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The growing number of satellite constellations in low Earth orbit (LEO) enhances global communications and Earth observation, and support of space commerce is a high priority of many governments. At the same time, the proliferation of satellites in LEO has negative effects on astronomical observations and research, and the preservation of the dark and quiet sky. These satellite constellations reflect sunlight onto optical telescopes, and their radio emission impacts radio observatories, jeopardising our access to essential scientific discoveries through astronomy. The changing visual appearance of the sky also impacts our cultural heritage and environment. Both ground-based observatories and space-based telescopes in LEO are affected, and there are no places on Earth that can escape the effects of satellite constellations given their global nature. The minimally disturbed dark and radio-quiet sky is crucial for conducting fundamental research in astronomy and important public services such as planetary defence, technology development, and high-precision geolocation. Some aspects of satellite deployment and operation are regulated by States and intergovernmental organisations. While regulatory agencies in some States have started to require operators to coordinate with their national astronomy agencies over impacts, mitigation of the impact of space objects on astronomical activities is not sufficiently regulated. To address this issue, the CPS urges States and the international community to take steps to protect the dark and quiet sky as specified in this paper., Comment: This position paper was developed by the IAU Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (CPS). It can also be downloaded at the CPS website at https://cps.iau.org/news/cps-urges-action-in-first-recommendations-paper/

This report arrives during a time of heightened scrutiny and civic engagement. While public attention is currently trained primarily on Milwaukee Public Schools (MPS), this research encompasses the whole of the city's education system, marking both commonalities and differences between different types of publicly funded schools. It highlights financial aspects of the system but also expands the discussion to include student enrollment and outcomes. The report is organized into three major sections: Milwaukee's Students and Schools, covering the basic schooling options available to students along with student enrollment and demographic trends; School Funding in Milwaukee, outlining the core funding mechanisms and their funding levels for schools in the city; and Student Outcomes and School Performance, summarizing broad trends in Milwaukee's academic results. The report concludes with key insights for the consideration of both policymakers and the public. It is hoped the report's findings ground both current and future policy discussions with important facts and nonpartisan insights. The authors further intend to inform those discussions with a second report this fall, which will highlight promising K-12 practices and innovations occurring both within Milwaukee and nationally.

Despite the popularity of teacher leadership since the 1980s, little research examines its effects on student achievement. In this paper, I assess the influence of the New York City Department of Education's Teacher Career Pathways program, a teacher leadership initiative, on student achievement in grades three through eight. Using difference-in-difference approaches, including new event study estimators, I find that where school leaders staffed teacher leaders into formal roles with defined responsibilities, positional authority, and commensurate salary increases, student achievement in ELA and math improves. Moreover, the improvement in scores compounds over time, with schools exhibiting increasing gains in each year following the initial introduction of teacher leaders. Schools that do not staff teacher leaders do not observe similar outcomes. I consider these results in the context of democratic policymaking and teacher empowerment, suggesting that teachers must be formally empowered in schools to lead meaningful changes that ultimately improve student achievement.

This chartbook is the 14th version to be produced for the Appalachian Regional Commission (ARC) by the Population Reference Bureau (PRB). The Chartbook describes the diversity of the Appalachian Region on a host of demographic and economic measures and provides an important annual view of the area and its people. The data contained in the 2018-2022 Chartbook describe how residents in the Appalachian Region were faring before and during the COVID-19 pandemic that began in March 2020. Nearly half of the years during this time period were pre-pandemic and half were during the pandemic era. Thus, this Chartbook is a blend of these two eras. As future data releases reflect the post-pandemic era, data users will have additional insights on the long-term changes that the pandemic brought to Appalachia's social and economic dynamics. Most of the data shown here come from the 2018-2022 American Community Survey (ACS), a nationwide study collected continuously every year in every county in the United States by the U.S. Census Bureau. The ACS is designed to provide communities with reliable and timely demographic, social, economic, and housing data each year. To provide as much county-level data as possible, we use ACS 5-year data files which provide reliable estimates for geographic areas with fewer than 20,000 people. Since many counties in the Appalachian Region have fewer than 20,000 residents, these data permit comparable statistics for all 423 counties in the Region. The primary purpose of the ACS is to measure the changing characteristics of the U.S. population in a way that is continually updated. The estimates in this Chartbook, therefore, are data collected over the five-year (or 60-month) period from January 2018 through December 2022. These ACS estimates are not averages of monthly or annual values, but rather an aggregation of data collected continuously over that time period.

This report describes results from the Long-Term Impact of Early College High Schools Study--funded by the Institute of Education Sciences (IES) (#R305A210017)--which aimed to explore the long-term impacts of early college (EC) high schools on students' academic outcomes (e.g., college enrollment, degree attainment) and workforce, financial, and later life outcomes after schooling. In this report--the second follow-up report on our initial EC impact study--we focus specifically on key questions about workforce, financial, and other life outcomes: What were the impacts of ECs on workforce, financial, and other life outcomes in the 12th to 14th years after expected high school graduation? Did the impacts of ECs vary by participant characteristics? This report describes findings which build on a previous EC impact study, which was a multisite student-level randomized controlled trial with randomization based on admission lotteries. The analyses in this report focus on individuals who originally participated in 17 admission lotteries conducted by seven ECs for three cohorts of students, and examined outcomes after formal schooling. Key takeaways include the following: (1) Participants who were admitted to an EC, regardless of whether they attended the EC, did not experience a significant effect on any of the workforce, financial, and other life outcomes measured with survey data 12 to 14 years after expected high school graduation; and (2) EC impacts on workforce, financial, and other life outcomes measured 12 to 14 years after expected high school graduation did not differ significantly by individuals' race/ethnicity, low-income status, or prior achievement. While our initial impact study and first follow-up study found that attending an EC had effects on secondary and postsecondary enrollment, and completion for participants with different background characteristics, we found that attending an EC had no impact on any of the workforce, financial, and other life outcomes that we analyzed 12 to 14 years after participants' expected high school graduation. Future studies could consider examining workforce, financial, and other life outcomes in years before the 12th to 14th year after expected high school graduation using administrative data sources (e.g., IRS or unemployment insurance records). Further research may also examine if other factors, such as EC students' college major or labor market opportunities, influence the impact of ECs on longer-term outcomes.

