Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical challenges in mediation analysis: A guide for applied researchers

Mediation analysis is a statistical approach that can provide insights regarding the intermediary processes by which an intervention or exposure affects a given outcome. Mediation analyses rose to prominence, particularly in social science research, with the publication of the seminal paper by Baron and Kenny and is now commonly applied in many research disciplines, including health services research. Despite the growth in popularity, applied researchers may still encounter challenges in terms of conducting mediation analyses in practice. In this paper, we provide an overview of conceptual and methodological challenges that researchers face when conducting mediation analyses. Specifically, we discuss the following key challenges: (1) Conceptually differentiating mediators from other third variables, (2) Extending beyond the single mediator context, (3) Identifying appropriate datasets in which measurement and temporal ordering supports the hypothesized mediation model, (4) Selecting mediation effects that reflect the scientific question of interest, (5) Assessing the validity of underlying assumptions of no omitted confounders, (6) Addressing measurement error regarding the mediator, and (7) Clearly reporting results from mediation analyses. We discuss each challenge and highlight ways in which the applied researcher can approach these challenges.
Improving Sequential Recommendations with LLMs

The sequential recommendation problem has attracted considerable research attention in the past few years, leading to the rise of numerous recommendation models. In this work, we explore how Large Language Models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or improve sequential recommendation approaches. Specifically, we design three orthogonal approaches and hybrids of those to leverage the power of LLMs in different ways. In addition, we investigate the potential of each approach by focusing on its comprising technical aspects and determining an array of alternative choices for each one. We conduct extensive experiments on three datasets and explore a large variety of configurations, including different language models and baseline recommendation models, to obtain a comprehensive picture of the performance of each approach. Among other observations, we highlight that initializing state-of-the-art sequential recommendation models such as BERT4Rec or SASRec with embeddings obtained from an LLM can lead to substantial performance gains in terms of accuracy. Furthermore, we find that fine-tuning an LLM for recommendation tasks enables it to learn not only the tasks, but also concepts of a domain to some extent. We also show that fine-tuning OpenAI GPT leads to considerably better performance than fine-tuning Google PaLM 2. Overall, our extensive experiments indicate a huge potential value of leveraging LLMs in future recommendation approaches. We publicly share the code and data of our experiments to ensure reproducibility.
MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation

Video foreground segmentation (VFS) is an important computer vision task wherein one aims to segment the objects under motion from the background. Most of the current methods are image-based, i.e., rely only on spatial cues while ignoring motion cues. Therefore, they tend to overfit the training data and don't generalize well to out-of-domain (OOD) distribution. To solve the above problem, prior works exploited several cues such as optical flow, background subtraction mask, etc. However, having a video data with annotations like optical flow is a challenging task. In this paper, we utilize the temporal information and the spatial cues from the video data to improve OOD performance. However, the challenge lies in how we model the temporal information given the video data in an interpretable way creates a very noticeable difference. We therefore devise a strategy that integrates the temporal context of the video in the development of VFS. Our approach give rise to deep learning architectures, namely MUSTAN1 and MUSTAN2 and they are based on the idea of multi-scale temporal context as an attention, i.e., aids our models to learn better representations that are beneficial for VFS. Further, we introduce a new video dataset, namely Indoor Surveillance Dataset (ISD) for VFS. It has multiple annotations on a frame level such as foreground binary mask, depth map, and instance semantic annotations. Therefore, ISD can benefit other computer vision tasks. We validate the efficacy of our architectures and compare the performance with baselines. We demonstrate that proposed methods significantly outperform the benchmark methods on OOD. In addition, the performance of MUSTAN2 is significantly improved on certain video categories on OOD data due to ISD.
Nano-ironing van der Waals Heterostructures Towards Electrically Controlled Quantum Dots

Assembling two-dimensional van der Waals layered materials into heterostructures is an exciting development that sparked the discovery of rich correlated electronic phenomena and offers possibilities for designer device applications. However, resist residue from fabrication processes is a major limitation. Resulting disordered interfaces degrade device performance and mask underlying transport physics. Conventional cleaning processes are inefficient and can cause material and device damage. Here, we show that thermal scanning probe based cleaning can effectively eliminate resist residue to recover pristine material surfaces. Our technique is compatible at both the material- and device-level, and we demonstrate the significant improvement in the electrical performance of 2D WS2 transistors. We also demonstrate the cleaning of van der Waals heterostructures to achieve interfaces with low disorder. This enables the electrical formation and control of quantum dots that can be tuned from macroscopic current flow to the single-electron tunnelling regime. Such material processing advances are crucial for constructing high-quality vdW heterostructures that are important platforms for fundamental studies and building blocks for quantum and nano-electronics applications.
Nanomechanically Induced Transparency

