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Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

Recent advances in test-time scaling have shown promising results in improving Large Language Models (LLMs) performance through strategic computation allocation during inference. While this approach has demonstrated strong performance improvements in logical and mathematical reasoning tasks, its application to natural language generation (NLG), especially summarization, has yet to be explored. Multi-Document Summarization (MDS) is a challenging task that focuses on extracting and synthesizing useful information from multiple lengthy documents. Unlike reasoning tasks, MDS requires a more nuanced approach to prompt design and ensemble, as there is no "best" prompt to satisfy diverse summarization requirements. To address this, we propose a novel framework that leverages inference-time scaling for this task. Precisely, we take prompt ensemble approach by leveraging various prompt to first generate candidate summaries and then ensemble them with an aggregator to produce a refined summary. We also introduce two new evaluation metrics: Consistency-Aware Preference (CAP) score and LLM Atom-Content-Unit (ACU) score, to enhance LLM's contextual understanding while mitigating its positional bias. Extensive experiments demonstrate the effectiveness of our approach in improving summary quality while identifying and analyzing the scaling boundaries in summarization tasks.

Deep neural networks (DNNs) struggle at systematic generalization (SG). Several studies have evaluated the possibility to promote SG through the proposal of novel architectures, loss functions or training methodologies. Few studies, however, have focused on the role of training data properties in promoting SG. In this work, we investigate the impact of certain data distributional properties, as inductive biases for the SG ability of a multi-modal language model. To this end, we study three different properties. First, data diversity, instantiated as an increase in the possible values a latent property in the training distribution may take. Second, burstiness, where we probabilistically restrict the number of possible values of latent factors on particular inputs during training. Third, latent intervention, where a particular latent factor is altered randomly during training. We find that all three factors significantly enhance SG, with diversity contributing an 89\% absolute increase in accuracy in the most affected property. Through a series of experiments, we test various hypotheses to understand why these properties promote SG. Finally, we find that Normalized Mutual Information (NMI) between latent attributes in the training distribution is strongly predictive of out-of-distribution generalization. We find that a mechanism by which lower NMI induces SG is in the geometry of representations. In particular, we find that NMI induces more parallelism in neural representations (i.e., input features coded in parallel neural vectors) of the model, a property related to the capacity of reasoning by analogy.

Automated chart summarization is crucial for enhancing data accessibility and enabling efficient information extraction from visual data. While recent advances in visual-language models (VLMs) have demonstrated promise, existing methods often suffer from limitations in matching the generated summary to the chart data and in reasoning about complex chart patterns. This paper introduces End-to-End Visual Chain-of-Thought (V-CoT) for chart summarization, a novel approach optimized for Large Vision-Language Models (LVLMs). Our method directly trains an LVLM to process chart images and generate textual summaries in an end-to-end fashion, eliminating the need for explicit chart parsing modules. We incorporate a visual Chain-of-Thought mechanism through instruction fine-tuning, implicitly guiding the LVLM to perform visual reasoning steps during summary generation. Evaluated on the large-scale Chart-Sum-QA dataset, our V-CoT method significantly outperforms state-of-the-art baselines across a range of automatic metrics, including BLEU, BLEURT, CIDEr, and CS, and demonstrates superior matching degree and reasoning correctness in human evaluations. Ablation studies and detailed analyses further validate the effectiveness and robustness of our proposed approach, establishing a new benchmark for end-to-end chart summarization.

The two-dimensional magnetic Laplacian is considered. We calculate the leading term of the splitting between the first two eigenvalues of the operator in the semiclassical limit under the assumption that the magnetic field does not vanish and has two symmetric magnetic wells with respect to the coordinate axes. This is the first result of quantum tunneling between purely magnetic wells under generic assumptions. The proof, which strongly relies on microlocal analysis, reveals a purely magnetic Agmon distance between the wells. Surprisingly, it is discovered that the exponential decay of the eigenfunctions away from the magnetic wells is not crucial to derive the tunneling formula. The key is a microlocal exponential decay inside the characteristic manifold, with respect to the variable quantizing the classical center guide motion.

We consider linear stochastic bandits where the set of actions is an ellipsoid. We provide the first known minimax optimal algorithm for this problem. We first derive a novel information-theoretic lower bound on the regret of any algorithm, which must be at least $\Omega(\min(d \sigma \sqrt{T} + d \|\theta\|_{A}, \|\theta\|_{A} T))$ where $d$ is the dimension, $T$ the time horizon, $\sigma^2$ the noise variance, $A$ a matrix defining the set of actions and $\theta$ the vector of unknown parameters. We then provide an algorithm whose regret matches this bound to a multiplicative universal constant. The algorithm is non-classical in the sense that it is not optimistic, and it is not a sampling algorithm. The main idea is to combine a novel sequential procedure to estimate $\|\theta\|$, followed by an explore-and-commit strategy informed by this estimate. The algorithm is highly computationally efficient, and a run requires only time $O(dT + d^2 \log(T/d) + d^3)$ and memory $O(d^2)$, in contrast with known optimistic algorithms, which are not implementable in polynomial time. We go beyond minimax optimality and show that our algorithm is locally asymptotically minimax optimal, a much stronger notion of optimality. We further provide numerical experiments to illustrate our theoretical findings., Comment: 20 pages, 3 figures