The three-loop banana integral with three equal masses and the conformal two-loop five-point traintrack integral in two dimensions are related to a two-parameter family of K3 surfaces. We compute the corresponding periods and the mirror map, and we show that they can be expressed in terms of ordinary modular forms and functions. In particular, we find that the maximal cuts of the three-loop banana integral with three equal masses can be written as a product of two copies of the maximal cuts of the two-loop equal-mass sunrise integral. Our computation reveals a hidden symmetry of the banana integral not manifest from the Feynman integral representation, which corresponds to exchanging the two copies of the sunrise elliptic curve., Comment: 27 pages, 2 figures

Coronal lines (CLs), which arise from collisionally excited forbidden transitions from highly ionized species, are a powerful diagnostic tool in uncovering active galactic nuclei (AGNs) and constraining their properties. However, recent optical surveys are finding that coronal lines are rarely detected in the majority of local AGNs, possibly as a result of the depletion of elements from the interstellar gas onto dust grains. Prominent CL emission may therefore only arise when dust is being destroyed in the highly ionized gas in AGNs. To explore the possibility that dust destruction may be caused by ionized gas outflows in galaxies with prominent CLs, we present the first large-scale systematic study of ionized outflows, as traced by the [O III] $\lambda$5007 emission, in galaxies displaying CL emission relative to a robust control sample of non-CL-emitting galaxies. We find: 1) galaxies that display CL emission have a significantly elevated outflow incidence rate compared to their matched controls, 2) the outflow luminosity is significantly higher in the CL-emitters, 3) the CL-emitters have systematically lower intrinsic extinction toward the ionized gas compared with the controls, 4) there are significant correlations between the CL luminosity and outflow velocity for the iron CLs, with similar relationships found between the CL FWHM and outflow luminosity. These observations are consistent with dust destruction in an outflowing wind from a dusty torus causing efficient CL emission., Comment: Submitted to AAS journals; comments welcome and appreciated

This work introduces the design, implementation, and validation of a virtual reality (VR) experience aimed at promoting the inclusion of individuals with dyslexia in university settings. Unlike traditional awareness methods, this immersive approach offers a novel way to foster empathy by allowing participants to experience firsthand the challenges faced by students with dyslexia. Specifically, the experience raises awareness by exposing non-dyslexic individuals to the difficulties commonly encountered by dyslexic students. In the virtual environment, participants explore a virtual campus with multiple buildings, navigating between them while completing tasks and simultaneously encountering barriers that simulate some of the challenges faced by individuals with dyslexia. These barriers include reading signs with shifting letters, following directional arrows that may point incorrectly, and dealing with a lack of assistance. The campus is a comprehensive model featuring both indoor and outdoor spaces and supporting various modes of locomotion. To validate the experience, more than 30 non-dyslexic participants from the university environment, mainly professors and students, evaluated it through ad hoc satisfaction surveys. The results indicated heightened awareness of the barriers encountered by students with dyslexia, with participants deeming the experience a valuable tool for increasing visibility and fostering understanding of dyslexic students., Comment: 20 pages, 9 figures

We present a computation of the one-loop QCD corrections to top-quark pair production in association with a $W$ boson, including terms up to order $\varepsilon^2$ in dimensional regularization. Providing a first glimpse into the complexity of the corresponding two-loop amplitude, this result is a first step towards a description of this process at next-to-next-to-leading order (NNLO) in QCD. We perform a tensor decomposition and express the corresponding form factors in terms of a basis of independent special functions with compact rational coefficients, providing a structured framework for future developments. In addition, we derive an explicit analytic representation of the form factors, valid up to order $\varepsilon^0$, expressed in terms of logarithms and dilogarithms. For the complete set of special functions required, we obtain a semi-numerical solution based on generalized power series expansion., Comment: 37 pages, 1 figure, 2 tables. The paper is complemented by a Zenodo repository found at https://zenodo.org/records/14810498

Understanding historical and cultural artifacts demands human expertise and advanced computational techniques, yet the process remains complex and time-intensive. While large multimodal models offer promising support, their evaluation and improvement require a standardized benchmark. To address this, we introduce TimeTravel, a benchmark of 10,250 expert-verified samples spanning 266 distinct cultures across 10 major historical regions. Designed for AI-driven analysis of manuscripts, artworks, inscriptions, and archaeological discoveries, TimeTravel provides a structured dataset and robust evaluation framework to assess AI models' capabilities in classification, interpretation, and historical comprehension. By integrating AI with historical research, TimeTravel fosters AI-powered tools for historians, archaeologists, researchers, and cultural tourists to extract valuable insights while ensuring technology contributes meaningfully to historical discovery and cultural heritage preservation. We evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement. Our goal is to establish AI as a reliable partner in preserving cultural heritage, ensuring that technological advancements contribute meaningfully to historical discovery. Our code is available at: \url{https://github.com/mbzuai-oryx/TimeTravel}., Comment: 4 pages, 6 figures

