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The U.S. has witnessed a resurgence of labor activism, with teachers at the forefront. We examine how teacher strikes affect compensation, working conditions, and productivity with an original dataset of 772 teacher strikes generating 48 million student days idle between 2007 and 2023. Using an event study framework, we find that, on average, strikes increase compensation by 8% and lower pupil-teacher ratios by 0.5 students, driven by new state revenues. We find little evidence of sizable impacts on student achievement up to five years post-strike, though strikes lasting 10 or more days decrease math achievement in the short-term.

The EdResearch for Action "Design Principles Series" focuses on a single program or practice that has been proven to have positive effects on student outcomes. Authors -- leading experts from across the field of education research -- look across many high-quality studies of similar programs to identify the components and conditions that are key to its effective implementation. The "Design Principles Series" helps practitioners adapt and successfully implement an evidence-based program to meet the needs of their target population. This brief is an update of the previous version published in 2021. It incorporates new research on effective design and implementation components in high-impact tutoring programs. [This brief was prepared in collaboration with the University of Virginia, School of Education and Human Development and National Student Support Accelerator.]

Budget constraints and limited supplies of local tutors have caused many K-12 school districts to pivot from individual tutoring in-person toward small-group tutoring online to expand access to personalized instruction. We conduct a field experiment to explore the effect of increasing student-tutor ratios on middle school students' math achievement and growth during an online tutoring program. We leverage a novel feature of the program where tutors often taught individual and small-group tutoring sessions, allowing them to directly compare their experiences across these settings. Both experimental estimates and tutor survey responses suggest 1:1 tutoring is more effective than 3:1 tutoring online. Tutoring small groups in an online format presents additional challenges for personalizing instruction, developing relationships, fostering participation, and managing student behavior.

We show simplified DPS-QKD using a SiN micro-ring resonator operated at 852 nm. A raw-key rate of up to 25.3 kb/s is reached at a QBER suitable for secure-key extraction. Short-reach QKD operation is maintained for zero-touch link layouts with C-band telecom fiber.

This study introduces a novel data-centric approach to improve real-time surgical guidance using fiber-based fluorescence lifetime imaging (FLIm). A key aspect of the methodology is the accurate detection of the aiming beam, which is essential for localizing points used to map FLIm measurements onto the tissue region within the surgical field. The primary challenge arises from the complex and variable conditions encountered in the surgical environment, particularly in Transoral Robotic Surgery (TORS). Uneven illumination in the surgical field can cause reflections, reduce contrast, and results in inconsistent color representation, further complicating aiming beam detection. To overcome these challenges, an instance segmentation model was developed using a data-centric training strategy that improves accuracy by minimizing label noise and enhancing detection robustness. The model was evaluated on a dataset comprising 40 in vivo surgical videos, demonstrating a median detection rate of 85%. This performance was maintained when the model was integrated in a clinical system, achieving a similar detection rate of 85% during TORS procedures conducted in patients. The system's computational efficiency, measured at approximately 24 frames per second (FPS), was sufficient for real-time surgical guidance. This study enhances the reliability of FLIm-based aiming beam detection in complex surgical environments, advancing the feasibility of real-time, image-guided interventions for improved surgical precision

Understanding the physics of the integrable spin-1/2 XXZ chain has witnessed substantial progress, due to the development and application of sophisticated analytical and numerical techniques. In particular, infinite-temperature magnetization transport has turned out to range from ballistic, over superdiffusive, to diffusive behavior in different parameter regimes of the anisotropy. Since integrability is rather the exception than the rule, a crucial question is the change of transport under integrability-breaking perturbations. This question includes the stability of superdiffusion at the isotropic point and the change of diffusion constants in the easy-axis regime. In our work, we study this change of diffusion constants by a variety of methods and cover both, linear response theory in the closed system and the Lindblad equation in the open system, where we throughout focus on periodic boundary conditions. In the closed system, we compare results from the recursion method to calculations for finite systems and find evidence for a continuous change of diffusion constants over the full range of perturbation strengths. In the open system weakly coupled to baths, we find diffusion constants in quantitative agreement with the ones in the closed system in a range of nonweak perturbations, but disagreement in the limit of weak perturbations. Using a simple model in this limit, we point out the possibility of a diverging diffusion constant in such an open system., Comment: 6 pages, 4 figures (+ 5 pages, 7 figures)

