-stationary from each player's perspective. In this context, the performance of a learning algorithm is often measured by its regret. However, no-regret algorithms are not created equal in terms of game-theoretic guarantees: depending on how they are tuned, some of them may drive the system to an equilibrium, while others could produce cyclic, chaotic, or otherwise divergent trajectories. To account for this, we propose a range of no-regret policies based on optimistic mirror descent, with the following desirable properties: i) they do not require any prior tuning or knowledge of the game; ii) they all achieve O(√ T) regret against arbitrary, adversarial opponents; and iii) they converge to the best response against convergent opponents. Also, if employed by all players, then iv) they guarantee O(1) social regret; while v) the induced sequence of play converges to Nash equilibrium with O(1) individual regret in all variationally stable games (a class of games that includes all monotone and convex-concave zero-sum games)

Osteopontin mediates tumorigenic transformation of a preneoplastic murine cell line by suppressing anoikis: An Arg‐Gly‐Asp‐dependent‐focal adhesion kinase‐caspase‐8 axis

Osteopontin (OPN), an adhesive, matricellular glycoprotein, is a rate‐limiting factor in tumor promotion of skin carcinogenesis. With a tumor promotion model, the JB6 Cl41.5a cell line, we have shown that suppressing 12‐O‐tetradecanoylphorbol‐13‐acetate (TPA)‐induced OPN expression markedly inhibits TPA‐induced colony formation in soft agar, an assay indicative of tumorigenic transformation. Further, the addition of exogenous OPN promotes colony formation of these cells. These findings support a function of OPN in mediating TPA‐induced neoplastic transformation of JB6 cells. In regard to the mechanism of action by OPN, we hypothesized that, for JB6 cells grown in soft‐agar, secreted OPN induced by TPA stimulates cell proliferation and/or prevents anoikis to facilitate TPA‐induced colony formation. Analyses of cell cycle and cyclin D1 expression, and direct cell counting of JB6 cells treated with OPN indicate that OPN does not stimulate cell proliferation relative to non‐treated controls. Instead, at 24 h, OPN decreases anoikis by 41%, as assessed by annexin V assays. Further, in suspended cells OPN suppresses caspase‐8 activation, which is mediated specifically through its RGD‐cell binding motif that transduces signals through integrin receptors. Transfection studies with wild‐type and mutant focal adhesion kinases (FAK) and Western blot analyses suggest that OPN suppression of caspase‐8 activation is mediated through phosphorylation of FAK at Tyr861. In summary, these studies indicate that induced OPN is a microenvironment modulator that facilitates tumorigenic transformation of JB6 cells by inhibiting anoikis through its RGD‐dependent suppression of caspase‐8 activity, which is mediated in part through the activation of FAK at Tyr861. © 2013 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111135/1/mc22108.pd

Multi-agent online optimization with delays: Asynchronicity, adaptivity, and optimism

International audienceIn this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities. Specifically, we propose and analyze a class of adaptive dual averaging schemes in which agents only need to accumulate gradient feedback received from the whole system, without requiring any between-agent coordination. In the single-agent case, the adaptivity of the proposed method allows us to extend a range of existing results to problems with potentially unbounded delays between playing an action and receiving the corresponding feedback. In the multi-agent case, the situation is significantly more complicated because agents may not have access to a global clock to use as a reference point; to overcome this, we focus on the information that is available for producing each prediction rather than the actual delay associated with each feedback. This allows us to derive adaptive learning strategies with optimal regret bounds, even in a fully decentralized, asynchronous environment. Finally, we also analyze an "optimistic" variant of the proposed algorithm which is capable of exploiting the predictability of problems with a slower variation and leads to improved regret bounds

Navigating Text-To-Image Customization:From LyCORIS Fine-Tuning to Model Evaluation

Text-to-image generative models have garnered immense attention for their ability to produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes itself as a leading open-source model in this fast-growing field. However, the intricacies of fine-tuning these models pose multiple challenges from new methodology integration to systematic evaluation. Addressing these issues, this paper introduces LyCORIS (Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion) [https://github.com/KohakuBlueleaf/LyCORIS], an open-source library that offers a wide selection of fine-tuning methodologies for Stable Diffusion. Furthermore, we present a thorough framework for the systematic assessment of varied fine-tuning techniques. This framework employs a diverse suite of metrics and delves into multiple facets of fine-tuning, including hyperparameter adjustments and the evaluation with different prompt types across various concept categories. Through this comprehensive approach, our work provides essential insights into the nuanced effects of fine-tuning parameters, bridging the gap between state-of-the-art research and practical application.Comment: 77 pages, 54 figures, 6 table