In the RC$^\star$ collaboration, we simulate lattice QCD+QED using $C-$periodic spatial boundary conditions to ensure that locality, gauge invariance, and translational invariance are preserved throughout the calculation. We present our progress in computing isospin-breaking (IB) corrections to the leading hadronic contribution to $(g-2)_\mu$. We compare two ways of including the IB corrections: the RM123 method and dynamical QCD+QED simulations, both with $C-$periodic boundary conditions. The two calculations are performed at $\beta=3.24$ with four flavours of $\mathcal{O}(a)-$improved Wilson fermions; the QCD ensemble features $SU(3)-$symmetric sea quarks plus charm, while down and strange quarks are degenerate in QCD+QED gauge ensembles., Comment: 10 pages, RC* collaboration, Contribution to the 41st International Symposium on Lattice Field Theory, 28th July-3rd August 2024, University of Liverpool, United Kingdom

Current- and next-generation Cosmic Microwave Background (CMB) experiments will measure polarization anisotropies with unprecedented sensitivities. The need for high precision in these measurements underscores the importance of gaining a comprehensive understanding of instrument properties, with a particular emphasis on the study of the beam properties and, in particular, their polarization characteristics, and the measurement of the polarization angle. In this context, a major challenge lies in the scarcity of millimeter polarized astrophysical sources with sufficient brightness and calibration knowledge to meet the stringent accuracy requirements of future CMB missions. This led to the development of a drone-borne calibration source designed for frequency band centered on approximately 90 GHz band, matching a commonly used channel in ground based CMB measurements. The PROTOtype CALibrator for Cosmology, PROTOCALC, has undergone thorough in-lab testing, and its properties have been subsequently modeled through simulation software integrated into the standard Simons Observatory (SO) analysis pipeline. Moreover, the PROTOCALC system has been tested in the field, having been deployed twice on calibration campaigns with CMB telescopes in the Atacama desert. The data collected constrain the roll angle of the source with a statistical accuracy of $0.045^\circ$.

Content creators often draw inspiration from multiple visual sources, combining distinct elements to craft new compositions. Modern computational approaches now aim to emulate this fundamental creative process. Although recent diffusion models excel at text-guided compositional synthesis, text as a medium often lacks precise control over visual details. Image-based composition approaches can capture more nuanced features, but existing methods are typically limited in the range of concepts they can capture, and require expensive training procedures or specialized data. We present IP-Composer, a novel training-free approach for compositional image generation that leverages multiple image references simultaneously, while using natural language to describe the concept to be extracted from each image. Our method builds on IP-Adapter, which synthesizes novel images conditioned on an input image's CLIP embedding. We extend this approach to multiple visual inputs by crafting composite embeddings, stitched from the projections of multiple input images onto concept-specific CLIP-subspaces identified through text. Through comprehensive evaluation, we show that our approach enables more precise control over a larger range of visual concept compositions., Comment: Project Page: https://ip-composer.github.io/IP-Composer/

We introduce Faina, the first dataset for fallacy detection that embraces multiple plausible answers and natural disagreement. Faina includes over 11K span-level annotations with overlaps across 20 fallacy types on social media posts in Italian about migration, climate change, and public health given by two expert annotators. Through an extensive annotation study that allowed discussion over multiple rounds, we minimize annotation errors whilst keeping signals of human label variation. Moreover, we devise a framework that goes beyond "single ground truth" evaluation and simultaneously accounts for multiple (equally reliable) test sets and the peculiarities of the task, i.e., partial span matches, overlaps, and the varying severity of labeling errors. Our experiments across four fallacy detection setups show that multi-task and multi-label transformer-based approaches are strong baselines across all settings. We release our data, code, and annotation guidelines to foster research on fallacy detection and human label variation more broadly., Comment: NAACL 2025

Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale, community-driven expansion of MTEB, covering over 500 quality-controlled evaluation tasks across 250+ languages. MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date. Using this collection, we develop several highly multilingual benchmarks, which we use to evaluate a representative set of models. We find that while large language models (LLMs) with billions of parameters can achieve state-of-the-art performance on certain language subsets and task categories, the best-performing publicly available model is multilingual-e5-large-instruct with only 560 million parameters. To facilitate accessibility and reduce computational cost, we introduce a novel downsampling method based on inter-task correlation, ensuring a diverse selection while preserving relative model rankings. Furthermore, we optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks that drastically reduce computational demands. For instance, our newly introduced zero-shot English benchmark maintains a ranking order similar to the full-scale version but at a fraction of the computational cost., Comment: Accepted for ICLR: https://openreview.net/forum?id=zl3pfz4VCV

Multi-Party Conversations (MPCs) are widely studied across disciplines, with social media as a primary data source due to their accessibility. However, these datasets raise privacy concerns and often reflect platform-specific properties. For example, interactions between speakers may be limited due to rigid platform structures (e.g., threads, tree-like discussions), which yield overly simplistic interaction patterns (e.g., as a consequence of ``reply-to'' links). This work explores the feasibility of generating diverse MPCs with instruction-tuned Large Language Models (LLMs) by providing deterministic constraints such as dialogue structure and participants' stance. We investigate two complementary strategies of leveraging LLMs in this context: (i.) LLMs as MPC generators, where we task the LLM to generate a whole MPC at once and (ii.) LLMs as MPC parties, where the LLM generates one turn of the conversation at a time, provided the conversation history. We next introduce an analytical framework to evaluate compliance with the constraints, content quality, and interaction complexity for both strategies. Finally, we assess the quality of obtained MPCs via human annotation and LLM-as-a-judge evaluations. We find stark differences among LLMs, with only some being able to generate high-quality MPCs. We also find that turn-by-turn generation yields better conformance to constraints and higher linguistic variability than generating MPCs in one pass. Nonetheless, our structural and qualitative evaluation indicates that both generation strategies can yield high-quality MPCs.

