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Calibrating large volumes of Very Long Baseline Interferometry (VLBI) data traditionally requires significant human intervention at every stage. While the Common Astronomy Software Applications (CASA) package is the standard data reduction tool across major radio observatories, no existing CASA-based pipeline operates in a fully automated manner across the heterogeneous data formats produced by the Very Long Baseline Array (VLBA) over three decades of operations. The Search for Milli-Lenses (SMILE) project, requiring the calibration of ~5000 VLBA sources, makes such blind automation a practical necessity. We introduce the Automated VLBI pipeline in CASA (AVICA), which automates the calibration of archival VLBA data. AVICA extends the CASA-based rPICARD framework by automating preprocessing of FITS-IDI and Measurement Set data formats, calibrator and reference antenna selection via FFT-based fringe detection, and execution of the full calibration workflow. Progress tracking is handled by ALFRD (Automated Logical Framework for executing Dynamic scripts), which orchestrates pipeline execution and records results in real time. AVICA was validated on 1000 NRAO archival sources spanning

The argument sufficiency assessment task aims to determine if the premises of a given argument support its conclusion. To tackle this task, existing works often train a classifier on data annotated by humans. However, annotating data is laborious, and annotations are often inconsistent due to subjective criteria. Motivated by the definition of probability of sufficiency (PS) in the causal literature, we proposeCASA, a zero-shot causality-driven argument sufficiency assessment framework. PS measures how likely introducing the premise event would lead to the conclusion when both the premise and conclusion events are absent. To estimate this probability, we propose to use large language models (LLMs) to generate contexts that are inconsistent with the premise and conclusion and revise them by injecting the premise event. Experiments on two logical fallacy detection datasets demonstrate that CASA accurately identifies insufficient arguments. We further deploy CASA in a writing assistance application, and find that suggestions generated by CASA enhance the sufficiency of student-written arguments. Code and data are available at https://github.com/xxxiaol/CASA.

Multivariate long-term time series forecasting is critical for applications such as weather prediction, and traffic analysis. In addition, the implementation of Transformer variants has improved prediction accuracy. Following these variants, different input data process approaches also enhanced the field, such as tokenization techniques including point-wise, channel-wise, and patch-wise tokenization. However, previous studies still have limitations in time complexity, computational resources, and cross-dimensional interactions. To address these limitations, we introduce a novel CNN Autoencoder-based Score Attention mechanism (CASA), which can be introduced in diverse Transformers model-agnosticically by reducing memory and leading to improvement in model performance. Experiments on eight real-world datasets validate that CASA decreases computational resources by up to 77.7%, accelerates inference by 44.0%, and achieves state-of-the-art performance, ranking first in 87.5% of evaluated metrics.

The Casas-Alvero conjecture states that if $f(X)$ is a monic univariate polynomial of degree $d$ over a characteristic $0$ field $\mathbb{K}$ such that $\gcd(f, f_{i})$ is non-trivial for each $i=1, \dots, d-1$, then $f(X)=(X-α)^d$ for some $α\in \mathbb{K}$. In this paper, we prove the Casas-Alvero conjecture for polynomials of any degree $d\geq 3$ over any characteristic $0$ field, by using Koszul homology. Along the way we show existence of various "almost counterexamples" over $\mathbb{C}$, satisfying mildly weaker hypotheses, using Brouwer degree techniques.

New functionality to process Very Long Baseline Interferometry (VLBI) data has been implemented in the CASA package. This includes two new tasks to handle fringe fitting and VLBI-specific amplitude calibration steps. Existing tasks have been adjusted to handle VLBI visibility data and calibration meta-data properly. With these updates, it is now possible to process VLBI continuum and spectral line observations in CASA. This article describes the development and implementation, and presents an outline for the workflow when calibrating European VLBI Network or Very Long Baseline Array data in CASA. Though the CASA VLBI functionality has already been vetted extensively as part of the Event Horizon Telescope data processing, in this paper we compare results for the same dataset processed in CASA and AIPS. We find identical results for the two packages and conclude that CASA in some cases performs better, though it cannot match AIPS for single-core processing time. The new functionality in CASA allows for easy development of pipelines or Jupyter notebooks, and thus contributes to raising VLBI data processing to present day standards for accessibility, reproducibility, and reusability.

