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This paper proposes a new data-driven methodology for predicting intervals of post-fault voltage trajectories in power systems. We begin by introducing the Quantile Attention-Fourier Deep Operator Network (QAF-DeepONet), designed to capture the complex dynamics of voltage trajectories and reliably estimate quantiles of the target trajectory without any distributional assumptions. The proposed operator regression model maps the observed portion of the voltage trajectory to its unobserved post-fault trajectory. Our methodology employs a pre-training and fine-tuning process to address the challenge of limited data availability. To ensure data privacy in learning the pre-trained model, we use merging via federated learning with data from neighboring buses, enabling the model to learn the underlying voltage dynamics from such buses without directly sharing their data. After pre-training, we fine-tune the model with data from the target bus, allowing it to adapt to unique dynamics and operating conditions. Finally, we integrate conformal prediction into the fine-tuned model to ensure coverage guarantees for the predicted intervals. We evaluated the performance of the proposed methodology using the New England 39-bus test system considering detailed models of voltage and frequency controllers. Two metrics, Prediction Interval Coverage Probability (PICP) and Prediction Interval Normalized Average Width (PINAW), are used to numerically assess the model's performance in predicting intervals. The results show that the proposed approach offers practical and reliable uncertainty quantification in predicting the interval of post-fault voltage trajectories.

Social dilemmas where the good of a group is at odds with individual interests are usually considered as static -- the dilemma does not change over time. In the COVID-19 pandemic, social dilemmas occurred in the mitigation of epidemic spread: Should I reduce my contacts or wear a mask to protect others? In the context of respiratory diseases, which are predominantly spreading during the winter months, some of these situations re-occur seasonally. We couple a game theoretical model, where individuals can adjust their behavior, to an epidemiological model with seasonal forcing. We find that social dilemmas can occur annually and that behavioral reactions to them can either decrease or increase the peaks of infections in a population. Our work has not only implications for seasonal infectious diseases, but also more generally for oscillatory social dilemmas: A complex interdependence between behavior and external dynamics emerges. To be effective and to exploit behavioral dynamics, intervention measures to mitigate re-occuring social dilemmas have to be timed carefully.

We present and analyze a model in which a Hatsugai-Kohmoto interaction is added to a system of fermions with a Weyl point in their non-interacting dispersion relation. We show that the interacting model exhibits a Weyl-Mott point, at which a single isolated Green's function zero is found for a wide range of interaction strengths. We show that such Weyl-Mott points imply an emergent non-Fermi-liquid state for the metallic regime and a gapped topological state for the insulating one. Furthermore, their presence is linked to a topological charge inherited from the original Green's function pole at the Weyl point. A Weyl-Mott point is therefore naturally associated with a strongly-correlated chiral anomaly.

Beyond conventional productivity metrics, human interaction and collaboration dynamics merit careful consideration in our increasingly digital workspace. This research proposes a conjectural neuro-adaptive room that enhances group interactions by adjusting the physical environment to desired internal states. Drawing inspiration from previous work on collective consciousness, the system leverages computer vision and machine learning models to analyze physiological and behavioral cues, such as facial expressions and speech analysis, to infer the overall internal state of occupants. Environmental conditions of the room, such as visual projections, lighting and sound, are actively adjusted to create an optimal setting for inducing the desired state, including focus or collaboration. Our goal is to create a dynamic and responsive environment to support group needs, fostering a sense of collective consciousness and improving workplace well-being., Comment: 5 pages, 2 figures. Longevity, AI, and Cognitive Research Hackathon

Cognitive decline is a natural part of aging, often resulting in reduced cognitive abilities. In some cases, however, this decline is more pronounced, typically due to disorders such as Alzheimer's disease. Early detection of anomalous cognitive decline is crucial, as it can facilitate timely professional intervention. While medical data can help in this detection, it often involves invasive procedures. An alternative approach is to employ non-intrusive techniques such as speech or handwriting analysis, which do not necessarily affect daily activities. This survey reviews the most relevant methodologies that use deep learning techniques to automate the cognitive decline estimation task, including audio, text, and visual processing. We discuss the key features and advantages of each modality and methodology, including state-of-the-art approaches like Transformer architecture and foundation models. In addition, we present works that integrate different modalities to develop multimodal models. We also highlight the most significant datasets and the quantitative results from studies using these resources. From this review, several conclusions emerge. In most cases, the textual modality achieves the best results and is the most relevant for detecting cognitive decline. Moreover, combining various approaches from individual modalities into a multimodal model consistently enhances performance across nearly all scenarios.