We present an end-to-end framework for generating synthetic users for evaluating interactive agents designed to encourage positive behavior changes, such as in health and lifestyle coaching. The synthetic users are grounded in health and lifestyle conditions, specifically sleep and diabetes management in this study, to ensure realistic interactions with the health coaching agent. Synthetic users are created in two stages: first, structured data are generated grounded in real-world health and lifestyle factors in addition to basic demographics and behavioral attributes; second, full profiles of the synthetic users are developed conditioned on the structured data. Interactions between synthetic users and the coaching agent are simulated using generative agent-based models such as Concordia, or directly by prompting a language model. Using two independently-developed agents for sleep and diabetes coaching as case studies, the validity of this framework is demonstrated by analyzing the coaching agent's understanding of the synthetic users' needs and challenges. Finally, through multiple blinded evaluations of user-coach interactions by human experts, we demonstrate that our synthetic users with health and behavioral attributes more accurately portray real human users with the same attributes, compared to generic synthetic users not grounded in such attributes. The proposed framework lays the foundation for efficient development of conversational agents through extensive, realistic, and grounded simulated interactions.

Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not guaranteed to be correct. Expert recommendations may contradict data-driven results, and their reliability can vary significantly depending on the domain or specific query. Existing methods based on soft constraints or inconsistencies in predicted causal relationships fail to account for these variations in expertise. To remedy this, we propose L2D-CD, a method for gauging the correctness of expert recommendations and optimally combining them with data-driven causal discovery results. By adapting learning-to-defer (L2D) algorithms for pairwise causal discovery (CD), we learn a deferral function that selects whether to rely on classical causal discovery methods using numerical data or expert recommendations based on textual meta-data. We evaluate L2D-CD on the canonical T\"ubingen pairs dataset and demonstrate its superior performance compared to both the causal discovery method and the expert used in isolation. Moreover, our approach identifies domains where the expert's performance is strong or weak. Finally, we outline a strategy for generalizing this approach to causal discovery on graphs with more than two variables, paving the way for further research in this area.

We describe how the annihilation of antiprotons can be utilized to generate highly charged synthetic qubits in an ion-trap setup. We identify the qubit transitions in the hyperfine splitting of Hydrogen-like atoms composed of an isomer and a single electron in the ground state. We identify promising candidates in the isomers of Y, Nb, Rh, In, and Sb, for which the hyperfine transition lies in the infrared and whose excited state level lifetime is in the hundreds of milliseconds, which is suitable for metrology applications., Comment: 11 pages, 2 figures, 6 tables

Despite progress in Arabic large language models, such as Jais and AceGPT, their evaluation on commonsense reasoning has largely relied on machine-translated datasets, which lack cultural depth and may introduce Anglocentric biases. Commonsense reasoning is shaped by geographical and cultural contexts, and existing English datasets fail to capture the diversity of the Arab world. To address this, we introduce \datasetname, a commonsense reasoning dataset in Modern Standard Arabic (MSA), covering cultures of 13 countries across the Gulf, Levant, North Africa, and the Nile Valley. The dataset was built from scratch by engaging native speakers to write and validate culturally relevant questions for their respective countries. \datasetname spans 12 daily life domains with 54 fine-grained subtopics, reflecting various aspects of social norms, traditions, and everyday experiences. Zero-shot evaluations show that open-weight language models with up to 32B parameters struggle to comprehend diverse Arab cultures, with performance varying across regions. These findings highlight the need for more culturally aware models and datasets tailored to the Arabic-speaking world.

Phase-averaging is a fundamental approach for investigating periodic and non-stationary phenomena. In fluid dynamics, these can be generated by rotating blades such as propellers/turbines or by pulsed jets. Traditional phase-averaging approaches often rely on synchronized data acquisition systems, which might require high-speed cameras, light sources, and precise delay generators and encoders, making them expensive and sometimes unfeasible. This work proposes an a posteriori data-driven approach that reconstructs phase information from randomly acquired uncorrelated photographic frames (snapshots) using the ISOMAP algorithm. The technique enables accurate reordering of snapshots in the phase space and subsequent computation of the phase-averaged flow field without the need for synchronization. The framework was validated through numerical simulations and experimental fluid dynamics datasets from an optical setup featuring single- and multi-propeller configurations. The results demonstrate that the proposed method effectively captures the periodic flow characteristics while addressing the challenges related to synchronization and hardware limitations. Furthermore, the ability to apply this technique to archival datasets extends its applicability to a wide range of experimental fluid dynamics studies. This approach provides a scalable and cost-effective alternative to traditional methods for the analysis of periodic phenomena.

Teaching the software engineers of the future to write high-quality code with good style and structure is important. This systematic literature review identifies existing instructional approaches, their objectives, and the strategies used for measuring their effectiveness. Building on an existing mapping study of code quality in education, we identified 53 papers on code structure instruction. We classified these studies into three categories: (1) studies focused on developing or evaluating automated tools and their usage (e.g., code analyzers, tutors, and refactoring tools), (2) studies discussing other instructional materials, such as learning resources (e.g., refactoring lessons and activities), rubrics, and catalogs of violations, and (3) studies discussing how to integrate code structure into the curriculum through a holistic approach to course design to support code quality. While most approaches use analyzers that point students to problems in their code, incorporating these tools into classrooms is not straightforward. Combined with further research on code structure instruction in the classroom, we call for more studies on effectiveness. Over 40% of instructional studies had no evaluation. Many studies show promise for their interventions by demonstrating improvement in student performance (e.g., reduced violations in student code when using the intervention compared with code that was written without access to the intervention). These interventions warrant further investigation on learning, to see how students apply their knowledge after the instructional supports are removed., Comment: Accepted to the 2025 IEEE/ACM 37th International Conference on Software Engineering Education and Training (CSEE&T)

