While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. This paper addresses this need by testing the applicability of AcME-AD in industrial settings. This recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. Thus, it seems suitable for seamless integration with industrial Decision Support Systems. We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. These tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments, paving the way for trustworthy and actionable insights in the age of Industry 5.0.
Pre-trained Transformer-based large models have revolutionized personal virtual assistants, but their deployment in cloud environments faces challenges related to data privacy and response latency. Deploying large models closer to the data and users has become a key research area to address these issues. However, applying these models directly often entails significant difficulties, such as model mismatching, resource constraints, and energy inefficiency. Automated design of customized models is necessary, but it faces three key challenges, namely, the high cost of centralized model customization, imbalanced performance from user heterogeneity, and suboptimal performance from data heterogeneity. In this paper, we propose ACME, an adaptive customization approach of Transformer-based large models via distributed systems. To avoid the low cost-efficiency of centralized methods, ACME employs a bidirectional single-loop distributed system to progressively achieve fine-grained collaborative model customization. In order to better match user heterogeneity, it begins by customizing the backbone generation and identifying the Pareto Front under model size constraints to ensure optimal resou
Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications. Traditional Anomaly Detection methods excel in outlier identification but are often black-boxes, providing scant insights into their decision-making process. This lack of transparency compromises their reliability and hampers their adoption in scenarios where comprehending the reasons behind anomaly detection is vital. At the same time, getting explanations quickly is paramount in practical scenarios. To bridge this gap, we present AcME-AD, a novel approach rooted in Explainable Artificial Intelligence principles, designed to clarify Anomaly Detection models for tabular data. AcME-AD transcends the constraints of model-specific or resource-heavy explainability techniques by delivering a model-agnostic, efficient solution for interoperability. It offers local feature importance scores and a what-if analysis tool, shedding light on the factors contributing to each anomaly, thus aiding root cause analysis and decision-making. This paper elucidates AcME-AD's foundation, its benefits over existing methods, and validates its effectiveness with tests on
The current best upper limit for electron electric dipole moment (EDM), $|d_e|<1.1\times10^{-29}\,$e$\cdot$cm ($90$% confidence), was set by the ACME collaboration in 2018. The ACME experiment uses a spin-precession measurement in a cold beam of ThO molecules to detect $d_e$. An improvement in statistical uncertainty would be possible with more efficient use of molecules from the cryogenic buffer gas beam source. Here, we demonstrate electrostatic focusing of the ThO beam with a hexapole lens. This results in a factor of $16$ enhancement in the molecular flux detectable downstream, in a beamline similar to that built for the next generation of ACME. We also demonstrate an upgraded rotational cooling scheme that increases the ground state population by $3.5$ times compared to no cooling, consistent with expectations and a factor of $1.4$ larger than previously in ACME. When combined with other demonstrated improvements, we project over an order of magnitude improvement in statistical sensitivity for the next generation ACME electron EDM search.
This report shows the design and the performance of a large area Silicon Photomultiplier (SiPM) module developed detection of fluorescent light emitted from a 10 cm scale volume. The module was optimized for the planned ACME III electron electric dipole moment (eEDM) search, which will be a powerful probe for the existence of physics beyond the Standard Model of particle physics. The ACME experiment searched for the eEDM with the world's highest sensitivity using cold ThO polar molecules (ACME II). In ACME III, SiPMs will be used for detection of fluorescent photons (the fundamental signal of the experiment) instead of PMTs, which were used in the previous measurement. We have developed an optimized SiPM module, based on a 16-channel SiPM array. Key operational parameters are characterized, including gain and noise. The SiPM dark count rate, background light sensitivity, and optical crosstalk are found to all be well suppressed and more than sufficient for the ACME III application.
Experimental searches for the electron electric dipole moment, $d_e$, probe new physics beyond the Standard Model. Recently, the ACME Collaboration set a new limit of $|d_e| <1.1\times 10^{-29}$ $e\cdot \textrm{cm}$ [Nature $\textbf{562}$, 355 (2018)], constraining time reversal symmetry (T) violating physics in the 3-100 TeV energy scale. ACME extracts $d_e$ from the measurement of electron spin precession due to the thorium monoxide (ThO) molecule's internal electric field. This recent ACME II measurement achieved an order of magnitude increased sensitivity over ACME I by reducing both statistical and systematic uncertainties in the measurement of the electric dipole precession frequency. The ACME II statistical uncertainty was a factor of 1.7 above the ideal shot-noise limit. We have since traced this excess noise to timing imperfections. When the experimental imperfections are eliminated, we show that shot noise limit is attained by acquiring noise-free data in the same configuration as ACME II.