This paper presents a parametric quadratic approximation of the AC optimal power flow (AC-OPF) problem for time-sensitive and market-based applications. The parametric approximation preserves the physics-based but simple representation provided by the DC-OPF model and leverages market and physics information encoded in the data-driven demand-dependent parameters. To enable the deployment of the proposed model for real-time applications, we propose a supervised learning approach to predict near-optimal parameters, given a certain metric concerning the dispatch quantities and locational marginal prices (LMPs). The training dataset is generated based on the solution of the accurate AC-OPF problem and a bilevel optimization problem, which calibrates parameters satisfying two market properties: cost recovery and revenue adequacy. We show the proposed approach's performance in various test systems in terms of cost and dispatch approximation errors, LMPs, market properties satisfaction, dispatch feasibility, and generalizability with respect to N-1 network topologies., Comment: 11 pages, 6 figures, 52nd Hawaii International Conference on System

Motivated by the recent appearance of the trillium lattice in the search for materials hosting spin liquids, we study the ground state of the classical Heisenberg model on its linegraph, the trilline lattice. We find that this network realises the recently proposed notion of a fragile spin liquid in three dimensions. Additionally, we analyze the Ising case and argue for a possible $\mathbb{Z}_2$ quantum spin liquid phase in the corresponding quantum dimer model. Like the well-known $U(1)$ spin liquids, the classical phase hosts moment fractionalisation evidenced in the diluted lattice, but unlike these, it exhibits exponential decay both in spin correlations and interactions between fractionalised moments. This provides the first instance of a purely short-range correlated classical Heisenberg spin liquid in three dimensions.

This article presents initial configuration work and use of the robotic platform and manipulator in question. The development of the ideal configuration for using this robot serves as a guide for new users and also validates its functionality for use in projects. Husky is a large payload capacity and power systems robotics development platform that accommodates a wide variety of payloads, customized to meet research needs. Together with the Cobot UR5 Manipulator attached to its base, it expands the application area of its capacity in projects. Advances in robots and mobile manipulators have revolutionized industries by automating tasks that previously required human intervention. These innovations alone increase productivity but also reduce operating costs, which makes the company more competitive in an evolving global market. Therefore, this article investigates the functionalities of this robot to validate its execution in robotics projects., Comment: in Portuguese language

As our understanding of autism and ableism continues to increase, so does our understanding of ableist language towards autistic people. Such language poses a significant challenge in NLP research due to its subtle and context-dependent nature. Yet, detecting anti-autistic ableist language remains underexplored, with existing NLP tools often failing to capture its nuanced expressions. We present AUTALIC, the first benchmark dataset dedicated to the detection of anti-autistic ableist language in context, addressing a significant gap in the field. The dataset comprises 2,400 autism-related sentences collected from Reddit, accompanied by surrounding context, and is annotated by trained experts with backgrounds in neurodiversity. Our comprehensive evaluation reveals that current language models, including state-of-the-art LLMs, struggle to reliably identify anti-autistic ableism and align with human judgments, underscoring their limitations in this domain. We publicly release AUTALIC along with the individual annotations which serve as a valuable resource to researchers working on ableism, neurodiversity, and also studying disagreements in annotation tasks. This dataset serves as a crucial step towards developing more inclusive and context-aware NLP systems that better reflect diverse perspectives., Comment: 9 pages, 5 figures, 7 tables

Bid shading has become a standard practice in the digital advertising industry, in which most auctions for advertising (ad) opportunities are now of first price type. Given an ad opportunity, performing bid shading requires estimating not only the value of the opportunity but also the distribution of the highest bid from competitors (i.e. the competitive landscape). Since these two estimates tend to be very noisy in practice, first-price auction participants need a bid shading policy that is robust against relatively significant estimation errors. In this work, we provide a max-min formulation in which we maximize the surplus against an adversary that chooses a distribution both for the value and the competitive landscape, each from a Kullback-Leibler-based ambiguity set. As we demonstrate, the two ambiguity sets are essential to adjusting the shape of the bid-shading policy in a principled way so as to effectively cope with uncertainty. Our distributionally robust bid shading policy is efficient to compute and systematically outperforms its non-robust counterpart on real datasets provided by Yahoo DSP.