In osteochondral tissue engineering (OCTE), simultaneously regenerating subchondral bone and cartilage tissue presents a significant challenge. Multiphasic scaffolds were created and manufactured using 3D printing to address this issue. Excellent interfacial mechanical properties and biocompatibility enhance the growth and chondrogenic differentiation of bone marrow mesenchymal stem cells (BM-MSCs). The subchondral bone bottom layer is mimicked by incorporating varying concentrations of graphene oxide (GO) (0%, 1%, and 2% w/v) into a bioink composed of alginate (Alg) and gelatin (Gel). Based on evaluations of mechanical and biocompatibility properties, 1% GO is selected for further studies. Subsequently, the GO concentration is kept constant while varying the platelet-rich plasma (PRP) dosage in the multiphasic scaffolds. Different PRP dosages (0%, 1%, 2%, and 3% w/v) are integrated into the Alg-Gel bioink to simulate cartilage tissues. Results indicate that 3D-printed scaffolds containing 1% or 2% PRP exhibit favorable biomechanical properties, with no significant differences observed. However, BM-MSCs exposed to 2% PRP demonstrate enhanced adhesion, growth, and viability. Additionally, real-time PCR and Alcian blue staining confirm increased chondrogenic expression and glycosaminoglycans (GAGs) synthesis. This work highlights the promising potential of 3D-printed multiphasic frameworks in the development of OCTE.

We have performed DEM simulations of elongated grains in a shear cell for various particle aspect ratios and contact frictions, with an additional heterogeneous force perturbation in the flow direction. For a perturbation in the form of a single Fourier mode, we show that the response of the system consists of transverse secondary flows that average onto a pattern of four vortices. We have also theoretically studied this phenomenon by generalizing the granular rheology $\mu(I)$ to the case of elongated grains and computing the linear response to such a perturbation. Even if the agreement between theory and simulations remains qualitative only, we can reproduce and understand the inversion of the vortex pattern when the cell aspect ratio is increased from a vertically to a horizontally elongated cell shape, emphasizing the key role of the second normal stress difference as well as the cell geometry.

Quantifying the evolution and complexity of materials is of importance in many areas of science and engineering, where a central open challenge is developing experimental complexity measurements to distinguish random structures from evolved or engineered materials. Assembly Theory (AT) was developed to measure complexity produced by selection, evolution and technology. Here, we extend the fundamentals of AT to quantify complexity in inorganic molecules and solid-state periodic objects such as crystals, minerals and microprocessors, showing how the framework of AT can be used to distinguish naturally formed materials from evolved and engineered ones by quantifying the amount of assembly using the assembly equation defined by AT. We show how tracking the Assembly of repeated structures within a material allows us formalizing the complexity of materials in a manner accessible to measurement. We confirm the physical relevance of our formal approach, by applying it to phase transformations in crystals using the HCP to FCC transformation as a model system. To explore this approach, we introduce random stacking faults in closed-packed systems simplified to one-dimensional strings and demonstrate how Assembly can track the phase transformation. We then compare the Assembly of closed-packed structures with random or engineered faults, demonstrating its utility in distinguishing engineered materials from randomly structured ones. Our results have implications for the study of pre-genetic minerals at the origin of life, optimization of material design in the trade-off between complexity and function, and new approaches to explore material technosignatures which can be unambiguously identified as products of engineered design., Comment: 34 pages, 7 figures, 55 references

The discovery of the most metal-poor stream, C-19, provides us with a fossil record of a stellar structure born very soon after the Big Bang. In this work, we search for new C-19 members over the whole sky by combining two complementary stream-searching algorithms, STREAMFINDER and StarGO,, and utilizing low-metallicity star samples from the Pristine survey as well as Gaia BP/RP spectro-photometric catalogues. We confirm twelve new members, spread over more than 100$^\circ$, using velocity and metallicity information from a set of spectroscopic follow-up programs that targeted a quasi-complete sample of our bright candidates ($G \lesssim 16.0$). From the updated set of stream members, we confirm that the stream is wide, with a stream width of $\sim200$ pc, and dynamically hot, with a derived velocity dispersion of $11.1^{+1.9}_{-1.6}$ km/s. The tension remains between these quantities and a purely baryonic scenario in which the relatively low-mass stream (even updated to a few $10^4M_{\odot}$) stems from a globular cluster progenitor, as suggested by its chemical abundances. Some heating mechanism, such as preheating of the cluster in its own dark matter halo or through interactions with halo sub-structures appears necessary to explain the tension. The impact of binaries on the measured dispersion also remains unknown. Detailed elemental abundances of more stream members as well as multi-epoch radial velocities from spectroscopic observations are therefore crucial to fully understand the nature and past history of the most metal-poor stream of the Milky Way., Comment: submitted to A&A

Extremal ideals are a class of square-free monomial ideals which dominate and determine many algebraic invariants of powers of all square-free monomial ideals. For example, the $r^{th}$ power ${\mathcal{E}_q}^r$ of the extremal ideal on $q$ generators has the maximum Betti numbers among the $r^{th}$ power of any square-free monomial ideal with $q$ generators. In this paper we study the combinatorial and geometric structure of the (minimal) free resolutions of powers of square-free monomial ideals via the resolutions of powers of extremal ideals. Although the end results are algebraic, this problem has a natural interpretation in terms of polytopes and discrete geometry. Our guiding conjecture is that all powers ${\mathcal{E}_q}^r$ of extremal ideals have resolutions supported on their Scarf simplicial complexes, and thus their resolutions are as small as possible. This conjecture is known to hold for $r \leq 2$ or $q \leq 4$. In this paper we prove the conjecture holds for $r=3$ and any $q\geq 1$ by giving a complete description of the Scarf complex of ${\mathcal{E}_q}^3$. This effectively gives us a sharp bound on the betti numbers and projective dimension of the third power of any square-free momomial ideal. For large $i$ and $q$, our bounds on the $i^{th}$ betti numbers are an exponential improvement over previously known bounds. We also describe a large number of faces of the Scarf complex of ${\mathcal{E}_q}^r$ for any $r,q \geq 1$., Comment: 34 pages, 5 figures. Comments are welcome!

