Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotics for non-degenerate multivariate $U$-statistics with estimated nuisance parameters under the null and local alternative hypotheses

The large-sample behavior of non-degenerate multivariate $U$-statistics of arbitrary degree is investigated under the assumption that their kernel depends on parameters that can be estimated consistently. Mild regularity conditions are given which guarantee that once properly normalized, such statistics are asymptotically multivariate Gaussian both under the null hypothesis and sequences of local alternatives. The work of Randles (1982, Ann. Statist.) is extended in three ways: the data and the kernel values can be multivariate rather than univariate, the limiting behavior under local alternatives is studied for the first time, and the effect of knowing some of the nuisance parameters is quantified. These results can be applied to a broad range of goodness-of-fit testing contexts, as shown in one specific example.
Prospects for Joint Detection of Gravitational Waves with Counterpart Gamma-Ray Bursts Detected by the HADAR Experiment

The detection of GW170817/GRB170817A implied the strong association between short gamma-ray bursts (SGRBs) and binary neutron star (BNS) mergers which produce gravitational waves (GWs). More evidence is needed to confirm the association and reveal the physical processes of BNS mergers. The upcoming High Altitude Detection of Astronomical Radiation (HADAR) experiment, excelling in a wide field of view (FOV) and a large effective area above tens of GeV, is a hope for the prompt detection of very-high-energy (VHE;>10 GeV) SGRBs. The aim of this paper is to simulate and analyse GW/SGRB joint detections by future GW detector networks in synergy with HADAR, including the second generation LIGO, Virgo and KAGRA and the third generation ET and CE. We provide a brief introduction of the HADAR experiment for SGRB simulations and its expected SGRB detections. For GW simulations, we adopt a phenomenological model to describe GWs produced by BNS mergers and introduce the signal-noise ratios (SNRs) as detector responses. Following a theoretical analysis we compute the redshift-dependent efficiency functions of GW detector networks. We then construct the simulation of GW detection by Monte Carlo sampling. We compare the simulated results of LIGO-Virgo O2 and O3 runs with their actual detections as a check. The combination of GW and SGRB models is then discussed for joint detection, including parameter correlations, triggered SNRs and efficiency skymaps. The estimated joint detection rates are 0.09-2.52 per year for LHVK network with HADAR under different possible configurations, and approximately 0.27-7.89 per year for ET+CE network with HADAR.
Clustering Molecular Energy Landscapes by Adaptive Network Embedding

In order to efficiently explore the chemical space of all possible small molecules, a common approach is to compress the dimension of the system to facilitate downstream machine learning tasks. Towards this end, we present a data-driven approach for clustering potential energy landscapes of molecular structures by applying recently developed Network Embedding techniques to obtain latent variables defined through the embedding function. To scale up the method, we also incorporate an entropy sensitive adaptive scheme for hierarchical sampling of the energy landscape, based on Metadynamics and Transition Path Theory. Taking into account the kinetic information implied by the energy landscape of a system, we can interpret dynamical node-node relationships in reduced dimensions. We demonstrate the framework through Lennard-Jones clusters and a human DNA sequence.
Constraining Annihilating Dark Matter Using the Multifrequency Radio Flux Profiles of the M33 Galaxy

Radio data can give stringent constraints for annihilating dark matter. In general, radio observations can detect very accurate radio flux density with high resolution and different frequencies for nearby galaxies. We are able to obtain the radio flux density as a function of distance from the galactic center and frequencies S(r, ν). In this article, we demonstrate a comprehensive radio analysis of the M33 galaxy, combining the radio flux density profile S(r) and the frequency spectrum S(ν) to get the constraints of dark matter annihilation parameters. By analyzing the archival radio data obtained from the Effelsberg telescope, we show that the dark matter annihilation contributing to the radio flux density might be insignificant in the disk region of the M33 galaxy. Moreover, by including the baryonic radio contribution, we constrain the 2σ conservative upper limits of the annihilation cross section, which can be complementary to the existing constraints based on neutrino, cosmic-ray, and gamma-ray observations. Our results indicate that analyzing the galactic multifrequency radio flux profiles can give useful and authentic constraints on dark matter for the leptophilic annihilation channels.
SyzRetrospector: A Large-Scale Retrospective Study of Syzbot

