Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Stochastic Optimal Control Using Kernel Gradients

We present an empirical, gradient-based method for solving data-driven stochastic optimal control problems using the theory of kernel embeddings of distributions. By embedding the integral operator of a stochastic kernel in a reproducing kernel Hilbert space (RKHS), we can compute an empirical approximation of stochastic optimal control problems, which can then be solved efficiently using the properties of the RKHS. Existing approaches typically rely upon finite control spaces or optimize over policies with finite support to enable optimization. In contrast, our approach uses kernel-based gradients computed using observed data to approximate the cost surface of the optimal control problem, which can then be optimized using gradient descent. We apply our technique to the area of data-driven stochastic optimal control, and demonstrate our proposed approach on a linear regulation problem for comparison and on a nonlinear target tracking problem.
Computing Anti-Derivatives using Deep Neural Networks

This paper presents a novel algorithm to obtain the closed-form anti-derivative of a function using Deep Neural Network architecture. In the past, mathematicians have developed several numerical techniques to approximate the values of definite integrals, but primitives or indefinite integrals are often non-elementary. Anti-derivatives are necessarily required when there are several parameters in an integrand and the integral obtained is a function of those parameters. There is no theoretical method that can do this for any given function. Some existing ways to get around this are primarily based on either curve fitting or infinite series approximation of the integrand, which is then integrated theoretically. Curve fitting approximations are inaccurate for highly non-linear functions and require a different approach for every problem. On the other hand, the infinite series approach does not give a closed-form solution, and their truncated forms are often inaccurate. We claim that using a single method for all integrals, our algorithm can approximate anti-derivatives to any required accuracy. We have used this algorithm to obtain the anti-derivatives of several functions, including non-elementary and oscillatory integrals. This paper also shows the applications of our method to get the closed-form expressions of elliptic integrals, Fermi-Dirac integrals, and cumulative distribution functions and decrease the computation time of the Galerkin method for differential equations.
Bias-Aware Design for Informed Decisions: Raising Awareness of Self-Selection Bias in User Ratings and Reviews

People often take user ratings/reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people's decisions and it is hard to resolve this issue completely by algorithms. In this work, we propose to raise people's awareness of the self-selection bias by making three types of information concerning user ratings/reviews transparent. We distill these three pieces of information, i.e., reviewers' experience, the extremity of emotion, and reported aspect(s), from the definition of self-selection bias and exploration of related literature. We further conduct an online survey to assess people's perceptions of the usefulness of such information and identify the exact facets (e.g., negative emotion) people care about in their decision process. Then, we propose a visual design to make such details behind user reviews transparent and integrate the design into an experimental website for evaluation. The results of a between-subjects study demonstrate that our bias-aware design significantly increases people's awareness of bias and their satisfaction with decision-making. We further offer a series of design implications for improving information transparency and awareness of bias in user-generated content.
Fairness in Face Presentation Attack Detection

Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios. Despite the growing number of fairness studies in FR, the fairness of face presentation attack detection (PAD) has been overlooked, mainly due to the lack of appropriately annotated data. To avoid and mitigate the potential negative impact of such behavior, it is essential to assess the fairness in face PAD and develop fair PAD models. To enable fairness analysis in face PAD, we present a Combined Attribute Annotated PAD Dataset (CAAD-PAD), offering seven human-annotated attribute labels. Then, we comprehensively analyze the fairness of PAD and its relation to the nature of the training data and the Operational Decision Threshold Assignment (ODTA) through a set of face PAD solutions. Additionally, we propose a novel metric, the Accuracy Balanced Fairness (ABF), that jointly represents both the PAD fairness and the absolute PAD performance. The experimental results pointed out that female and faces with occluding features (e.g. eyeglasses, beard, etc.) are relatively less protected than male and non-occlusion groups by all PAD solutions. To alleviate this observed unfairness, we propose a plug-and-play data augmentation method, FairSWAP, to disrupt the identity/semantic information and encourage models to mine the attack clues. The extensive experimental results indicate that FairSWAP leads to better-performing and fairer face PADs in 10 out of 12 investigated cases.
Scaling and Load-Balancing Equi-Joins