In this paper, we investigate a nanomechanically induced transparency (NIT) effects that arises from the coupling of a nanoelectromechanical system and a trapped ion. By confining the ion in mesoscopic traps and capacitively coupling it with a nanoelectromechanical system suspended as electrodes, the research is intricately focussed on the implications of including the ion's degrees of freedom. The Lamb--Dicke approximation is crucial to understanding the effects of phonon exchange with electronic qubits and revealing transparency phenomena in this unique coupling. The results underline the importance of the Lamb--Dicke approximation in modelling the effects of transparency windows in nanoelectromechanical systems.
Privacy and Security Implications of Cloud-Based AI Services : A Survey

This paper details the privacy and security landscape in today's cloud ecosystem and identifies that there is a gap in addressing the risks introduced by machine learning models. As machine learning algorithms continue to evolve and find applications across diverse domains, the need to categorize and quantify privacy and security risks becomes increasingly critical. With the emerging trend of AI-as-a-Service (AIaaS), machine learned AI models (or ML models) are deployed on the cloud by model providers and used by model consumers. We first survey the AIaaS landscape to document the various kinds of liabilities that ML models, especially Deep Neural Networks pose and then introduce a taxonomy to bridge this gap by holistically examining the risks that creators and consumers of ML models are exposed to and their known defences till date. Such a structured approach will be beneficial for ML model providers to create robust solutions. Likewise, ML model consumers will find it valuable to evaluate such solutions and understand the implications of their engagement with such services. The proposed taxonomies provide a foundational basis for solutions in private, secure and robust ML, paving the way for more transparent and resilient AI systems.
Investigation of the mass spectra of singly heavy baryons $\Sigma_{Q}$, $\Xi^{\prime}_{Q}$ and $\Omega_{Q}$ $(Q=c, b)$ in the Regge trajectory model

Very recently, LHCb Collaboration observed that two new $\Omega_{c}^{0}$ states decay into $\Xi^{+}_{c}K^{-}$ with masses of about $3185$ MeV and $3327$ MeV. However, their spin parity quantum numbers $J^{P}$ have not been determined. In this paper, we exploit the quark-diquark model, the linear Regge trajectory and the perturbation treatment method to analyze the mass spectra of the discovered experimental data for the singly heavy baryons $\Sigma_{c}/\Sigma_{b}$, $\Xi^{\prime}_{c}/\Xi^{\prime}_{b}$ and $\Omega_{c}/\Omega_{b}$. In addition, we further predict the mass spectra of several unobserved $\Sigma_{c}/\Sigma_{b}$, $\Xi^{\prime}_{c}/\Xi^{\prime}_{b}$ and $\Omega_{c}/\Omega_{b}$ baryons. In the case of the $\Omega_c(3185)^{0}$ and $\Omega_c(3327)^{0}$ states, we determine $\Omega_{c}(3185)^{0}$ as $2S$ state and $\Omega_{c}(3327)^{0}$ as $1D$ state with $J^{P}=1/2^{+}$ and $J^{P}=3/2^{+}$, respectively. An overall good agreement of the obtained predictions with available experimental data are found.
Upland Farming System Erosion Yields and Their Constraints to Change for Sustainable Agricultural Conservation Practices: A Case Study of Land Use and Land Cover (LULC) Change in Indonesia

In the upper catchments of Southeast Asia, land use change from forest to agricultural systems generated land degradation and conflicts between uplanders and lowlanders. More sustainable cropping systems are proposed to upper‐catchment farmers. Grass fodder strip (GFS) is an effective anti‐erosion practice, and it involves lower costs for farmers. However, labour and cash constraints are sometimes preventing farmers to implement it. To evaluate farms' current impact and adaptation capacities, we need a comprehensive understanding of farm and farm household characteristics that influence their activities. This paper proposes an approach that combines farm household surveys and modelling of farm erosion yield to help project planners and policy makers to identify such farmers in a data‐scarce environment. We developed two farm typologies—one based on both farm and farm household characteristics and one based on their erosion yield and constraints. We calculated erosion yields on plot level by using revised universal soil loss equation method and identified their constraints. We found that a typology based on farm constraints and calculated farm erosion was a good complement to identify farmers who are generating the highest erosion yields and would be able to change their production systems. This methodology is mainly useful at the beginning of conservation projects, when very few hard data are available. Copyright © 2016 John Wiley & Sons, Ltd.
Automating the Design of Asynchronous Logic Control for AMS Electronics