The Light-only Liquid Xenon experiment (LoLX) employs a small-scale detector equipped with 96 Hamamatsu VUV4 silicon photomultipliers (SiPMs) submerged in 5 kg of liquid xenon (LXe) to perform characterization measurements of light production, transport and detection in xenon. In this work, we perform a novel measurement of the "external cross-talk" (ExCT) of SiPMs, where photons produced in the avalanche process escape the device and produce correlated signals on other SiPMs. SiPMs are the photodetector technology of choice for next generation rare-event search experiments; understanding the sources and effects of correlated noise in SiPMs is critical for producing accurate estimates of detector performance and sensitivity projections. We measure the probability to observe ExCT through timing correlation of detected photons in low-light conditions within LoLX. Measurements of SiPM ExCT are highly detector dependent; thus the ExCT process is simulated and modelled using the Geant4 framework. Using the simulation, we determine the average transport and detection efficiency for ExCT photons within LoLX, a necessary input to extract the expected ExCT probability from the data. For an applied overvoltage of 4 V and 5 V, we measure a mean number of photons emitted into the LXe per avalanche of $0.5^{+0.3}_{-0.2}$ and $0.6^{+0.3}_{-0.2}$, respectively. Using an optical model to describe photon transmission through the SiPM surface, this corresponds to an estimated photon yield inside the bulk silicon of $20^{+11}_{-9}$ and $25^{+12}_{-9}$ photons per avalanche. The relative increase in intensity of SiPM ExCT emission between 4 V and 5 V is consistent with expectation for the linear increase of gain with respect to overvoltage., Comment: 15 pages, 11 figures, submitted to EPJC

Conversational query rewriting is crucial for effective conversational search, yet traditional supervised methods require substantial labeled data, which is scarce in low-resource settings. This paper introduces Prompt-Guided In-Context Learning, a novel approach that leverages the in-context learning capabilities of Large Language Models (LLMs) for few-shot conversational query rewriting. Our method employs carefully designed prompts, incorporating task descriptions, input/output format specifications, and a small set of illustrative examples, to guide pre-trained LLMs to generate context-independent queries without explicit fine-tuning. Extensive experiments on benchmark datasets, TREC and Taskmaster-1, demonstrate that our approach significantly outperforms strong baselines, including supervised models and contrastive co-training methods, across various evaluation metrics such as BLEU, ROUGE-L, Success Rate, and MRR. Ablation studies confirm the importance of in-context examples, and human evaluations further validate the superior fluency, relevance, and context utilization of our generated rewrites. The results highlight the potential of prompt-guided in-context learning as an efficient and effective paradigm for low-resource conversational query rewriting, reducing the reliance on extensive labeled data and complex training procedures.

Patients have long sought health information online, and increasingly, they are turning to generative AI to answer their health-related queries. Given the high stakes of the medical domain, techniques like retrieval-augmented generation and citation grounding have been widely promoted as methods to reduce hallucinations and improve the accuracy of AI-generated responses and have been widely adopted into search engines. This paper argues that even when these methods produce literally accurate content drawn from source documents sans hallucinations, they can still be highly misleading. Patients may derive significantly different interpretations from AI-generated outputs than they would from reading the original source material, let alone consulting a knowledgeable clinician. Through a large-scale query analysis on topics including disputed diagnoses and procedure safety, we support our argument with quantitative and qualitative evidence of the suboptimal answers resulting from current systems. In particular, we highlight how these models tend to decontextualize facts, omit critical relevant sources, and reinforce patient misconceptions or biases. We propose a series of recommendations -- such as the incorporation of communication pragmatics and enhanced comprehension of source documents -- that could help mitigate these issues and extend beyond the medical domain., Comment: Preprint

Physics-informed neural networks (PINNs) have shown promise in solving partial differential equations (PDEs), with growing interest in their energy-efficient, real-time training on edge devices. Photonic computing offers a potential solution to achieve this goal because of its ultra-high operation speed. However, the lack of photonic memory and the large device sizes prevent training real-size PINNs on photonic chips. This paper proposes a completely back-propagation-free (BP-free) and highly salable framework for training real-size PINNs on silicon photonic platforms. Our approach involves three key innovations: (1) a sparse-grid Stein derivative estimator to avoid the BP in the loss evaluation of a PINN, (2) a dimension-reduced zeroth-order optimization via tensor-train decomposition to achieve better scalability and convergence in BP-free training, and (3) a scalable on-chip photonic PINN training accelerator design using photonic tensor cores. We validate our numerical methods on both low- and high-dimensional PDE benchmarks. Through circuit simulation based on real device parameters, we further demonstrate the significant performance benefit (e.g., real-time training, huge chip area reduction) of our photonic accelerator.