The task of joining two tables is fundamental for querying databases. In this paper, we focus on the equi-join problem, where a pair of records from the two joined tables are part of the join results if equality holds between their values in the join column(s). While this is a tractable problem when the number of records in the joined tables is relatively small, it becomes very challenging as the table sizes increase, especially if hot keys (join column values with a large number of records) exist in both joined tables. This paper, an extended version of [metwally-SIGMOD-2022], proposes Adaptive-Multistage-Join (AM-Join) for scalable and fast equi-joins in distributed shared-nothing architectures. AM-Join utilizes (a) Tree-Join, a proposed novel algorithm that scales well when the joined tables share hot keys, and (b) Broadcast-Join, the known fastest when joining keys that are hot in only one table. Unlike the state-of-the-art algorithms, AM-Join (a) holistically solves the join-skew problem by achieving load balancing throughout the join execution, and (b) supports all outer-join variants without record deduplication or custom table partitioning. For the fastest AM-Join outer-join performance, we propose the Index-Broadcast-Join (IB-Join) family of algorithms for Small-Large joins, where one table fits in memory and the other can be up to orders of magnitude larger. The outer-join variants of IB-Join improves on the state-of-the-art Small-Large outer-join algorithms. The proposed algorithms can be adopted in any shared-nothing architecture. We implemented a MapReduce version using Spark. Our evaluation shows the proposed algorithms execute significantly faster and scale to more skewed and orders-of-magnitude bigger tables when compared to the state-of-the-art algorithms.
Sequence-to-Set Generative Models

In this paper, we propose a sequence-to-set method that can transform any sequence generative model based on maximum likelihood to a set generative model where we can evaluate the utility/probability of any set. An efficient importance sampling algorithm is devised to tackle the computational challenge of learning our sequence-to-set model. We present GRU2Set, which is an instance of our sequence-to-set method and employs the famous GRU model as the sequence generative model. To further obtain permutation invariant representation of sets, we devise the SetNN model which is also an instance of the sequence-to-set model. A direct application of our models is to learn an order/set distribution from a collection of e-commerce orders, which is an essential step in many important operational decisions such as inventory arrangement for fast delivery. Based on the intuition that small-sized sets are usually easier to learn than large sets, we propose a size-bias trick that can help learn better set distributions with respect to the $\ell_1$-distance evaluation metric. Two e-commerce order datasets, TMALL and HKTVMALL, are used to conduct extensive experiments to show the effectiveness of our models. The experimental results demonstrate that our models can learn better set/order distributions from order data than the baselines. Moreover, no matter what model we use, applying the size-bias trick can always improve the quality of the set distribution learned from data.
EMaP: Explainable AI with Manifold-based Perturbations

In the last few years, many explanation methods based on the perturbations of input data have been introduced to improve our understanding of decisions made by black-box models. The goal of this work is to introduce a novel perturbation scheme so that more faithful and robust explanations can be obtained. Our study focuses on the impact of perturbing directions on the data topology. We show that perturbing along the orthogonal directions of the input manifold better preserves the data topology, both in the worst-case analysis of the discrete Gromov-Hausdorff distance and in the average-case analysis via persistent homology. From those results, we introduce EMaP algorithm, realizing the orthogonal perturbation scheme. Our experiments show that EMaP not only improves the explainers' performance but also helps them overcome a recently-developed attack against perturbation-based methods.
Thermal annealing effects on graphene/n-Si Schottky junction solar cell: removal of PMMA residues

Thermal annealing is one of the most effective ways to improve the efficiency of a graphene/n-Si Schottky junction solar cell. Here, its underlying mechanism has been investigated by comparative studies in terms of the removal of polymethyl methacrylate (PMMA) residues, using the J–V characteristics and transient photocurrent and photovoltage measurements. The obtained results have revealed that there are trap states that originate from the PMMA residues and cause the large photocurrent leakage as the intensity of the incident light increases. It is also found that the PMMA residues accelerate deterioration and rapidly invalidate hole doping effects. Such undesirable PMMA residues were effectively removed by the thermal annealing treatments, serving to reduce the photocurrent leakage and increase the stability.
Quantum jet clustering with LHC simulated data

We study the case where quantum computing could improve jet clustering by considering two new quantum algorithms that might speed up classical jet clustering algorithms. The ﬁrst one is a quantum subroutine to compute a Minkowski-based distance between two data points, while the second one consists of a quantum circuit to track the rough maximum into a list of unsorted data. When one or both algorithms are implemented in classical versions of well-known clustering algorithms ( K-means , Aﬃnity Propagation and 𝑘 𝑇 -jet) we obtain eﬃciencies comparable to those of their classical counterparts. Furthermore, in the ﬁrst two algorithms, an exponential speed up in dimensionality and data length can be achieved when applying the distance or the maximum search algorithm. In the 𝑘 𝑇 algorithm, a quantum version of the same order as FastJet is achieved.
Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation without Clean Data