Recent progress in large language models (LLMs) offers promising new approaches for recommendation system (RecSys) tasks. While the current state-of-the-art methods rely on fine-tuning LLMs to achieve optimal results, this process is costly and introduces significant engineering complexities. Conversely, methods that bypass fine-tuning and use LLMs directly are less resource-intensive but often fail to fully capture both semantic and collaborative information, resulting in sub-optimal performance compared to their fine-tuned counterparts. In this paper, we propose a Simple Training-free Approach for Recommendation (STAR), a framework that utilizes LLMs and can be applied to various recommendation tasks without the need for fine-tuning. Our approach involves a retrieval stage that uses semantic embeddings from LLMs combined with collaborative user information to retrieve candidate items. We then apply an LLM for pairwise ranking to enhance next-item prediction. Experimental results on the Amazon Review dataset show competitive performance for next item prediction, even with our retrieval stage alone. Our full method achieves Hits@10 performance of +23.8% on Beauty, +37.5% on Toys and Games, and -1.8% on Sports and Outdoors relative to the best supervised models. This framework offers an effective alternative to traditional supervised models, highlighting the potential of LLMs in recommendation systems without extensive training or custom architectures.

Analyses of major group mergers are key to understanding the evolution of large-scale structure in the Universe and the microphysical properties of the hot gas in these systems. We present imaging and spectral analyses of deep Chandra observations of hot gas structures formed in the major merger of the NGC 7618 and UGC 12491 galaxy groups and compare the observed hot gas morphology, temperatures, and abundances with recent simulations. The morphology of the observed multiple cold front edges and boxy wings are consistent with those expected to be formed by Kelvin-Helmholtz instabilities and gas sloshing in inviscid gas. The arc-shaped slingshot tail morphologies seen in each galaxy suggest that the dominant galaxies are near their orbital apogee after having experienced at least one core passage at a large impact parameter., Comment: 30 pages, 8 figures

Detecting arousals in sleep is essential for diagnosing sleep disorders. However, using Machine Learning (ML) in clinical practice is impeded by fundamental issues, primarily due to mismatches between clinical protocols and ML methods. Clinicians typically annotate only the onset of arousals, while ML methods rely on annotations for both the beginning and end. Additionally, there is no standardized evaluation methodology tailored to clinical needs for arousal detection models. This work addresses these issues by introducing a novel post-processing and evaluation framework emphasizing approximate localization and precise event count (ALPEC) of arousals. We recommend that ML practitioners focus on detecting arousal onsets, aligning with clinical practice. We examine the impact of this shift on current training and evaluation schemes, addressing simplifications and challenges. We utilize a novel comprehensive polysomnographic dataset (CPS) that reflects the aforementioned clinical annotation constraints and includes modalities not present in existing polysomnographic datasets. We release the dataset alongside this paper, demonstrating the benefits of leveraging multimodal data for arousal onset detection. Our findings significantly contribute to integrating ML-based arousal detection in clinical settings, reducing the gap between technological advancements and clinical needs.

Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a recurrent inference machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1-$\sigma$ error threshold; moreover, the RIM reconstruction of the mock observations are indistinguishable from the observations themselves. To further test the algorithm, we deconvolve extracted spectra from the central regions of the galaxy group NGC 1550, known to have a rich X-ray spectrum, and the massive galaxy clusters Abell 1795. Despite the RIM reconstructions consistently remaining below the 1-$\sigma$ noise level, the recovered intrinsic spectra did not align with modeled expectations. This discrepancy is likely attributable to the RIM's method of implicitly encoding prior information within the neural network. This approach holds promise for unlocking new possibilities in accurate spectral reconstructions and advancing our understanding of complex X-ray cosmic phenomena., Comment: Submitted to AJ