Text corpora are essential for training models used in tasks like summarization, translation, and large language models (LLMs). While various efforts have been made to collect monolingual and multilingual datasets in many languages, Persian has often been underrepresented due to limited resources for data collection and preprocessing. Existing Persian datasets are typically small and lack content diversity, consisting mainly of weblogs and news articles. This shortage of high-quality, varied data has slowed the development of NLP models and open-source LLMs for Persian. Since model performance depends heavily on the quality of training data, we address this gap by introducing the Matina corpus, a new Persian dataset of 72.9B tokens, carefully preprocessed and deduplicated to ensure high data quality. We further assess its effectiveness by training and evaluating transformer-based models on key NLP tasks. Both the dataset and preprocessing codes are publicly available, enabling researchers to build on and improve this resource for future Persian NLP advancements.

Synthetically-generated data plays an increasingly larger role in training large language models. However, while synthetic data has been found to be useful, studies have also shown that without proper curation it can cause LLM performance to plateau, or even "collapse", after many training iterations. In this paper, we formalize this question and develop a theoretical framework to investigate how much curation is needed in order to ensure that LLM performance continually improves. We find that the requirements are nearly minimal. We describe a training procedure that converges to an optimal LLM even if almost all of the non-synthetic training data is of poor quality. Our analysis is inspired by boosting, a classic machine learning technique that leverages a very weak learning algorithm to produce an arbitrarily good classifier. Our training procedure subsumes many recently proposed methods for training LLMs on synthetic data, and thus our analysis sheds light on why they are successful, and also suggests opportunities for future improvement. We present experiments that validate our theory, and show that dynamically focusing labeling resources on the most challenging examples -- in much the same way that boosting focuses the efforts of the weak learner -- leads to improved performance.

The Mpemba effect, where a hotter system can equilibrate faster than a cooler one, has long been a subject of fascination in classical physics. In the past few years, significant theoretical and experimental progress has been made in understanding its occurrence in both classical and quantum systems. In this review, we provide a concise overview of the Mpemba effect in quantum systems, with a focus on both open and isolated dynamics which give rise to distinct manifestations of this anomalous non-equilibrium phenomenon. We discuss key theoretical frameworks, highlight experimental observations, and explore the fundamental mechanisms that give rise to anomalous relaxation behaviors. Particular attention is given to the role of quantum fluctuations, integrability, and symmetry in shaping equilibration pathways. Finally, we outline open questions and future directions., Comment: 14 pages, 4 figures

Causal discovery can be computationally demanding for large numbers of variables. If we only wish to estimate the causal effects on a small subset of target variables, we might not need to learn the causal graph for all variables, but only a small subgraph that includes the targets and their adjustment sets. In this paper, we focus on identifying causal effects between target variables in a computationally and statistically efficient way. This task combines causal discovery and effect estimation, aligning the discovery objective with the effects to be estimated. We show that definite non-ancestors of the targets are unnecessary to learn causal relations between the targets and to identify efficient adjustments sets. We sequentially identify and prune these definite non-ancestors with our Sequential Non-Ancestor Pruning (SNAP) framework, which can be used either as a preprocessing step to standard causal discovery methods, or as a standalone sound and complete causal discovery algorithm. Our results on synthetic and real data show that both approaches substantially reduce the number of independence tests and the computation time without compromising the quality of causal effect estimations., Comment: Accepted at AISTATS 2025

Analysis of the previously classified delta Scuti variable star MW Camelopardalis using data from the Transiting Exoplanet Survey Telescope sparked a deeper inquiry due to the unexpected patterns within the target's observed-calculated graph. From the shape of the O-C diagram we have designed these objects as Staircase delta Scuti. The pattern was found to be replicated in the O-C graphs of seven additional targets. The objects are TIC 17931346, TIC 44845403, TIC 123580083, TIC 173503902, TIC 302394816, TIC 194944219, and TIC 396465600. The Q value for the targets, their position in the delta Scuti Leavitt Law, and location in the instability strip would show these objects to be low mass, fundamental pulsators, near the red edge of the instability strip. We also discuss the impact this phenomenon could have on the analysis of all pulsating variable stars., Comment: 10 pages, 9 figures, 2 tables, will be submitted to Astronomical Journal

The peer review process is crucial for ensuring the quality and reliability of scholarly work, yet assigning suitable reviewers remains a significant challenge. Traditional manual methods are labor-intensive and often ineffective, leading to nonconstructive or biased reviews. This paper introduces the exHarmony (eHarmony but for connecting experts to manuscripts) benchmark, designed to address these challenges by re-imagining the Reviewer Assignment Problem (RAP) as a retrieval task. Utilizing the extensive data from OpenAlex, we propose a novel approach that considers a host of signals from the authors, most similar experts, and the citation relations as potential indicators for a suitable reviewer for a manuscript. This approach allows us to develop a standard benchmark dataset for evaluating the reviewer assignment problem without needing explicit labels. We benchmark various methods, including traditional lexical matching, static neural embeddings, and contextualized neural embeddings, and introduce evaluation metrics that assess both relevance and diversity in the context of RAP. Our results indicate that while traditional methods perform reasonably well, contextualized embeddings trained on scholarly literature show the best performance. The findings underscore the importance of further research to enhance the diversity and effectiveness of reviewer assignments.

