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Gradient boosting for decision tree algorithms are increasingly used in actuarial applications as they show superior predictive performance over traditional generalized linear models. Many improvements and sophistications to the first gradient boosting machine algorithm exist. We present in a unified notation, and contrast, all the existing point and probabilistic gradient boosting for decision tree algorithms: GBM, XGBoost, DART, LightGBM, CatBoost, EGBM, PGBM, XGBoostLSS, cyclic GBM, and NGBoost. In this comprehensive numerical study, we compare their performance on five publicly available datasets for claim frequency and severity, of various size and comprising different number of (high cardinality) categorical variables. We explain how varying exposure-to-risk can be handled with boosting in frequency models. We compare the algorithms on the basis of computational efficiency, predictive performance, and model adequacy. LightGBM and XGBoostLSS win in terms of computational efficiency. The fully interpretable EGBM achieves competitive predictive performance compared to the black box algorithms considered. We find that there is no trade-off between model adequacy and predictive accuracy: both are achievable simultaneously., Comment: 26 pages, 4 figures, 26 tables, 7 algorithms

While the subclass of interacting supernovae with narrow hydrogen emission lines (SNe IIn) consists of some of the longest-lasting and brightest SNe ever discovered, their progenitors are still not well understood. Investigating SNe IIn as they emit across the electromagnetic spectrum is the most robust way to understand the progenitor evolution before the explosion. This work presents X-Ray, optical, infrared, and radio observations of the strongly interacting Type IIn SN 2020ywx covering a period $>1200$ days after discovery. Through multiwavelength modeling, we find that the progenitor of 2020ywx was losing mass at $\sim10^{-2}$--$10^{-3} \mathrm{\,M_{\odot}\,yr^{-1}}$ for at least 100 yr pre-explosion using the circumstellar medium (CSM) speed of $120$ km/s measured from our optical and NIR spectra. Despite the similar magnitude of mass-loss measured in different wavelength ranges, we find discrepancies between the X-ray and optical/radio-derived mass-loss evolution, which suggest asymmetries in the CSM. Furthermore, we find evidence for dust formation due to the combination of a growing blueshift in optical emission lines and near-infrared continuum emission which we fit with blackbodies at $\sim$ 1000 K. Based on the observed elevated mass loss over more than 100 years and the configuration of the CSM inferred from the multiwavelength observations, we invoke binary interaction as the most plausible mechanism to explain the overall mass-loss evolution. SN 2020ywx is thus a case that may support the growing observational consensus that SNe IIn mass loss is explained by binary interaction., Comment: Submitted to ApJ, 31 pages, 19 figures

This paper deals with the problem of infrared image database generation for ATR assessment purposes. Huge databases are required to have quantitative and objective performance evaluations. We propose a method which superimpose targets and occultants on background under image quality metrics constraints to generate realistic images. We also propose a method to generate target signatures with intrinsic thermal variability based on 3D models plated with real infrared textures., Comment: arXiv admin note: substantial text overlap with arXiv:2411.06695

In this communication, we propose a method which permits to simulate images of targets in infrared imagery by superimposition of vehicle signatures in background, eventually with occultants. We develop a principle which authorizes us to generate different thermal configurations of target signatures. This method enables us to easily generate huge datasets for ATR algorithms performance evaluation., Comment: in French language, GRETSI Symposium on Signal and Image Processing, Dijon, France, September 2009

In this communication, we deal with the question of automatic target detection, recognition and tracking (ATD/R/T) algorithms performance assessment. We propose a complete methodology of evaluation which approaches objective image datasets development and adapted metrics definition for the different tasks (detection, recognition and tracking). We present some performance results which are currently processed in a French-MoD program called 2ACI (``Acquisition Automatique de Cibles par Imagerie``).

We study optimal liquidation strategies under partial information for a single asset within a finite time horizon. We propose a model tailored for high-frequency trading, capturing price formation driven solely by order flow through mutually stimulating marked Hawkes processes. The model assumes a limit order book framework, accounting for both permanent price impact and transient market impact. Importantly, we incorporate liquidity as a hidden Markov process, influencing the intensities of the point processes governing bid and ask prices. Within this setting, we formulate the optimal liquidation problem as an impulse control problem. We elucidate the dynamics of the hidden Markov chain's filter and determine the related normalized filtering equations. We then express the value function as the limit of a sequence of auxiliary continuous functions, defined recursively. This characterization enables the use of a dynamic programming principle for optimal stopping problems and the determination of an optimal strategy. It also facilitates the development of an implementable algorithm to approximate the original liquidation problem. We enrich our analysis with numerical results and visualizations of candidate optimal strategies., Comment: 36 pages