NoSQL databases have become increasingly popular due to their outstanding performance in handling large-scale, unstructured, and semi-structured data, highlighting the need for user-friendly interfaces to bridge the gap between non-technical users and complex database queries. In this paper, we introduce the Text-to-NoSQL task, which aims to convert natural language queries into NoSQL queries, thereby lowering the technical barrier for non-expert users. To promote research in this area, we developed a novel automated dataset construction process and released a large-scale and open-source dataset for this task, named TEND (short for Text-to-NoSQL Dataset). Additionally, we designed a SLM (Small Language Model)-assisted and RAG (Retrieval-augmented Generation)-assisted multi-step framework called SMART, which is specifically designed for Text-to-NoSQL conversion. To ensure comprehensive evaluation of the models, we also introduced a detailed set of metrics that assess the model's performance from both the query itself and its execution results. Our experimental results demonstrate the effectiveness of our approach and establish a benchmark for future research in this emerging field. We believe that our contributions will pave the way for more accessible and intuitive interactions with NoSQL databases.

Entity tracking is a fundamental challenge in natural language understanding, requiring models to maintain coherent representations of entities. Previous work has benchmarked entity tracking performance in purely text-based tasks. We introduce MET-Bench, a multimodal entity tracking benchmark designed to evaluate the ability of vision-language models to track entity states across modalities. Using two structured domains, Chess and the Shell Game, we assess how effectively current models integrate textual and image-based state updates. Our findings reveal a significant performance gap between text-based and image-based tracking and that this performance gap stems from deficits in visual reasoning rather than perception. We further show that explicit text-based reasoning strategies improve performance, yet substantial limitations remain, especially in long-horizon multimodal scenarios. Our results highlight the need for improved multimodal representations and reasoning techniques to bridge the gap between textual and visual entity tracking.

A science is defined by a set of encyclopedic knowledge related to facts or phenomena following rules or evidenced by experimentally-driven observations. Computer Science and in particular computer networks is a relatively new scientific domain maturing over years and adopting the best practices inherited from more fundamental disciplines. The design of past, present and future networking components and architectures have been assisted, among other methods, by experimentally-driven research and in particular by the deployment of test platforms, usually named as testbeds. However, often experimentally-driven networking research used scattered methodologies, based on ad-hoc, small-sized testbeds, producing hardly repeatable results. We believe that computer networks needs to adopt a more structured methodology, supported by appropriate instruments, to produce credible experimental results supporting radical and incremental innovations. This paper reports lessons learned from the design and operation of test platforms for the scientific community dealing with digital infrastructures. We introduce the SLICES initiative as the outcome of several years of evolution of the concept of a networking test platform transformed into a scientific instrument. We address the challenges, requirements and opportunities that our community is facing to manage the full research-life cycle necessary to support a scientific methodology.

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

To design social robots to effectively promote health behavior change, it is essential to understand how people respond to various health communication strategies employed by these robots. This study examines the effectiveness of two types of social control strategies from a social robot, relationship-focused strategies (emphasizing relational consequences) and target-focused strategies (emphasizing health consequences), in encouraging people to reduce sedentary behavior. A two-session lab experiment was conducted (n = 135), where participants first played a game with a robot, followed by the robot persuading them to stand up and move using one of the strategies. Half of the participants joined a second session to have a repeated interaction with the robot. Results showed that relationship-focused strategies motivated participants to stay active longer. Repeated sessions did not strengthen participants' relationship with the robot, but those who felt more attached to the robot responded more actively to the target-focused strategies. These findings offer valuable insights for designing persuasive strategies for social robots in health communication contexts., Comment: Accepted to HRI 2025

Tuning the chemical potential of a solid to the vicinity of a van Hove singularity (vHS) is a well-established route to discovering emergent quantum phases. In monolayer graphene, the use of electron-donating metal layers has recently emerged as a method to dope the chemical potential to the nearest vHS, as evidenced by Angle-Resolved Photoemission Spectroscopy (ARPES) measurements. In this work, we study the spatial uniformity of the doping from this process using spectroscopic imaging scanning tunneling microscopy (SI-STM). Using molecular beam epitaxy (MBE), we achieve electron doping of graphene on SiC using Ytterbium (Yb-Graphene). We show using in-situ ARPES that the chemical potential is shifted to within 250 meV of the vHS. Using in-situ SI-STM, we establish that there exists significant inhomogeneity in the vHS position in overdoped graphene. We find two separate reasons for this. First, the spatial inhomogeneity of the intercalated Yb leads to local variations in the doping, with a length scale of inhomogeneity set by the screening length of ~ 3 nm. Second, we observe the presence of substitutional Yb dopants in the graphene basal plane. These Yb dopants cause a strong local shift of the doping, along with a renormalization of the quasiparticle amplitude. Theoretical calculations confirm that the Yb impurities effectively change the local potential, thus energetically shifting the position of the van Hove singularity. Our results point to the importance of considering the spatial structure of doping and its inextricable link to electronic structure.