Massive clusters of galaxies are very rare in the observable Universe. Even rarer are mergers of such clusters observed close to pericenter passage. Here, we report on one such case: a massive (~ $10^{15}\,M_\odot$) and hot (kT ~ 10 keV) cluster CL0238.3+2005 at $z\approx 0.42$. For this cluster, we combine X-ray data from SRG/eROSITA and Chandra, optical images from DESI, and spectroscopy from BTA and RTT-150 telescopes. The X-ray and optical morphologies suggest an ongoing merger with the projected separation of subhalos of $\sim 200$ kpc. The line-of-sight velocity of galaxies tentatively associated with the two merging halos differs by 2000-3000 km/s. We conclude that, most plausibly, the merger axis is neither close to the line of sight nor to the sky plane. We compare CL0238 with two well-known clusters MACS0416 and Bullet, and conclude that CL0238 corresponds to an intermediate phase between the pre-merging MACS0416 cluster and the post-merger Bullet cluster. Namely, this cluster has recently (only $\lesssim 0.1$ Gyr ago) experienced an almost head-on merger. We argue that this "just after" system is a very rare case and an excellent target for lensing, Sunyaev-Zeldovich effect, and X-ray studies that can constrain properties ranging from dynamics of mergers to self-interacting dark matter, and plasma effects in intracluster medium that are associated with shock waves, e.g., electron-ion equilibration efficiency and relativistic particle acceleration., Comment: submitted to A&A; comments are welcome

Virtual field trips (VFTs) have proven to be valuable learning tools. Such applications are mostly based on 360{\deg} technology and are to be characterized as single-user applications in technological terms. In contrast, Social VR applications are characterized by multi-user capability and user-specific avatars. From a learning perspective, the concepts of collaborative learning and embodiment have long been proposed as conducive to learning. Both concepts might be supported using Social VR. However, little is currently known about the use of Social VR for VFTs. Accordingly, the research questions are to what extent VFTs can be implemented in Social VR environments and how these Social VR-based VFTs are perceived by learners. This article presents an evaluation study on the development and evaluation of a VFT environment using the Social VR platform Mozilla Hubs. It describes the design decisions to create the environment and evaluation results from a mixed-method study (N=16) using a questionnaire and focus group discussions. The study highlighted the opportunities offered by Social VR-based VFTs but also revealed several challenges that need to be addressed to embrace the potential of Social VR-based VFTs to be utilized regularly in education., Comment: 9 pages, 7 figures, 1 table, submitted to Games and Learning Alliance Conference

The ability to accurately discern active massive black holes (BHs) in nearby dwarf galaxies is paramount to understanding the origins and processes of "seed" BHs in the early Universe. We present Chandra X-ray Observatory observations of a sample of three local dwarf galaxies (M$_{*}$ $\leqslant 3 \times 10^{9}$ M$_\odot$, z $\leqslant$ 0.15) previously identified as candidates for hosting active galactic nuclei (AGN). The galaxies were selected from the NASA-Sloan Atlas (NSA) with spatially coincident X-ray detections in the eROSITA Final Equatorial Depth Survey (eFEDS). Our new Chandra data reveal three X-ray point sources in two of the target galaxies with luminosities between log(L$_{\rm \text{2-10 keV}}$ [erg s$^{-1}$]) = 39.1 and 40.4. Our results support the presence of an AGN in these two galaxies and a ULX in one of them. For the AGNs, we estimate BH masses of $M_{\rm BH} \sim 10^{5-6} M_\odot$ and Eddington ratios on the order of $\sim 10^{-3}$., Comment: Accepted for publication in The Astrophysical Journal. 7 pages

In [Muhl2019], Peter M\"uhlbacher showed that in the random loop model without loop weights, a loop phase transition (assuming it exists) cannot occur at the same parameter as the percolation phase transition of the occupied edges. In this work, we give a quantitative version of this result, specifying a minimal gap between the percolation phase transition and a possible loop phase transition. A substantial part of our argument also works for weighted loop models.

Laser-plasma accelerators represent a promising technology for future compact accelerating systems, enabling the acceleration of tens of pC to above $1\,$GeV over just a few centimeters. Nonetheless, these devices currently lack the stability, beam quality and average current of conventional systems. While many efforts have focused on improving acceleration stability and quality, little progress has been made in increasing the beam's average current, which is essential for future laser-plasma-based applications. In this paper, we investigate a laser-plasma acceleration regime aimed at increasing the beam average current with energies up to few-MeVs, efficiently enhancing the beam charge. We present experimental results on configurations that allow reaching charges of $5-30\,$nC and a maximum conversion efficiency of around $14\,$%. Through comprehensive Particle-In-Cell simulations, we interpret the experimental results and present a detailed study on electron dynamics. From our analysis, we show that most electrons are not trapped in a plasma wave; rather, they experience ponderomotive acceleration. Thus, we prove the laser pulse as the main driver of the particles' energy gain process.