V2PSense: Enabling Cellular-based V2P Collision Warning Service Through Mobile Sensing

The C-V2X (Cellular Vehicle-to-Everything) technology is developing in full swing. One of its mainstream services can be the Vehicle-to-Pedestrian (V2P) service. It can protect pedestrians who are mostly vulnerable on the road. In this work, we seek to enable a V2P service that can identify which pedestrians may be nearby a dangerous driving event and then notify them of warning messages. To enable this V2P service, there are two major challenges. First, a low-latency V2P message transport is required for this infrastructure-based service. Second, the pedestrian’s smartphone requires an energy-efficient outdoor positioning method instead of power-hungry GPS due to its limited battery life. We thus propose a novel solution, V2PSense, which trades off positioning precision for energy savings while achieving low-latency message transport with LTE high-priority bearers. It does a coarse-grained positioning by leveraging intermittent GPS information and mobile sensing data, which includes step count from the pedometer and cellular signal strength changes. Though the V2PSense’s positioning is not as precise as the GPS, it can still ensure that all the pedestrians nearby dangerous spots can be notified. Our results show that it can achieve the average precision ratio 92.6% for estimating where the pedestrian is while saving 20.8% energy, compared with the GPS always-on case.This work was partially supported by the Ministry of Science and Tech-nology, Taiwan, under grant numbers 106-2622-8-009-017 and 106-2218-E-009-018, and by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586

The Development of Spatial Attention U-Net for The Recovery of Ionospheric Measurements and The Extraction of Ionospheric Parameters

We train a deep learning artificial neural network model, Spatial Attention U-Net to recover useful ionospheric signals from noisy ionogram data measured by Hualien's Vertical Incidence Pulsed Ionospheric Radar. Our results show that the model can well identify F2 layer ordinary and extraordinary modes (F2o, F2x) and the combined signals of the E layer (ordinary and extraordinary modes and sporadic Es). The model is also capable of identifying some signals that were not labeled. The performance of the model can be significantly degraded by insufficient number of samples in the data set. From the recovered signals, we determine the critical frequencies of F2o and F2x and the intersection frequency between the two signals. The difference between the two critical frequencies is peaking at 0.63 MHz, with the uncertainty being 0.18 MHz.Comment: 17 pages, 7 figures, 3 table

Potato miR828 is associated with purple tuber skin and flesh color

Anthocyanins are plant pigments responsible for the colors of many flowers, fruits and storage organs and have roles in abiotic and biotic stress resistance. Anthocyanins and polyphenols are bioactive compounds in plants including potato (Solanum tuberosum L.) which is the most important non-cereal crop in the world, cultivated for its tubers rich in starch and nutrients. The genetic regulation of the flavonoid biosynthetic pathway is relatively well known leading to the formation of anthocyanins. However, our knowledge of post-transcriptional regulation of anthocyanin biosynthesis is limited. There is increasing evidence that micro RNAs (miRNAs) and other small RNAs can regulate the expression level of key factors in anthocyanin production. In this study we have found strong associations between the high levels of miR828, TAS4 D4(-) and purple/red color of tuber skin and flesh. This was confirmed not only in different cultivars but in pigmented and non-pigmented sectors of the same tuber. Phytochemical analyses verified the levels of anthocyanins and polyphenols in different tissues. We showed that miR828 is able to direct cleavage of the RNA originating from Trans-acting siRNA gene 4 (TAS4) and initiate the production of phased small interfering RNAs (siRNAs) whose production depends on RNA-dependent RNA polymerase 6 (RDR6). MYB transcription factors were predicted as potential targets of miR828 and TAS4 D4(-) and their expression was characterized. MYB12 and R2R3-MYB genes showed decreased expression levels in purple skin and flesh in contrast with high levels of small RNAs in the same tissues. Moreover, we confirmed that R2R3-MYB and MYB-36284 are direct targets of the small RNAs. Overall, this study sheds light on the small RNA directed anthocyanin regulation in potato, which is an important member of the Solanaceae family