In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the users wait. In this paper, we propose Accelerated Model-agnostic Explanations (AcME), an interpretability approach that quickly provides feature importance scores both at the global and the local level. AcME can be applied a posteriori to each regression or classification model. Not only does AcME compute feature ranking, but it also provides a what-if analysis tool to assess how changes in features values would affect model predictions. We evaluated the proposed approach on synthetic and real-world datasets, also in comparison with SHapley Additive exPlanations (SHAP), the approach we drew inspiration from, which is currently one of the state-of-the-art model-agnostic interpretability approaches. We achieved comparable results in terms of quality of produced explanations while reducing dramatically the computational time and providing consistent visualization for global and local interpretations. To foster research in this field, and for the sake of reproducibility, we also provide a repository with the
The best upper limit for the electron electric dipole moment was recently set by the ACME collaboration. This experiment measures an electron spin-precession in a cold beam of ThO molecules in their metastable $H~(^3Δ_1)$ state. Improvement in the statistical and systematic uncertainties is possible with more efficient use of molecules from the source and better magnetometry in the experiment, respectively. Here, we report measurements of several relevant properties of the long-lived $Q~(^3Δ_2)$ state of ThO, and show that this state is a very useful resource for both these purposes. The $Q$ state lifetime is long enough that its decay during the time of flight in the ACME beam experiment is negligible. The large electric dipole moment measured for the $Q$ state, giving rise to a large linear Stark shift, is ideal for an electrostatic lens that increases the fraction of molecules detected downstream. The measured magnetic moment of the $Q$ state is also large enough to be used as a sensitive co-magnetometer in ACME. Finally, we show that the $Q$ state has a large transition dipole moment to the $C~(^1Π_1)$ state, which allows for efficient population transfer between the ground sta
Deep reinforcement learning (RL) has led to many recent and groundbreaking advances. However, these advances have often come at the cost of both increased scale in the underlying architectures being trained as well as increased complexity of the RL algorithms used to train them. These increases have in turn made it more difficult for researchers to rapidly prototype new ideas or reproduce published RL algorithms. To address these concerns this work describes Acme, a framework for constructing novel RL algorithms that is specifically designed to enable agents that are built using simple, modular components that can be used at various scales of execution. While the primary goal of Acme is to provide a framework for algorithm development, a secondary goal is to provide simple reference implementations of important or state-of-the-art algorithms. These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research. In this work we describe the major design decisions made within Acme and give further details as to how its components can be used to implement various algorithms. Our experiments provide baselines fo
Analyzing and controlling large distributed services under a wide range of conditions is difficult. Yet these capabilities are essential to a number of important development and operational tasks such as benchmarking, testing, and system management. To facilitate these tasks, we have built the Application Control and Monitoring Environment (ACME), a scalable, flexible infrastructure for monitoring, analyzing, and controlling Internet-scale systems. ACME consists of two parts. ISING, the Internet Sensor In-Network agGregator, queries sensors and aggregates the results as they are routed through an overlay network. ENTRIE, the ENgine for TRiggering Internet Events, uses the data streams supplied by ISING, in combination with a user's XML configuration file, to trigger actuators such as killing processes during a robustness benchmark or paging a system administrator when predefined anomalous conditions are observed. In this paper we describe the design, implementation, and evaluation of ACME and its constituent parts. We find that for a 512-node system running atop an emulated Internet topology, ISING's use of in-network aggregation can reduce end-to-end query-response latency by more
On the one hand, ACME is a language designed in the late 90s as an interchange format for software architectures. The need for recon guration at runtime has led to extend the language with speci c support in Plastik. On the other hand, PDDL is a predicative language for the description of planning problems. It has been designed in the AI community for the International Planning Competition of the ICAPS conferences. Several related works have already proposed to encode software architectures into PDDL. Existing planning algorithms can then be used in order to generate automatically a plan that updates an architecture to another one, i.e., the program of a recon guration. In this paper, we improve the encoding in PDDL. Noticeably we propose how to encode ADL types and constraints in the PDDL representation. That way, we can statically check our design and express PDDL constraints in order to ensure that the generated plan never goes through any bad or inconsistent architecture, not even temporarily.
The ACME Spectra project provides absolutely calibrated, mostly empirical spectra of exoplanet host stars for use in analysis of the stars and their planets. Spectra are obtained from ground-based telescopes and are tied directly to calibrated ground- and space-based photometry. The spectra remain only "mostly" empirical because of telluric absorption, but interpolation of stellar models over the gaps in wavelength coverage provides continuous stellar spectra. Among other uses, the spectra are suitable for precisely converting observed secondary eclipses (occultations) into absolute flux units with minimal recourse to models. In this letter I introduce ACME's methods and present a calibrated spectrum of the nearby, super-Earth hosting star 55 Cancri that spans the range from 0.81-5.05 micron. This spectrum is well-suited for interpreting near- and thermal-infrared eclipse observations. With this spectrum I show that the brightness temperature of the small, low-mass transiting planet 55 Cnc e is 1950 +260/-190 K at 4.5 micron (cooler than previously reported), which corresponds to a planetary flux of 0.44 +0.12/-0.08 mJy. This result suggests the planet has some combination of a non
Estimating the mortality of birds and bats at wind turbines based on periodic carcass counts is challenging because carcasses may be removed by scavengers or may be missed in investigators' searches, leading to undercounting. Existing mortality estimators intended to correct for this offer wildly different estimates when search intervals are short. We introduce a new estimator that includes many existing ones as special cases but extends and improves them in three ways to reflect phenomena discovered in the field: * Decreasing removal rate by scavengers as carcasses age; * Diminishing proficiency of Field Technicians in discovering carcasses as they age; * Possibility that some (but not all) carcasses arriving in earlier search periods may be discovered in the current period. It is this feature that makes the new estimator "partially periodic". Both point estimates and 50% and 90% Objective Bayes interval estimates are provided for mortality. The new ACME mortality estimator will significantly improve the accuracy and credibility of mortality estimates for birds and bats near wind turbines.
