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This paper presents a novel data-driven hierarchical approach to open set recognition (OSR) for robust perception in robotics and computer vision, utilizing constrained agglomerative clustering to automatically build a hierarchy of known classes in embedding space without requiring manual relational information. The method, demonstrated on the Animals with Attributes 2 (AwA2) dataset, achieves competitive results with an AUC ROC score of 0.82 and utility score of 0.85, while introducing two classification approaches (score-based and traversal-based) and a new Concentration Centrality (CC) metric for measuring hierarchical classification consistency. Although not surpassing existing models in accuracy, the approach provides valuable additional information about unknown classes through automatically generated hierarchies, requires no supplementary information beyond typical supervised model requirements, and introduces the Class Concentration Centrality (CCC) metric for evaluating unknown class placement consistency, with future work aimed at improving accuracy, validating the CC metric, and expanding to Large-Scale Open-Set Classification Protocols for ImageNet., Comment: Accepted as Extended Abstract to the IEEE ICRA@40 2024

We used attitude data from the Mars Ingenuity helicopter with a simple steady-state model to estimate windspeeds and directions at altitudes of 3 meters up to 24 meters, the first time winds at such altitudes have been probed on Mars. We compared our estimates to concurrent wind data at 1.5 m height from the meteorology package MEDA onboard the Mars 2020 Perseverance rover and to predictions from meteorological models. Wind directions inferred from the Ingenuity data agreed to within uncertainties with the directions measured by MEDA, when the latter were available, but deviated from model-predicted directions by as much as 180 deg in some cases. Also, the inferred windspeeds are often much higher than expected. For example, meteorological predictions tailored to the time and location of Ingenuity's 59th flight suggest Ingenuity should not have seen windspeeds above about 15 m/s, but we inferred speeds reaching nearly 25 m/s. By contrast, the 61st flight was at a similar time and season and showed weaker winds then the 59th flight, suggesting winds shaped by transient phenomena. For flights during which we have MEDA data to compare to, inferred windspeeds imply friction velocities exceeding 1 m/s and roughness lengths of more than 10 cm based on a boundary layer model that incorporates convective instability, which seem implausibly large. These results suggest Ingenuity was probing winds sensitive to aerodynamic conditions hundreds of meters upwind instead of the conditions very near Mars 2020, but they may also reflect a need for updated boundary layer wind models. An improved model for Ingenuity's aerodynamic response that includes the effects of transient winds may also modify our results. In any case, the work here provides a foundation for exploration of planetary boundary layers using drones and suggests important future avenues for research and development., Comment: Accepted by PSJ

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

This article is concerned with locally flatly immersed surfaces in simply-connected $4$-manifolds where the complement of the surface has fundamental group $\mathbb{Z}$. Once the genus and number of double points are fixed, we classify such immersed surfaces in terms of the equivariant intersection form of their exterior and a secondary invariant. Applications include criteria for deciding when an immersed $\mathbb{Z}$-surface in $S^4$ is isotopic to the standard immersed surface that is obtained from an unknotted surface by adding local double points. As another application, we enumerate $\mathbb{Z}$-disks in $D^4$ with a single double point and boundary a given knot; we prove that the number of such disks may be infinite. We also prove that a knot bounds a $\mathbb{Z}$-disk in $D^4$ with $c_+$ positive double points and $c_-$ negative double points if and only if it can be converted into an Alexander polynomial one knot via changing $c_+$ positive crossings and $c_-$ negative crossings. In $4$-manifolds other than $D^4$ and $S^4$, applications include measuring the extent to which immersed $\mathbb{Z}$-surfaces are determined by the equivariant intersection form of their exterior. Along the way, we prove that any two $\mathbb{Z}^2$-concordances between the Hopf link and an Alexander polynomial one link $L$ are homeomorphic rel. boundary., Comment: 76 pages, 15 figures

