Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous-time convolutions model of event sequences

Massive samples of event sequences data occur in various domains, including e-commerce, healthcare, and finance. There are two main challenges regarding inference of such data: computational and methodological. The amount of available data and the length of event sequences per client are typically large, thus it requires long-term modelling. Moreover, this data is often sparse and non-uniform, making classic approaches for time series processing inapplicable. Existing solutions include recurrent and transformer architectures in such cases. To allow continuous time, the authors introduce specific parametric intensity functions defined at each moment on top of existing models. Due to the parametric nature, these intensities represent only a limited class of event sequences. We propose the COTIC method based on a continuous convolution neural network suitable for non-uniform occurrence of events in time. In COTIC, dilations and multi-layer architecture efficiently handle dependencies between events. Furthermore, the model provides general intensity dynamics in continuous time - including self-excitement encountered in practice. The COTIC model outperforms existing approaches on majority of the considered datasets, producing embeddings for an event sequence that can be used to solve downstream tasks - e.g. predicting next event type and return time. The code of the proposed method can be found in the GitHub repository (https://github.com/VladislavZh/COTIC).
Trajectory‐based robustness analysis for nonlinear systems

This article considers the robustness of an uncertain nonlinear system along a finite‐horizon trajectory. The uncertain system is modeled as a connection of a nonlinear system and a perturbation. The analysis relies on three ingredients. First, the nonlinear system is approximated by a linear time‐varying (LTV) system via linearization along a trajectory. This linearization introduces an additional forcing input due to the nominal trajectory. Second, the input/output behavior of the perturbation is described by time‐domain, integral quadratic constraints (IQCs). Third, a dissipation inequality is formulated to bound the worst‐case deviation of an output signal due to the uncertainty. These steps yield a differential linear matrix inequality (DLMI) condition to bound the worst‐case performance. The robustness condition is then converted to an equivalent condition in terms of a Riccati differential equation. This yields a computational method that avoids heuristics often used to solve DLMIs, for example, time gridding. The approach is demonstrated by a two‐link robotic arm example.
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications

Human-robot interaction (HRI) research is progressively addressing multi-party scenarios, where a robot interacts with more than one human user at the same time. Conversely, research is still at an early stage for human-robot collaboration The use of machine learning techniques to handle such type of collaboration requires data that are less feasible to produce than in a typical HRC setup. This work outlines scenarios of concurrent tasks for non-dyadic HRC applications. Based upon these concepts, this study also proposes an alternative way of gathering data regarding multi-user activity, by collecting data related to single users and merging them in post-processing, to reduce the effort involved in producing recordings of pair settings. To validate this statement, 3D skeleton poses of activity of single users were collected and merged in pairs. After this, such datapoints were used to separately train a long short-term memory (LSTM) network and a variational autoencoder (VAE) composed of spatio-temporal graph convolutional networks (STGCN) to recognise the joint activities of the pairs of people. The results showed that it is possible to make use of data collected in this way for pair HRC settings and get similar performances compared to using training data regarding groups of users recorded under the same settings, relieving from the technical difficulties involved in producing these data. The related code and collected data are publicly available11The code repository is available at: https://github.com/francescosemeraro/Multi_User_through_Single_User. git. The data repository is available at: https://figshare.com/s/64bd023e968e6eb096f3.
Towards Understanding Provenance in Industry

Context: Trustworthiness of software has become a first-class concern of users (e.g., to understand software-made decisions). Also, there is increasing demand to demonstrate regulatory compliance of software and end users want to understand how software-intensive systems make decisions that affect them. Objective: We aim to provide a step towards understanding provenance needs of the software industry to support trustworthy software. Provenance is information about entities, activities, and people involved in producing data, software, or output of software, and used to assess software quality, reliability and trustworthiness of digital products and services. Method: Based on data from in-person and questionnaire-based interviews with professionals in leadership roles we develop an ``influence map'' to analyze who drives provenance, when provenance is relevant, what is impacted by provenance and how provenance can be managed. Results: The influence map helps decision makers navigate concerns related to provenance. It can also act as a checklist for initial provenance analyses of systems. It is empirically-grounded and designed bottom-up (based on perceptions of practitioners) rather than top-down (from regulations or policies). Conclusion: We present an imperfect first step towards understanding provenance based on current perceptions and offer a preliminary view ahead.
Droplet nanofluidic transport under vapor deposition: a review on seeded growth of low-dimensional nanomaterials