In this note we show that the twisted convolution algebra $L^1_{\alpha,\omega}({\sf G},\mathfrak A)$ associated to a twisted action of a locally compact group ${\sf G}$ on a $C^*$-algebra $\mathfrak A$ has the following property: Every quotient by a closed two-sided ideal of finite codimension produces a semisimple algebra. We use this property, together with results of H. Dales and G. Willis, to build up on previous results of the author and produce large classes of examples of algebras with properties of automatic continuity., Comment: 7 pages. Comments are welcomed

Downsampling or under-sampling is a technique that is utilized in the context of large and highly imbalanced classification models. We study optimal downsampling for imbalanced classification using generalized linear models (GLMs). We propose a pseudo maximum likelihood estimator and study its asymptotic normality in the context of increasingly imbalanced populations relative to an increasingly large sample size. We provide theoretical guarantees for the introduced estimator. Additionally, we compute the optimal downsampling rate using a criterion that balances statistical accuracy and computational efficiency. Our numerical experiments, conducted on both synthetic and empirical data, further validate our theoretical results, and demonstrate that the introduced estimator outperforms commonly available alternatives.

The rise of Large Language Models (LLMs) has revolutionized natural language processing across numerous languages and tasks. However, evaluating LLM performance in a consistent and meaningful way across multiple European languages remains challenging, especially due to the scarcity of language-parallel multilingual benchmarks. We introduce a multilingual evaluation approach tailored for European languages. We employ translated versions of five widely-used benchmarks to assess the capabilities of 40 LLMs across 21 European languages. Our contributions include examining the effectiveness of translated benchmarks, assessing the impact of different translation services, and offering a multilingual evaluation framework for LLMs that includes newly created datasets: EU20-MMLU, EU20-HellaSwag, EU20-ARC, EU20-TruthfulQA, and EU20-GSM8K. The benchmarks and results are made publicly available to encourage further research in multilingual LLM evaluation.

This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performance multilingual large language models (LLMs). The project goal is to deliver models that cover all major European languages, with a particular focus on real-world applications within the European Union. We explain all data processing steps, starting with the data selection and requirement definition to the preparation of the final datasets for model training. We distinguish between curated data and web data, as each of these categories is handled by distinct pipelines, with curated data undergoing minimal filtering and web data requiring extensive filtering and deduplication. This distinction guided the development of specialized algorithmic solutions for both pipelines. In addition to describing the processing methodologies, we provide an in-depth analysis of the datasets, increasing transparency and alignment with European data regulations. Finally, we share key insights and challenges faced during the project, offering recommendations for future endeavors in large-scale multilingual data preparation for LLMs.

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Metals are commonly used as electrostatic gates in devices due to their abundant charge carrier densities that are necessary for efficient charging and discharging. A semiconducting gate can be beneficial for certain fabrication processes, in low light conditions, and for specific gating properties. Here, we determine the effectiveness and limitations of a semiconducting gate in graphene and bilayer graphene devices. Using the semiconducting transition metal dichalcogenides molybdenum disulfide (MoS2) and molybdenum diselenide (MoSe2), we show that these semiconductors can be used to suitably gate the graphene devices for certain operating conditions. For singly gated devices, we find that the semiconducting gates provide gating characteristics comparable with metallic gates below liquid helium temperatures but include resistivity features resulting from gate voltage clamping of the MoS$_2$. A 1D potential model is developed that corroborates the clamping effect observed in the measurements. In doubly gated devices, we pin down the parameter range of effective operation and show that the semiconducting depletion regime results in clamping and hysteresis from defect state charge trapping. Our results provide a guide for the appropriate operating conditions for employing semiconducting gates and open the door to novel device architectures., Comment: 15 pages, 3 figures