This report describes the implications for eVACS of two cryptographic errors in the Ada Web Services Library that it depends on. We identified these errors in the course of examining and testing the 2024 eVACS code, which was made publicly available in March 2024. We disclosed the problems to AdaCore, and explained the implications at the time to the relevant electoral authorities., Comment: 10 pages, 1 figure

The emergence of demanding space applications has modified the traditional landscape of computing systems in space. When reliability is a first-class concern, in addition to enhanced performance-per-Watt, radiation-hardened FPGAs are favored. In this context, the current paper evaluates the first European radiation-hardened SoC FPGA, i.e., NanoXplore's NG-ULTRA, for accelerating high-performance DSP algorithms from space applications. The proposed development & testing methodologies provide efficient implementations, while they also aim to test the new NG-ULTRA hardware and its associated software tools. The results show that NG-ULTRA achieves competitive resource utilization and performance, constituting it as a very promising device for space missions, especially for Europe., Comment: Accepted for publication at the 31st IEEE ICECS Conference, 18-20 Nov, 2024, Nancy, France

Single Transferable Vote (STV) counting, used in several jurisdictions in Australia, is a system for choosing multiple election winners given voters' preferences over candidates. There are a variety of different versions of STV legislated and/or applied across Australia. This paper shows some of the unintuitive properties of some of these systems.

This paper investigates the computational mechanisms underlying a type of metacognitive monitoring known as detached mindfulness, a particularly effective therapeutic technique within cognitive psychology. While research strongly supports the capacity of detached mindfulness to reduce depression and anxiety, its cognitive and computational underpinnings remain largely unexplained. We employ a computational model of metacognitive skill to articulate the mechanisms through which a detached perception of affect reduces emotional reactivity., Comment: International Conference on Cognitive Modeling (ICCM 2024) https://mathpsych.org/presentation/1634#/abstract

The Atacama Large Millimetre/submillimetre Array (ALMA) receivers and technical papers are cited fewer than once in every six publications. This citation shortage is impeding the development of future (sub)millimetre instruments. In an effort to facilitate the correct citations of ALMA receivers and technical papers, this memo provides a comprehensive list of papers for the scientific community. This list was produced in discussion with the scientific and instrumentalist community, based on a June 2024 survey at the European Southern Observatory workshop on the ALMA Wideband Sensitivity Upgrade, as well as with the ALMA technical staff. The authors now encourage the community to enhance their already-excellent ALMA science with the appropriate references to ensure future (sub)millimetre instrumentation can keep addressing the key questions about our Universe., Comment: 8 pages; 1 figure; 3 tables; continuously updated as more papers become available. Suggestions are warmly welcomed. ALMA memos solely contains the opinion of the authors in discussion with the astronomer and instrument builder community, and does not reflect the official policy of the ALMA telescope

Distilling conversational skills into Small Language Models (SLMs) with approximately 1 billion parameters presents significant challenges. Firstly, SLMs have limited capacity in their model parameters to learn extensive knowledge compared to larger models. Secondly, high-quality conversational datasets are often scarce, small, and domain-specific. Addressing these challenges, we introduce a novel data distillation framework named CoDi (short for Conversational Distillation, pronounced "Cody"), allowing us to synthesize large-scale, assistant-style datasets in a steerable and diverse manner. Specifically, while our framework is task agnostic at its core, we explore and evaluate the potential of CoDi on the task of conversational grounded reasoning for question answering. This is a typical on-device scenario for specialist SLMs, allowing for open-domain model responses, without requiring the model to "memorize" world knowledge in its limited weights. Our evaluations show that SLMs trained with CoDi-synthesized data achieve performance comparable to models trained on human-annotated data in standard metrics. Additionally, when using our framework to generate larger datasets from web data, our models surpass larger, instruction-tuned models in zero-shot conversational grounded reasoning tasks., Comment: 13 pages

