Miracle-dz/UniFAR-FTUs huggingface dataset:
Suppressing Uncertainties for Large-Scale Facial Expression Recognition

Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties suspend the progress of large-scale Facial Expression Recognition (FER) in data-driven deep learning era. To address this problelm, this paper proposes to suppress the uncertainties by a simple yet efficient Self-Cure Network (SCN). Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over FER dataset to weight each sample in training with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with \textbf{88.14}\% on RAF-DB, \textbf{60.23}\% on AffectNet, and \textbf{89.35}\% on FERPlus.
Embedded-physics machine learning for coarse-graining and collective variable discovery without data

We present a novel learning framework that consistently embeds underlying physics while bypassing a significant drawback of most modern, data-driven coarse-grained approaches in the context of molecular dynamics (MD), i.e., the availability of big data. The generation of a sufficiently large training dataset poses a computationally demanding task, while complete coverage of the atomistic configuration space is not guaranteed. As a result, the explorative capabilities of data-driven coarse-grained models are limited and may yield biased "predictive" tools. We propose a novel objective based on reverse Kullback-Leibler divergence that fully incorporates the available physics in the form of the atomistic force field. Rather than separating model learning from the data-generation procedure - the latter relies on simulating atomistic motions governed by force fields - we query the atomistic force field at sample configurations proposed by the predictive coarse-grained model. Thus, learning relies on the evaluation of the force field but does not require any MD simulation. The resulting generative coarse-grained model serves as an efficient surrogate model for predicting atomistic configurations and estimating relevant observables. Beyond obtaining a predictive coarse-grained model, we demonstrate that in the discovered lower-dimensional representation, the collective variables (CVs) are related to physicochemical properties, which are essential for gaining understanding of unexplored complex systems. We demonstrate the algorithmic advances in terms of predictive ability and the physical meaning of the revealed CVs for a bimodal potential energy function and the alanine dipeptide.
Knot Selection in Sparse Gaussian Processes

Knot-based, sparse Gaussian processes have enjoyed considerable success as scalable approximations to full Gaussian processes. Problems can occur, however, when knot selection is done by optimizing the marginal likelihood. For example, the marginal likelihood surface is highly multimodal, which can cause suboptimal knot placement where some knots serve practically no function. This is especially a problem when many more knots are used than are necessary, resulting in extra computational cost for little to no gains in accuracy. We propose a one-at-a-time knot selection algorithm to select both the number and placement of knots. Our algorithm uses Bayesian optimization to efficiently propose knots that are likely to be good and largely avoids the pathologies encountered when using the marginal likelihood as the objective function. We provide empirical results showing improved accuracy and speed over the current standard approaches.
Hydrogen Recombination Line Luminosities and Variability from Forming Planets

We calculated hydrogen recombination line luminosities (H-α, Paschen-β and Brackett-γ) from 3D thermohydrodynamical simulations of forming planets from 1 to 10 Jupiter mass (MJup). We explored various opacities to estimate the line emissions with extinction, in each case assuming boundary-layer accretion. When realistic opacities are considered, only lines from planets ≥10 MJup can be detected with current instrumentation, highlighting that most planets do not have detectable emission. This might explain the very low detection rate of H-α from forming planets from observations. While the line emission comes from both the forming planet and its circumplanetary disk, we found that only the disk component could be detected due to extinction. We examined the line variability as well, and found that it is higher for higher-mass planets. Furthermore, we determine for the first time the parametric relationship between the mass of the planet and the luminosity of the hydrogen recombination lines, as well as the equation between the accretion luminosity and hydrogen recombination line luminosities.
EDEN: Sensitivity Analysis and Transiting Planet Detection Limits for Nearby Late Red Dwarfs

Small planets are common around late-M dwarfs and can be detected through highly precise photometry by the transit method. Planets orbiting nearby stars are particularly important as they are often the best-suited for future follow-up studies. We present observations of three nearby M dwarfs referred to as EIC-1, EIC-2, and EIC-3, and use them to search for transits and set limits on the presence of planets. On most nights our observations are sensitive to Earth-sized transiting planets, and photometric precision is similar to or better than TESS for faint late-M dwarfs of the same magnitude (I ≈ 15 mag). We present our photometry and transit search pipeline, which utilizes simple median detrending in combination with transit least-squares-based transit detection. For these targets, and transiting planets between one and two Earth radii, we achieve an average transit detection probability of ∼60% between periods of 0.5 and 2 days, ∼30% between 2 and 5 days, and ∼10% between 5 and 10 days. These sensitivities are conservative compared to visual searches.
Higher order Massey products and applications