The long-term solar magnetic activity and its cyclical behaviour, which is maintained by a dynamo mechanism, are both still challenging for the astrophysics. In particular, an atypical event occurred between 1645 and 1715 when the solar activity was remarkably decreased and the number of sunspots got extremely reduced. However, it is still unclear what happened to the solar cycle. The discovery of longer activity minima in cool stars may shed light on the nature of the complex mechanisms involved in the long-term behaviour of the solar-stellar dynamo. Our aim is to explore if the G5V solar-like star HD 4915, which showed a striking chromospheric activity pattern in a previous study performed with HIRES data, could be considered a bona fide Maunder Minimum (hereafter MM) candidate. We have analyzed over 380 spectra acquired between 2003 and 2022 using HARPS and HIRES spectrographs. We carried out a detailed search of activity signatures in HD 4915 by using the Mount Wilson and the Balmer H$_{\alpha}$ activity indexes. This task was performed by means of the GLS periodogram. The new HARPS data show that the chromospheric activity of HD 4915 is not decreasing. In fact, the rise of the activity after the broad minimum in three years gets to the level of activity before that phase, suggesting that it is not entering into a MM phase. HD 4915 shows a distinctive activity behaviour initially attributed to a possible and incipient MM phase. The additional HARPS data allow us to discard a MM in the star. Our analysis shows that the complex activity pattern of HD 4915 could be ruled by a multiple activity cycle, being a shorter cycle of 4.8-yr modulated by a potential longer one. More activity surveys with extensive records and suitable cadence are crucial for accurate identification of stars in Magnetic Grand Minima., Comment: (5 pages, 4 figures)

Cell-free (CF) systems have the potential to fulfill the increasing performance demand of future wireless applications by employing distributed access points (APs) that transmit the information over the same time-frequency resources. Due to the simultaneous transmission, multiuser interference (MUI) degrades the overall performance. To cope with the MUI in the downlink several linear precoding techniques, which rely on perfect channel state information at the transmitter (CSIT), have been studied. However, perfect CSIT is hardly obtained in practical systems. In this context, rate-splitting (RS) has arisen as a potential solution to deal with CSIT imperfections. In contrast to existing works, we explore non-linear precoding techniques along with RS-CF systems. Furthermore, the multi-branch (MB) concept is included to further enhance the overall performance of the system. Simulations show that the proposed MB-THP for RS-based CF systems outperforms the conventional linear precoders., Comment: 7 pages, 3 figures

Centipedes exhibit great maneuverability in diverse environments due to their many legs and body-driven control. By leveraging similar morphologies, their robotic counterparts also demonstrate effective terrestrial locomotion. However, the success of these multi-legged robots is largely limited to forward locomotion; steering is substantially less studied, in part due to the challenges in coordinating their many body joints. Furthermore, steering behavior is complex and can include different combinations of desired rotational/translational displacement. In this paper, we explore steering strategies in multi-legged robots based on tools derived from geometric mechanics (GM). We characterize the steering motion in the plane by the rotation angle, the steering radius, and the heading direction angle. We identify an effective turning strategy by superimposing two traveling waves in the lateral body undulation and further explore variations of the "turning wave" to enable a broad spectrum of steering behaviors. By combining an amplitude modulation and a phase modulation, we develop a control strategy for steering behaviors that enables steering with a range of rotation angles (from 0{\deg} to 20{\deg}) and steering radius (from 0.28 to 0.38 body length) while keeping the heading direction angle close to 0. Lastly, we test our control framework on an elongate multi-legged robot model to verify the effectiveness of our proposed strategy. Our work demonstrates the generality of the two-wave template for effective steering of multi-legged elongate robots.

We present two multilingual LLMs designed to embrace Europe's linguistic diversity by supporting all 24 official languages of the European Union. Trained on a dataset comprising around 60% non-English data and utilizing a custom multilingual tokenizer, our models address the limitations of existing LLMs that predominantly focus on English or a few high-resource languages. We detail the models' development principles, i.e., data composition, tokenizer optimization, and training methodologies. The models demonstrate competitive performance across multilingual benchmarks, as evidenced by their performance on European versions of ARC, HellaSwag, MMLU, and TruthfulQA.

$\beta$-NMR is an advancing technique that enables measurements relevant to various fields of research, ranging from physics to chemistry and biology. Among the recent achievements of the $\beta$-NMR setup located at the ISOLDE facility at CERN is the determination of the magnetic moment of a shortlived nucleus with a part-per-million accuracy. Presented here are major upgrades and extensions of that $\beta$-NMR setup. The most important advancement is the installation of a 4.7 T superconducting solenoidal magnet, leading to sub-ppm spatial homogeneity and temporal stability of the magnetic field. A detector array optimised for such magnetic field has also been implemented and a more powerful, time-resolved, fully-digital data acquisition system has been deployed. To commission the upgraded beamline, $\beta$-NMR resonances of short-lived 26Na were recorded in solid and liquid samples. These showed 3-fold narrower linewidths and 15-fold higher resolving power than using the previous setup. Hence, the improvements achieved here permit more accurate bio-$\beta$-NMR studies, investigating, e.g., the interaction of metal ions with biomolecules, such as DNA. They also pave the way for the first studies of the distribution of the magnetisation inside short-lived nuclei.