We analyze the long-term evolution of hierarchical triple systems in Newtonian gravity to second order in the quadrupolar perturbation parameter, and to sixth order in $\epsilon = a/A$, the ratio of the semimajor axes of the inner and outer orbits. We apply the ``two-timescale'' method from applied mathematics to the Lagrange Planetary Equations for the inner and outer orbits, in which each osculating orbit element is split into an orbit averaged part that evolves on the long perturbative timescale, and an ``average-free'' part that is oscillatory in the orbital timescales. Averages over the two orbital timescales are performed using the well-known secular approximation. We also incorporate perturbative corrections to the relation between time and the orbital phases. We place no restrictions on the masses, on the relative orbit inclinations or on the eccentricities, beyond the requirement that the quadrupolar parameter and $\epsilon$ both be small. The result is a complete set of long-timescale evolution equations for the averaged elements of the inner and outer orbits. At first order in perturbation theory, we obtain the dotriacontapole contributions explicitly at order $\epsilon^6$. At second order in perturbation theory, i.e. quadratic in the quadrupole perturbation amplitude, we find contributions that scale as $\epsilon^{9/2}$ (found in earlier work), $\epsilon^{5}$, $\epsilon^{11/2}$, and $\epsilon^{6}$. At first perturbative order and dotriacontapole order, the two averaged semimajor axes are constant in time (and we prove that this holds to arbitrary multipole orders); but at second perturbative order, beginning at $O( \epsilon^{5})$, they are no longer constant. Nevertheless we verify that the total averaged energy of the system is conserved, and we argue that this behavior is not incompatible with classical theorems on secular evolution of the semimajor axes., Comment: 21 pages, 2 figures, minor changes to align with published version

Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex are crucial for building these chatbots. However, creating effective enterprise chatbots is challenging and requires meticulous RAG pipeline engineering. This includes fine-tuning embeddings and LLMs, extracting documents from vector databases, rephrasing queries, reranking results, designing prompts, honoring document access controls, providing concise responses, including references, safeguarding personal information, and building orchestration agents. We present a framework for building RAG-based chatbots based on our experience with three NVIDIA chatbots: for IT/HR benefits, financial earnings, and general content. Our contributions are three-fold: introducing the FACTS framework (Freshness, Architectures, Cost, Testing, Security), presenting fifteen RAG pipeline control points, and providing empirical results on accuracy-latency tradeoffs between large and small LLMs. To the best of our knowledge, this is the first paper of its kind that provides a holistic view of the factors as well as solutions for building secure enterprise-grade chatbots.", Comment: 8 pages, 6 figures, 2 tables, Preprint submission to ACM CIKM 2024

Following long-standing predictions associated with hydrogen, high-temperature superconductivity has recently been observed in several hydride-based materials. Nevertheless, these high-$T_c$ phases only exist at extremely high pressures, and achieving high transition temperatures at ambient pressure remains a major challenge. Recent predictions of the complex hydride Mg$_{2}$IrH$_{6}$ may help overcome this challenge with calculations of high-$T_c$ superconductivity (65 K$~<~T_c~<~$ 170 K) in a material that is stable at atmospheric pressure. In this work, the synthesis of Mg$_{2}$IrH$_{6}$ was targeted over a broad range of $P$-$T$ conditions, and the resulting products were characterized using X-ray diffraction (XRD) and vibrational spectroscopy, in concert with first-principles calculations. The results indicate that the charge-balanced complex hydride Mg$_{2}$IrH$_{5}$ is more stable over all conditions tested up to ca 28 GPa. The resulting hydride is isostructural with the predicted superconducting Mg$_{2}$IrH$_{6}$ phase except for a single hydrogen vacancy, which shows a favorable replacement barrier upon insertion of hydrogen into the lattice. Bulk Mg$_{2}$IrH$_{5}$ is readily accessible at mild $P$-$T$ conditions and may thus represent a convenient platform to access superconducting Mg$_{2}$IrH$_{6}$ via non-equilibrium processing methods., Comment: 18 pages, 17 Figures