We present the results from our extensive hard-to-soft X-ray (NuSTAR, Swift-XRT, XMM-Newton, Chandra) and meter-to-mm wave radio (GMRT, VLA, NOEMA) monitoring campaign of the very nearby (d $=6.9$ Mpc) Type II SN2023ixf spanning $\approx$ 4--165 d post-explosion. This unprecedented dataset enables inferences on the explosion's circumstellar medium (CSM) density and geometry. Specifically, we find that the luminous X-ray emission is well modeled by thermal free-free radiation from the forward shock with rapidly decreasing photo-electric absorption with time. The radio spectrum is dominated by synchrotron radiation from the same shock, and the NOEMA detection of high-frequency radio emission may indicate a new component consistent with the secondary origin. Similar to the X-rays, the level of free-free absorption affecting the radio spectrum rapidly decreases with time as a consequence of the shock propagation into the dense CSM. While the X-ray and the radio modeling independently support the presence of a dense medium corresponding to an \emph{effective} mass-loss rate $\dot{M} \approx 10^{-4}\, \rm M_{\odot}\,yr^{-1}$ at $R = (0.4-14) \times 10^{15}$ (for $v_{\rm w}=\rm 25 \,km\,s^{-1}$), our study points at a complex CSM density structure with asymmetries and clumps. The inferred densities are $\approx$10--100 times those of typical red supergiants, indicating an extreme mass-loss phase of the progenitor in the $\approx$200 years preceding core collapse, which leads to the most X-ray luminous Type II SN and the one with the most delayed emergence of radio emission. These results add to the picture of the complex mass-loss history of massive stars on the verge of collapse and demonstrate the need for panchromatic campaigns to fully map their intricate environments., Comment: 32 pages, 16 figures, 9 Tables

Antiterminators are essential components of bacterial transcriptional regulation, allowing the control of gene expression in response to fluctuating environmental conditions. RNA-binding antiterminators are particularly important regulatory proteins that play a significant role in preventing transcription termination by binding to specific RNA sequences. These RNA-binding antiterminators have been extensively studied for their roles in regulating various metabolic pathways. However, their role in modulating the physiology of pathogens requires further investigations. This review focuses on these RNA-binding proteins in both Gram-positive and Gram-negative bacteria, particularly on their structures, mechanism of action, and target genes. Additionally, the involvement of the antitermination mechanisms in bacterial pathogenicity will be discussed. This knowledge is crucial for understanding the regulatory mechanisms that govern bacterial pathogenicity, opening up exciting prospects for future research, and potentially new alternative strategies to fight against infectious diseases., Comment: arXiv admin comment: This version has been removed by arXiv administrators as the submitter did not have the rights to agree to the license at the time of submission

We introduce "visual writing", an approach to writing stories by manipulating visuals instead of words. Visual writing relies on editable visual representations of time, entities, events, and locations to offer representations more suited to specific editing tasks. We propose a taxonomy for these representations and implement a prototype software supporting the visual writing workflow. The system allows writers to edit the story by alternating between modifying the text and manipulating visual representations to edit entities, actions, locations, and order of events. We evaluate this workflow with eight creative writers and find visual writing can help find specific passages, keep track of story elements, specify edits, and explore story variations in a way that encourages creativity.

Electricity markets currently fail to incorporate preferences of buyers, treating polluting and renewable energy sources as having equal social benefit under a system of uniform clearing prices. Meanwhile, renewable energy is prone to curtailment due to transmission constraints, forcing grid operators to reduce or shut down renewable energy production despite its availability and need. This paper proposes a ``dual pricing mechanism" which allows buyers to bid both their willingness to pay for electricity, and additionally, their preference for green energy. Designed for use in deregulated electricity markets, this mechanism prioritizes the dispatch of more renewable energy sources according to consumer preferences. Traditional uniform clearing prices, which treat all energy sources equally, do not reflect the growing share of green energy in the power grid and the environmental values of consumers. By allowing load-serving entities to bid their willingness to pay for renewable energy directly into the clearing market, our proposed framework generates distinct pricing signals for green and ``black" electricity.