Over the past 6 years, Syzbot has fuzzed the Linux kernel day and night to report over 5570 bugs, of which 4604 have been patched [11]. While this is impressive, we have found the average time to find a bug is over 405 days. Moreover, we have found that current metrics commonly used, such as time-to-find and number of bugs found, are inaccurate in evaluating Syzbot since bugs often spend the majority of their lives hidden from the fuzzer. In this paper, we set out to better understand and quantify Syzbot's performance and improvement in finding bugs. Our tool, SyzRetrospector, takes a different approach to evaluating Syzbot by finding the earliest that Syzbot was capable of finding a bug, and why that bug was revealed. We use SyzRetrospector on a large scale to analyze 559 bugs and find that bugs are hidden for an average of 331.17 days before Syzbot is even able to find them. We further present findings on the behaviors of revealing factors, how some bugs are harder to reveal than others, the trends in delays over the past 6 years, and how bug location relates to delays. We also provide key takeaways for improving Syzbot's delays.
Evaluating Driver Readiness in Conditionally Automated Vehicles from Eye-Tracking Data and Head Pose

As automated driving technology advances, the role of the driver to resume control of the vehicle in conditionally automated vehicles becomes increasingly critical. In the SAE Level 3 or partly automated vehicles, the driver needs to be available and ready to intervene when necessary. This makes it essential to evaluate their readiness accurately. This article presents a comprehensive analysis of driver readiness assessment by combining head pose features and eye-tracking data. The study explores the effectiveness of predictive models in evaluating driver readiness, addressing the challenges of dataset limitations and limited ground truth labels. Machine learning techniques, including LSTM architectures, are utilised to model driver readiness based on the Spatio-temporal status of the driver's head pose and eye gaze. The experiments in this article revealed that a Bidirectional LSTM architecture, combining both feature sets, achieves a mean absolute error of 0.363 on the DMD dataset, demonstrating superior performance in assessing driver readiness. The modular architecture of the proposed model also allows the integration of additional driver-specific features, such as steering wheel activity, enhancing its adaptability and real-world applicability.
LearnedWMP: Workload Memory Prediction Using Distribution of Query Templates

In a modern DBMS, working memory is frequently the limiting factor when processing in-memory analytic query operations such as joins, sorting, and aggregation. Existing resource estimation approaches for a DBMS estimate the resource consumption of a query by computing an estimate of each individual database operator in the query execution plan. Such an approach is slow and error-prone as it relies upon simplifying assumptions, such as uniformity and independence of the underlying data. Additionally, the existing approach focuses on individual queries separately and does not factor in other queries in the workload that may be executed concurrently. In this research, we are interested in query performance optimization under concurrent execution of a batch of queries (a workload). Specifically, we focus on predicting the memory demand for a workload rather than providing separate estimates for each query within it. We introduce the problem of workload memory prediction and formalize it as a distribution regression problem. We propose Learned Workload Memory Prediction (LearnedWMP) to improve and simplify estimating the working memory demands of workloads. Through a comprehensive experimental evaluation, we show that LearnedWMP reduces the memory estimation error of the state-of-the-practice method by up to 47.6%. Compared to an alternative single-query model, during training and inferencing, the LearnedWMP model and its variants were 3x to 10x faster. Moreover, LearnedWMP-based models were at least 50% smaller in most cases. Overall, the results demonstrate the advantages of the LearnedWMP approach and its potential for a broader impact on query performance optimization.
Higher-order Laplacian Renormalization

We propose a cross-order Laplacian renormalization group (X-LRG) scheme for arbitrary higher-order networks. The renormalization group is a pillar of the theory of scaling, scale-invariance, and universality in physics. An RG scheme based on diffusion dynamics was recently introduced for complex networks with dyadic interactions. Despite mounting evidence of the importance of polyadic interactions, we still lack a general RG scheme for higher-order networks. Our approach uses a diffusion process to group nodes or simplices, where information can flow between nodes and between simplices (higher-order interactions). This approach allows us (i) to probe higher-order structures, defining scale-invariance at various orders, and (ii) to propose a coarse-graining scheme. We demonstrate our approach on controlled synthetic higher-order systems and then use it to detect the presence of order-specific scale-invariant profiles of real-world complex systems from multiple domains.
A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model

