Miracle-dz/UniFAR-FTUs huggingface dataset:
NMA: Neural Multi-slot Auctions with Externalities for Online Advertising

Online advertising driven by auctions brings billions of dollars in revenue for social networking services and e-commerce platforms. GSP auctions, which are simple and easy to understand for advertisers, have almost become the benchmark for ad auction mechanisms in the industry. However, most GSP-based industrial practices assume that the user click only relies on the ad itself, which overlook the effect of external items, referred to as externalities. Recently, DNA has attempted to upgrade GSP with deep neural networks and models local externalities to some extent. However, it only considers set-level contexts from auctions and ignores the order and displayed position of ads, which is still suboptimal. Although VCG-based multi-slot auctions (e.g., VCG, WVCG) make it theoretically possible to model global externalities (e.g., the order and positions of ads and so on), they lack an efficient balance of both revenue and social welfare. In this paper, we propose novel auction mechanisms named Neural Multi-slot Auctions (NMA) to tackle the above-mentioned challenges. Specifically, we model the global externalities effectively with a context-aware list-wise prediction module to achieve better performance. We design a list-wise deep rank module to guarantee incentive compatibility in end-to-end learning. Furthermore, we propose an auxiliary loss for social welfare to effectively reduce the decline of social welfare while maximizing revenue. Experiment results on both offline large-scale datasets and online A/B tests demonstrate that NMA obtains higher revenue with balanced social welfare than other existing auction mechanisms (i.e., GSP, DNA, WVCG) in industrial practice, and we have successfully deployed NMA on Meituan food delivery platform.
Trend analysis and forecasting air pollution in Rwanda

Air pollution is a major public health problem worldwide although the lack of data is a global issue for most low and middle income countries. Ambient air pollution in the form of fine particulate matter (PM2.5) exceeds the World Health Organization guidelines in Rwanda with a daily average of around 42.6 microgram per meter cube. Monitoring and mitigation strategies require an expensive investment in equipment to collect pollution data. Low-cost sensor technology and machine learning methods have appeared as an alternative solution to get reliable information for decision making. This paper analyzes the trend of air pollution in Rwanda and proposes forecasting models suitable to data collected by a network of low-cost sensors deployed in Rwanda.
Real Time Multi-Object Detection for Helmet Safety

The National Football League (NFL) and Amazon Web Services (9) (AWS) teamed up to develop the best sports injury surveillance and mitigation program via the Kaggle competition. Through which the NFL wants to assign speciﬁc players to each helmet, which would help accurately identify each player’s “ex-posures” throughout a football play. We are trying to implement a computer vision based ML algorithms capable of assigning detected helmet impacts to correct players via tracking information. Our paper will explain the approach to automati-cally track player helmets and their collisions. This will also allow them to review previous plays and explore the trends in exposure over time.
Axion and FIMP dark matter in a 𝖴(1) extension of the Standard Model

In the Standard Model a Dark Matter candidate is missing, but it is relatively simple to enlarge the model including one or more suitable particles. We consider in this paper one such extension, inspired by simplicity and by the goal to solve more than just the Dark Matter issue. Indeed we consider a local U(1) extension of the SM providing an axion particle to solve the strong CP problem and including RH neutrinos with appropriate mass terms. One of the latter is decoupled from the SM leptons and can constitute stable sterile neutrino DM. In this setting, the PQ symmetry arises only as an accidental symmetry but its breaking by higher order operators is sufficiently suppressed to avoid introducing a large θ contribution. The axion decay constant and the RH neutrino masses are related to the same v.e. v.s and the PQ scale and both DM densities are determined by the parameters of the axion and scalar sector. The model predicts in general a mixed Dark Matter scenario with both axion and sterile neutrino DM and is characterised by a reduced density and observational signals from each single component.
A Demographic Attribute Guided Approach to Age Estimation

Face-based age estimation has attracted enormous attention due to wide applications to public security surveillance, human-computer interaction, etc. With vigorous development of deep learning, age estimation based on deep neural network has become the mainstream practice. However, seeking a more suitable problem paradigm for age change characteristics, designing the corresponding loss function and designing a more effective feature extraction module still needs to be studied. What is more, change of face age is also related to demographic attributes such as ethnicity and gender, and the dynamics of different age groups is also quite different. This problem has so far not been paid enough attention to. How to use demographic attribute information to improve the performance of age estimation remains to be further explored. In light of these issues, this research makes full use of auxiliary information of face attributes and proposes a new age estimation approach with an attribute guidance module. We first design a multi-scale attention residual convolution unit (MARCU) to extract robust facial features other than simply using other standard feature modules such as VGG and ResNet. Then, after being especially treated through full connection (FC) layers, the facial demographic attributes are weight-summed by 1*1 convolutional layer and eventually merged with the age features by a global FC layer. Lastly, we propose a new error compression ranking (ECR) loss to better converge the age regression value. Experimental results on three public datasets of UTKFace, LAP2016 and Morph show that our proposed approach achieves superior performance compared to other state-of-the-art methods.
End-to-End Zero-Shot Voice Style Transfer with Location-Variable Convolutions