With recent deep learning based approaches showing promising results in removing noise from images, the best denoising performance has been reported in a supervised learning setup that requires a large set of paired noisy images and ground truth for training. The strong data requirement can be mitigated by unsupervised learning techniques, however, accurate modelling of images or noise variance is still crucial for high-quality solutions. The learning problem is ill-posed for unknown noise distributions. This paper investigates the tasks of image denoising and noise variance estimation in a single, joint learning framework. To address the ill-posedness of the problem, we present deep variation prior (DVP), which states that the variation of a properly learnt denoiser with respect to the change of noise satisfies some smoothness properties, as a key criterion for good denoisers. Building upon DVP, an unsupervised deep learning framework, that simultaneously learns a denoiser and estimates noise variances, is developed. Our method does not require any clean training images or an external step of noise estimation, and instead, approximates the minimum mean squared error denoisers using only a set of noisy images. With the two underlying tasks being considered in a single framework, we allow them to be optimised for each other. The experimental results show a denoising quality comparable to that of supervised learning and accurate noise variance estimates.
A Robust and Constrained Multi-Agent Reinforcement Learning Framework for Electric Vehicle AMoD Systems

— Electric vehicles (EVs) play critical roles in autonomous mobility-on-demand (AMoD) systems, but their unique charging patterns increase the model uncertainties in AMoD systems (e.g. state transition probability). Since there usually exists a mismatch between the training and test (true) environments, incorporating model uncertainty into system design is of critical importance in real-world applications. However, model uncertainties have not been considered explicitly in EV AMoD system rebalancing by existing literature yet and remain an urgent and challenging task. In this work, we design a robust and constrained multi-agent reinforcement learning (MARL) framework with transition kernel uncertainty for the EV rebalancing and charging problem. We then propose a robust and constrained MARL algorithm (ROCOMA) that trains a robust EV rebalancing policy to balance the supply-demand ratio and the charging utilization rate across the whole city under state transition uncertainty. Experiments show that the ROCOMA can learn an effective and robust rebalancing policy. It outperforms non-robust MARL methods when there are model uncertainties. It increases the system fairness by 19.6% and decreases the rebalancing costs by 75.8%.
Continuously Controllable Facial Expression Editing in Talking Face Videos

Recently audio-driven talking face video generation has attracted considerable attention. However, very few researches address the issue of emotional editing of these talking face videos with continuously controllable expressions, which is a strong demand in the industry. The challenge is that speech-related expressions and emotion-related expressions are often highly coupled. Meanwhile, traditional image-to-image translation methods cannot work well in our application due to the coupling of expressions with other attributes such as poses, i.e., translating the expression of the character in each frame may simultaneously change the head pose due to the bias of the training data distribution. In this paper, we propose a high-quality facial expression editing method for talking face videos, allowing the user to control the target emotion in the edited video continuously. We present a new perspective for this task as a special case of motion information editing, where we use a 3DMM to capture major facial movements and an associated texture map modeled by a StyleGAN to capture appearance details. Both representations (3DMM and texture map) contain emotional information and can be continuously modified by neural networks and easily smoothed by averaging in coefficient/latent spaces, making our method simple yet effective. We also introduce a mouth shape preservation loss to control the trade-off between lip synchronization and the degree of exaggeration of the edited expression. Extensive experiments and a user study show that our method achieves state-of-the-art performance across various evaluation criteria.
Comparative study of machine learning and deep learning methods on ASD classification

The autism dataset is studied to identify the differences between autistic and healthy groups. For this, the resting-state Functional Magnetic Resonance Imaging (rs-fMRI) data of the two groups are analyzed, and networks of connections between brain regions were created. Several classification frameworks are developed to distinguish the connectivity patterns between the groups. The best models for statistical inference and precision were compared, and the tradeoff between precision and model interpretability was analyzed. Finally, the classification accuracy measures were reported to justify the performance of our framework. Our best model can classify autistic and healthy patients on the multisite ABIDE I data with 71% accuracy.
Bistable All‐Optical Devices Based on Nonlinear Epsilon‐Near‐Zero (ENZ) Materials