We explore how interactions inspired by drawing software can help edit text. Making an analogy between visual and text editing, we consider words as pixels, sentences as regions, and tones as colours. For instance, direct manipulations move, shorten, expand, and reorder text; tools change number, tense, and grammar; colours map to tones explored along three dimensions in a tone picker; and layers help organize and version text. This analogy also leads to new workflows, such as boolean operations on text fragments to construct more elaborated text. A study shows participants were more successful at editing text and preferred using the proposed interface over existing solutions. Broadly, our work highlights the potential of interaction analogies to rethink existing workflows, while capitalizing on familiar features.

Studies on human decision-making focused on humanitarian aid have found that cognitive biases can hinder the fair allocation of resources. However, few HCI and Information Visualization studies have explored ways to overcome those cognitive biases. This work investigates whether the design of interactive resource allocation tools can help to promote allocation fairness. We specifically study the effect of presentation format (using text or visualization) and a specific framing strategy (showing resources allocated to groups or individuals). In our three crowdsourced experiments, we provided different tool designs to split money between two fictional programs that benefit two distinct communities. Our main finding indicates that individual-framed visualizations and text may be able to curb unfair allocations caused by group-framed designs. This work opens new perspectives that can motivate research on how interactive tools and visualizations can be engineered to combat cognitive biases that lead to inequitable decisions., Comment: Published as a conference paper at CHI 2023

In the field of formal verification, Neural Networks (NNs) are typically reformulated into equivalent mathematical programs which are optimized over. To overcome the inherent non-convexity of these reformulations, convex relaxations of nonlinear activation functions are typically utilized. Common relaxations (i.e., static linear cuts) of "S-shaped" activation functions, however, can be overly loose, slowing down the overall verification process. In this paper, we derive tuneable hyperplanes which upper and lower bound the sigmoid activation function. When tuned in the dual space, these affine bounds smoothly rotate around the nonlinear manifold of the sigmoid activation function. This approach, termed $\alpha$-sig, allows us to tractably incorporate the tightest possible, element-wise convex relaxation of the sigmoid activation function into a formal verification framework. We embed these relaxations inside of large verification tasks and compare their performance to LiRPA and $\alpha$-CROWN, a state-of-the-art verification duo.

This paper presents an interactive technique to explain visual patterns in network visualizations to analysts who do not understand these visualizations and who are learning to read them. Learning a visualization requires mastering its visual grammar and decoding information presented through visual marks, graphical encodings, and spatial configurations. To help people learn network visualization designs and extract meaningful information, we introduce the concept of interactive pattern explanation that allows viewers to select an arbitrary area in a visualization, then automatically mines the underlying data patterns, and explains both visual and data patterns present in the viewer's selection. In a qualitative and a quantitative user study with a total of 32 participants, we compare interactive pattern explanations to textual-only and visual-only (cheatsheets) explanations. Our results show that interactive explanations increase learning of i) unfamiliar visualizations, ii) patterns in network science, and iii) the respective network terminology., Comment: to be published in IEEE VIS 2024

Literature search questions, such as "Where can I find research on the evaluation of consistency in generated summaries?" pose significant challenges for modern search engines and retrieval systems. These questions often require a deep understanding of research concepts and the ability to reason across entire articles. In this work, we introduce LitSearch, a retrieval benchmark comprising 597 realistic literature search queries about recent ML and NLP papers. LitSearch is constructed using a combination of (1) questions generated by GPT-4 based on paragraphs containing inline citations from research papers and (2) questions manually written by authors about their recently published papers. All LitSearch questions were manually examined or edited by experts to ensure high quality. We extensively benchmark state-of-the-art retrieval models and also evaluate two LLM-based reranking pipelines. We find a significant performance gap between BM25 and state-of-the-art dense retrievers, with a 24.8% absolute difference in recall@5. The LLM-based reranking strategies further improve the best-performing dense retriever by 4.4%. Additionally, commercial search engines and research tools like Google Search perform poorly on LitSearch, lagging behind the best dense retriever by up to 32 recall points. Taken together, these results show that LitSearch is an informative new testbed for retrieval systems while catering to a real-world use case., Comment: Accepted by EMNLP 2024. Dataset and code are available at https://github.com/princeton-nlp/LitSearch