Let \((A, L)\) be a polarized abelian variety of dimension \(g \geq 1\) over an algebraically closed field of characteristic zero. We prove that every nontrivial line bundle \(P\) in the connected component \(\operatorname{Pic}^0(A)\) of the Picard variety is arithmetically Cohen--Macaulay (ACM) with respect to \(L\). For \(g \geq 2\) and any fixed nontrivial \(P \in \operatorname{Pic}^0(A)\), we construct by induction an infinite sequence of indecomposable ACM vector bundles \(E_r\) of every rank \(r \geq 1\). In addition, this paper studies classification questions for ACM line bundles and shows that, for abelian varieties of dimension at least two, the category of ACM bundles is of wild representation type. This paper settles the existence problem for nontrivial ACM bundles on polarized abelian varieties and supply large explicit families of indecomposable examples
We classify ACM curves contained in a surface of degree d in $\mathbb{P}^{3}$ in terms of weak admissible pairs. In the case of a very general smooth determinantal quartic surface, we provide a geometric description of these curves and compute their Picard classes on the surface. Finally, we present a generalization to ACM closed subvarieties of codimension $1$ on a hypersurface in $\mathbb{P}^{n}$.
ACM and IEEE are the two premier associations on computing and electrical/electronics engineering which publish and organize the great majority of periodicals and conferences, respectively, serving these disciplines. Science is a constantly evolving process, and these publication fora are expected to follow the trends. In this article, we focus on the periodicals published by the two associations and seek to detect and/or confirm any contemporary science trends as these are reflected to the periodical titles established recently. Our study is rather qualitative than quantitative, aiming at revealing patterns immediately comprehensible and validatable by the reader. Among the most notable patterns, we see a growing preference of both associations for the open access mode of publication; we also observe ACM's orientation toward AI-focused periodicals, and most importantly, a significant theme overlap among periodicals of the same association and this is valid for both ACM and IEEE.
Despite the rise in affordable eXtended Reality (XR) technologies, accessibility still remains a key concern, often excluding people with disabilities from accessing these immersive XR platforms. Consequently, there has been a notable surge in HCI research on creating accessible XR solutions (also known as, assistive XR). This increased focus in assistive XR research is also reflected in the number of research and innovative solutions submitted at the ACM Conference on Accessible Computing (ASSETS), with an aim to make XR experiences inclusive for disabled communities. However, till date, there is little to no work that provides a comprehensive overview of state-of-the-art research in assistive XR for disability at ACM ASSETS, a premier conference dedicated for research in HCI for people with disabilities. This study aims to fill this research gap by conducting a scoping review of literature delineating the key focus areas, research methods, statistical and temporal trends in XR research for disability at ACM ASSETS (2019-2023). From a pool of 1595 articles submitted to ASSETS, 26 articles are identified that specifically focus on XR research for disability. Through a detailed anal
Let $\mathbb{X}$ be a Geigle-Lenzing projective plane of type $(2,2,2,p)$ and $\mathsf{coh} \mathbb{X}$ the category of coherent sheaves on $\mathbb{X}$. This paper is devoted to study ACM tilting bundles over $\mathbb{X}$, that is, tilting objects in the derived category $\mathsf{D}^{\rm b}(\mathsf{coh} \, \mathbb{X})$ that are also ACM bundles. We show that a tilting bundle consisting of line bundles is the $2$-canonical tilting bundle up to degree shift. We also provide a program to construct ACM tilting bundles, which give a rich source of (almost) $2$-representation infinite algebras. As an application, we give a classification result of ACM tilting bundles.
The U-shaped encoder-decoder architecture with skip connections has become a prevailing paradigm in medical image segmentation due to its simplicity and effectiveness. While many recent works aim to improve this framework by designing more powerful encoders and decoders, employing advanced convolutional neural networks (CNNs) for local feature extraction, Transformers or state space models (SSMs) such as Mamba for global context modeling, or hybrid combinations of both, these methods often struggle to fully utilize pretrained vision backbones (e.g., ResNet, ViT, VMamba) due to structural mismatches. To bridge this gap, we introduce ACM-UNet, a general-purpose segmentation framework that retains a simple UNet-like design while effectively incorporating pretrained CNNs and Mamba models through a lightweight adapter mechanism. This adapter resolves architectural incompatibilities and enables the model to harness the complementary strengths of CNNs and SSMs-namely, fine-grained local detail extraction and long-range dependency modeling. Additionally, we propose a hierarchical multi-scale wavelet transform module in the decoder to enhance feature fusion and reconstruction fidelity. Exte