Nonlinear and bistable optical systems are a key enabling technology for the next generation optical networks and photonic neural systems with many potential applications in optical logic and information processing. Here, a novel bistable resonator‐free all‐optical waveguide device based on indium tin oxide as nonlinear epsilon‐near‐zero material providing a cost‐efficient and high‐performance binarity photonic platform is proposed. The salient features of the proposed device are compatibility with silicon photonics, enabling sub‐picosecond operation speeds with moderate switching power. The device can act as an optical analogue of memristor or thyristor and can become an enabling element of photonic neural networks not requiring OEO conversions.
MiNL: Micro-images based Neural Representation for Light Fields

Traditional representations for light fields can be separated into two types: explicit representation and implicit representation. Unlike explicit representation that represents light fields as Sub-Aperture Images (SAIs) based arrays or Micro-Images (MIs) based lenslet images, implicit representation treats light fields as neural networks, which is inherently a continuous representation in contrast to discrete explicit representation. However, at present almost all the implicit representations for light fields utilize SAIs to train an MLP to learn a pixel-wise mapping from 4D spatial-angular coordinate to pixel colors, which is neither compact nor of low complexity. Instead, in this paper we propose MiNL, a novel MI-wise implicit neural representation for light fields that train an MLP + CNN to learn a mapping from 2D MI coordinates to MI colors. Given the micro-image's coordinate, MiNL outputs the corresponding micro-image's RGB values. Light field encoding in MiNL is just training a neural network to regress the micro-images and the decoding process is a simple feedforward operation. Compared with common pixel-wise implicit representation, MiNL is more compact and efficient that has faster decoding speed (\textbf{$\times$80$\sim$180} speed-up) as well as better visual quality (\textbf{1$\sim$4dB} PSNR improvement on average).
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes

We study the offline reinforcement learning (RL) in the face of unmeasured confounders. Due to the lack of online interaction with the environment, offline RL is facing the following two significant challenges: (i) the agent may be confounded by the unobserved state variables; (ii) the offline data collected a prior does not provide sufficient coverage for the environment. To tackle the above challenges, we study the policy learning in the confounded MDPs with the aid of instrumental variables. Specifically, we first establish value function (VF)-based and marginalized importance sampling (MIS)-based identification results for the expected total reward in the confounded MDPs. Then by leveraging pessimism and our identification results, we propose various policy learning methods with the finite-sample suboptimality guarantee of finding the optimal in-class policy under minimal data coverage and modeling assumptions. Lastly, our extensive theoretical investigations and one numerical study motivated by the kidney transplantation demonstrate the promising performance of the proposed methods.
Impact of anisotropic birefringence on measuring cosmic microwave background lensing

The power spectrum of cosmic microwave background lensing is a powerful tool for constraining fundamental physics such as the sum of neutrino masses and the dark energy equation of state. Current lensing measurements primarily come from distortions to the microwave background temperature field, but the polarization lensing signal will dominate upcoming experiments with greater sensitivity. Cosmic birefringence refers to the rotation of the linear polarization direction of microwave photons propagating from the last scattering surface to us, which can be induced by parity-violating physics such as axion-like dark matter or primordial magnetic fields. We find that, for an upcoming CMB-S4-like experiment, if there exists the scale-invariant anisotropic birefringence with an amplitude corresponding to the current $95\%$ upper bound, the measured lensing power spectrum could be biased by up to a factor of few at small scales, $L\gtrsim 1000$. We show that the bias scales linearly with the amplitude of the scale-invariant birefringence spectrum. The signal-to-noise of the contribution from anisotropic birefringence is larger than unity even if the birefringence amplitude decreases to $\sim 5\%$ of the current upper bound. Our results indicate that a measurement and characterization of the anisotropic birefringence is important for lensing analysis in future low-noise polarization experiments.
A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization

Over the last two decades, the robotics community witnessed the emergence of various motion representations that have been used extensively, particularly in behavorial cloning, to compactly encode and generalize skills. Among these, probabilistic approaches have earned a relevant place, owing to their encoding of variations, correlations and adaptability to new task conditions. Modulating such primitives, however, is often cumbersome due to the need for parameter re-optimization which frequently entails computationally costly operations. In this paper we derive a non-parametric movement primitive formulation that contains a null space projector. We show that such formulation allows for fast and efficient motion generation and adaptation with computational complexity O(n2) without involving matrix inversions, whose complexity is O(n3). This is achieved by using the null space to track secondary targets, with a precision determined by the training dataset. Using a 2D example associated with time input we show that our non-parametric solution compares favourably with a state-of-the-art parametric approach. For demonstrated skills with high-dimensional inputs we show that it permits on-the-fly adaptation as well.