This research, focused on small work groups, proposes to examine the role of the leader in the appropriation of collaborative information systems. It is based on a three-year action research conducted during the implementation of the Microsoft Teams collaborative platform at a university. The aim is to understand how groups of students organize themselves to carry out complex projects over a long period of time and with varying distance constraints. Differences in the appropriation of the new tool appeared depending on whether the mode of work was cooperative or collaborative. To understand these different dynamics, we mobilize Vygotsky's activity theories (TA), more precisely Engestr{\"o}m's 2nd generation TA model (2000). The results reveal the role of leaders through the cooperation versus collaboration tension. In teams, a leader emerges who organizes cooperation but slows down the appropriation of collaborative features of the new information system., Comment: in French language. 28{\`e}me conf{\'e}rence de l'AIM, Association information et management, May 2023, DIJON, France

Electrical networks contain thousands of interconnected nodes and edges, which leads to computational challenges in some power system studies. To address these challenges, we contend that network reductions can serve as a framework to enable scalable computing in power systems. By building upon a prior AC "Opti-KRON" formulation, this paper presents a DC power flow formulation for finding network reductions that are optimal within the context of large transmission analysis. Opti-KRON previously formulated optimal Kron-based network reductions as a mixed integer linear program (MILP), where the number of binary variables scaled with the number of nodes. To improve the scalability of the Opti-KRON approach, we augment the MILP formulation with a community detection (CD) technique that segments a large network into smaller, disjoint, but contiguous sub-graphs (i.e., communities). For each sub-graph, we then (in parallel) apply MILP-based along with a new cutting plane constraint, thus, enhancing scalability. Ultimately, the new DC-based Opti-KRON method can achieve a 80-95\% reduction of networks (in terms of nodes) while statistically outperforming other CD- and Kron-based methods. We present simulation results for the IEEE RTS-96 and the 2383-bus Polish networks.

Chart understanding plays a pivotal role when applying Multimodal Large Language Models (MLLMs) to real-world tasks such as analyzing scientific papers or financial reports. However, existing datasets often focus on oversimplified and homogeneous charts with template-based questions, leading to an over-optimistic measure of progress. We demonstrate that although open-source models can appear to outperform strong proprietary models on these benchmarks, a simple stress test with slightly different charts or questions can deteriorate performance by up to 34.5%. In this work, we propose CharXiv, a comprehensive evaluation suite involving 2,323 natural, challenging, and diverse charts from arXiv papers. CharXiv includes two types of questions: 1) descriptive questions about examining basic chart elements and 2) reasoning questions that require synthesizing information across complex visual elements in the chart. To ensure quality, all charts and questions are handpicked, curated, and verified by human experts. Our results reveal a substantial, previously underestimated gap between the reasoning skills of the strongest proprietary model (i.e., GPT-4o), which achieves 47.1% accuracy, and the strongest open-source model (i.e., InternVL Chat V1.5), which achieves 29.2%. All models lag far behind human performance of 80.5%, underscoring weaknesses in the chart understanding capabilities of existing MLLMs. We hope CharXiv facilitates future research on MLLM chart understanding by providing a more realistic and faithful measure of progress. Project page and leaderboard: https://charxiv.github.io/, Comment: 121 pages, 90 figures

DC Optimal Power Flow (DCOPF) is a key operational tool for power system operators, and it is embedded as a subproblem in many challenging optimization problems (e.g., line switching). However, traditional CPU-based solve routines (e.g., simplex) have saturated in speed and are hard to parallelize. This paper focuses on solving DCOPF problems using gradient-based routines on Graphics Processing Units (GPUs), which have massive parallelization capability. To formulate these problems, we pose a Lagrange dual associated with DCOPF (linear and quadratic cost curves), and then we explicitly solve the inner (primal) minimization problem with a dual norm. The resulting dual problem can be efficiently iterated using projected gradient ascent. After solving the dual problem on both CPUs and GPUs to find tight lower bounds, we benchmark against Gurobi and MOSEK, comparing convergence speed and tightness on the IEEE 2000, 4601, and 10000 bus systems. We provide reliable and tight lower bounds for these problems with, at best, 5.4x speedup over a conventional solver.

