Search

Features (a.k.a. context) are critical for contextual multi-armed bandits (MAB) performance. In practice of large scale online system, it is important to select and implement important features for the model: missing important features can led to sub-optimal reward outcome, and including irrelevant features can cause overfitting, poor model interpretability, and implementation cost. However, feature selection methods for conventional machine learning models fail short for contextual MAB use cases, as conventional methods select features correlated with the outcome variable, but not necessarily causing heterogeneuous treatment effect among arms which are truely important for contextual MAB. In this paper, we introduce model-free feature selection methods designed for contexutal MAB problem, based on heterogeneous causal effect contributed by the feature to the reward distribution. Empirical evaluation is conducted based on synthetic data as well as real data from an online experiment for optimizing content cover image in a recommender system. The results show this feature selection method effectively selects the important features that lead to higher contextual MAB reward than unimportant features. Compared with model embedded method, this model-free method has advantage of fast computation speed, ease of implementation, and prune of model mis-specification issues.

Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI large model, more and more large models in subdivided fields are gradually being proposed, especially large models in radiology imaging field. This article first introduces the development history of large models, technical details, workflow, working principles of multimodal large models and working principles of video generation large models. Secondly, we summarize the latest research progress of AI large models in radiology education, radiology report generation, applications of unimodal and multimodal radiology. Finally, this paper also summarizes some of the challenges of large AI models in radiology, with the aim of better promoting the rapid revolution in the field of radiography.

In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one's outcomes, and (2) non-random treatment assignments influenced by confounders. To improve the efficiency of estimating potentially complex effects functions, we introduce an novel active learning approach: Active Learning in Causal Inference with Interference (ACI). This approach uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. The ACI framework sequentially identifies the experimental settings that demand further data. It further optimizes the treatment assignments under the network interference structure using genetic algorithms to achieve efficient learning outcome. By applying our method to simulation data and a Tencent game dataset, we demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements. This ACI approach marks a significant advancement in the realm of data efficiency for causal inference, offering a robust and efficient alternative to traditional methodologies, particularly in scenarios characterized by complex interference patterns., Comment: conference paper

The evolving landscape of online multiplayer gaming presents unique challenges in assessing the causal impacts of game features. Traditional A/B testing methodologies fall short due to complex player interactions, leading to violations of fundamental assumptions like the Stable Unit Treatment Value Assumption (SUTVA). Unlike traditional social networks with stable and long-term connections, networks in online games are often dynamic and short-lived. Players are temporarily teamed up for the duration of a game, forming transient networks that dissolve once the game ends. This fleeting nature of interactions presents a new challenge compared with running experiments in a stable social network. This study introduces a novel framework for treatment effect estimation in online gaming environments, considering the dynamic and ephemeral network interference that occurs among players. We propose an innovative estimator tailored for scenarios where a completely randomized experimental design is implemented without explicit knowledge of network structures. Notably, our method facilitates post-hoc interference adjustment on experimental data, significantly reducing the complexities and costs associated with intricate experimental designs and randomization strategies. The proposed framework stands out for its ability to accommodate varying levels of interference, thereby yielding more accurate and robust estimations. Through comprehensive simulations set against a variety of interference scenarios, along with empirical validation using real-world data from a mobile gaming environment, we demonstrate the efficacy of our approach. This study represents a pioneering effort in exploring causal inference in user-randomized experiments impacted by dynamic network effects.

Due to the hydrolytic instability of $LiPF_6$ in carbonate-based solvents, HF is a typical impurity in Li-ion battery electrolytes. HF significantly influences the performance of Li-ion batteries, for example by impacting the formation of the solid electrolyte interphase at the anode and by affecting transition metal dissolution at the cathode. Additionally, HF complicates studying fundamental interfacial electrochemistry of Li-ion battery electrolytes, such as direct anion reduction, because it is electrocatalytically relatively unstable, resulting in LiF passivation layers. Methods to selectively remove ppm levels of HF from $LiPF_6$-containing carbonate-based electrolytes are limited. We introduce and benchmark a simple yet efficient electrochemical in situ method to selectively remove ppm amounts of HF from $LiPF_6$-containing carbonate-based electrolytes. The basic idea is the application of a suitable potential to a high surface-area metallic electrode upon which only HF reacts (electrocatalytically) while all other electrolyte components are unaffected under the respective conditions., Comment: 31 pages, 19 figures

Fractional calculus with respect to function $\psi$, also named as $\psi$-fractional calculus, generalizes the Hadamard and the Riemann-Liouville fractional calculi, which causes challenge in numerical treatment. In this paper we study spectral-type methods using mapped Jacobi functions (MJFs) as basis functions and obtain efficient algorithms to solve $\psi$-fractional differential equations. In particular, we setup the Petrov-Galerkin spectral method and spectral collocation method for initial and boundary value problems involving $\psi$-fractional derivatives. We develop basic approximation theory for the MJFs and conduct the error estimates of the derived methods. We also establish a recurrence relation to evaluate the collocation differentiation matrix for implementing the spectral collocation algorithm. Numerical examples confirm the theoretical results and demonstrate the effectiveness of the spectral and collocation methods., Comment: This is a full length version of a submission to TWMS

Diffusion models have become a mainstream approach for high-resolution image synthesis. However, directly generating higher-resolution images from pretrained diffusion models will encounter unreasonable object duplication and exponentially increase the generation time. In this paper, we discover that object duplication arises from feature duplication in the deep blocks of the U-Net. Concurrently, We pinpoint the extended generation times to self-attention redundancy in U-Net's top blocks. To address these issues, we propose a tuning-free higher-resolution framework named HiDiffusion. Specifically, HiDiffusion contains Resolution-Aware U-Net (RAU-Net) that dynamically adjusts the feature map size to resolve object duplication and engages Modified Shifted Window Multi-head Self-Attention (MSW-MSA) that utilizes optimized window attention to reduce computations. we can integrate HiDiffusion into various pretrained diffusion models to scale image generation resolutions even to 4096x4096 at 1.5-6x the inference speed of previous methods. Extensive experiments demonstrate that our approach can address object duplication and heavy computation issues, achieving state-of-the-art performance on higher-resolution image synthesis tasks.