Zero-shot voice conversion is becoming an increasingly popular research direction, as it promises the ability to transform speech to match the voice style of any speaker. However, little work has been done on end-to-end methods for this task, which are appealing because they remove the need for a separate vocoder to generate audio from intermediate features. In this work, we propose Location-Variable Convolution-based Voice Conversion (LVC-VC), a model for performing end-to-end zero-shot voice conversion that is based on a neural vocoder. LVC-VC utilizes carefully designed input features that have disentangled content and speaker style information, and the vocoder-like architecture learns to combine them to simultaneously perform voice conversion while synthesizing audio. To the best of our knowledge, LVC-VC is one of the ﬁrst models to be proposed that can perform zero-shot voice conversion in an end-to-end manner, and it is the ﬁrst to do so using a vocoder-like neural framework. Experiments show that our model achieves competitive or better voice style transfer performance compared to several baselines while maintaining the intelligibility of transformed speech much better.
Explainable Graph Theory-Based Identification of Meter-Transformer Mapping

Distributed energy resources are better for the environment but may cause transformer overload in distribution grids, calling for recovering meter-transformer mapping to provide situational awareness, i.e., the transformer loading. The challenge lies in recovering meter-transformer (M.T.) mapping for two common scenarios, e.g., large distances between a meter and its parent transformer or high similarity of a meter's consumption pattern to a non-parent transformer's meters. Past methods either assume a variety of data as in the transmission grid or ignore the two common scenarios mentioned above. Therefore, we propose to utilize the above observation via spectral embedding by using the property that inter-transformer meter consumptions are not the same and that the noise in data is limited so that all the k smallest eigenvalues of the voltage-based Laplacian matrix are smaller than the next smallest eigenvalue of the ideal Laplacian matrix. We also provide a guarantee based on this understanding. Furthermore, we partially relax the assumption by utilizing location information to aid voltage information for areas geographically far away but with similar voltages. Numerical simulations on the IEEE test systems and real feeders from our partner utility show that the proposed method correctly identifies M.T. mapping.
Mosaic Zonotope Shadow Matching for Risk-Aware Autonomous Localization in Harsh Urban Environments

Risk-aware urban localization with the Global Navigation Satellite System (GNSS) remains an unsolved problem with frequent misdetection of the user’s street or side of the street. Signiﬁcant advances in 3D map-aided GNSS use grid-based GNSS shadow matching alongside AI-driven line-of-sight (LOS) classiﬁers and server-based processing to improve localization accuracy, especially in the cross-street direction. Our prior work introduces a new paradigm for shadow matching that proposes set-valued localization with computationally eﬃcient zonotope set representations. While existing literature improved accuracy and eﬃciency, the current state of shadow matching theory does not address the needs of risk-aware autonomous systems. We extend our prior work to propose Mosaic Zonotope Shadow Matching (MZSM) that employs a classiﬁer-agnostic polytope mosaic architecture to provide risk-awareness and certiﬁable guarantees on urban positioning. We formulate a recursively expanding binary tree that reﬁnes an initial location estimate with set operations into smaller polytopes. Together, the smaller polytopes form a mosaic. We weight the tree branches with the probability that the user is in line of sight of the satellite and expand the tree with each new satellite observation. Our method yields an exact shadow matching distribution from which we guarantee uncertainty bounds on the user localization. We perform high-ﬁdelity simulations using a 3D building map of San Francisco to validate our algorithm’s risk-aware improvements. We demonstrate that MZSM provides certiﬁable guarantees across varied data-driven LOS classiﬁer accuracies and yields a more precise understanding of the uncertainty over existing methods. We validate that our tree-based construction is eﬃcient and tractable, computing a mosaic from 14 satellites in 0.63 seconds and growing quadratically in the satellite number.
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks

Monte Carlo (MC) integration is the de facto method for approximating the predictive distribution of Bayesian neural networks (BNNs). But, even with many MC samples, Gaussian-based BNNs could still yield bad predictive performance due to the posterior approximation's error. Meanwhile, alternatives to MC integration tend to be more expensive or biased. In this work, we experimentally show that the key to good MC-approximated predictive distributions is the quality of the approximate posterior itself. However, previous methods for obtaining accurate posterior approximations are expensive and non-trivial to implement. We, therefore, propose to refine Gaussian approximate posteriors with normalizing flows. When applied to last-layer BNNs, it yields a simple \emph{post hoc} method for improving pre-existing parametric approximations. We show that the resulting posterior approximation is competitive with even the gold-standard full-batch Hamiltonian Monte Carlo.
A Review of Safe Reinforcement Learning: Methods, Theory and Applications