Analog and mixed signal (AMS) electronics becomes increasingly complex and needs to be digitally enhanced by its own control circuitry. The RTL synthesis flow routinely used for digital logic is, however, optimized for synchronous data processing and produces inefficient control for AMS. In this paper, we demonstrate the evident benefits of asynchronous circuits in the context of AMS systems, and propose an asynchronous design for analog electronics (A4A) flow for their specification, synthesis, and formal verification. A library of specialized analog-to-asynchronous (A2A) components is developed for interfacing analog and asynchronous worlds. A4A flow is automated in the Workcraft framework and evaluated using a multiphase buck converter case study, where A2A components are employed to sanitize analog sensor readings. Timing analysis of asynchronous buck control shows improved response time: $4\boldsymbol \times $ faster reaction to high-load and $7\boldsymbol \times $ to under-voltage condition, compared with a 333 MHz clocked controller (to achieve a similar response time, a clocked controller would require ~3 GHz frequency). The simulation results of a 4-phase asynchronous buck demonstrate improved voltage ripple and peak current −16% and 12% reduction, respectively. These benefits lead to the higher efficiency of power conversion, and can be traded off for the cost of analog components, e.g., coils. Moreover, the use of the proposed design flow and tools helps to improve design productivity and overall robustness of AMS circuits.
Scalable Higher-Order Tensor Product Spline Models

In the current era of vast data and transparent machine learning, it is essential for techniques to operate at a large scale while providing a clear mathematical comprehension of the internal workings of the method. Although there already exist interpretable semi-parametric regression methods for large-scale applications that take into account non-linearity in the data, the complexity of the models is still often limited. One of the main challenges is the absence of interactions in these models, which are left out for the sake of better interpretability but also due to impractical computational costs. To overcome this limitation, we propose a new approach using a factorization method to derive a highly scalable higher-order tensor product spline model. Our method allows for the incorporation of all (higher-order) interactions of non-linear feature effects while having computational costs proportional to a model without interactions. We further develop a meaningful penalization scheme and examine the induced optimization problem. We conclude by evaluating the predictive and estimation performance of our method.
Dynamic Occupancy Grids for Object Detection: A Radar-Centric Approach

Dynamic Occupancy Grid Mapping is a technique used to generate a local map of the environment containing both static and dynamic information. Typically, these maps are primarily generated using lidar measurements. However, with improvements in radar sensing, resulting in better accuracy and higher resolution, radar is emerging as a viable alternative to lidar as the primary sensor for mapping. In this paper, we propose a radar-centric dynamic occupancy grid mapping algorithm with adaptations to the state computation, inverse sensor model, and field-of-view computation tailored to the specifics of radar measurements. We extensively evaluate our approach using real data to demonstrate its effectiveness and establish the first benchmark for radar-based dynamic occupancy grid mapping using the publicly available Radarscenes dataset.
Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data

Image-to-image translation is a common task in computer vision and has been rapidly increasing the impact on the field of medical imaging. Deep learning-based methods that employ conditional generative adversarial networks (cGANs), such as Pix2PixGAN, have been extensively explored to perform image-to-image translation tasks. However, when noisy medical image data are considered, such methods cannot be directly applied to produce clean images. Recently, an augmented GAN architecture named AmbientGAN has been proposed that can be trained on noisy measurement data to synthesize high-quality clean medical images. Inspired by AmbientGAN, in this work, we propose a new cGAN architecture, Ambient-Pix2PixGAN, for performing medical image-to-image translation tasks by use of noisy measurement data. Numerical studies that consider MRI-to-PET translation are conducted. Both traditional image quality metrics and task-based image quality metrics are employed to assess the proposed Ambient-Pix2PixGAN. It is demonstrated that our proposed Ambient-Pix2PixGAN can be successfully trained on noisy measurement data to produce high-quality translated images in target imaging modality.
Towards Understanding the Challenges of Bug Localization in Deep Learning Systems

Software bugs cost the global economy billions of dollars annually and claim ~50\% of the programming time from software developers. Locating these bugs is crucial for their resolution but challenging. It is even more challenging in deep-learning systems due to their black-box nature. Bugs in these systems are also hidden not only in the code but also in the models and training data, which might make traditional debugging methods less effective. In this article, we conduct a large-scale empirical study to better understand the challenges of localizing bugs in deep-learning systems. First, we determine the bug localization performance of four existing techniques using 2,365 bugs from deep-learning systems and 2,913 from traditional software. We found these techniques significantly underperform in localizing deep-learning system bugs. Second, we evaluate how different bug types in deep learning systems impact bug localization. We found that the effectiveness of localization techniques varies with bug type due to their unique challenges. For example, tensor bugs were more accessible to locate due to their structural nature, while all techniques struggled with GPU bugs due to their external dependencies. Third, we investigate the impact of bugs' extrinsic nature on localization in deep-learning systems. We found that deep learning bugs are often extrinsic and thus connected to artifacts other than source code (e.g., GPU, training data), contributing to the poor performance of existing localization methods.
Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization

[$^{18}$F]fluorodeoxyglucose (FDG) positron emission tomography (PET) has emerged as a crucial tool in identifying the epileptic focus, especially in cases where magnetic resonance imaging (MRI) diagnosis yields indeterminate results. FDG PET can provide the metabolic information of glucose and help identify abnormal areas that are not easily found through MRI. However, the effectiveness of FDG PET-based assessment and diagnosis depends on the selection of a healthy control group. The healthy control group typically consists of healthy individuals similar to epilepsy patients in terms of age, gender, and other aspects for providing normal FDG PET data, which will be used as a reference for enhancing the accuracy and reliability of the epilepsy diagnosis. However, significant challenges arise when a healthy PET control group is unattainable. Yaakub \emph{et al.} have previously introduced a Pix2PixGAN-based method for MRI to PET translation. This method used paired MRI and FDG PET scans from healthy individuals for training, and produced pseudo normal FDG PET images from patient MRIs that are subsequently used for lesion detection. However, this approach requires a large amount of high-quality, paired MRI and PET images from healthy control subjects, which may not always be available. In this study, we investigated unsupervised learning methods for unpaired MRI to PET translation for generating pseudo normal FDG PET for epileptic focus localization. Two deep learning methods, CycleGAN and SynDiff, were employed, and we found that diffusion-based method achieved improved performance in accurately localizing the epileptic focus.
Parametric-Task MAP-Elites

Optimizing a set of functions simultaneously by leveraging their similarity is called multi-task optimization. Current black-box multi-task algorithms only solve a finite set of tasks, even when the tasks originate from a continuous space. In this paper, we introduce Parametric-task MAP-Elites (PT-ME), a novel black-box algorithm to solve continuous multi-task optimization problems. This algorithm (1) solves a new task at each iteration, effectively covering the continuous space, and (2) exploits a new variation operator based on local linear regression. The resulting dataset of solutions makes it possible to create a function that maps any task parameter to its optimal solution. We show on two parametric-task toy problems and a more realistic and challenging robotic problem in simulation that PT-ME outperforms all baselines, including the deep reinforcement learning algorithm PPO.
Training Adversarial yet Safe Agent to Characterize Safety Performance of Highly Automated Vehicles

This paper focuses on safety performance testing and characterization of black-box highly automated vehicles (HAV). Existing testing approaches typically obtain the testing outcomes by deploying the HAV into a specific testing environment. Such a testing environment can involve various passively given testing strategies presented by other traffic participants such as (i) the naturalistic driving policy learned from human drivers, (ii) extracted concrete scenarios from real-world driving data, and (iii) model-based or data-driven adversarial testing methodologies focusing on forcing safety-critical events. The safety performance of HAV is further characterized by analyzing the obtained testing outcomes with a particular selected measure, such as the observed collision risk. The aforementioned testing practices suffer from the scarcity of safety-critical events, have limited operational design domain (ODD) coverage, or are biased toward long-tail unsafe cases. This paper presents a novel and informative testing strategy that differs from these existing practices. The proposal is inspired by the intuition that a relatively safer HAV driving policy would allow the traffic vehicles to exhibit a higher level of aggressiveness to achieve a certain fixed level of an overall safe outcome. One can specifically characterize such a HAV and traffic interactive strategy and use it as a safety performance indicator for the HAV. Under the proposed testing scheme, the HAV is evaluated under its full ODD with a reward function that represents a trade-off between safety and adversity in generating safety-critical events. The proposed methodology is demonstrated in simulation with various HAV designs under different operational design domains.
On the return probability of the simple random walk on Galton-Watson trees

We consider the simple random walk on Galton-Watson trees with supercritical offspring distribution, conditioned on non-extinction. In case the offspring distribution has finite support, we prove an upper bound for the annealed return probability to the root which decays subexponentially in time with exponent 1/3. This exponent is optimal. Our result improves the previously known subexponential upper bound with exponent 1/5 by Piau [Ann. Probab. 26, 1016-1040 (1998)]. For offspring distributions with unbounded support but sufficiently fast decay, our method also yields improved subexponential upper bounds.