We present JWST NIRCam (F356W and F444W filters) and MIRI (F770W) images and NIRSpec- IFU spectroscopy of the young supernova remnant Cassiopeia A (Cas A). We obtained the data as part of a JWST survey of Cas A. The NIRCam and MIRI images map the spatial distributions of synchrotron radiation, Ar-rich ejecta, and CO on both large and small scales, revealing remarkably complex structures. The CO emission is stronger at the outer layers than the Ar ejecta, which indicates the reformation of CO molecules behind the reverse shock. NIRSpec-IFU spectra (3 - 5.5 microns) were obtained toward two representative knots in the NE and S fields. Both regions are dominated by the bright fundamental rovibrational band of CO in the two R and P branches, with strong [Ar VI] and relatively weaker, variable strength ejecta lines of [Si IX], [Ca IV], [Ca V] and [Mg IV]. The NIRSpec-IFU data resolve individual ejecta knots and filaments spatially and in velocity space. The fundamental CO band in the JWST spectra reveals unique shapes of CO, showing a few tens of sinusoidal patterns of rovibrational lines with pseudo-continuum underneath, which is attributed to the high-velocity widths of CO lines. The CO also shows high J lines at different vibrational transitions. Our results with LTE modeling of CO emission indicate a temperature of 1080 K and provide unique insight into the correlations between dust, molecules, and highly ionized ejecta in supernovae, and have strong ramifications for modeling dust formation that is led by CO cooling in the early Universe., Comment: accepted for the ApJ letter (17 pages and 10 figures)

Exciton-polaritons, hybrid light-matter elementary excitations arising from the strong coupling regime between excitons in semiconductors and photons in photonic nanostructures, offer a fruitful playground to explore the physics of quantum fluids of light as well as to develop all-optical devices. However, achieving room temperature propagation of polaritons with a large excitonic fraction, which would be crucial, e.g., for nonlinear light transport in prospective devices, remains a significant challenge. } Here we report on experimental studies of exciton-polariton propagation at room temperature in resonant metasurfaces made from a sub-wavelength lattice of perovskite pillars. Thanks to the large Rabi splitting, an order of magnitude larger than the optical phonon energy, the lower polariton band is completely decoupled from the phonon bath of perovskite crystals. The long lifetime of these cooled polaritons, in combination with the high group velocity achieved through the metasurface design, enables long-range propagation regardless of the polariton excitonic fraction. Remarkably, we observed propagation distances exceeding hundreds of micrometers at room temperature, even when the polaritons possess a very high excitonic component, approximately {80}$\%$. Furthermore, the design of the metasurface introduces an original mechanism for directing uni-directional propagation through polarization control. This discovery of a ballistic propagation mode, leveraging high-speed cooled polaritons, heralds a promising avenue for the development of advanced polaritonic devices.

The oscillator bases expansion stands as an efficient approximation method for the time-independent Schr\"odinger equation. The method, originally formulated with one non-linear variational parameter, can be extended to incorporate two such parameters. It handles both non- and semi-relativistic kinematics with generic two-body interactions. In the current work, focusing on systems of three identical bodies, the method is generalised to include the management of a given class of three-body forces. The computational cost of this generalisation proves to not exceed the one for two-body interactions. The accuracy of the generalisation is assessed by comparing with results from Lagrange mesh method and hyperspherical harmonic expansions. Extensions for systems of $N$ identical bodies and for systems of two identical particles and one distinct are also discussed., Comment: 18 pages

In May 2022, mpox (formerly monkeypox) spread to non-endemic countries rapidly. Human judgment is a forecasting approach that has been sparsely evaluated during the beginning of an outbreak. We collected -- between May 19, 2022 and July 31, 2022 -- 1275 forecasts from 442 individuals of six questions about the mpox outbreak where ground truth data are now available. Individual human judgment forecasts and an equally weighted ensemble were evaluated, as well as compared to a random walk, autoregressive, and doubling time model. We found (1) individual human judgment forecasts underestimated outbreak size, (2) the ensemble forecast median moved closer to the ground truth over time but uncertainty around the median did not appreciably decrease, and (3) compared to computational models, for 2-8 week ahead forecasts, the human judgment ensemble outperformed all three models when using median absolute error and weighted interval score; for one week ahead forecasts a random walk outperformed human judgment. We propose two possible explanations: at the time a forecast was submitted, the mode was correlated with the most recent (and smaller) observation that would eventually determine ground truth. Several forecasts were solicited on a logarithmic scale which may have caused humans to generate forecasts with unintended, large uncertainty intervals. To aide in outbreak preparedness, platforms that solicit human judgment forecasts may wish to assess whether specifying a forecast on logarithmic scale matches an individual's intended forecast, support human judgment by finding cues that are typically used to build forecasts, and, to improve performance, tailor their platform to allow forecasters to assign zero probability to events.