Reinforcement learning (RL) has achieved tremendous success in many complex decision making tasks. When it comes to deploying RL in the real world, safety concerns are usually raised, leading to a growing demand for safe RL algorithms, such as in autonomous driving and robotics scenarios. While safety control has a long history, the study of safe RL algorithms is still in the early stages. To establish a good foundation for future research in this thread, in this paper, we provide a review for safe RL from the perspectives of methods, theory and applications. Firstly, we review the progress of safe RL from five dimensions and come up with five problems that are crucial for safe RL being deployed in real-world applications, coined as"2H3W". Secondly, we analyze the theory and algorithm progress from the perspectives of answering the"2H3W"problems. Then, the sample complexity of safe RL methods is reviewed and discussed, followed by an introduction of the applications and benchmarks of safe RL algorithms. Finally, we open the discussion of the challenging problems in safe RL, hoping to inspire more future research on this thread. To advance the study of safe RL algorithms, we release a benchmark suite, an open-sourced repository containing the implementations of major safe RL algorithms, along with tutorials at the link: https://github.com/chauncygu/Safe-Reinforcement-Learning-Baselines.git.
Gender Bias in Meta-Embeddings

Different methods have been proposed to develop meta-embeddings from a given set of source embeddings. However, the source embeddings can contain unfair gender-related biases, and how these influence the meta-embeddings has not been studied yet. We study the gender bias in meta-embeddings created under three different settings: (1) meta-embedding multiple sources without performing any debiasing (Multi-Source No-Debiasing), (2) meta-embedding multiple sources debiased by a single method (Multi-Source Single-Debiasing), and (3) meta-embedding a single source debiased by different methods (Single-Source Multi-Debiasing). Our experimental results show that meta-embedding amplifies the gender biases compared to input source embeddings. We find that debiasing not only the sources but also their meta-embedding is needed to mitigate those biases. Moreover, we propose a novel debiasing method based on meta-embedding learning where we use multiple debiasing methods on a single source embedding and then create a single unbiased meta-embedding.
Learning Interface Conditions in Domain Decomposition Solvers

Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requires tedious analysis and is often available only in simplified, structured-grid settings, limiting their use for more complex problems. In this work, we generalize optimized Schwarz domain decomposition methods to unstructured-grid problems, using Graph Convolutional Neural Networks (GCNNs) and unsupervised learning to learn optimal modifications at subdomain interfaces. A key ingredient in our approach is an improved loss function, enabling effective training on relatively small problems, but robust performance on arbitrarily large problems, with computational cost linear in problem size. The performance of the learned linear solvers is compared with both classical and optimized domain decomposition algorithms, for both structured- and unstructured-grid problems.
Atmospheric resonant production for light dark sectors

Cosmic ray atmospheric showers provide an eﬀective environment for the production of MeV-scale dark sector particles. We show that, when available, the resonant annihilation of positrons from the shower on atmospheric electrons is the dominant production mechanism by more than an order of magnitude. We provide a quantitative example based on dark photon production and update existing constraints on a corresponding light dark matter model from kilotons neutrino experiments and xenon-based direct detection experiments. particles. In particular, the relevant processes become e + e − → χχ and π 0 → γχχ . We expect a similar enhancement of the production ﬂuxes for MeV-scale millicharge particles and leave to future work a thorough study of this scenario.
A Method for Quantifying Position Reconstruction Uncertainty in Astroparticle Physics using Bayesian Networks

Robust position reconstruction is paramount for enabling discoveries in astroparticle physics as backgrounds are signiﬁcantly reduced by only considering interactions within the ﬁducial volume. In this work, we present for the ﬁrst time a method for position reconstruction using a Bayesian network which provides per interaction uncertainties. We demonstrate the utility of this method with simulated data based on the XENONnT detector design, a dual-phase xenon time-projection chamber, as a proof-of-concept. The network structure includes variables representing the 2D position of the interaction within the detector, the number of electrons entering the gaseous phase, and the hits measured by each sensor in the top array of the detector. The precision of the position reconstruction (diﬀerence between the true and expectation value of position) is comparable to the state-of-the-art methods — an RMS of 0.69 cm, ∼ 0.09 of the sensor spacing, for the inner part of the detector ( < 60 cm) and 0.98 cm, ∼ 0.12 of the sensor spacing, near the wall of the detector ( ≥ 60 cm). More importantly, the uncertainty of each interaction position was directly computed, which is not possible with other reconstruction methods. The method found a median 3- σ conﬁdence region of 11 cm 2 for the inner part of the detector and 21 cm 2 near the wall of the detector. We found the Bayesian network framework to be well suited to the problem of position reconstruction. The performance of this proof-of-concept, even with several simplifying assumptions, shows that this is a promising method for providing per interaction uncertainty, which can be extended to energy reconstruction and signal classiﬁcation.
Confident Clustering via PCA Compression Ratio and Its Application to Single-cell RNA-seq Analysis