The aim of this study is to probe the sub-mJy polarized source population with LOFAR. We present the method used to stack LOFAR polarization datasets, the resulting catalog of polarized sources, and the derived polarized source counts. The ELAIS-N1 field was selected for a polarimetric study at 114.9-177.4 MHz. A total area of 25 deg2 was imaged at 6"- resolution in the Stokes Q and U parameters. Alignment of polarization angles was done both in frequency and in Faraday space before stacking datasets from 19 eight-hour-long epochs. A search for polarized sources was carried out in the final, stacked dataset, and the properties of the detected sources were examined. The depolarization level of sources known to be polarized at 1.4 GHz was quantified. A one-sigma noise level of 19 {\mu}Jy/beam was reached in the central part of the field after stacking. Twenty-five polarized sources were detected above 8\sigma, five of which had not been detected in polarization at any other radio frequencies before. Seven additional polarized components were found by lowering the threshold to 6\sigma at positions corresponding to sources known to be polarized at 1.4 GHz. In two radio galaxies, polarization was detected from both radio lobes, so the final number of associated radio continuum sources is 31. The detected sources are weakly polarized, with a median degree of polarization of 1.75% for the sample of sources detected in polarized emission. The sources previously detected in polarization at 1.4 GHz are significantly depolarized at 150 MHz. The catalog is used to derive the polarized source counts at 150 MHz. This is the deepest and highest-resolution polarization study at 150 MHz to date., Comment: 18 pages, 10 figures, accepted for publication in A&A

On-device training is currently the most common approach for training machine learning (ML) models on private, distributed user data. Despite this, on-device training has several drawbacks: (1) most user devices are too small to train large models on-device, (2) on-device training is communication- and computation-intensive, and (3) on-device training can be difficult to debug and deploy. To address these problems, we propose Private Evolution-Text (PrE-Text), a method for generating differentially private (DP) synthetic textual data. First, we show that across multiple datasets, training small models (models that fit on user devices) with PrE-Text synthetic data outperforms small models trained on-device under practical privacy regimes ($\epsilon=1.29$, $\epsilon=7.58$). We achieve these results while using 9$\times$ fewer rounds, 6$\times$ less client computation per round, and 100$\times$ less communication per round. Second, finetuning large models on PrE-Text's DP synthetic data improves large language model (LLM) performance on private data across the same range of privacy budgets. Altogether, these results suggest that training on DP synthetic data can be a better option than training a model on-device on private distributed data. Code is available at https://github.com/houcharlie/PrE-Text., Comment: ICML 2024 (Oral). Latest revision corrects a discussion on concurrent work arXiv:2403.01749. We described their work as reliant on using closed-sourced models when in reality they also evaluate and use open source models. This has been corrected in this version

Las Cumbres Observatory (LCOGT) operates a global network of robotic 0.4, 1.0, and 2.0-meter telescopes to facilitate scientific research and education in time-domain astronomy. LCOGT's flagship educational program, Global Sky Partners (GSP), awards up to 1500 hours per year of telescope time to individuals and organizations that run their own, fully supported, educational programs. The GSP has a presence in 40 countries and 45% of the Partners target under-served, under-represented, and developing world audiences. The degradation and obsolescence of the original 0.4-meter telescope network prompted LCOGT to update the fleet of 10 telescopes to a new system consisting of predominantly off-the-shelf products. New PlaneWave DeltaRho 350 telescopes with Gemini Focuser/Rotators, LCOGT filter wheels, and QHY600 CMOS cameras, complement the original, custom-built mount. The deployment of all ten telescopes was completed in March 2024. We describe the design and performance of this new system and its components. We comment on modifications made to the QHY600 cameras, as well as on the treatment of random telegraph noise of its CMOS detectors within our data processing system BANZAI. The new telescope network supports the GSP program as well as multiple key science projects, including follow-up observations for the TESS satellite mission., Comment: submitted to SPIE Astronomical Telescopes + Instrumentation proceedings 13096-146