The first images of the jet and low velocity component (LVC) from the strongly accreting classical T Tauri star RU Lupi are presented. Adaptive optics assisted spectro-imaging of forbidden emission lines was used. The main aim of the observations was to test the conclusion from a recent spectro-astrometric study that the narrow component of the LVC is tracing an MHD disk wind, and to estimate the mass loss rate in the wind. The structure and morphology of the component supports a wind origin for the NC. An upper limit to the launch radius and semi-opening angle of the wind in [O I]{\lambda}6300 emission are estimated to be 2 au and 19{\deg} in agreement with MHD wind models for high accretors. The height of the [O I]{\lambda}6300 wind emitting region, a key parameter for the derivation of the mass loss rate, is estimated for the first time at approximately 35 au giving M_out = 2.6 x 10^-11 M_sun/yr. When compared to the derived mass accretion rate of M_acc = 1.6 x 10^-7 M_sun/yr, the efficiency in the wind is too low for the wind to be significantly contributing to angular momentum removal., Comment: 15 pages, 15 figures, 2 tables, submitted to A&A

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

As AI-generated summaries proliferate, how can we help people understand the veracity of those summaries? In this short paper, we design a simple interaction primitive, traceable text, to support critical examination of generated summaries and the source texts they were derived from. In a traceable text, passages of a generated summary link to passages of the source text that informed them. A traceable text can be generated with a straightforward prompt chaining approach, and optionally adjusted by human authors depending on application. In a usability study, we examined the impact of traceable texts on reading and understanding patient medical records. Traceable text helped readers answer questions about the content of the source text more quickly and markedly improved correctness of answers in cases where there were hallucinations in the summaries. When asked to read a text of personal importance with traceable text, readers employed traceable text as an understanding aid and as an index into the source note.

The in-vivo identification of the kidney stone types during an ureteroscopy would be a major medical advance in urology, as it could reduce the time of the tedious renal calculi extraction process, while diminishing infection risks. Furthermore, such an automated procedure would make possible to prescribe anti-recurrence treatments immediately. Nowadays, only few experienced urologists are able to recognize the kidney stone types in the images of the videos displayed on a screen during the endoscopy. Thus, several deep learning (DL) models have recently been proposed to automatically recognize the kidney stone types using ureteroscopic images. However, these DL models are of black box nature whicl limits their applicability in clinical settings. This contribution proposes a case-based reasoning DL model which uses prototypical parts (PPs) and generates local and global descriptors. The PPs encode for each class (i.e., kidney stone type) visual feature information (hue, saturation, intensity and textures) similar to that used by biologists. The PPs are optimally generated due a new loss function used during the model training. Moreover, the local and global descriptors of PPs allow to explain the decisions ("what" information, "where in the images") in an understandable way for biologists and urologists. The proposed DL model has been tested on a database including images of the six most widespread kidney stone types. The overall average classification accuracy was 90.37. When comparing this results with that of the eight other DL models of the kidney stone state-of-the-art, it can be seen that the valuable gain in explanability was not reached at the expense of accuracy which was even slightly increased with respect to that (88.2) of the best method of the literature. These promising and interpretable results also encourage urologists to put their trust in AI-based solutions., Comment: Paper submitted to Artificial Intelligence in Medicine. (AIIM), Elsevier

It has been shown in \href{https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.108.170402}{\textit{Phys. Rev. Lett.}, \textbf{108} 170402 (2012)}, that quantum tunneling is instantaneous using a time-of-arrival (TOA) operator constructed by Weyl quantization of the classical TOA. However, there are infinitely many possible quantum images of the classical TOA, leaving it unclear if one is uniquely preferred over the others. This raises the question on whether instantaneous tunneling time is simply an artifact of the chosen ordering rule. Here, we demonstrate that tunneling time vanishes for all possible quantum images of the classical arrival time, irrespective of the ordering rule between the position and momentum observables. The result still holds for TOA-operators that are constructed independent of canonical quantization, while still imposing the correct algebra defined by the time-energy canonical commutation relation.