Unsupervised clustering algorithms for vectors has been widely used in the area of machine learning. Many applications, including the biological data we studied in this paper, contain some boundary datapoints which show combination properties of two underlying clusters and could lower the performance of the traditional clustering algorithms. We develop a confident clustering method aiming to diminish the influence of these datapoints and improve the clustering results. Concretely, for a list of datapoints, we give two clustering results. The first-round clustering attempts to classify only pure vectors with high confidence. Based on it, we classify more vectors with less confidence in the second round. We validate our algorithm on single-cell RNA-seq data, which is a powerful and widely used tool in biology area. Our confident clustering shows a high accuracy on our tested datasets. In addition, unlike traditional clustering methods in single-cell analysis, the confident clustering shows high stability under different choices of parameters.
DPER: Dynamic Programming for Exist-Random Stochastic SAT

In Bayesian inference, the maximum a posteriori (MAP) problem combines the most probable explanation (MPE) and marginalization (MAR) problems. The counterpart in propositional logic is the exist-random stochastic satisﬁability (ER-SSAT) problem, which combines the satisﬁability (SAT) and weighted model counting (WMC) problems. Both MAP and ER-SSAT have the form argmax X (cid:80) Y f ( X, Y ), where f is a real-valued function over disjoint sets X and Y of variables. These two optimization problems request a value assignment for the X variables that maximizes the weighted sum of f ( X, Y ) over all value assignments for the Y variables. ER-SSAT has been shown to be a promising approach to formally verify fairness in supervised learning. Recently, dynamic programming on graded project-join trees has been proposed to solve weighted projected model counting (WPMC) , a related problem that has the form (cid:80) X max Y f ( X, Y ). We extend this WPMC framework to exactly solve ER-SSAT and implement a dynamic-programming solver named DPER . Our empirical evaluation indicates that DPER contributes to the portfolio of state-of-the-art ER-SSAT solvers ( DC-SSAT and erSSAT ) through competitive performance on low-width problem instances.
Charge Exchange X-Ray Emission Detected in Multiple Shells of Supernova Remnant G296.1–0.5

Recent high-resolution X-ray spectroscopy revealed the possible presence of charge exchange (CX) X-ray emission in supernova remnants (SNRs). Although CX is expected to take place at the outermost edges of SNR shells, no significant measurement has been reported so far due to the lack of nearby SNR samples. Here we present an X-ray study of SNR G296.1−0.5, which has a complicated multiple-shell structure, with the Reflection Grating Spectrometer on board XMM-Newton. We select two shells in different regions and find that in both regions the O vii line shows a high forbidden-to-resonance (f/r) ratio that cannot be reproduced by a simple thermal model. Our spectral analysis suggests a presence of CX and the result is also supported by our new radio observation, where we discover evidence of molecular clouds associated with these shells. Assuming G296.1−0.5 has a spherical shock, we estimate that CX is dominant in a thin layer with a thickness of 0.2%–0.3% of the shock radius. The result is consistent with a previous theoretical expectation and we therefore conclude that CX occurs in G296.1−0.5.
Non-Hermitian higher-order topological superconductors in two dimensions: Statics and dynamics

Being motivated by intriguing phenomena such as the breakdown of conventional bulk boundary correspondence and emergence of skin modes in the context of non-Hermitian (NH) topological insulators, we here propose a NH second-order topological superconductor (SOTSC) model that hosts Majorana zero modes (MZMs). Employing the non-Bloch form of NH Hamiltonian, we topologically characterize the above modes by biorthogonal nested polarization and resolve the apparent breakdown of the bulk boundary correspondence. Unlike the Hermitian SOTSC, we notice that the MZMs inhabit only one corner out of four in the two-dimensional NH SOTSC. We extend the static MZMs into the realm of Floquet drive. We find anomalous $\pi$-mode following low-frequency mass-kick in addition to the regular $0$-mode that is usually engineered in a high-frequency regime. We further characterize the regular $0$-mode with biorthogonal Floquet nested polarization. Our proposal is not limited to the $d$-wave superconductivity only and can be realized in the experiment with strongly correlated optical lattice platforms.