Adaptive filters, such as telescoping and adaptive cuckoo filters, update their representation upon detecting a false positive to avoid repeating the same error in the future. Adaptive filters require an auxiliary structure, typically much larger than the main filter and often residing on slow storage, to facilitate adaptation. However, existing adaptive filters are not practical and have seen no adoption in real-world systems due to two main reasons. Firstly, they offer weak adaptivity guarantees, meaning that fixing a new false positive can cause a previously fixed false positive to come back. Secondly, the sub-optimal design of the auxiliary structure results in adaptivity overheads so substantial that they can actually diminish the overall system performance compared to a traditional filter. In this paper, we design and implement AdaptiveQF, the first practical adaptive filter with minimal adaptivity overhead and strong adaptivity guarantees, which means that the performance and false-positive guarantees continue to hold even for adversarial workloads. The AdaptiveQF is based on the state-of-the-art quotient filter design and preserves all the critical features of the quotient filter such as cache efficiency and mergeability. Furthermore, we employ a new auxiliary structure design which results in considerably low adaptivity overhead and makes the AdaptiveQF practical in real systems.

The list-labeling problem captures the basic task of storing a dynamically changing set of up to $n$ elements in sorted order in an array of size $m = (1 + \Theta(1))n$. The goal is to support insertions and deletions while moving around elements within the array as little as possible. Until recently, the best known upper bound stood at $O(\log^2 n)$ amortized cost. This bound, which was first established in 1981, was finally improved two years ago, when a randomized $O(\log^{3/2} n)$ expected-cost algorithm was discovered. The best randomized lower bound for this problem remains $\Omega(\log n)$, and closing this gap is considered to be a major open problem in data structures. In this paper, we present the See-Saw Algorithm, a randomized list-labeling solution that achieves a nearly optimal bound of $O(\log n \operatorname{polyloglog} n)$ amortized expected cost. This bound is achieved despite at least three lower bounds showing that this type of result is impossible for large classes of solutions., Comment: 39 pages

The list-labeling problem is one of the most basic and well-studied algorithmic primitives in data structures, with an extensive literature spanning upper bounds, lower bounds, and data management applications. The classical algorithm for this problem, dating back to 1981, has amortized cost $O(\log^2 n)$. Subsequent work has led to improvements in three directions: \emph{low-latency} (worst-case) bounds; \emph{high-throughput} (expected) bounds; and (adaptive) bounds for \emph{important workloads}. Perhaps surprisingly, these three directions of research have remained almost entirely disjoint -- this is because, so far, the techniques that allow for progress in one direction have forced worsening bounds in the others. Thus there would appear to be a tension between worst-case, adaptive, and expected bounds. List labeling has been proposed for use in databases at least as early as PODS'99, but a database needs good throughput, response time, and needs to adapt to common workloads (e.g., bulk loads), and no current list-labeling algorithm achieve good bounds for all three. We show that this tension is not fundamental. In fact, with the help of new data-structural techniques, one can actually \emph{combine} any three list-labeling solutions in order to cherry-pick the best worst-case, adaptive, and expected bounds from each of them., Comment: PODS 2024, 19 pages, 4 figures

Transformer-based language models spread FLOPs uniformly across input sequences. In this work we demonstrate that transformers can instead learn to dynamically allocate FLOPs (or compute) to specific positions in a sequence, optimising the allocation along the sequence for different layers across the model depth. Our method enforces a total compute budget by capping the number of tokens ($k$) that can participate in the self-attention and MLP computations at a given layer. The tokens to be processed are determined by the network using a top-$k$ routing mechanism. Since $k$ is defined a priori, this simple procedure uses a static computation graph with known tensor sizes, unlike other conditional computation techniques. Nevertheless, since the identities of the $k$ tokens are fluid, this method can expend FLOPs non-uniformly across the time and model depth dimensions. Thus, compute expenditure is entirely predictable in sum total, but dynamic and context-sensitive at the token-level. Not only do models trained in this way learn to dynamically allocate compute, they do so efficiently. These models match baseline performance for equivalent FLOPS and wall-clock times to train, but require a fraction of the FLOPs per forward pass, and can be upwards of 50\% faster to step during post-training sampling.