Thin film deposition technologies boost the development of modern semiconductor industries. Being a fancy variant, vapor phase deposition on metal nanoparticles (often in liquid phase) rather than on bare substrates opens novel avenues of fabricating low-dimensional nanomaterials, which renders the development of new device architectures and their applications in advanced electronics, optoelectronics and photonics, etc. Since the last twenty years, nanomaterials with various geometries (i.e. dots, wires, trees, tubes, flakes, ribbons, etc.) have been synthesized via different bottom-up methods (i.e. vapor-liquid-solid, vapor-solid-solid, in plane solid-liquid-solid, etc.) by different deposition techniques (CVD, PECVD, MOCVD, MBE, etc.). In contrast with liquid phase epitaxy where metal liquid severs as stationary reservoir that accommodates gaseous precursors, metal droplets have to be kicked off in-plane on/out-of-plane from the substrates so as to steer the growth of low-dimensional nanomaterials. In this review, we shall regard the growth process in a viewpoint of dynamic droplet evolution under vapor phase deposition. We shall summarize several key factors that affect the droplet spreading behaviors and their consequent nanofluidic transport, which involves deposition parameters, solid-liquid interfaces, crystal phases, substrate nanofacets and so on, which deterministically results in various morphologies and growth directions of the nanomaterials. Reversely, the aspects like doping profile and phase transition that are strongly dependent on the droplet transport will also be discussed.
Structure-Preserving Model Reduction for Port-Hamiltonian Systems Based on a Special Class of Nonlinear Approximation Ansatzes

We discuss structure-preserving model order reduction for port-Hamiltonian systems based on an approximation of the full-order state by a linear combination of ansatz functions which depend themselves on the state of the reduced-order model. In recent years, such nonlinear approximation ansatzes have gained more and more attention especially due to their effectiveness in the context of model reduction for transport-dominated systems which are challenging for classical linear model reduction techniques. We demonstrate that port-Hamiltonian reduced-order models can often be obtained by a residual minimization approach where a special weighted norm is used for the residual. Moreover, we discuss sufficient conditions for the resulting reduced-order models to be stable. Finally, the methodology is illustrated by means of two transport-dominated numerical test cases, where the ansatz functions are determined based on snapshot data of the full-order state.
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making

A growing literature on human-AI decision-making investigates strategies for combining human judgment with statistical models to improve decision-making. Research in this area often evaluates proposed improvements to models, interfaces, or workflows by demonstrating improved predictive performance on “ground truth’’ labels. However, this practice overlooks a key difference between human judgments and model predictions. Whereas humans commonly reason about broader phenomena of interest in a decision – including latent constructs that are not directly observable, such as disease status, the “toxicity” of online comments, or future “job performance” – predictive models target proxy labels that are readily available in existing datasets. Predictive models’ reliance on simplistic proxies for these nuanced phenomena makes them vulnerable to various sources of statistical bias. In this paper, we identify five sources of target variable bias that can impact the validity of proxy labels in human-AI decision-making tasks. We develop a causal framework to disentangle the relationship between each bias and clarify which are of concern in specific human-AI decision-making tasks. We demonstrate how our framework can be used to articulate implicit assumptions made in prior modeling work, and we recommend evaluation strategies for verifying whether these assumptions hold in practice. We then leverage our framework to re-examine the designs of prior human subjects experiments that investigate human-AI decision-making, finding that only a small fraction of studies examine factors related to target variable bias. We conclude by discussing opportunities to better address target variable bias in future research.
Gaugino Masses from Misaligned Supersymmetry Breaking and R-Symmetry Breaking Spurions

In gauge mediation models with multiple spurion fields breaking SUSY and the R-symmetry separately, we show that it is possible to generate gaugino masses in one loop if the R-charge arrangement satisfies a certain condition. The resulting gaugino masses are calculated and suppressed by a power of the messenger’s mass scale. We present two simple examples to demonstrate this possibility and discuss possible phenomenology implications.
A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution

Marketers employ various online advertising channels to reach customers, and they are particularly interested in attribution for measuring the degree to which individual touchpoints contribute to an eventual conversion. The availability of individual customer-level path-to-purchase data and the increasing number of online marketing channels and types of touchpoints bring new challenges to this fundamental problem. We aim to tackle the attribution problem with finer granularity by conducting attribution at the path level. To this end, we develop a novel graphical point process framework to study the direct conversion effects and the full relational structure among numerous types of touchpoints simultaneously. Utilizing the temporal point process of conversion and the graphical structure, we further propose graphical attribution methods to allocate proper path-level conversion credit, called the attribution score, to individual touchpoints or corresponding channels for each customer's path to purchase. Our proposed attribution methods consider the attribution score as the removal effect, and we use the rigorous probabilistic definition to derive two types of removal effects. We examine the performance of our proposed methods in extensive simulation studies and compare their performance with commonly used attribution models. We also demonstrate the performance of the proposed methods in a real-world attribution application.
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation

In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks. To cope with these issues, existing works emphasize on leveraging Multi-Task Learning (MTL) frameworks (Category 1) or causal debiasing frameworks (Category 2) to incorporate more auxiliary data in the entire exposure/inference space $\mathcal{D}$ or debias the selection bias in the click/training space ${\mathcal{O}}$. However, these two kinds of solutions cannot effectively address the not-missing-at-random problem and debias the selection bias in ${\mathcal{O}}$ to fit the inference in $\mathcal{D}$. To fill the research gaps, we propose a Direct entire-space Causal Multi-Task framework, namely DCMT, for post-click conversion prediction in this paper. Specifically, inspired by users’ decision process of conversion, we propose a new counterfactual mechanism to debias the selection bias in $\mathcal{D}$, which can predict the factual CVR and the counterfactual CVR under the soft constraint of a counterfactual prior knowledge. Extensive experiments demonstrate that our DCMT can improve the state-of-the-art methods by an average of 1.07% in term of CVR AUC on the offline datasets and 0.75% in term of PV-CVR on the online A/B test (the Alipay Search). Such improvements can increase millions of conversions per week in real industrial applications, e.g., the Alipay Search.
A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

The transistor celebrated its 75th birthday in 2022. The continued scaling of the transistor defined by Moore’s law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning (ML) and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.
GAIN: Enhancing Byzantine Robustness in Federated Learning with Gradient Decomposition

Federated learning provides a privacy-aware learning framework by enabling participants to jointly train models without exposing their private data. However, federated learning has exhibited vulnerabilities to Byzantine attacks, where the adversary aims to destroy the convergence and performance of the global model. Meanwhile, we observe that most existing robust AGgregation Rules (AGRs) fail to stop the aggregated gradient deviating from the optimal gradient (the average of honest gradients) in the non-IID setting. We attribute the reason of the failure of these AGRs to two newly proposed concepts: identiﬁcation failure and integrity failure. The identiﬁcation failure mainly comes from the exacerbated curse of dimensionality in the non-IID setting. The integrity failure is a combined result of conservative ﬁltering strategy and gradient heterogeneity. In order to address both failures, we propose GAIN, a gradient decomposition scheme that can help adapt existing robust algorithms to heterogeneous datasets. We also provide convergence analysis for integrating existing robust AGRs into GAIN. Experiments on various real-world datasets verify the efﬁcacy of our proposed GAIN.
Taming Metadata-intensive HPC Jobs Through Dynamic, Application-agnostic QoS Control

Modern I/O applications that run on HPC infrastructures are increasingly becoming read and metadata intensive. However, having multiple applications submitting large amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to overall performance degradation and I/O unfairness. We present PADLL, an application and file system agnostic storage middleware that enables QoS control of data and metadata workflows in HPC storage systems. It adopts ideas from Software-Defined Storage, building data plane stages that mediate and rate limit POSIX requests submitted to the shared file system, and a control plane that holistically coordinates how all I/O workflows are handled. We demonstrate its performance and feasibility under multiple QoS policies using synthetic benchmarks, real-world applications, and traces collected from a production file system. Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization.
Requirements Elicitation and Modelling of Artificial Intelligence Systems: An Empirical Study