Prompting has emerged as the dominant paradigm for adapting large, pre-trained transformer-based models to downstream tasks. The Prompting Decision Transformer (PDT) enables large-scale, multi-task offline reinforcement learning pre-training by leveraging stochastic trajectory prompts to identify the target task. However, these prompts are sampled uniformly from expert demonstrations, overlooking a critical limitation: Not all prompts are equally informative for differentiating between tasks. To address this, we propose an inference time bandit-based prompt-tuning framework that explores and optimizes trajectory prompt selection to enhance task performance. Our experiments indicate not only clear performance gains due to bandit-based prompt-tuning, but also better sample complexity, scalability, and prompt space exploration compared to prompt-tuning baselines.

Soft-tissue surgeries, such as tumor resections, are complicated by tissue deformations that can obscure the accurate location and shape of tissues. By representing tissue surfaces as point clouds and applying non-rigid point cloud registration (PCR) methods, surgeons can better understand tissue deformations before, during, and after surgery. Existing non-rigid PCR methods, such as feature-based approaches, struggle with robustness against challenges like noise, outliers, partial data, and large deformations, making accurate point correspondence difficult. Although learning-based PCR methods, particularly Transformer-based approaches, have recently shown promise due to their attention mechanisms for capturing interactions, their robustness remains limited in challenging scenarios. In this paper, we present DefTransNet, a novel end-to-end Transformer-based architecture for non-rigid PCR. DefTransNet is designed to address the key challenges of deformable registration, including large deformations, outliers, noise, and partial data, by inputting source and target point clouds and outputting displacement vector fields. The proposed method incorporates a learnable transformation matrix to enhance robustness to affine transformations, integrates global and local geometric information, and captures long-range dependencies among points using Transformers. We validate our approach on four datasets: ModelNet, SynBench, 4DMatch, and DeformedTissue, using both synthetic and real-world data to demonstrate the generalization of our proposed method. Experimental results demonstrate that DefTransNet outperforms current state-of-the-art registration networks across various challenging conditions. Our code and data are publicly available.

The emergence of THz (Terahertz) frequency wireless networks holds great potential for advancing various high-demand services, including Industrial Internet of Things (IIoT) applications. These use cases benefit significantly from the ultra-high data rates, low latency, and high spatial resolution offered by THz frequencies. However, a primary well-known challenge of THz networks is their limited coverage range due to high path loss and vulnerability to obstructions. This paper addresses this limitation by proposing two novel multi-hop protocols, Table-Less (TL) and Table-Based (TB), respectively, both avoiding centralized control and/or control plane transmissions. Indeed, both solutions are distributed, simple, and rapidly adaptable to network changes. Simulation results demonstrate the effectiveness of our approaches, as well as revealing interesting trade-offs between TL and TB routing protocols, both in a real IIoT THz network and under static and dynamic conditions.

Domain specificity of embedding models is critical for effective performance. However, existing benchmarks, such as FinMTEB, are primarily designed for high-resource languages, leaving low-resource settings, such as Korean, under-explored. Directly translating established English benchmarks often fails to capture the linguistic and cultural nuances present in low-resource domains. In this paper, titled TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Models Bring? A Case Study on Korea Financial Texts, we introduce KorFinMTEB, a novel benchmark for the Korean financial domain, specifically tailored to reflect its unique cultural characteristics in low-resource languages. Our experimental results reveal that while the models perform robustly on a translated version of FinMTEB, their performance on KorFinMTEB uncovers subtle yet critical discrepancies, especially in tasks requiring deeper semantic understanding, that underscore the limitations of direct translation. This discrepancy highlights the necessity of benchmarks that incorporate language-specific idiosyncrasies and cultural nuances. The insights from our study advocate for the development of domain-specific evaluation frameworks that can more accurately assess and drive the progress of embedding models in low-resource settings., Comment: Submitted to ICLR@Financial AI

The goal of the TANGERINE project is to develop the next generation of monolithic silicon pixel detectors using a 65 nm CMOS imaging process, which offers a higher logic density and overall lower power consumption compared to previously used processes. A combination of Technology Computer-Aided Design (TCAD) and Monte Carlo (MC) simulations is used to understand the physical processes within the sensing element and thus the overall performance of the pixel detector. The response of the sensors can then be tested in laboratory and test beam facilities and compared to simulation results. Transient simulations allow for studying the response of the sensor as a function of time, such as the signal produced after a charged particle passes through the sensor. The study of these signals is important to understand the magnitude and timing of the response from the sensors and improve upon them. While TCAD simulations are accurate, the time required to produce a single pulse is large compared to a combination of MC and TCAD simulations. In this work, a validation of the transient simulation approach and studies on charge collection are presented., Comment: TIPP 2023 Proceedings

The high energy physics community recently gained access to the TPSCo 65 nm ISC (Image Sensor CMOS), which enables a higher in-pixel logic density in monolithic active pixel sensors (MAPS) compared to processes with larger feature sizes. To explore this novel technology, the Hybrid-to-Monolithic (H2M) test chip has been designed and manufactured. The design followed a digital-on-top design workflow and ports a hybrid pixel-detector architecture, with digital pulse processing in each pixel, into a monolithic chip. The chip matrix consists of 64$\times$16 square pixels with a size of 35$\times$35 um2, and a total active area of approximately 1.25 um2. The chip has been successfully integrated into the Caribou DAQ system. It is fully functional, and the measured threshold dispersion and noise agree with the expectation from front-end simulations. However, a non-uniform in-pixel response related to the size and location of the n-wells in the analog circuitry has been observed in test beam measurements and will be discussed in this contribution. This asymmetry in the pixel response, enhanced by the 35 um pixel pitch - larger than in other prototypes - and certain features of the readout circuit, has not been observed in prototypes with smaller pixel pitches in this technology.