In this survey, we discuss two research areas related to Massey’s higher operations. The first direction is connected with the cohomology of Lie algebras and the theory of representations. The second main theme is at the intersection of toric topology, homotopy theory of polyhedral products, and the homology theory of local rings, Stanley–Reisner rings of simplicial complexes.
Use of Historical Individual Patient Data in Analysis of Clinical Trials

Historical data from previous clinical trials, observational studies and health records may be utilized in analysis of clinical trials data to strengthen inference. Under the Bayesian framework incorporation of information obtained from any source other than the current data is facilitated through construction of an informative prior. The existing methodology for defining an informative prior based on historical data relies on measuring similarity to the current data at the study level that can result in discarding useful individual patient data (IPD). This paper proposes a family of priors that utilize IPD to empower statistical inference. IPD-based priors can be obtained as a weighted likelihood of the historical data where each individual's weight is a function of their similarity to the current study population. We demonstrate that the proposed prior construction approach can considerably improve estimation accuracy and precision in compare with existing methods.
A CORRELATIONAL STUDY OF PRINCIPALS' LEADERSHIP STYLES ON TEACHERS' JOB SATISFACTION IN GIRLS' PRIVATE ELEMENTARY SCHOOLS IN SAUDI ARABIA

The purpose of this quantitative, correlational study is to explore the relationship between principals’ leadership styles and teachers’ job satisfaction in private elementary schools for girls in Jeddah, Saudi Arabia. In this study, I measure two main constructs: leadership styles and job satisfaction. Within leadership style, I measure three sub-constructs: transformational leadership, transactional leadership and passive-avoidant leadership. This study is motivated by two research questions aimed at identifying the primary leadership style adopted by school principals and the relationship between principals’ leadership styles and teachers’ job satisfaction in private elementary schools for girls in Jeddah, Saudi Arabia. Correlational analysis results are based on quantitative survey data from a sample of principals and teacher pairs from 55 schools. I discuss implications of my findings and provide suggestions for future research.
A new hybrid approach for crude oil price forecasting: Evidence from multi-scale data

Faced with the growing research towards crude oil price fluctuations influential factors following the accelerated development of Internet technology, accessible data such as Google search volume index are increasingly quantified and incorporated into forecasting approaches. In this paper, we apply multi-scale data that including both GSVI data and traditional economic data related to crude oil price as independent variables and propose a new hybrid approach for monthly crude oil price forecasting. This hybrid approach, based on divide and conquer strategy, consists of K-means method, kernel principal component analysis and kernel extreme learning machine , where K-means method is adopted to divide input data into certain clusters, KPCA is applied to reduce dimension, and KELM is employed for final crude oil price forecasting. The empirical result can be analyzed from data and method levels. At the data level, GSVI data perform better than economic data in level forecasting accuracy but with opposite performance in directional forecasting accuracy because of Herd Behavior, while hybrid data combined their advantages and obtain best forecasting performance in both level and directional accuracy. At the method level, the approaches with K-means perform better than those without K-means, which demonstrates that divide and conquer strategy can effectively improve the forecasting performance.
Adversarial training applied to Convolutional Neural Network for photometric redshift predictions