Miniaturized spectrometry system is playing an essential role for materials analysis in the development of in-situ or portable sensing platforms across research and industry. However, there unavoidably exists trade-offs between the resolution and operation bandwidth as the device scale down. Here, we report an extreme miniaturized computational photonic molecule (PM) spectrometer utilizing the diverse spectral characteristics and mode-hybridization effect of split eigenfrequencies and super-modes, which effectively eliminates the inherent periodicity and expands operation bandwidth with ultra-high spectral resolution. These results of dynamic control of the frequency, amplitude, and phase of photons in the photonic multi-atomic systems, pave the way to the development of benchtop sensing platforms for applications previously unfeasible due to resolution-bandwidth-footprint limitations, such as in gas sensing or nanoscale biomedical sensing.

On-chip implementation of optical nonlinear activation functions (NAFs) is essential for realizing large-scale photonic neural chips. To implement different neural processing and machine learning tasks with optimal performances, different NAFs are explored with the use of different devices. From the perspective of on-chip integration and reconfigurability of photonic neural network (PNN), it is highly preferred that a single compact device can fulfill multiple NAFs. Here, we propose and experimentally demonstrate a compact high-speed microdisk modulator to realize multiple NAFs. The fabricated microdisk modulator has an add-drop configuration in which a lateral PN junction is incorporated for tuning. Based on high-speed nonlinear electrical-optical (E-O) effect, multiple NAFs are realized by electrically controlling free-carrier injection. Thanks to its strong optical confinement of the disk cavity, all-optical thermo-optic (TO) nonlinear effect can also be leveraged to realize other four different NAFs, which is difficult to be realized with the use of electrical-optical effect. With the use of the realized nonlinear activation function, a convolutional neural network (CNN) is studied to perform handwritten digit classification task, and an accuracy as large as 98% is demonstrated, which verifies the effectiveness of the use of the high-speed microdisk modulator to realize the NAFs. Thanks to its compact footprint and strong electrical-optical or all-optical effects, the microdisk modulator features multiple NAFs, which could serve as a flexible nonlinear unit for large-scale PNNs.

We present a study of the measurement of the effective weak mixing angle parameter ($\sin^2\theta^{\ell}_\text{eff}$) at the Circular Electron Positron Collider (CEPC). As a fundamental physics parameter, $\sin^2\theta^{\ell}_\text{eff}$ plays a key role not only in the global test of the standard model electroweak sector, but also in constraining the potential beyond standard model new physics at high energy frontier. CEPC proposes a two year running period around the Z boson mass pole at high instataneous luminosity, providing a large data sample with $4\times 10^{12}$ $Z$ candidates generated in total. It allows a high precision measurement of $\sin^2\theta^{\ell}_\text{eff}$ both in the lepton and quark final states, of which the uncertainty can be one order of magnitude lower than any previous measurement at the LEP, SLC, Tevatron and LHC. It will not only improve the overall precision of the $\sin^2\theta^\ell_\text{eff}$ experimental determination to be comparable to the preicision of the theoretical calculation with two-loop radiative corrections, but also provide direct comparisons between different final states. In this paper, we also study the measurement of $\sin^2\theta^{\ell}_\text{eff}$ at high mass region. With one month data taken, the precision of $\sin^2\theta^{\ell}_\text{eff}$ measured at 130 GeV from $b$ quark final state is 0.00010, which will be an important experimental observation on the energy-running effect of $\sin^2\theta^{\ell}_\text{eff}$.

In-circuit impedance measurement provides useful information for many EMC applications. The inductive coupling approach is a promising in-circuit impedance measurement method due to its non-contact characteristics and simple on-site implementation. Many measurement setups of this approach were reported. However, a comprehensive survey and comparison of these setups have not been found in the literature. This paper reviews these setups in terms of their characteristics, limitations, and applications. In addition, recommendations for future research are also presented., Comment: The paper has been accepted to be presented at the 2022 Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC) in July 2022

Impedance is one of the vital parameters that provides useful information for many power electronics related applications. A lot of impedance measurement methods in power electronics have been reported. However, a comprehensive investigation among these methods in terms of their characteristics, advantages, and limitations has not been found in the literature. In order to bridge this gap, a survey of the impedance measurement methods is conducted in this paper. These methods are introduced, discussed, and then classified into different categories depending on the measurement modes, principles, and instruments. Moreover, recommendations for the future research on the impedance measurement are also presented., Comment: The paper has been accepted to be presented at the 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), and will be published in the IEEE Xplore later

In-circuit impedance provides key information for many EMC applications. The inductive coupling approach is a promising method for in-circuit impedance measurement because its measurement setups have no direct electrical contact with the energized system under test, thus greatly simplifying the on-site implementation. This paper presents and summaries the latest research on the inductive coupling approach and its EMC applications. First of all, three common measurement setups for this approach and their respective pros and cons are discussed. Subsequently, their EMC applications are introduced. Finally, recommendations for future research are listed.