Machine learning is a vital part of many real-world systems, but several concerns remain about the lack of interpretability, explainability and robustness of black-box AI systems. Concept-based models (CBM) address some of these challenges by learning interpretable concepts from high-dimensional data, e.g. images, which are used to predict labels. An important issue in CBMs is concept leakage, i.e., spurious information in the learned concepts, which effectively leads to learning "wrong" concepts. Current mitigating strategies are heuristic, have strong assumptions, e.g., they assume that the concepts are statistically independent of each other, or require substantial human interaction in terms of both interventions and labels provided by annotators. In this paper, we describe a framework that provides theoretical guarantees on the correctness of the learned concepts and on the number of required labels, without requiring any interventions. Our framework leverages causal representation learning (CRL) to learn high-level causal variables from low-level data, and learns to align these variables with interpretable concepts. We propose a linear and a non-parametric estimator for this mapping, providing a finite-sample high probability result in the linear case and an asymptotic consistency result for the non-parametric estimator. We implement our framework with state-of-the-art CRL methods, and show its efficacy in learning the correct concepts in synthetic and image benchmarks., Comment: 47 pages, 16 figures, 9 Tables, Preprint

Large language Models (LLMs) are highly sensitive to variations in prompt formulation, which can significantly impact their ability to generate accurate responses. In this paper, we introduce a new task, Prompt Sensitivity Prediction, and a dataset PromptSET designed to investigate the effects of slight prompt variations on LLM performance. Using TriviaQA and HotpotQA datasets as the foundation of our work, we generate prompt variations and evaluate their effectiveness across multiple LLMs. We benchmark the prompt sensitivity prediction task employing state-of-the-art methods from related tasks, including LLM-based self-evaluation, text classification, and query performance prediction techniques. Our findings reveal that existing methods struggle to effectively address prompt sensitivity prediction, underscoring the need to understand how information needs should be phrased for accurate LLM responses.

Real-world sensing challenges such as sensor failures, communication issues, and power constraints lead to data intermittency. An issue that is known to undermine the traditional classification task that assumes a continuous data stream. Previous works addressed this issue by designing bespoke solutions (i.e. task-specific and/or modality-specific imputation). These approaches, while effective for their intended purposes, had limitations in their applicability across different tasks and sensor modalities. This raises an important question: Can we build a task-agnostic imputation pipeline that is transferable to new sensors without requiring additional training? In this work, we formalise the concept of zero-shot imputation and propose a novel approach that enables the adaptation of pre-trained models to handle data intermittency. This framework, named NeuralPrefix, is a generative neural component that precedes a task model during inference, filling in gaps caused by data intermittency. NeuralPrefix is built as a continuous dynamical system, where its internal state can be estimated at any point in time by solving an Ordinary Differential Equation (ODE). This approach allows for a more versatile and adaptable imputation method, overcoming the limitations of task-specific and modality-specific solutions. We conduct a comprehensive evaluation of NeuralPrefix on multiple sensory datasets, demonstrating its effectiveness across various domains. When tested on intermittent data with a high 50% missing data rate, NeuralPreifx accurately recovers all the missing samples, achieving SSIM score between 0.93-0.96. Zero-shot evaluations show that NeuralPrefix generalises well to unseen datasets, even when the measurements come from a different modality., Comment: Accepted in PerCom 25

Data-driven advancements significantly contribute to societal progress, yet they also pose substantial risks to privacy. In this landscape, differential privacy (DP) has become a cornerstone in privacy preservation efforts. However, the adequacy of DP in scenarios involving correlated datasets has sometimes been questioned and multiple studies have hinted at potential vulnerabilities. In this work, we delve into the nuances of applying DP to correlated datasets by leveraging the concept of pointwise maximal leakage (PML) for a quantitative assessment of information leakage. Our investigation reveals that DP's guarantees can be arbitrarily weak for correlated databases when assessed through the lens of PML. More precisely, we prove the existence of a pure DP mechanism with PML levels arbitrarily close to that of a mechanism which releases individual entries from a database without any perturbation. By shedding light on the limitations of DP on correlated datasets, our work aims to foster a deeper understanding of subtle privacy risks and highlight the need for the development of more effective privacy-preserving mechanisms tailored to diverse scenarios., Comment: Published in the proceedings of the Annual Privacy Forum (APF) 2024

Accurate estimates of salmon escapement - the number of fish migrating upstream to spawn - are key data for conservation and fishery management. Existing methods for salmon counting using high-resolution imaging sonar hardware are non-invasive and compatible with computer vision processing. Prior work in this area has utilized object detection and tracking based methods for automated salmon counting. However, these techniques remain inaccessible to many sonar deployment sites due to limited compute and connectivity in the field. We propose an alternative lightweight computer vision method for fish counting based on analyzing echograms - temporal representations that compress several hundred frames of imaging sonar video into a single image. We predict upstream and downstream counts within 200-frame time windows directly from echograms using a ResNet-18 model, and propose a set of domain-specific image augmentations and a weakly-supervised training protocol to further improve results. We achieve a count error of 23% on representative data from the Kenai River in Alaska, demonstrating the feasibility of our approach., Comment: ECCV 2024. 6 pages, 2 figures