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal, and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein-truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5 to 37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke-Fahn-Marsden Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year, and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002, and P = 0.012)., Comment: PMID: 33150406; PMCID: PMC7719027

Large foundation models have shown strong open-world generalization to complex problems in vision and language, but similar levels of generalization have yet to be achieved in robotics. One fundamental challenge is the lack of robotic data, which are typically obtained through expensive on-robot operation. A promising remedy is to leverage cheaper, off-domain data such as action-free videos, hand-drawn sketches or simulation data. In this work, we posit that hierarchical vision-language-action (VLA) models can be more effective in utilizing off-domain data than standard monolithic VLA models that directly finetune vision-language models (VLMs) to predict actions. In particular, we study a class of hierarchical VLA models, where the high-level VLM is finetuned to produce a coarse 2D path indicating the desired robot end-effector trajectory given an RGB image and a task description. The intermediate 2D path prediction is then served as guidance to the low-level, 3D-aware control policy capable of precise manipulation. Doing so alleviates the high-level VLM from fine-grained action prediction, while reducing the low-level policy's burden on complex task-level reasoning. We show that, with the hierarchical design, the high-level VLM can transfer across significant domain gaps between the off-domain finetuning data and real-robot testing scenarios, including differences on embodiments, dynamics, visual appearances and task semantics, etc. In the real-robot experiments, we observe an average of 20% improvement in success rate across seven different axes of generalization over OpenVLA, representing a 50% relative gain. Visual results are provided at: https://hamster-robot.github.io/, Comment: to be published in ICLR 2025

Giant impacts dominate the late stages of accretion of rocky planets. They contribute to the heating, melting, and sometimes vaporizing of the bodies involved in the impacts. Due to fractionation during melting and vaporization, planet-building impacts can significantly change the composition and geochemical signatures of rocky objects. Using first-principles molecular dynamics simulations, we analyze the shock behavior of complex realistic silicate systems, representative of both rocky bodies. We introduce a novel criterion for vapor formation that uses entropy calculations to determine the minimum impact velocity required to pass the threshold for vapor production. We derive impact velocity criteria for vapor formation (7.1 km per s for chondritic bodies) and show that this threshold is reached in 61 and 89 percent of impacts in dynamical simulations of the late stages of accretion with classical and annulus starting configuration (respectively) for analogs of Earth. These outcomes should be nuanced by factors such as the impact angle and the mass of the impacting bodies, which further influence the vaporization dynamics and the resultant material distribution. Our findings indicate that vaporization was common during accretion and likely played a crucial role in shaping the early environments and material properties of terrestrial planets., Comment: 17 pages, 4 figures

Solving hard optimization problems is one of the most promising application domains for quantum computers due to the ubiquity of such problems in industry and the availability of broadly applicable quantum speedups. However, the ability of near-term quantum computers to tackle industrial-scale optimization problems is limited by their size and the overheads of quantum error correction. Quantum Random Access Optimization (QRAO) has been proposed to reduce the space requirements of quantum optimization. However, to date QRAO has only been implemented using variational algorithms, which suffer from the need to train instance-specific variational parameters, making them difficult to scale. We propose and benchmark a non-variational approach to QRAO based on the Quantum Alternating Operator Ansatz (QAOA) for the MaxCut problem. We show that instance-independent ``fixed'' parameters achieve good performance, removing the need for variational parameter optimization. Additionally, we evaluate different design choices, such as various mixers and initial states, as well as QAOA operator implementations when customizing for QRAO, and identify a strategy that performs well in practice. Our results pave the way for the practical execution of QRAO on early fault-tolerant quantum computers., Comment: 8 pages, 8 figures

Randomized experiments are the preferred approach for evaluating the effects of interventions, but they are costly and often yield estimates with substantial uncertainty. On the other hand, in silico experiments leveraging foundation models offer a cost-effective alternative that can potentially attain higher statistical precision. However, the benefits of in silico experiments come with a significant risk: statistical inferences are not valid if the models fail to accurately predict experimental responses to interventions. In this paper, we propose a novel approach that integrates the predictions from multiple foundation models with experimental data while preserving valid statistical inference. Our estimator is consistent and asymptotically normal, with asymptotic variance no larger than the standard estimator based on experimental data alone. Importantly, these statistical properties hold even when model predictions are arbitrarily biased. Empirical results across several randomized experiments show that our estimator offers substantial precision gains, equivalent to a reduction of up to 20% in the sample size needed to match the same precision as the standard estimator based on experimental data alone.

The garnet compound Yb$_3$Ga$_5$O$_{12}$ is a fascinating material that is considered highly suitable for low-temperature refrigeration, via the magnetocaloric effect, in addition to enabling the exploration of quantum states with long-range dipolar interactions. It has previously been theorized that the magnetocaloric effect can be enhanced, in Yb$_3$Ga$_5$O$_{12}$ , via magnetic soft mode excitations which in the hyperkagome structure would be derived from an emergent magnetic structure formed from nanosized 10-spin loops. We study the magnetic field dependence of bands of magnetic soft mode excitations in the effective spin $S = 1/2$ hyperkagome compound Yb$_3$Ga$_5$O$_{12}$ using single crystal inelastic neutron scattering. We probe the magnetically short ranged ordered state, in which we determine magnetic nanoscale structures coexisting with a fluctuating state, and the magnetically saturated state. We determine that Yb$_3$Ga$_5$O$_{12}$ can be described as a quantum dipolar magnet with perturbative weak near-neighbor and inter-hyperkagome exchange interaction. The magnetic excitations, under the application of a magnetic field, reveal highly robust soft modes with distinctive signatures of the quantum nature of the Yb3+ spins. Our results enhance our understanding of soft modes in topological frustrated magnets that drive both the unusual physics of quantum dipolar systems and future refrigerant material design.