Recent advancements in AI safety have led to increased efforts in training and red-teaming large language models (LLMs) to mitigate unsafe content generation. However, these safety mechanisms may not be comprehensive, leaving potential vulnerabilities unexplored. This paper introduces MathPrompt, a novel jailbreaking technique that exploits LLMs' advanced capabilities in symbolic mathematics to bypass their safety mechanisms. By encoding harmful natural language prompts into mathematical problems, we demonstrate a critical vulnerability in current AI safety measures. Our experiments across 13 state-of-the-art LLMs reveal an average attack success rate of 73.6\%, highlighting the inability of existing safety training mechanisms to generalize to mathematically encoded inputs. Analysis of embedding vectors shows a substantial semantic shift between original and encoded prompts, helping explain the attack's success. This work emphasizes the importance of a holistic approach to AI safety, calling for expanded red-teaming efforts to develop robust safeguards across all potential input types and their associated risks.

Understanding faint dwarf galaxies is fundamental to the development of a robust theory of galaxy formation on small scales. Since the discovery of a population of ultra diffuse galaxies (UDGs) rich in globular clusters (GCs) in Coma, an increasing number of studies on low surface brightness dwarf galaxies (LSBds) have been published in recent years. The most massive LSBds have been observed predominantly in groups and clusters, with properties displaying dependence on the environment. In this work, we use deep DECam imaging to systematically identify LSBds and their GC populations around the low-density environment of NGC 3115. We carefully analyse the structure and morphology of 24 candidates, 18 of which are reported for the first time. Most candidates exhibit red colours suggesting a connection between their colour and distance to NGC 3115. We followed up with Gemini GMOS imaging 9 LSBds to properly identify their GC populations. We derive lower limits for the number of GCs associated with each galaxy. Our analysis reveals that they occur around of the same loci of Fornax LSB dwarf GC systems. The relationship between the number of GCs and total mass provides a tool in which, by counting the GCs in these galaxies, we estimate an upper limit for the total mass of these LSB dwarfs, obtaining the mean value of $\sim 3.3\times10^{10}$ M$_{\odot}$. Our results align with expectations for dwarf-sized galaxies, particularly regarding the distribution and specific frequency of their GC systems., Comment: 25 pages, 22 figures, accepted for publication in MNRAS

Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cross-sections sliced from 3D biopsy tissue specimens. This process impedes the analysis of complex tissue structures such as glands, which can vary depending on the tissue slice examined. We propose a novel digital pathology data source called a "volumetric core," obtained via the extraction and co-alignment of serially sectioned tissue sections using a novel morphology-preserving alignment framework. We trained an attention-based multiple-instance learning (ABMIL) framework on deep features extracted from volumetric patches to automatically classify the Gleason Grade Group (GGG). To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning. We ran our morphology-preserving alignment framework to construct 10,210 volumetric cores, leaving out 30% for pretraining. The rest of the dataset was used to train ABMIL, which resulted in a 0.958 macro-average AUC, 0.671 F1 score, 0.661 precision, and 0.695 recall averaged across all five GGG significantly outperforming the 2D baselines.

Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction process overcomes the limitation presented by the former approaches based on the independent estimation of wall-parallel planes. The network is capable of estimating the 3-D flow field with a level of error at each wall-normal distance comparable to that reported from wall-parallel plane estimations and at a lower training cost in terms of computational resources. The direct full 3-D reconstruction also unveils a direct interpretation in terms of coherent structures. It is shown that the accuracy of the network depends directly on the wall footprint of each individual turbulent structure. It is observed that wall-attached structures are predicted more accurately than wall-detached ones, especially at larger distances from the wall. Among wall-attached structures, smaller sweeps are reconstructed better than small ejections, while large ejections are reconstructed better than large sweeps as a consequence of their more intense footprint.

Many educators in California are unaware of just how harmful out of school suspensions can be. When suspended students are barred from attending school, more often than not, the rule broken was some form of minor misconduct. This update of "Lost Instruction Time in California Schools" demonstrates that despite the important efforts by the state of California to reduce suspensions, those efforts are seriously insufficient. The most recent statewide rates of lost instruction due to out of school suspensions show a widespread increase in the days lost per 100 students. The increase in these lost instruction rates, is not large for every group, but the 2023 data reverses a consistent downward 6-year trend. In 2023, nearly every racial and ethnic group, as well as students with disabilities, experienced an increase over their rate of lost instruction from the prior year. Using the recently released data for 2023, show that not only are the rates for homeless youth increasing for every demographic, but they increased far more for Black and Native American homeless youth than any other groups.