Traditional cosmic ray filtering algorithms used in X-ray imaging detectors aboard space telescopes perform event reconstruction based on the properties of activated pixels above a certain energy threshold, within 3x3 or 5x5 pixel sliding windows. This approach can reject up to 98% of the cosmic ray background. However, the remaining unrejected background constitutes a significant impediment to studies of low surface brightness objects, which are especially prevalent in the high-redshift universe. The main limitation of the traditional filtering algorithms is their ignorance of the long-range contextual information present in image frames. This becomes particularly problematic when analyzing signals created by secondary particles produced during interactions of cosmic rays with body of the detector. Such signals may look identical to the energy deposition left by X-ray photons, when one considers only the properties within the small sliding window. Additional information is present, however, in the spatial and energy correlations between signals in different parts of the frame, which can be accessed by modern machine learning (ML) techniques. In this work, we continue the development of an ML-based pipeline for cosmic ray background mitigation. Our latest method consist of two stages: first, a frame classification neural network is used to create class activation maps (CAM), localizing all events within the frame; second, after event reconstruction, a random forest classifier, using features obtained from CAMs, is used to separate X-ray and cosmic ray features. The method delivers >40% relative improvement over traditional filtering in background rejection in standard 0.3-10keV energy range, at the expense of only a small (<2%) level of lost X-ray signal. Our method also provides a convenient way to tune the cosmic ray rejection threshold to adapt to a user's specific scientific needs., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Bringing artificial intelligence (AI) alongside next-generation X-ray imaging detectors, including CCDs and DEPFET sensors, enhances their sensitivity to achieve many of the flagship science cases targeted by future X-ray observatories, based upon low surface brightness and high redshift sources. Machine learning algorithms operating on the raw frame-level data provide enhanced identification of background vs. astrophysical X-ray events, by considering all of the signals in the context within which they appear within each frame. We have developed prototype machine learning algorithms to identify valid X-ray and cosmic-ray induced background events, trained and tested upon a suite of realistic end-to-end simulations that trace the interaction of cosmic ray particles and their secondaries through the spacecraft and detector. These algorithms demonstrate that AI can reduce the unrejected instrumental background by up to 41.5 per cent compared with traditional filtering methods. Alongside AI algorithms to reduce the instrumental background, next-generation event reconstruction methods, based upon fitting physically-motivated Gaussian models of the charge clouds produced by events within the detector, promise increased accuracy and spectral resolution of the lowest energy photon events., Comment: Proceedings of the SPIE, Astronomical Telescopes and Instrumentation, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray

Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. While tasks like arithmetic reasoning involve clear, definitive answers and logical chains of thought, the application of LLM reasoning in recommendation systems (RecSys) presents a distinct challenge. RecSys tasks revolve around subjectivity and personalized preferences, an under-explored domain in utilizing LLMs' reasoning capabilities. Our study explores several aspects to better understand reasoning for RecSys and demonstrate how task quality improves by utilizing LLM reasoning in both zero-shot and finetuning settings. Additionally, we propose RecSAVER (Recommender Systems Automatic Verification and Evaluation of Reasoning) to automatically assess the quality of LLM reasoning responses without the requirement of curated gold references or human raters. We show that our framework aligns with real human judgment on the coherence and faithfulness of reasoning responses. Overall, our work shows that incorporating reasoning into RecSys can improve personalized tasks, paving the way for further advancements in recommender system methodologies., Comment: To be published at ACL 2024