We use JWST Near-Infrared Spectrograph (NIRSpec) observations from the the Cosmic Evolution Early Release survey (CEERS), GLASS-JWST ERS (GLASS), and JWST Advanced Deep Extragalactic Survey (JADES) to measure rest-frame optical emission-line ratios of 90 galaxies at z>4. The stacked spectra of galaxies with and without a broad-line feature reveal a difference in the [OIII]$\lambda$ 4363 and H$\gamma$ ratios. This motivated our investigation of the [OIII]/H$\gamma$ vs [NeIII]/[OII] diagram. We define two AGN/SF classification lines based on 1869 SDSS galaxies at z$\sim$0. After applying a redshift correction to the AGN/SF lines we find 76.8% of BLAGN continue to land in the AGN region of the diagnostic largely due to the [NeIII]/[OII] ratio. However, 40.2% of non-BLAGN land in the AGN region as well, this could be due to star forming galaxies having harder ionization of there are narrow line AGN which are not accounted for. This indicates the potential of the [NeIII]/[OII] ratio to continue classifying galaxies to z$\sim$6. We further inspect galaxies without broad emission lines in each region of [OIII]/H\gamma vs [NeIII]/[OII] diagram and found that they have slightly stronger CIII]$\lambda$1908 fluxes and equivalent width when landing in the BLAGN region. However, the cause of this higher ionization is unclear. Additionally, we find that BLAGN are characterized by a higher ionization (at constant electron temperature) compared to non-broad line galaxies., Comment: 14 pages, 8 figures

Segmentation of brain tumors is a critical step in treatment planning, yet manual segmentation is both time-consuming and subjective, relying heavily on the expertise of radiologists. In Sub-Saharan Africa, this challenge is magnified by overburdened medical systems and limited access to advanced imaging modalities and expert radiologists. Automating brain tumor segmentation using deep learning offers a promising solution. Convolutional Neural Networks (CNNs), especially the U-Net architecture, have shown significant potential. However, a major challenge remains: achieving generalizability across different datasets. This study addresses this gap by developing a deep learning ensemble that integrates UNet3D, V-Net, and MSA-VNet models for the semantic segmentation of gliomas. By initially training on the BraTS-GLI dataset and fine-tuning with the BraTS-SSA dataset, we enhance model performance. Our ensemble approach significantly outperforms individual models, achieving DICE scores of 0.8358 for Tumor Core, 0.8521 for Whole Tumor, and 0.8167 for Enhancing Tumor. These results underscore the potential of ensemble methods in improving the accuracy and reliability of automated brain tumor segmentation, particularly in resource-limited settings.

Surveying prior literature to establish a foundation for new knowledge is essential for scholarly progress. However, survey articles are resource-intensive and challenging to create, and can quickly become outdated as new research is published, risking information staleness and inaccuracy. Keeping survey articles current with the latest evidence is therefore desirable, though there is a limited understanding of why, when, and how these surveys should be updated. Toward this end, through a series of in-depth retrospective interviews with 11 researchers, we present an empirical examination of the work practices in authoring and updating survey articles in computing research. We find that while computing researchers acknowledge the value in maintaining an updated survey, continuous updating remains unmanageable and misaligned with academic incentives. Our findings suggest key leverage points within current workflows that present opportunities for enabling technologies to facilitate more efficient and effective updates., Comment: 10 pages; To appear at CHI 2025

Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks., Comment: Forecasting, environment monitoring, transfer learning, recurrent neural network, airborne particle matter

This paper examines the systemic risks posed by incremental advancements in artificial intelligence, developing the concept of `gradual disempowerment', in contrast to the abrupt takeover scenarios commonly discussed in AI safety. We analyze how even incremental improvements in AI capabilities can undermine human influence over large-scale systems that society depends on, including the economy, culture, and nation-states. As AI increasingly replaces human labor and cognition in these domains, it can weaken both explicit human control mechanisms (like voting and consumer choice) and the implicit alignments with human interests that often arise from societal systems' reliance on human participation to function. Furthermore, to the extent that these systems incentivise outcomes that do not line up with human preferences, AIs may optimize for those outcomes more aggressively. These effects may be mutually reinforcing across different domains: economic power shapes cultural narratives and political decisions, while cultural shifts alter economic and political behavior. We argue that this dynamic could lead to an effectively irreversible loss of human influence over crucial societal systems, precipitating an existential catastrophe through the permanent disempowerment of humanity. This suggests the need for both technical research and governance approaches that specifically address the risk of incremental erosion of human influence across interconnected societal systems., Comment: 19 pages, 2 figures

Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. The transition metal and nonmetallic atoms in MXenes can exhibit distinct coordination environments, potentially leading to a wide variety of 2D phases. Despite extensive research and significant advancements, a fundamental understanding of MXenes' phase diversity and its relationship with their hierarchical precursors, including intermediate MAX phases and parent bulk phases, remains limited. Using high-throughput modeling based on first-principles density functional theory, we unveil a wide range of MXenes and comprehensively evaluate their relative stabilities across a large chemical space. The key lies in considering both octahedral and trigonal prismatic coordination environments characteristic of various bulk phases. Through this comprehensive structural library of MXenes, we uncover a close alignment between the phase stability of MXenes and that of their hierarchical 3D counterparts. Building on this, we demonstrate a new design strategy where the atomic coordination environments in parent bulk phases can serve as reliable predictors for the design of MXenes, reducing reliance on intermediate MAX phases. Our study significantly expands the landscape of MXenes, at least doubling the number of possible structures., Comment: 17 pages, 5 illustrations (3 figures and 2 tables)

This article deals with a generalization of the superadiabatic projectors method. In a general framework, the well-known superadiabatic projectors are constructed and accurately described in the case of rank one, when a remarkable factorization occurs. We apply these ideas to spectral theory and we explain how our abstract results allow to recover or improve recent results about the semiclassical magnetic Laplacian.