Disentangling the radio flux contribution from star formation (SF) and active-galactic-nuclei (AGN) activity is a long-standing problem in extragalactic astronomy, since at frequencies of $\lesssim$ 10 GHz, both processes emit synchrotron radiation. We present in this work the general objectives of the PARADIGM Project, a multi-instrument concept to explore star-formation and mass assembly of galaxies. We introduce two novel general approaches for a detailed multiscale study of the radio emission in local U/LIRGs. In this work, we use archival interferometric data from the Very Large Array (VLA) centred at ~ 6 GHz (C band) and present new observations from the e-Multi-Element Radio-Linked Interferometer Network (e-MERLIN) for UGC5101, VV705, VV250 and UGC8696. Using our image decomposition methods, we robustly disentangle the radio emission into distinct components by combining information from the two interferometric arrays. We use e-MERLIN as a probe of the core-compact radio emission (AGN or starburst) at ~ 20 pc scales, and as a probe of nuclear diffuse emission, at scales ~ 100 - 200 pc. With VLA, we characterise the source morphology and the flux density on scales from 200 pc up to and above 1 kpc. As a result, we find deconvolved and convolved sizes for nuclear regions from ~ 10 pc to ~ 200 pc. At larger scales, we find sizes of 1.5 - 2 kpc for diffuse structures (with effective sizes of ~ 300 - 400 pc). We demonstrate that the radio emission from nuclear extended structures (~ 100 pc) can dominate over core-compact components, providing a significant fraction of the total multiscale SF output. We establish a multiscale radio tracer for star formation by combining information from different instruments. Consequently, this work sets a starting point to potentially correct for overestimations of AGN fractions and underestimates of SF activity., Comment: Accepted for publication in MNRAS. 31 pages (9 pages appendix), 13 figures, 9 tables. Online code repository: morphen - https://github.com/lucatelli/morphen

Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval. As the widespread adoption of LLMs continues to surge, it becomes increasingly crucial to ensure that these models yield responses that are politically impartial, with the aim of preventing information bubbles, upholding fairness in representation, and mitigating confirmation bias. In this paper, we propose a quantitative framework and pipeline designed to systematically investigate the political orientation of LLMs. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. Across topics, the results indicate that LLMs exhibit a tendency to provide responses that closely align with liberal or left-leaning perspectives rather than conservative or right-leaning ones when user queries include details pertaining to occupation, race, or political affiliation. The findings presented in this study not only reaffirm earlier observations regarding the left-leaning characteristics of LLMs but also surface particular attributes, such as occupation, that are particularly susceptible to such inclinations even when directly steered towards conservatism. As a recommendation to avoid these models providing politicised responses, users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.

We study locally flat disks in $(\mathbb{C} P^2)^\circ:=(\mathbb{C} P^2)\setminus \mathring{B^4}$ with boundary a fixed knot $K$ and whose complement has fundamental group $\mathbb{Z}$. We show that up to topological isotopy rel. boundary, such disks necessarily arise by performing a positive crossing change on $K$ to an Alexander polynomial one knot and capping off with a $\mathbb{Z}$-disk in $D^4.$ Such a crossing change determines a loop in $S^3 \setminus K$ and we prove that the homology class of its lift to the infinite cyclic cover leads to a complete invariant of the disk. We prove that this determines a bijection between the set of rel. boundary topological isotopy classes of $\mathbb{Z}$-disks with boundary $K$ and a quotient of the set of unitary units of the ring $\mathbb{Z}[t^{\pm 1}]/(\Delta_K)$. Number-theoretic considerations allow us to deduce that a knot $K \subset S^3$ with quadratic Alexander polynomial bounds $0,1,2,4$, or infinitely many $\mathbb{Z}$-disks in $(\mathbb{C} P^2)^\circ$. This leads to the first examples of knots bounding infinitely many topologically distinct disks whose exteriors have the same fundamental group and equivariant intersection form. Finally we give several examples where these disks are realized smoothly., Comment: 40 pages, 9 figures

In the fall of 2020, ACT and the Mississippi Community College Board (MCCB) entered into a data sharing agreement in order to examine the validity of ACT's® WorkKeys® Assessments. WorkKeys Assessments measure students' foundational workplace skills. The focus of this technical brief is to summarize the results of several validity studies using a sample from MCCB. This brief examines three different types of validity: (1) convergent; (2) discriminant; and (3) criterion-related. To examine convergent and discriminant validity, the relationship between WorkKeys and the Test of Adult Basic Education (TABE) was examined. To assess criterion-related validity, the relationships between WorkKeys and several educational outcomes were examined.