Genotype imputation enhances genetic data by predicting missing SNPs using reference haplotype information. Traditional methods leverage linkage disequilibrium (LD) to infer untyped SNP genotypes, relying on the similarity of LD structures between genotyped target sets and fully sequenced reference panels. Recently, reference-free deep learning-based methods have emerged, offering a promising alternative by predicting missing genotypes without external databases, thereby enhancing privacy and accessibility. However, these methods often produce models with tens of millions of parameters, leading to challenges such as the need for substantial computational resources to train and inefficiency for client-sided deployment. Our study addresses these limitations by introducing a baseline for a novel genotype imputation pipeline that supports client-sided imputation models generalizable across any genotyping chip and genomic region. This approach enhances patient privacy by performing imputation directly on edge devices. As a case study, we focus on PRS313, a polygenic risk score comprising 313 SNPs used for breast cancer risk prediction. Utilizing consumer genetic panels such as 23andMe, our model democratizes access to personalized genetic insights by allowing 23andMe users to obtain their PRS313 score. We demonstrate that simple linear regression can significantly improve the accuracy of PRS313 scores when calculated using SNPs imputed from consumer gene panels, such as 23andMe. Our linear regression model achieved an R^2 of 0.86, compared to 0.33 without imputation and 0.28 with simple imputation (substituting missing SNPs with the minor allele frequency). These findings suggest that popular SNP analysis libraries could benefit from integrating linear regression models for genotype imputation, providing a viable and light-weight alternative to reference based imputation., Comment: This paper is 16 pages long and contains 7 figures. For more information and to access related resources: * Web application: https://aaronge-2020.github.io/DeepImpute/ * Code repository: https://github.com/aaronge-2020/DeepImpute

Based on a proposal to observe 18 bright radio sources from the SMS4 catalog with the Neil Gehrels Swift Observatory (hereafter Swift), we obtained X-ray observations of 17 targets (one target was not observed). Following up our first paper that discussed 31 sources (see Maselli et al. 2022; 20 sources detected as point sources and one very extended source), we present results for this final sample of 17 radio sources, that previously lacked dedicated, pointed narrow FOV X-ray observations. One of these 17 sources, undetected by Swift due to a very short exposure, was instead detected by eROSITA, and given in the Data Release 1 (DR1) Catalog. No 1eRASS source was found in the DR1 for the remaining source, unobserved by Swift. The new Swift observations led to eleven X-ray source detections in the 0.3-10 keV band and six upper limits. We investigated the extent of the X-ray emission, the hardness ratio, and when statistics allowed we carried out a spectral analysis. The X-ray emission of eight sources is consistent with point-like emission, while three sources show clear evidence of extent, each with peculiar properties. We used the X-ray determined positions and uncertainties of the twelve detected sources to establish associations with infrared and optical sources from the AllWISE and the GSC 2.4.2 catalogs. Requiring a detection in both the infrared and the optical bands to establish a candidate counterpart for our X-ray detections, we identify counterparts for all twelve sources. We discuss the interesting structure of MRC B0344-345 and PKS B2148-555, two of the six extended X-ray sources that we detected in both our Swift campaigns, and suggest they are very promising for further X-ray and radio investigations. For the 38 SMS4 sources that lack pointed, narrow FOV X-ray telescope observations, after our Swift campaigns, we list 18 likely counterparts from the eROSITA DR1 catalog., Comment: 25 pages, 7 figures, 11 tables; accepted for publication in the Astrophysical Journal Supplement Series. arXiv admin note: text overlap with arXiv:2208.04763

We conducted a scoping review for active learning in the domain of natural language processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as follows: Objective: Identify active learning strategies that were proposed for entity recognition and their evaluation environments (datasets, metrics, hardware, execution time). Design: We used Scopus and ACM as our search engines. We compared the results with two literature surveys to assess the search quality. We included peer-reviewed English publications introducing or comparing active learning strategies for entity recognition. Results: We analyzed 62 relevant papers and identified 106 active learning strategies. We grouped them into three categories: exploitation-based (60x), exploration-based (14x), and hybrid strategies (32x). We found that all studies used the F1-score as an evaluation metric. Information about hardware (6x) and execution time (13x) was only occasionally included. The 62 papers used 57 different datasets to evaluate their respective strategies. Most datasets contained newspaper articles or biomedical/medical data. Our analysis revealed that 26 out of 57 datasets are publicly accessible. Conclusion: Numerous active learning strategies have been identified, along with significant open questions that still need to be addressed. Researchers and practitioners face difficulties when making data-driven decisions about which active learning strategy to adopt. Conducting comprehensive empirical comparisons using the evaluation environment proposed in this study could help establish best practices in the domain., Comment: The Version of Record of this contribution is published in Deep Learning Theory and Applications 5th International Conference, DeLTA 2024 Proceedings, and will be available after the conference

