Wireless Regional Imaging through Reconfigurable Intelligent Surfaces: Passive Mode

In this paper, we propose a multi-RIS-aided wireless imaging framework in 3D facing the distributed placement of multi-sensor networks. The system creates a randomized reflection pattern by adjusting the RIS phase shift, enabling the receiver to capture signals within the designated space of interest (SoI). Firstly, a multi-RIS-aided linear imaging channel modeling is proposed. We introduce a theoretical framework of computational imaging to recover the signal strength distribution of the SOI. For the RIS-aided imaging system, the impact of multiple parameters on the performance of the imaging system is analyzed. The simulation results verify the correctness of the proposal. Furthermore, we propose an amplitude-only imaging algorithm for the RIS-aided imaging system to mitigate the problem of phase unpredictability. Finally, the performance verification of the imaging algorithm is carried out by proof of concept experiments under reasonable parameter settings.

XRMDN: A Recurrent Mixture Density Networks-based Architecture for Short-Term Probabilistic Demand Forecasting in Mobility-on-Demand Systems with High Volatility

In real Mobility-on-Demand (MoD) systems, demand is subject to high and dynamic volatility, which is difficult to predict by conventional time-series forecasting approaches. Most existing forecasting approaches yield the point value as the prediction result, which ignores the uncertainty that exists in the forecasting result. This will lead to the forecasting result severely deviating from the true demand value due to the high volatility existing in demand. To fill the gap, we propose an extended recurrent mixture density network (XRMDN), which extends the weight and mean neural networks to recurrent neural networks. The recurrent neurons for mean and variance can capture the trend of the historical data-series data, which enables a better forecasting result in dynamic and high volatility. We conduct comprehensive experiments on one taxi trip record and one bike-sharing real MoD data set to validate the performance of XRMDN. Specifically, we compare our model to three types of benchmark models, including statistical, machine learning, and deep learning models on three evaluation metrics. The validation results show that XRMDN outperforms the three groups of benchmark models in terms of the evaluation metrics. Most importantly, XRMDN substantially improves the forecasting accuracy with the demands in strong volatility. Last but not least, this probabilistic demand forecasting model contributes not only to the demand prediction in MoD systems but also to other optimization application problems, especially optimization under uncertainty, in MoD applications.

Linear pretraining in recurrent mixture density networks

We present a method for pretraining a recurrent mixture density network (RMDN). We also propose a slight modification to the architecture of the RMDN-GARCH proposed by Nikolaev et al. [2012]. The pretraining method helps the RMDN avoid bad local minima during training and improves its robustness to the persistent NaN problem, as defined by Guillaumes [2017], which is often encountered with mixture density networks. Such problem consists in frequently obtaining "Not a number" (NaN) values during training. The pretraining method proposed resolves these issues by training the linear nodes in the hidden layer of the RMDN before starting including non-linear node updates. Such an approach improves the performance of the RMDN and ensures it surpasses that of the GARCH model, which is the RMDN's linear counterpart.

Lip movements information disentanglement for lip sync

The lip movements information is critical for many audio-visual tasks. However, extracting lip movements information from videos is challenging, as it can be easily perturbed by factors like personal identities and head poses. This paper proposes utilizing the parametric 3D face model to disentangle lip movements information explicitly. Building on top of the recent 3D face reconstruction advances, we firstly offer a method that can consistently disentangle expression information, where the lip movements information lies. Then we demonstrate that once the influences of perturbing factors are alleviated by synthesizing faces with the disentangled lip movements information, the lip-sync task can be done better with much fewer data. Finally, we show its effectiveness in the wild by testing it on an unseen dataset for the active speaker detection task and achieving competitive performance.