The convergence of Artificial Intelligence (AI) and blockchain technology is reshaping the digital world, offering decentralized, secure, and efficient AI services on blockchain platforms. Despite the promise, the high computational demands of AI on blockchain raise significant privacy and efficiency concerns. The Optimistic Privacy-Preserving AI (opp/ai) framework is introduced as a pioneering solution to these issues, striking a balance between privacy protection and computational efficiency. The framework integrates Zero-Knowledge Machine Learning (zkML) for privacy with Optimistic Machine Learning (opML) for efficiency, creating a hybrid model tailored for blockchain AI services. This study presents the opp/ai framework, delves into the privacy features of zkML, and assesses the framework's performance and adaptability across different scenarios.

3C84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of VLBI above the hitherto available maximum frequency of 86GHz. Using ultrahigh resolution VLBI observations at the highest available frequency of 228GHz, we aim to directly detect compact structures and understand the physical conditions in the compact region of 3C84. We used EHT 228GHz observations and, given the limited (u,v)-coverage, applied geometric model fitting to the data. We also employed quasi-simultaneously observed, multi-frequency VLBI data for the source in order to carry out a comprehensive analysis of the core structure. We report the detection of a highly ordered, strong magnetic field around the central, SMBH of 3C84. The brightness temperature analysis suggests that the system is in equipartition. We determined a turnover frequency of $\nu_m=(113\pm4)$GHz, a corresponding synchrotron self-absorbed magnetic field of $B_{SSA}=(2.9\pm1.6)$G, and an equipartition magnetic field of $B_{eq}=(5.2\pm0.6)$G. Three components are resolved with the highest fractional polarisation detected for this object ($m_\textrm{net}=(17.0\pm3.9)$%). The positions of the components are compatible with those seen in low-frequency VLBI observations since 2017-2018. We report a steeply negative slope of the spectrum at 228GHz. We used these findings to test models of jet formation, propagation, and Faraday rotation in 3C84. The findings of our investigation into different flow geometries and black hole spins support an advection-dominated accretion flow in a magnetically arrested state around a rapidly rotating supermassive black hole as a model of the jet-launching system in the core of 3C84. However, systematic uncertainties due to the limited (u,v)-coverage, however, cannot be ignored., Comment: 15 pages, 6 figures, published in A&A

The integration of machine learning with blockchain technology has witnessed increasing interest, driven by the vision of decentralized, secure, and transparent AI services. In this context, we introduce opML (Optimistic Machine Learning on chain), an innovative approach that empowers blockchain systems to conduct AI model inference. opML lies a interactive fraud proof protocol, reminiscent of the optimistic rollup systems. This mechanism ensures decentralized and verifiable consensus for ML services, enhancing trust and transparency. Unlike zkML (Zero-Knowledge Machine Learning), opML offers cost-efficient and highly efficient ML services, with minimal participation requirements. Remarkably, opML enables the execution of extensive language models, such as 7B-LLaMA, on standard PCs without GPUs, significantly expanding accessibility. By combining the capabilities of blockchain and AI through opML, we embark on a transformative journey toward accessible, secure, and efficient on-chain machine learning.

This article presents a theoretical framework for adapting the Common Model of Cognition to large generative network models within the field of artificial intelligence. This can be accomplished by restructuring modules within the Common Model into shadow production systems that are peripheral to a central production system, which handles higher-level reasoning based on the shadow productions' output. Implementing this novel structure within the Common Model allows for a seamless connection between cognitive architectures and generative neural networks.