In online advertising scenario, sellers often create multiple creatives to provide comprehensive demonstrations, making it essential to present the most appealing design to maximize the Click-Through Rate (CTR). However, sellers generally struggle to consider users preferences for creative design, leading to the relatively lower aesthetics and quantities compared to Artificial Intelligence (AI)-based approaches. Traditional AI-based approaches still face the same problem of not considering user information while having limited aesthetic knowledge from designers. In fact that fusing the user information, the generated creatives can be more attractive because different users may have different preferences. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. The ranking model can predict the CTR score for each creative considering user features. However, the two above stages are regarded as two different tasks and are optimized separately. In this paper, we proposed a new automated Creative Generation pipeline for Click-Through Rate (CG4CTR) with the goal of improving CTR during the creative generation stage. Our contributions have 4 parts: 1) The inpainting mode in stable diffusion is firstly applied to creative generation task in online advertising scene. A self-cyclic generation pipeline is proposed to ensure the convergence of training. 2) Prompt model is designed to generate individualized creatives for different user groups, which can further improve the diversity and quality. 3) Reward model comprehensively considers the multimodal features of image and text to improve the effectiveness of creative ranking task, and it is also critical in self-cyclic pipeline. 4) The significant benefits obtained in online and offline experiments verify the significance of our proposed method.
With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation

Long text generation, such as novel writing and discourse-level translation with extremely long contexts, presents significant challenges to current language models. Existing methods mainly focus on extending the model's context window through strategies like length extrapolation. However, these approaches demand substantial hardware resources during the training and/or inference phases. Our proposed method, Temp-Lora, introduces an alternative concept. Instead of relying on the KV cache to store all context information, we embeds this information directly into a temporary Lora module. In the process of long text generation, this module is progressively trained with text generated previously. This approach not only efficiently preserves contextual knowledge but also prevents any permanent alteration to the model's parameters given that the module is discarded post-generation. Extensive experiments on the PG19 language modeling benchmark and the GuoFeng discourse-level translation benchmark validate the effectiveness of Temp-Lora. Our results show that: 1) Temp-Lora substantially enhances generation quality for long text, as indicated by a 13.2% decrease in perplexity (PPL) on a subset of PG19, and a 29.3% decrease in PPL along with a 113.2% increase in BLEU score on a subset of GuoFeng, 2) Temp-Lora is compatible with and enhances most existing long text generation methods, and 3) Temp-Lora can greatly reduce computational costs by shortening the context window. For example, we can ensure a moderate improvement in generation quality (a decrease of 3.8% in PPL) while enabling a 51.5% memory usage reduction and a 60.0% decrease in latency for inference.
Expert-Driven Monitoring of Operational ML Models

We propose Expert Monitoring, an approach that leverages domain expertise to enhance the detection and mitigation of concept drift in machine learning (ML) models. Our approach supports practitioners by consolidating domain expertise related to concept drift-inducing events, making this expertise accessible to on-call personnel, and enabling automatic adaptability with expert oversight.
A Finger on the Pulse of Cardiovascular Health: Smartphone Photoplethysmography-Based Pulse Waveform Analysis for Blood Pressure Measurement

Routine blood pressure (BP) monitoring, crucial for health assessment, faces challenges such as limited access to medical-grade equipment and expertise. Portable cuff BP devices, on the other hand, are cumbersome to carry all day and often cost-prohibitive in less developed countries. Besides, these sphygmomanometer-based devices can cause discomfort and disrupt blood flow during measurement. This study explores the use of smartphones for continuous BP monitoring, focusing on overcoming the trust barriers associated with the opacity of machine learning models in predicting BP from low-quality PPG signals. Our approach included developing models based on cardiovascular literature, using simple statistical methods to estimate BP from smartphone PPG signals with comprehensive data pre-processing, applying SHAP for enhanced interpretability and feature identification, and comparing our methods against standard references using Bland-Altman analysis. Validated with data from 125 participants, the study demonstrated significant correlations in waveform features between smartphone and reference BP monitoring devices. The cross-validation of linear regression [MAE=9.86 and 8.01 mmHg for systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively] and random forest model (MAE=8.91 and 6.68 mmHg for SBP and DBP) using waveform-only variables demonstrated the feasibility of using a smartphone to estimate BP. Although SHAP analysis identified key feature sets, Bland-Altman results did not fully meet established thresholds (84.64% and 94.69% of MAE<15 mmHg for SBP and DBP, respectively). The study suggests the potential of smartphone cameras to enhance the accuracy and interpretability of machine learning models for daily BP estimation, but also indicates that smartphone PPG-based BP prediction is not yet a replacement for traditional medical devices.
Task-specific regularization loss towards model calibration for reliable lung cancer detection