Resilience in social systems is crucial for mitigating the impacts of crises, such as climate change, which poses an existential threat to communities globally. As disasters become more frequent and severe, enhancing community resilience has become imperative. This study introduces a cutting-edge framework, quantitative network-based modeling called Complex Analysis for Socio-environmental Adaptation (CASA) to evaluate and strengthen social resilience. CASA transforms resilience models' linear and static structure into a complex network that integrates complexity and systems thinking, utilizing global scientific knowledge and complex network methodologies. The resulting resilience framework features rich interdependencies, and subsequent dimensionality reduction produces robust resilience indicators. This innovative application of network sciences is then demonstrated by quantitatively assessing what are known as "Sacrifice Zones," socio-environmentally sensitive areas. Results unveil the potential of this novel application of complex network methodologies as tools for systemic diagnostics, identifying vulnerabilities, and guiding policies and practices to enhance climate resilienc

The Casas-Alvero conjecture predicts that every univariate polynomial over an algebraically closed field of characteristic zero sharing a common factor with each of its Hasse-Schmidt derivatives is a power of a linear polynomial. The conjecture for polynomials of a fixed degree is equivalent to the projective variety of such polynomials being one-dimensional. In this paper, we show that for any algebraically closed field of arbitrary characteristic, this variety is at most two-dimensional for all positive degrees. Consequently, we show that the associated arithmetic Casas-Alvero scheme in any positive degree has finitely many rational points over any field. Along the way, we prove several rigidity results towards the conjecture. We also introduce intermediate arithmetic Casas-Alvero schemes and show that their $\mathbb{K}$ points form an almost complete intersection over any algebraically closed field $\mathbb{K}$. Furthermore, we consider the question of when they form a complete intersection.

Very Long Baseline Interferometry (VLBI) provides the highest-resolution radio intensity maps, crucial for detailed studies of compact sources like active galactic nuclei (AGN) and their relativistic jets. Analyzing jet components in these maps traditionally involves manual Gaussian fitting, a time-consuming bottleneck for large datasets. To address this, we present an automated batch-processing tool, based on the Gaussian fitting capabilities of CASA, designed to streamline VLBI jet component characterization (AAP-Imfit). Our algorithm sets a detection limit, performs automatic 2D Gaussian fitting, and removes model artifacts, efficiently extracting component flux densities and positions. This method enables systematic and reproducible analysis, significantly reducing the time required for fitting extensive VLBI datasets. We validated AAP-Imfit by using VLBI observations of the blazars 3C 279 and 3C 454.3, comparing our results with published fits. The close agreement in residual root mean square (RMS) values and model/residual-to-map RMS ratios confirms the accuracy of our automated approach in reproducing original flux distributions. While visual inspection remains important for

CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is frequently used also for other radio telescopes. The CASA software can handle data from single-dish, aperture-synthesis, and Very Long Baseline Interferometery (VLBI) telescopes. One of its core functionalities is to support the calibration and imaging pipelines for ALMA, VLA, VLA Sky Survey (VLASS), and the Nobeyama 45m telescope. This paper presents a high-level overview of the basic structure of the CASA software, as well as procedures for calibrating and imaging astronomical radio data in CASA. CASA is being developed by an international consortium of scientists and software engineers based at the National Radio Astronomical Observatory (NRAO), the European Southern Observatory (ESO), the National Astronomical Observatory of Japan (NAOJ), and the Joint Institute for VLBI European Research Infrastructure Consortium (JIV-ERIC), under the guidance of NRAO.

Recently, speaker-attributed automatic speech recognition (SA-ASR) has attracted a wide attention, which aims at answering the question ``who spoke what''. Different from modular systems, end-to-end (E2E) SA-ASR minimizes the speaker-dependent recognition errors directly and shows a promising applicability. In this paper, we propose a context-aware SA-ASR (CASA-ASR) model by enhancing the contextual modeling ability of E2E SA-ASR. Specifically, in CASA-ASR, a contextual text encoder is involved to aggregate the semantic information of the whole utterance, and a context-dependent scorer is employed to model the speaker discriminability by contrasting with speakers in the context. In addition, a two-pass decoding strategy is further proposed to fully leverage the contextual modeling ability resulting in a better recognition performance. Experimental results on AliMeeting corpus show that the proposed CASA-ASR model outperforms the original E2E SA-ASR system with a relative improvement of 11.76% in terms of speaker-dependent character error rate.

Serverless computing is an emerging cloud computing paradigm that can reduce costs for cloud providers and their customers. However, serverless cloud platforms have stringent performance requirements (due to the need to execute short duration functions in a timely manner) and a growing carbon footprint. Traditional carbon-reducing techniques such as shutting down idle containers can reduce performance by increasing cold-start latencies of containers required in the future. This can cause higher violation rates of service level objectives (SLOs). Conversely, traditional latency-reduction approaches of prewarming containers or keeping them alive when not in use can improve performance but increase the associated carbon footprint of the serverless cluster platform. To strike a balance between sustainability and performance, in this paper, we propose a novel carbon- and SLO-aware framework called CASA to schedule and autoscale containers in a serverless cloud computing cluster. Experimental results indicate that CASA reduces the operational carbon footprint of a FaaS cluster by up to 2.6x while also reducing the SLO violation rate by up to 1.4x compared to the state-of-the-art.