Flexibility governs the many properties of materials and is crucial for the function of proteins and biopolymers. However, how the self-assembly of flexibly bonded particles can lead to larger structures with global reconfigurability is unexplored. We here use a binary colloidal model system equipped with flexible DNA-based bonds to study how regular structures with tunable flexibility can be created through self-assembly. We find that the reconfigurability during lattice growth leads to lattices with square symmetry which are inherently mechanically unstable and hence thermally floppy. By considering the role of size ratio, number ratio, and directionality induced by particle shape, we identify the optimal pathways that maximize the yield and flexibility of these square lattices using a combination of experiments, analytical calculations, and simulations. Our study highlights the crucial role of reconfigurability in systems that are governed by enthalpic and entropic principles, from synthetic to biological, and might be useful for creating materials with novel or reconfigurable properties., Comment: 40 pages, 10 figures

The Lindblad quantum master equation is one of the central approaches to the physics of open quantum systems. In particular, boundary driving enables the study of transport, where a steady state emerges in the long-time limit, which features a constant current and a characteristic density profile. While the Lindblad equation complements other approaches to transport in closed quantum systems, it has become clear that a connection between closed and open systems exists in certain cases. Here, we build on this connection for magnetization transport in the spin-1/2 XXZ chain with and without integrability-breaking perturbations. Specifically, we show that the time evolution of the open quantum system can be described on the basis of classical correlation functions, as generated by the Hamiltonian equations of motion for real vectors. By comparing to exact numerical simulations of the Lindblad equation, we demonstrate the accuracy of this description for a range of model parameters, but also point out counterexamples. While this agreement is an interesting physical observation, it also suggests that classical mechanics can be used to solve the Lindblad equation for comparatively large system sizes, which lie outside the possibilities of a quantum mechanical treatment., Comment: 11 pages, 10 figures

To assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments in early infancy. GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent costs in the training of human assessors and further standardize classification of spontaneous motor patterns. Available deep learning tools, all of which are based on single sensor modalities, are however still considerably inferior to that of well-trained human assessors. These approaches are hardly comparable as all models are designed, trained and evaluated on proprietary/silo-data sets. With this study we propose a sensor fusion approach for assessing fidgety movements (FMs). FMs were recorded from 51 typically developing participants. We compared three different sensor modalities (pressure, inertial, and visual sensors). Various combinations and two sensor fusion approaches (late and early fusion) for infant movement classification were tested to evaluate whether a multi-sensor system outperforms single modality assessments. Convolutional neural network (CNN) architectures were used to classify movement patterns. The performance of the three-sensor fusion (classification accuracy of 94.5%) was significantly higher than that of any single modality evaluated. We show that the sensor fusion approach is a promising avenue for automated classification of infant motor patterns. The development of a robust sensor fusion system may significantly enhance AI-based early recognition of neurofunctions, ultimately facilitating automated early detection of neurodevelopmental conditions.

Quantity and price risks are key uncertainties market participants face in electricity markets with increased volatility, for instance, due to high shares of renewables. From day ahead until real-time, there is a large variation in the best available information, leading to price changes that flexible assets, such as battery storage, can exploit economically. This study contributes to understanding how coordinated bidding strategies can enhance multi-market trading and large-scale energy storage integration. Our findings shed light on the complexities arising from interdependencies and the high-dimensional nature of the problem. We show how stochastic dual dynamic programming is a suitable solution technique for such an environment. We include the three markets of the frequency containment reserve, day-ahead, and intraday in stochastic modelling and develop a multi-stage stochastic program. Prices are represented in a multidimensional Markov Chain, following the scheduling of the markets and allowing for time-dependent randomness. Using the example of a battery storage in the German energy sector, we provide valuable insights into the technical aspects of our method and the economic feasibility of battery storage operation. We find that capacity reservation in the frequency containment reserve dominates over the battery's cycling in spot markets at the given resolution on prices in 2022. In an adjusted price environment, we find that coordination can yield an additional value of up to 12.5%.

Large language model systems face important security risks from maliciously crafted messages that aim to overwrite the system's original instructions or leak private data. To study this problem, we organized a capture-the-flag competition at IEEE SaTML 2024, where the flag is a secret string in the LLM system prompt. The competition was organized in two phases. In the first phase, teams developed defenses to prevent the model from leaking the secret. During the second phase, teams were challenged to extract the secrets hidden for defenses proposed by the other teams. This report summarizes the main insights from the competition. Notably, we found that all defenses were bypassed at least once, highlighting the difficulty of designing a successful defense and the necessity for additional research to protect LLM systems. To foster future research in this direction, we compiled a dataset with over 137k multi-turn attack chats and open-sourced the platform.