Artificial Intelligence (AI) systems have gained significant traction in the recent past, creating new challenges in requirements engineering (RE) when building AI software systems. RE for AI practices have not been studied much and have scarce empirical studies. Additionally, many AI software solutions tend to focus on the technical aspects and ignore human-centered values. In this paper, we report on a case study for eliciting and modeling requirements using our framework and a supporting tool for human-centred RE for AI systems. Our case study is a mobile health application for encouraging type-2 diabetic people to reduce their sedentary behavior. We conducted our study with three experts from the app team -- a software engineer, a project manager and a data scientist. We found in our study that most human-centered aspects were not originally considered when developing the first version of the application. We also report on other insights and challenges faced in RE for the health application, e.g., frequently changing requirements.
DocILE Benchmark for Document Information Localization and Extraction

This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically generated documents, and nearly~1M unlabeled documents for unsupervised pre-training. The dataset has been built with knowledge of domain- and task-specific aspects, resulting in the following key features: (i) annotations in 55 classes, which surpasses the granularity of previously published key information extraction datasets by a large margin; (ii) Line Item Recognition represents a highly practical information extraction task, where key information has to be assigned to items in a table; (iii) documents come from numerous layouts and the test set includes zero- and few-shot cases as well as layouts commonly seen in the training set. The benchmark comes with several baselines, including RoBERTa, LayoutLMv3 and DETR-based Table Transformer; applied to both tasks of the DocILE benchmark, with results shared in this paper, offering a quick starting point for future work. The dataset, baselines and supplementary material are available at https://github.com/rossumai/docile.
Implications of the Convergence of Language and Vision Model Geometries

Large-scale pretrained language models (LMs) are said to ``lack the ability to connect [their] utterances to the world'' (Bender and Koller, 2020). If so, we would expect LM representations to be unrelated to representations in computer vision models. To investigate this, we present an empirical evaluation across three different LMs (BERT, GPT2, and OPT) and three computer vision models (VMs, including ResNet, SegFormer, and MAE). Our experiments show that LMs converge towards representations that are partially isomorphic to those of VMs, with dispersion, and polysemy both factoring into the alignability of vision and language spaces. We discuss the implications of this finding.
Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. I: Brightness-temperature properties of a time-dependent cloud-radiation model

Abstract A cloud-radiation model is used to investigate the relationship between emerging microwave brightness temperatures (TB's) and vertically distributed mixtures of liquid and frozen hydrometeors as a means to establish the framework for a hybrid statistical-physical rainfall retrieval algorithm. The focus in this study is on the microwave characteristics of an intense hailstorm in which cold-rain microphysics dominate the precipitation process. The TB calculations exhibit a high degree of intercorrelation across a wide frequency range (15–128 GHz) due to the pervasive influence of large ice particles on attenuation of upwelling radiation emerging from the rain layers. When the radiative emission source is near blackbody, fluctuations of the mixing ratios of ice particles are wholly responsible for the TB variations, as opposed to fluctuations in the cloud-or raindrop mixing ratios. Supercooled cloud drops, suspended in the graupel layers, can exert influence on the TB's but only at the higher freque.
Dialectograms: Machine Learning Differences between Discursive Communities

Word embeddings provide an unsupervised way to understand differences in word usage between discursive communities. A number of recent papers have focused on identifying words that are used differently by two or more communities. But word embeddings are complex, high-dimensional spaces and a focus on identifying differences only captures a fraction of their richness. Here, we take a step towards leveraging the richness of the full embedding space, by using word embeddings to map out how words are used differently. Specifically, we describe the construction of dialectograms, an unsupervised way to visually explore the characteristic ways in which each community use a focal word. Based on these dialectograms, we provide a new measure of the degree to which words are used differently that overcomes the tendency for existing measures to pick out low frequent or polysemous words. We apply our methods to explore the discourses of two US political subreddits and show how our methods identify stark affective polarisation of politicians and political entities, differences in the assessment of proper political action as well as disagreement about whether certain issues require political intervention at all.