The use of Convolutional Neural Networks (CNN) to estimate the galaxy photometric redshift probability distribution by analysing the images in different wavelength bands has been developed in the recent years thanks to the rapid development of the Machine Learning (ML) ecosystem. Authors have set-up CNN architectures and studied their performances and some sources of systematics using standard methods of training and testing to ensure the generalisation power of their models. So far so good, but one piece was missing : does the model generalisation power is well measured? The present article shows clearly that very small image perturbations can fool the model completely and opens the Pandora's box of \textit{adversarial} attack. Among the different techniques and scenarios, we have chosen to use the Fast Sign Gradient one-step Method and its Projected Gradient Descent iterative extension as adversarial generator tool kit. However, as unlikely as it may seem these adversarial samples which fool not only a single model, reveal a weakness both of the model and the classical training. A revisited algorithm is shown and applied by injecting a fraction of adversarial samples during the training phase. Numerical experiments have been conducted using a specific CNN model for illustration although our study could be applied to other models - not only CNN ones - and in other contexts - not only redshift measurements - as it deals with the complexity of the boundary decision surface.
Survey Bandits with Regret Guarantees

We consider a variant of the contextual bandit problem. In standard contextual bandits, when a user arrives we get the user's complete feature vector and then assign a treatment (arm) to that user. In a number of applications (like healthcare), collecting features from users can be costly. To address this issue, we propose algorithms that avoid needless feature collection while maintaining strong regret guarantees.
The candy wrapper problem - a temporal multiscale approach for pde/pde systems

We describe a temporal multiscale approach for the simulation of long-term processes with short-term influences involving partial differential equations. The specific problem under consideration is a growth process in blood vessels. The \emph{Candy Wrapper Process} describes a restenosis in a vessel that has previously be widened by inserting a stent. The development of a new stenosis takes place on a long time horizon (months) while the acting forces are mainly given by the pulsating blood flow. We describe a coupled pde model and a finite element simulation that is used as basis for our multiscale approach, which is based on averaging the long scale equation and approximating the fast scale impact by localized periodic-in-time problems. Numerical test cases in prototypical 3d configurations demonstrate the power of the approach.
Exploiting Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network

Targeted sentiment classification predicts the sentiment polarity on given target mentions in input texts. Dominant methods employ neural networks for encoding the input sentence and extracting relations between target mentions and their contexts. Recently, graph neural network has been investigated for integrating dependency syntax for the task, achieving the state-of-the-art results. However, existing methods do not consider dependency label information, which can be intuitively useful. To solve the problem, we investigate a novel relational graph attention network that integrates typed syntactic dependency information. Results on standard benchmarks show that our method can effectively leverage label information for improving targeted sentiment classification performances. Our final model significantly outperforms state-of-the-art syntax-based approaches.
Fill in the BLANC: Human-free quality estimation of document summaries

We present BLANC, a new approach to the automatic estimation of document summary quality. Our goal is to measure the functional performance of a summary with an objective, reproducible, and fully automated method. Our approach achieves this by measuring the performance boost gained by a pre-trained language model with access to a document summary while carrying out its language understanding task on the document’s text. We present evidence that BLANC scores have as good correlation with human evaluations as do the ROUGE family of summary quality measurements. And unlike ROUGE, the BLANC method does not require human-written reference summaries, allowing for fully human-free summary quality estimation.
APAC-Net: Alternating the Population and Agent Control via Two Neural Networks to Solve High-Dimensional Stochastic Mean Field Games

We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean field games (MFGs). Our algorithm is geared toward high-dimensional instances of MFGs that are beyond reach with existing solution methods. We achieve this in two steps. First, we take advantage of the underlying variational primal-dual structure that MFGs exhibit and phrase it as a convex-concave saddle point problem. Second, we parameterize the value and density functions by two neural networks, respectively. By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN). We show the potential of our method on up to 100-dimensional MFG problems.
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks

The monitoring and management of numerous and diverse time series data at Alibaba Group calls for an effective and scalable time series anomaly detection service. In this paper, we propose RobustTAD, a Robust Time series Anomaly Detection framework by integrating robust seasonal-trend decomposition and convolutional neural network for time series data. The seasonal-trend decomposition can effectively handle complicated patterns in time series, and meanwhile significantly simplifies the architecture of the neural network, which is an encoder-decoder architecture with skip connections. This architecture can effectively capture the multi-scale information from time series, which is very useful in anomaly detection. Due to the limited labeled data in time series anomaly detection, we systematically investigate data augmentation methods in both time and frequency domains. We also introduce label-based weight and value-based weight in the loss function by utilizing the unbalanced nature of the time series anomaly detection problem. Compared with the widely used forecasting-based anomaly detection algorithms, decomposition-based algorithms, traditional statistical algorithms, as well as recent neural network based algorithms, RobustTAD performs significantly better on public benchmark datasets. It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection

Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system. In many of these applications, multiple periodic components exist and are often interlaced with each other. Such dynamic and complicated periodic patterns make the accurate periodicity detection difficult. In addition, other components in the time series, such as trend, outliers and noises, also pose additional challenges for accurate periodicity detection. In this paper, we propose a robust and general framework for multiple periodicity detection. Our algorithm applies maximal overlap discrete wavelet transform to transform the time series into multiple temporal-frequency scales such that different periodic components can be isolated. We rank them by wavelet variance, and then at each scale detect single periodicity by our proposed Huber-periodogram and Huber-ACF robustly. We rigorously prove the theoretical properties of Huber-periodogram and justify the use of Fisher's test on Huber-periodogram for periodicity detection. To further refine the detected periods, we compute unbiased autocorrelation function based on Wiener-Khinchin theorem from Huber-periodogram for improved robustness and efficiency. Experiments on synthetic and real-world datasets show that our algorithm outperforms other popular ones for both single and multiple periodicity detection.
Practical Verification of MapReduce Computation Integrity via Partial Re-execution

Big data processing is often outsourced to powerful, but untrusted cloud service providers that provide agile and scalable computing resources to weaker clients. However, untrusted cloud services do not ensure the integrity of data and computations while clients have no control over the outsourced computation or no means to check the correctness of the execution. Despite a growing interest and recent progress in verifiable computation, the existing techniques are still not practical enough for big data processing due to high verification overhead. In this paper, we present a solution called V-MR (Verifiable MapReduce), which is a framework that verifies the integrity of MapReduce computation outsourced in the untrusted cloud via partial re-execution. V-MR is practically effective and efficient in that (1) it can detect the violation of MapReduce computation integrity and identify the malicious workers involved in the that produced the incorrect computation. (2) it can reduce the overhead of verification via partial re-execution with carefully selected input data and program code using program analysis. The experiment results of a prototype of V-MR show that V-MR can verify the integrity of MapReduce computation effectively with small overhead for partial re-execution.
Flexible Prior Elicitation via the Prior Predictive Distribution

The prior distribution for the unknown model parameters plays a crucial role in the process of statistical inference based on Bayesian methods. However, specifying suitable priors is often difficult even when detailed prior knowledge is available in principle. The challenge is to express quantitative information in the form of a probability distribution. Prior elicitation addresses this question by extracting subjective information from an expert and transforming it into a valid prior. Most existing methods, however, require information to be provided on the unobservable parameters, whose effect on the data generating process is often complicated and hard to understand. We propose an alternative approach that only requires knowledge about the observable outcomes - knowledge which is often much easier for experts to provide. Building upon a principled statistical framework, our approach utilizes the prior predictive distribution implied by the model to automatically transform experts judgements about plausible outcome values to suitable priors on the parameters. We also provide computational strategies to perform inference and guidelines to facilitate practical use.
Adversarial attacks on machine learning systems for high-frequency trading

Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain. Nonetheless, little is known about the robustness properties of these models. We study valuation models for algorithmic trading from the perspective of adversarial machine learning. We introduce new attacks specific to this domain with size constraints that minimize attack costs. We further discuss how these attacks can be used as an analysis tool to study and evaluate the robustness properties of financial models. Finally, we investigate the feasibility of realistic adversarial attacks in which an adversarial trader fools automated trading systems into making inaccurate predictions.
Automatic Estimation of Sphere Centers from Images of Calibrated Cameras

Calibration of devices with different modalities is a key problem in robotic vision. Regular spatial objects, such as planes, are frequently used for this task. This paper deals with the automatic detection of ellipses in camera images, as well as to estimate the 3D position of the spheres corresponding to the detected 2D ellipses. We propose two novel methods to (i) detect an ellipse in camera images and (ii) estimate the spatial location of the corresponding sphere if its size is known. The algorithms are tested both quantitatively and qualitatively. They are applied for calibrating the sensor system of autonomous cars equipped with digital cameras, depth sensors and LiDAR devices.