The large data volumes expected from the High Luminosity LHC (HL-LHC) present challenges to existing paradigms and facilities for end-user data analysis. Modern cyberinfrastructure tools provide a diverse set of services that can be composed into a system that provides physicists with powerful tools that give them straightforward access to large computing resources, with low barriers to entry. The Coffea-Casa analysis facility (AF) provides an environment for end users enabling the execution of increasingly complex analyses such as those demonstrated by the Analysis Grand Challenge (AGC) and capturing the features that physicists will need for the HL-LHC. We describe the development progress of the Coffea-Casa facility featuring its modularity while demonstrating the ability to port and customize the facility software stack to other locations. The facility also facilitates the support of batch systems while staying Kubernetes-native. We present the evolved architecture of the facility, such as the integration of advanced data delivery services (e.g. ServiceX) and making data caching services (e.g. XCache) available to end users of the facility. We also highlight the composability o

Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge. Prevailing animal reconstruction methods often adopt a control-point driven animation model and optimize bone transforms individually without considering skeletal topology, yielding unsatisfactory shape and articulation. In contrast, humans can easily infer the articulation structure of an unknown animal by associating it with a seen articulated character in their memory. Inspired by this fact, we present CASA, a novel Category-Agnostic Skeletal Animal reconstruction method consisting of two major components: a video-to-shape retrieval process and a neural inverse graphics framework. During inference, CASA first retrieves an articulated shape from a 3D character assets bank so that the input video scores highly with the rendered image, according to a pretrained language-vision model. CASA then integrates the retrieved character into an inverse graphics framework and jointly infers the shape deformation, skeleton structure, and skinning weights through optimization. Experiments validate the efficacy of CASA regarding shape reconstruction and articulation. We further demonstrate that the re

The Casas--Alvero conjecture predicts that every univariate polynomial $f$ over a field $K$ of characteristic zero having a common factor with each of its derivatives $H\_i(f)$ is a power of a linear polynomial. Let $f=x^d+a\_1x^{d-1}+\cdots+a\_1x \in K[a\_1,\ldots,a\_{d-1}][x]$ and let $R\_i = Res(f,H\_i(f))\in K[a\_1,\ldots,a\_{d-1}]$ be the resultant of $f$ and $H\_i(f)$, $i \in \{1,\ldots,d-1\}$. The Casas-Alvero Conjecture is equivalent to saying that $R\_1,\ldots,R\_{d-1}$ are ``independent'' in a certain sense, namely that the height $ht(R\_1,\ldots,R\_{d-1})=d-1$ in $K[a\_1,\ldots,a\_{d-1}]$. In this paper we prove a very partial result in this direction : if $i \in \{d-3,d-2,d-1\}$ then $R\_i otin \sqrt{(R\_1,\ldots,\breve{R\_i},\ldots,R\_{d-1}}$.

CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is often used also for other radio telescopes. CASA has always been distributed as a single, integrated application, including a Python interpreter and all the libraries, packages and modules. As part of the ongoing development of CASA 6, and the switch from Python 2 to 3, CASA will provide greater flexibility for users to integrate CASA into existing Python workflows by using a modular architecture and standard pip wheel installation. These proceedings of the 2019 Astronomical Data Analysis Software & Systems (ADASS) conference will give an overview of the CASA 6 project.

In recent years new functionality for VLBI data processing has been added to the CASA package. This paper presents the new CASA tasks 'fringefit' and 'accor', which are closely matched to their AIPS counterparts FRING and ACCOR. Several CASA tasks received upgrades to handle VLBI specific metadata. With the current CASA release VLBI data processing is possible, and functionality will be expanded in the upcoming release. Longer term developments include fringe fitting of broad, non-continuous frequency bands and dispersive delays, which will ensure that the number of use cases for VLBI calibration will increase in future CASA releases.

Based on Casas-Alvero conjecture \textit{[J. Algebra, 2001]} we formulate the following conjecture.\\ \textbf{C*-algebraic Casas-Alvero Conjecture : Let $\mathcal{A}$ be a commutative C*-algebra, $n\in \mathbb{N}$ and let $P(z) = (z-a_1)(z-a_2)\cdots (z-a_n)$ be a polynomial over $\mathcal{A}$ with $a_1, a_2, \dots, a_n \in \mathcal{A}$. If $P$ shares a common zero with each of its (first) $n-1$ derivatives, then it is $n^\text{th}$ power of a linear monic C*-algebraic polynomial.}\\ We show that C*-algebraic Casas-Alvero Conjecture holds for C*-algebraic polynomials of degree 2.

CASA, the Common Astronomy Software Applications package, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and NSF's Karl G. Jansky Very Large Array (VLA), and is frequently used also for other radio telescopes. The CASA software can process data from both single-dish and aperture-synthesis telescopes, and one of its core functionalities is to support the data reduction and imaging pipelines for ALMA, VLA and the VLA Sky Survey (VLASS). CASA has recently undergone several exciting new developments, including an increased flexibility in Python (CASA 6), support of Very Long Baseline Interferometry (VLBI), performance gains through parallel imaging, data visualization with the new Cube Analysis Rendering Tool for Astronomy (CARTA), enhanced reliability and testing, and modernized documentation. These proceedings of the 2019 Astronomical Data Analysis Software & Systems (ADASS) conference give an update of the CASA project, and detail how these new developments will enhance user experience of CASA.