Lung cancer is one of the significant causes of cancer-related deaths globally. Early detection and treatment improve the chances of survival. Traditionally CT scans have been used to extract the most significant lung infection information and diagnose cancer. This process is carried out manually by an expert radiologist. The imbalance in the radiologists-to-population ratio in a country like India implies significant work pressure on them and thus raises the need to automate a few of their responsibilities. The tendency of modern-day Deep Neural networks to make overconfident mistakes limit their usage to detect cancer. In this paper, we propose a new task-specific loss function to calibrate the neural network to reduce the risk of overconfident mistakes. We use the state-of-the-art Multi-class Difference in Confidence and Accuracy (MDCA) loss in conjunction with the proposed task-specific loss function to achieve the same. We also integrate post-hoc calibration by performing temperature scaling on top of the train-time calibrated model. We demonstrate 5.98% improvement in the Expected Calibration Error (ECE) and a 17.9% improvement in Maximum Calibration Error (MCE) as compared to the best-performing SOTA algorithm.
"I Got Flagged for Supposed Bullying, Even Though It Was in Response to Someone Harassing Me About My Disability.": A Study of Blind TikTokers' Content Moderation Experiences

The Human-Computer Interaction (HCI) community has consistently focused on the experiences of users moderated by social media platforms. Recently, scholars have noticed that moderation practices could perpetuate biases, resulting in the marginalization of user groups undergoing moderation. However, most studies have primarily addressed marginalization related to issues such as racism or sexism, with little attention given to the experiences of people with disabilities. In this paper, we present a study on the moderation experiences of blind users on TikTok, also known as"BlindToker,"to address this gap. We conducted semi-structured interviews with 20 BlindTokers and used thematic analysis to analyze the data. Two main themes emerged: BlindTokers' situated content moderation experiences and their reactions to content moderation. We reported on the lack of accessibility on TikTok's platform, contributing to the moderation and marginalization of BlindTokers. Additionally, we discovered instances of harassment from trolls that prompted BlindTokers to respond with harsh language, triggering further moderation. We discussed these findings in the context of the literature on moderation, marginalization, and transformative justice, seeking solutions to address such issues.
Exploring descriptors for titanium microstructure via digital fingerprints from variational autoencoders

Microstructure is key to controlling and understanding the properties of metallic materials, but traditional approaches to describing microstructure capture only a small number of features. To enable data-centric approaches to materials discovery, allow efficient storage of microstructural data and assist in quality control in metals processing, we require more complete descriptors of microstructure. The concept of microstructural fingerprinting, using machine learning (ML) to develop quantitative, low-dimensional descriptors of microstructures, has recently attracted significant attention. However, it is difficult to interpret conclusions drawn by ML algorithms, which are commonly referred to as"black boxes". Here we explore variational autoencoders (VAEs), which can be trained to produce microstructural fingerprints in a continuous latent space. VAEs enable the reconstruction of images from fingerprints, allowing us to explore how key features of microstructure are encoded. We develop a VAE architecture based on ResNet18 and train it on Ti-6Al-4V optical micrographs as an example of an industrially important alloy where microstructural control is critical to performance. The latent space is explored in several ways, including by supplying interpolated and randomly perturbed fingerprints to the trained decoder and via dimensionality reduction to explore the distribution of microstructural features within the latent space of fingerprints. We show that the VAE fingerprints exhibit smooth, interpolable behaviour with stability to local perturbations, supporting their suitability as general purpose descriptors for microstructure. We also show that key properties of the microstructures are strongly correlated with position in the latent space, supporting the use of VAE fingerprints for quantitative exploration of process-structure-property relationships.
S3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving

Semantic segmentation and stereo matching are two essential components of 3D environmental perception systems for autonomous driving. Nevertheless, conventional approaches often address these two problems independently, employing separate models for each task. This approach poses practical limitations in real-world scenarios, particularly when computational resources are scarce or real-time performance is imperative. Hence, in this article, we introduce S$^3$M-Net, a novel joint learning framework developed to perform semantic segmentation and stereo matching simultaneously. Specifically, S$^3$M-Net shares the features extracted from RGB images between both tasks, resulting in an improved overall scene understanding capability. This feature sharing process is realized using a feature fusion adaption (FFA) module, which effectively transforms the shared features into semantic space and subsequently fuses them with the encoded disparity features. The entire joint learning framework is trained by minimizing a novel semantic consistency-guided (SCG) loss, which places emphasis on the structural consistency in both tasks. Extensive experimental results conducted on the vKITTI2 and KITTI datasets demonstrate the effectiveness of our proposed joint learning framework and its superior performance compared to other state-of-the-art single-task networks. Our project webpage is accessible at mias.group/S3M-Net.