We report measurements of anisotropic triple-$q$ charge density wave (CDW) fluctuations in the transition metal dichalcogenide 1$T$-TiSe$_2$ over a large volume of reciprocal space with X-ray diffuse scattering. Above the transition temperature, $T_{\text{CDW}}$, the out-of-plane diffuse scattering is characterized by rod-like structures which indicate that the CDW fluctuations in neighboring layers are largely decoupled. In addition, the in-plane diffuse scattering is marked by ellipses which reveal that the in-plane fluctuations are anisotropic. Our analysis of the diffuse scattering line shapes and orientations suggests that the three charge density wave components contain independent phase fluctuations. At $T_{\text{CDW}}$, long range coherence is established in both the in-plane and out-of-plane directions, consistent with the large observed value of the CDW gap compared to $T_{\text{CDW}}$, and the predicted presence of a hierarchy of energy scales.

Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we developed CT-FM, a large-scale 3D image-based pre-trained model designed explicitly for various radiological tasks. CT-FM was pre-trained using 148,000 computed tomography (CT) scans from the Imaging Data Commons through label-agnostic contrastive learning. We evaluated CT-FM across four categories of tasks, namely, whole-body and tumor segmentation, head CT triage, medical image retrieval, and semantic understanding, showing superior performance against state-of-the-art models. Beyond quantitative success, CT-FM demonstrated the ability to cluster regions anatomically and identify similar anatomical and structural concepts across scans. Furthermore, it remained robust across test-retest settings and indicated reasonable salient regions attached to its embeddings. This study demonstrates the value of large-scale medical imaging foundation models and by open-sourcing the model weights, code, and data, aims to support more adaptable, reliable, and interpretable AI solutions in radiology., Comment: 6 figures, followed by 9 Extended Data Figures and a Supplementary Information document

The \emph{$r$-neighbourhood complexity} of a graph $G$ is the function counting, for a given integer $k$, the largest possible number, over all vertex-subsets $A$ of size $k$, of subsets of $A$ realized as the intersection between the $r$-neighbourhood of some vertex and $A$. A~refinement of this notion is the \emph{$r$-profile complexity}, that counts the maximum number of distinct distance-vectors from any vertex to the vertices of $A$, ignoring distances larger than~$r$. Typically, in structured graph classes such as graphs of bounded VC-dimension or chordal graphs, these functions are bounded, leading to insights into their structural properties and efficient algorithms. We improve existing bounds on the $r$-profile complexity (and thus on the $r$-neighbourhood complexity) for graphs in several structured graph classes. We show that the $r$-profile complexity of graphs excluding $K_h$ as a minor is in $O_h(r^{3h-3}k)$. For graphs of treewidth at most~$t$, we give a bound in $O_t(r^{t+1}k)$, which is tight up to a function of~$t$ as a factor. These bounds improve results of Joret and Rambaud and answer a question of their paper [Combinatorica, 2024]. We also apply our methods to other classes of bounded expansion such as graphs excluding a fixed complete graph as a subdivision. For outerplanar graphs, we can improve our treewidth bound by a factor of $r$ and conjecture that a similar improvement holds for graphs with bounded simple treewidth. For graphs of treelength at most~$\ell$, we give the upper bound of $O(k(r^2(\ell+1)^k))$, which we improve to $O\left (k\cdot (r 2^k + r^2k^2) \right)$ in the case of chordal graphs and $O(k^2r)$ for interval graphs. Our bounds also imply relations between the order, diameter and metric dimension of graphs in these classes, improving results from [Beaudou et al., SIDMA 2017]., Comment: expanded version

With the development of deep learning, Dynamic Portfolio Optimization (DPO) problem has received a lot of attention in recent years, not only in the field of finance but also in the field of deep learning. Some advanced research in recent years has proposed the application of Deep Reinforcement Learning (DRL) to the DPO problem, which demonstrated to be more advantageous than supervised learning in solving the DPO problem. However, there are still certain unsolved issues: 1) DRL algorithms usually have the problems of slow learning speed and high sample complexity, which is especially problematic when dealing with complex financial data. 2) researchers use DRL simply for the purpose of obtaining high returns, but pay little attention to the problem of risk control and trading strategy, which will affect the stability of model returns. In order to address these issues, in this study we revamped the intrinsic structure of the model based on the Deep Deterministic Policy Gradient (DDPG) and proposed the Augmented DDPG model. Besides, we also proposed an innovative risk control strategy based on Quantum Price Levels (QPLs) derived from Quantum Finance Theory (QFT). Our experimental results revealed that our model has better profitability as well as risk control ability with less sample complexity in the DPO problem compared to the baseline models., Comment: 8 pages