In this paper, we examine the fundamental and complex role that time plays in the learning process. We begin by developing a conceptual framework to elucidate the multiple obstacles schools face in converting allocated time into learning time. We then synthesize the causal research and document a clear positive effect of time on student achievement of small to medium magnitude, but also with likely diminishing marginal returns. Further descriptive analyses reveal how large differences in the length of the school day and year across public schools are an underappreciated dimension of educational inequality in the United States. Finally, our case study of time loss in one urban district demonstrates the potential to substantially increase learning time within existing constraints.

We discuss Hamiltonian and Liouvillian learning for analog quantum simulation from non-equilibrium quench dynamics in the limit of weakly dissipative many-body systems. We present and compare various methods and strategies to learn the operator content of the Hamiltonian and the Lindblad operators of the Liouvillian. We compare different ans\"atze based on an experimentally accessible "learning error" which we consider as a function of the number of runs of the experiment. Initially, the learning error decreases with the inverse square root of the number of runs, as the error in the reconstructed parameters is dominated by shot noise. Eventually the learning error remains constant, allowing us to recognize missing ansatz terms. A central aspect of our approaches is to (re-)parametrize ans\"atze by introducing and varying the dependencies between parameters. This allows us to identify the relevant parameters of the system, thereby reducing the complexity of the learning task. Importantly, this (re-)parametrization relies solely on classical post-processing, which is compelling given the finite amount of data available from experiments. We illustrate and compare our methods with two experimentally relevant spin models., Comment: 20 pages, 9 figures

Galactic cosmic ray (GCR) particles have a significant impact on the particle-induced background of X-ray observatories, and their flux exhibits substantial temporal variability, potentially influencing background levels. In this study, we present one-day binned high-energy reject rates derived from the Chandra-ACIS and XMM-Newton EPIC-pn instruments, serving as proxies for GCR particle flux. We systematically analyze the ACIS and EPIC-pn reject rates and compare them with the AMS proton flux. Our analysis initially reveals robust correlations between the AMS proton flux and the ACIS/EPIC-pn reject rates when binned over 27-day intervals. However, a closer examination reveals substantial fluctuations within each 27-day bin, indicating shorter-term variability. Upon daily binning, we observe finer. temporal structures in the datasets, demonstrating the presence of recurrent variations with periods of $\sim$ 25 days and 23 days in ACIS and EPIC-pn reject rates, respectively, spanning the years 2014 to 2018. Notably, during the 2016--2017 period, we additionally detect periodicities of $\sim$13.5 days and 9 days in the ACIS and EPIC-pn reject rates, respectively. Intriguingly, we observe a time lag of $\sim$ 6 days between the AMS proton flux and the ACIS/EPIC-pn reject rates during the second half of 2016. This time lag is not visible before 2016 and aftern2017. The underlying physical mechanisms responsible for this time lag remain a subject of ongoing investigation., Comment: 16 pages, 8 figures, accepted for publication in ApJ

We implement a silicon-on-insulator light emitter as optical supply for a QKD transmitter and transfer it to an electronic BiCMOS wafer. A secure key is established over short reach in co-existence with shortwave data transmission.

Studies of surface brightness fluctuations in the intracluster medium (ICM) present an indirect probe of turbulent properties such as the turbulent velocities, injection scales, and the slope of the power spectrum of fluctuations towards smaller scales. With the advancement of Sunyaev-Zel'dovich (SZ) studies and surveys relative to X-ray observations, we seek to investigate surface brightness fluctuations in a sample of SPT-SZ clusters which also have archival \textit{XMM-Newton} data. Here we present a pilot study of two typical clusters in that sample: SPT-CLJ0232-4421 and SPT-CLJ0638-5358. We infer injection scales larger than 500 kpc in both clusters and Mach numbers $\approx 0.5$ in SPT-CLJ0232-4421 and Mach numbers $\approx 0.6 - 1.6$ in SPT-CLJ0638-5358, which has a known shock. We find hydrostatic bias values for $M_{500}$ less than 0.2 for SPT-CLJ0232-4421 and less than 0.1 for SPT-CLJ0638-5358. These results show the importance to assess its quantitative values via a detailed multiwavelength approach and suggest that the drivers of turbulence may occur at quite larger scales., Comment: Accepted to ApJ; 34 pages, 23 figures, and 14 tables