Double-Barreled Question Detection at Momentive

Momentive offers solutions in market research, customer experience, and enterprise feedback. The technology is gleaned from the billions of real responses to questions asked on the platform. However, people may create biased questions. A double-barreled question (DBQ) is a common type of biased question that asks two aspects in one question. For example, "Do you agree with the statement: The food is yummy, and the service is great.". This DBQ confuses survey respondents because there are two parts in a question. DBQs impact both the survey respondents and the survey owners. Momentive aims to detect DBQs and recommend survey creators to make a change towards gathering high quality unbiased survey data. Previous research work has suggested detecting DBQs by checking the existence of grammatical conjunction. While this is a simple rule-based approach, this method is error-prone because conjunctions can also exist in properly constructed questions. We present an end-to-end machine learning approach for DBQ classification in this work. We handled this imbalanced data using active learning, and compared state-of-the-art embedding algorithms to transform text data into vectors. Furthermore, we proposed a model interpretation technique propagating the vector-level SHAP values to a SHAP value for each word in the questions. We concluded that the word2vec subword embedding with maximum pooling is the optimal word embedding representation in terms of precision and running time in the offline experiments using the survey data at Momentive. The A/B test and production metrics indicate that this model brings a positive change to the business. To the best of our knowledge, this is the first machine learning framework for DBQ detection, and it successfully differentiates Momentive from the competitors. We hope our work sheds light on machine learning approaches for bias question detection.

Distributionally Robust Prescriptive Analytics with Wasserstein Distance

In prescriptive analytics, the decision-maker observes historical samples of $(X, Y)$, where $Y$ is the uncertain problem parameter and $X$ is the concurrent covariate, without knowing the joint distribution. Given an additional covariate observation $x$, the goal is to choose a decision $z$ conditional on this observation to minimize the cost $\mathbb{E}[c(z,Y)|X=x]$. This paper proposes a new distributionally robust approach under Wasserstein ambiguity sets, in which the nominal distribution of $Y|X=x$ is constructed based on the Nadaraya-Watson kernel estimator concerning the historical data. We show that the nominal distribution converges to the actual conditional distribution under the Wasserstein distance. We establish the out-of-sample guarantees and the computational tractability of the framework. Through synthetic and empirical experiments about the newsvendor problem and portfolio optimization, we demonstrate the strong performance and practical value of the proposed framework.

A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming

The conventional deep learning approaches for solving time-series problem such as long-short term memory (LSTM) and gated recurrent unit (GRU) both consider the time-series data sequence as the input with one single unit as the output (predicted time-series result). Those deep learning approaches have made tremendous success in many time-series related problems, however, this cannot be applied in data-driven stochastic programming problems since the output of either LSTM or GRU is a scalar rather than probability distribution which is required by stochastic programming model. To fill the gap, in this work, we propose an innovative data-driven dynamic stochastic programming (DD-DSP) framework for time-series decision-making problem, which involves three components: GRU, Gaussian Mixture Model (GMM) and SP. Specifically, we devise the deep neural network that integrates GRU and GMM which is called GRU-based Mixture Density Network (MDN), where GRU is used to predict the time-series outcomes based on the recent historical data, and GMM is used to extract the corresponding probability distribution of predicted outcomes, then the results will be input as the parameters for SP. To validate our approach, we apply the framework on the car-sharing relocation problem. The experiment validations show that our framework is superior to data-driven optimization based on LSTM with the vehicle average moving lower than LSTM.

Learning and Optimization with Seasonal Patterns

Seasonality is a common form of non-stationary patterns in the business world. We study a decision maker who tries to learn the optimal decision over time when the environment is unknown and evolving with seasonality. We consider a multi-armed bandit (MAB) framework where the mean rewards are periodic. The unknown periods of the arms can be different and scale with the length of the horizon $T$ polynomially. We propose a two-staged policy that combines Fourier analysis with a confidence-bound based learning procedure to learn the periods and minimize the regret. In stage one, the policy is able to correctly estimate the periods of all arms with high probability. In stage two, the policy explores mean rewards of arms in each phase using the periods estimated in stage one and exploits the optimal arm in the long run. We show that our policy achieves the rate of regret $\tilde{O}(\sqrt{T\sum_{k=1}^K T_k})$, where $K$ is the number of arms and $T_k$ is the period of arm $k$. It matches the optimal rate of regret of the classic MAB problem $O(\sqrt{TK})$ if we regard each phase of an arm as a separate arm.