Using optical and near-infrared images of the Cassiopeia A (Cas A) supernova remnant covering the time period 1951 to 2022, together with optical spectra of selected filaments, we present an investigation of Cas A's reverse shock velocity and the effects it has on the remnant's metal-rich ejecta. We find the sequence of optical ejecta brightening and the appearance of new optical ejecta indicating the advancement of the remnant's reverse shock in the remnant's main shell has velocities typically between 1000 and 2000 km/s, which is ~1000 km/s less than recent measurements made in X-rays. We further find the reverse shock appears to move much more slowly and is nearly even stationary in the sky frame along the remnant's western limb. However, we do not find the reverse shock to move inward at velocities as large as ~2000 km/s as has been reported. Optical ejecta in Cas A's main emission shell have proper motions indicating outward tangential motions ~3500 - 6000 km/s, with the smaller values preferentially along the remnant's southern regions which we speculate may be partially the cause of the remnant's faint and more slowly evolving southern sections. Following interaction with the reverse shock, ejecta knots exhibit extended mass ablated trails 0.2" - 0.5" in length leading to extended emission indicating reverse shock induced decelerated velocities as large as 1000 km/s. Such ablated material is most prominently seen in higher ionization line emissions, whereas denser parts of ejecta knots show surprisingly little deceleration., Comment: 36 pages, 20 figures

One of the main unknowns in galaxy evolution is how gas flows into and out of galaxies in the circumgalactic medium (CGM). Studies observing the CGM in absorption using multiple or extended background objects suggest a high degree of variation on relatively small ($\lesssim 1$ kpc) spatial scales. Similarly, high-resolution simulations generally exhibit small-scale substructure in the gas around galaxies. We examine the small-scale structure of the $z = 1$ CGM using simulations from the FOGGIE (Figuring Out Gas & Galaxies in Enzo) project. We select gaseous substructures ("clumps") by their local overdensity and investigate their physical properties, including temperature, metallicity, and kinematics with respect to the galaxy and the nearby surroundings. FOGGIE resolves clumps down to sphericalized radii $R \sim 0.25$ kpc at $z = 1$. The distribution of clumps peaks at $\sim 10^5$ $\rm M_{\odot}$ and $10^{4}$ K, consistent with relatively condensed, cool gas with a slight preference for inflow-like velocities. Many clumps show internal temperature and density variations, and thus internally varying ionization levels for key diagnostic ions such as HI, MgII, and OVI. The average metallicity in clumps is about a factor 1.5--2$\times$ lower in metallicity than nearby gas, suggesting that the metals are not well-mixed between structured and diffuse CGM, which may have implications for observational metallicity estimations of dense CGM clouds. We estimate the survivability of CGM clumps and find that structures larger than 0.5 kpc are generally long-lived. Finally, we qualitatively compare the simulated cloud properties to Milky Way high-velocity clouds., Comment: submitted to ApJ

We investigate the entanglement of a ladder of spins formed by two sublattices, a ''cluster'' chain and the ''environment'', consisting of independent spins, both coupled by an exchange interaction and evolving under a unitary discrete time dynamics. The automaton is defined by the composition of the two body spin swap gate (between sublattices) and the three body cluster interaction. We observe that, depending on the set of coupling constants, the cluster subsystem evolves towards states corresponding to different entanglement phases. In the weak coupling regime the subsystem remains near the topological cluster state. Increasing the coupling strength leads to random states which transform from almost pure to fully mixed, according to the effective number of the environment active degrees of freedom.

In this arxiv-post I present my solutions (published or not) to Problems that appeared in Amer. Math. Monthly, Math. Magazine, Elemente der Mathematik and CRUX, that were mostly done in collaboration with Rudolf Rupp. Some of them (including a few own proposals which were published) were also done in cooperation with Rainer Br\"uck, Bikash Chakraborty, Pamela Gorkin, Gerd Herzog, J\'er\^ome No\"el, Peter Pflug and Amol Sasane., Comment: This submission will be updated on a regular basis

Network coding enhances performance in network communications and distributed storage by increasing throughput and robustness while reducing latency. Batched Sparse (BATS) codes are a class of capacity-achieving network codes, but their practical applications are hindered by their structure, computational intensity, and power demands of finite field operations. Most literature focuses on algorithmic-level techniques to improve coding efficiency. Optimization with an algorithm/hardware co-designing approach has long been neglected. Leveraging the unique structure of BATS codes, we first present CS-BATS, a hardware-friendly variant. Next we propose a simple but effective bounded-value generator, to reduce the size of a finite field multiplier by up to 70%. Finally, we report on a scalable and resource-efficient FPGA-based network coding accelerator that achieves a throughput of 27 Gbps, a speedup of more than 300 over software.

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables

In the neighborhood growth dynamics on a Hamming rectangle $[0,m-1]\times[0,n-1]\subseteq \mathbb{Z}_+^2$, the decision to add a point is made by counting the currently occupied points on the horizontal and the vertical line through it, and checking whether the pair of counts lies outside a fixed Young diagram. After the initially occupied set is chosen, the synchronous rule is iterated. The Young domination number with a fixed latency $L$ is the smallest cardinality of an initial set that covers the rectangle by $L$ steps, for $L=0,1,\ldots$ We compute this number for some special cases, including $k$-domination for any $k$ when $m=n$, and devise approximation algorithms in the general case. These results have implications in extremal graph theory, via an equivalence between the case $L = 1$ and bipartite Tur\'an numbers for families of double stars. Our approach is based on a variety of techniques including duality, algebraic formulations, explicit constructions, and dynamic programming.