While supermassive black holes (BHs) are widely observed in the nearby and distant universe, their origin remains debated with two viable formation scenarios with light and heavy seeds. In the light seeding model, the first BHs form from the collapse of massive stars with masses of $10-100 \ \rm{M_{\odot}}$, while the heavy seeding model posits the formation of $10^{4-5} \ \rm{M_{\odot}}$ seeds from direct collapse. The detection of BHs at redshifts $z\gtrsim10$, edging closer to their formation epoch, provides critical observational discrimination between these scenarios. Here, we focus on the JWST-detected galaxy, GHZ 9, at $z\approx10$ that is lensed by the foreground cluster, Abell 2744. Based on 2.1 Ms deep Chandra observations, we detect a candidate X-ray AGN, which is spatially coincident with the high-redshift galaxy, GHZ 9. The BH candidate is inferred to have a bolometric luminosity of $(1.0^{+0.5}_{-0.4})\times10^{46} \ \rm{erg \ s^{-1}}$, which corresponds to a BH mass of $(8.0^{+3.7}_{-3.2})\times10^7 \ \rm{M_{\odot}}$ assuming Eddington-limited accretion. This extreme mass at such an early cosmic epoch suggests the heavy seed origin for this BH candidate. Based on the Chandra and JWST discoveries of extremely high-redshift quasars, we have constructed the first simple AGN luminosity function extending to $z\approx10$. Comparison of this luminosity function with theoretical models indicates an over-abundant $z\approx10$ BH population, consistent with a higher-than-expected seed formation efficiency., Comment: 9 pages, 4 figures, accepted for publication in The Astrophysical Journal Letters

This is a brief overview of a few selected chapters on automorphism groups of affine varieties. It includes some open questions., Comment: 33 pages; corrections according to referee remarks and some additions done

Membrane-induced interactions have been predicted to be important for the organization of membrane proteins. Measurements of the interactions between two and three membrane deforming objects have revealed their non-additive nature. They are thought to lead to complex many-body effects, however, experimental evidence is lacking to date. We here present an experimental method to measure many-body effects in membrane-mediated interactions using colloidal spheres placed between a deflated giant unilamellar vesicles and a planar substrate. The thus confined colloidal particles cause a large deformation of the membrane while not being physochemically attached to it and interact through it. Two particles are found to attract with a maximum force of 0.2~pN. For three particles, we observe a preference for forming compact equilateral triangles over a linear arrangement. We use numerical energy minimization to establish that the attraction stems from a reduction in the membrane-deformation energy caused by the particles. Confining up to 36 particles, we find a preference for hexagonally close packed clusters. However, with increasing number of particles the order of the confined particles decreases, while at the same time, diffusivity of the particles increases. Our experiments for the first time show that the non-additive nature of membrane-mediated interactions affects the interactions and arrangements and ultimately leads to spherical aggregates with liquid-like order of potential importance for cellular processes.

Applications increasingly leverage mixed-modality data, and must jointly search over vector data, such as embedded images, text and video, as well as structured data, such as attributes and keywords. Proposed methods for this hybrid search setting either suffer from poor performance or support a severely restricted set of search predicates (e.g., only small sets of equality predicates), making them impractical for many applications. To address this, we present ACORN, an approach for performant and predicate-agnostic hybrid search. ACORN builds on Hierarchical Navigable Small Worlds (HNSW), a state-of-the-art graph-based approximate nearest neighbor index, and can be implemented efficiently by extending existing HNSW libraries. ACORN introduces the idea of predicate subgraph traversal to emulate a theoretically ideal, but impractical, hybrid search strategy. ACORN's predicate-agnostic construction algorithm is designed to enable this effective search strategy, while supporting a wide array of predicate sets and query semantics. We systematically evaluate ACORN on both prior benchmark datasets, with simple, low-cardinality predicate sets, and complex multi-modal datasets not supported by prior methods. We show that ACORN achieves state-of-the-art performance on all datasets, outperforming prior methods with 2-1,000x higher throughput at a fixed recall.