In Sub-Saharan Africa (SSA), the utilization of lower-quality Magnetic Resonance Imaging (MRI) technology raises questions about the applicability of machine learning methods for clinical tasks. This study aims to provide a robust deep learning-based brain tumor segmentation (BraTS) method tailored for the SSA population using a threefold approach. Firstly, the impact of domain shift from the SSA training data on model efficacy was examined, revealing no significant effect. Secondly, a comparative analysis of 3D and 2D full-resolution models using the nnU-Net framework indicates similar performance of both the models trained for 300 epochs achieving a five-fold cross-validation score of 0.93. Lastly, addressing the performance gap observed in SSA validation as opposed to the relatively larger BraTS glioma (GLI) validation set, two strategies are proposed: fine-tuning SSA cases using the GLI+SSA best-pretrained 2D fullres model at 300 epochs, and introducing a novel neural style transfer-based data augmentation technique for the SSA cases. This investigation underscores the potential of enhancing brain tumor prediction within SSA's unique healthcare landscape.

Operator-splitting methods are widely used to solve differential equations, especially those that arise from multi-scale or multi-physics models, because a monolithic (single-method) approach may be inefficient or even infeasible. The most common operator-splitting methods are the first-order Lie--Trotter (or Godunov) and the second-order Strang (Strang--Marchuk) splitting methods. High-order splitting methods with real coefficients require backward-in-time integration in each operator and hence may be adversely impacted by instability for certain operators such as diffusion. However, besides the method coefficients, there are many other ancillary aspects to an overall operator-splitting method that are important but often overlooked. For example, the operator ordering and the choice of sub-integration methods can significantly affect the stability and efficiency of an operator-splitting method. In this paper, we investigate some design principles for the construction of operator-splitting methods, including minimization of local error measure, choice of sub-integration method, maximization of linear stability, and minimization of overall computational cost. We propose a new four-stage, third-order, 2-split operator-splitting method with seven sub-integrations per step and optimized linear stability for a benchmark problem from cardiac electrophysiology. We then propose a general principle to further improve stability and efficiency of such operator-splitting methods by using low-order, explicit sub-integrators for unstable sub-integrations. We demonstrate an almost 30\% improvement in the performance of methods derived from these design principles compared to the best-known third-order methods., Comment: 20 pages, 5 figures

Health outcomes depend on complex environmental and sociodemographic factors whose effects change over location and time. Only recently has fine-grained spatial and temporal data become available to study these effects, namely the MEDSAT dataset of English health, environmental, and sociodemographic information. Leveraging this new resource, we use a variety of variable importance techniques to robustly identify the most informative predictors across multiple health outcomes. We then develop an interpretable machine learning framework based on Generalized Additive Models (GAMs) and Multiscale Geographically Weighted Regression (MGWR) to analyze both local and global spatial dependencies of each variable on various health outcomes. Our findings identify NO2 as a global predictor for asthma, hypertension, and anxiety, alongside other outcome-specific predictors related to occupation, marriage, and vegetation. Regional analyses reveal local variations with air pollution and solar radiation, with notable shifts during COVID. This comprehensive approach provides actionable insights for addressing health disparities, and advocates for the integration of interpretable machine learning in public health., Comment: AAAI

In the corona, plasma is accelerated to hundreds of kilometers per second, and heated to temperatures hundreds of times hotter than the Sun's surface, before it escapes to form the solar wind. Decades of space-based experiments have shown that the energization process does not stop after it escapes. Instead, the solar wind continues to accelerate and it cools far more slowly than a freely-expanding adiabatic gas. Recent work suggests that fast solar wind requires additional momentum beyond what can be provided by the observed thermal pressure gradients alone whereas it is sufficient for the slowest wind. The additional acceleration for fast wind can be provided through an Alfv\'en wave pressure gradient. Beyond this fast-slow categorization, however, a subset of slow solar wind exhibits high Alfv\'enicity that suggest Alfv\'en waves could play a larger role in its acceleration compared to conventional slow wind outflows. Through a well-timed conjunction between Solar Orbiter and Parker Solar Probe, we trace the energetics of slow wind to compare with a neighboring Alfv\'enic slow solar wind stream. An analysis that integrates remote and heliospheric properties and modeling of the two distinct solar wind streams finds Alfv\'enic slow solar wind behaves like fast wind, where a wave pressure gradient is required to reconcile its full acceleration, while non-Alfv\'enic slow wind can be driven by its non-adiabatic electron and proton thermal pressure gradients. Derived coronal conditions of the source region indicate good model compatibility but extended coronal observations are required to effectively trace solar wind energetics below Parker's orbit., Comment: 20 pages, 9 figures, accepted for publication to ApJ on January 3, 2025

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

Partial differential equation (PDE) is an important math tool in science and engineering. This paper experimentally demonstrates an optical neural PDE solver by leveraging the back-propagation-free on-photonic-chip training of physics-informed neural networks.

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