Mixed Integer Linear Programming (MILP) can be considered the backbone of the modern power system optimization process, with a large application spectrum, from Unit Commitment and Optimal Transmission Switching to verifying Neural Networks for power system applications. The main issue of these formulations is the computational complexity of the solution algorithms, as they are considered NP-Hard problems. Quantum computing has been tested as a potential solution towards reducing the computational burden imposed by these problems, providing promising results, motivating the can be used to speedup the solution of MILPs. In this work, we present a general framework for solving power system optimization problems with a Quantum Computer (QC), which leverages mathematical tools and QCs' sampling ability to provide accelerated solutions. Our guiding applications are the optimal transmission switching and the verification of neural networks trained to solve a DC Optimal Power Flow. Specifically, using an accelerated version of Benders Decomposition , we split a given MILP into an Integer Master Problem and a linear Subproblem and solve it through a hybrid ``quantum-classical'' approach, getting the best of both worlds. We provide 2 use cases, and benchmark the developed framework against other classical and hybrid methodologies, to demonstrate the opportunities and challenges of hybrid quantum-classical algorithms for power system mixed integer optimization problems.

JWST/NIRCam obtained high angular-resolution (0.05-0.1''), deep near-infrared 1--5 micron imaging of Supernova (SN) 1987A taken 35 years after the explosion. In the NIRCam images, we identify: 1) faint H2 crescents, which are emissions located between the ejecta and the equatorial ring, 2) a bar, which is a substructure of the ejecta, and 3) the bright 3-5 micron continuum emission exterior to the equatorial ring. The emission of the remnant in the NIRCam 1-2.3 micron images is mostly due to line emission, which is mostly emitted in the ejecta and in the hot spots within the equatorial ring. In contrast, the NIRCam 3-5 micron images are dominated by continuum emission. In the ejecta, the continuum is due to dust, obscuring the centre of the ejecta. In contrast, in the ring and exterior to the ring, synchrotron emission contributes a substantial fraction to the continuum. Dust emission contributes to the continuum at outer spots and diffuse emission exterior to the ring, but little within the ring. This shows that dust cooling and destruction time scales are shorter than the synchrotron cooling time scale, and the time scale of hydrogen recombination in the ring is even longer than the synchrotron cooling time scale. With the advent of high sensitivity and high angular resolution images provided by JWST/NIRCam, our observations of SN 1987A demonstrate that NIRCam opens up a window to study particle-acceleration and shock physics in unprecedented details, probed by near-infrared synchrotron emission, building a precise picture of how a SN evolves., Comment: Accepted for publication in MNRAS; 18 pages

This first workshop on visualization for climate action and sustainability aims to explore and consolidate the role of data visualization in accelerating action towards addressing the current environmental crisis. Given the urgency and impact of the environmental crisis, we ask how our skills, research methods, and innovations can help by empowering people and organizations. We believe visualization holds an enormous power to aid understanding, decision making, communication, discussion, participation, education, and exploration of complex topics around climate action and sustainability. Hence, this workshop invites submissions and discussion around these topics with the goal of establishing a visible and actionable link between these fields and their respective stakeholders. The workshop solicits work-in-progress and research papers as well as pictorials and interactive demos from the whole range of visualization research (dashboards, interactive spaces, scientific visualization, storytelling, visual analytics, explainability etc.), within the context of environmentalism (climate science, sustainability, energy, circular economy, biodiversity, etc.) and across a range of scenarios from public awareness and understanding, visual analysis, expert decision making, science communication, personal decision making etc. After presentations of submissions, the workshop will feature dedicated discussion groups around data driven interactive experiences for the public, and tools for personal and professional decision making., Comment: Accepted Workshop at IEEE VIS Conference, 2024

We present a framework to deal with a range of large scale compressive sensing problems using a quantum subroutine. We apply a quantum approximate optimization algorithm (QAOA) to support detection in a sparse signal reconstruction algorithm: matching pursuit. The constrained optimization required in this algorithm is difficult to handle when the size of the problem is large and constraints are given by unstructured patterns. Our framework utilizes specially designed structured constraints that are easy to manipulate and reduce the optimization problem to the solution of an Ising model which can be found using Ising solvers. In this research, we test the performance of QAOA for this purpose on a simulator of quantum computer. We observe that our method can outperform reference classical methods. Our results explore a promising path of applying quantum computers in the compressive sensing field., Comment: 10 pages, 11 figures

Exciton-polaritons represent a promising platform that combines the strengths of both photonic and electronic systems for future optoelectronic devices. However, their application is currently limited to laboratory research due to the high cost and complexity of fabrication methods, which are not compatible with the mature CMOS technology developed for microelectronics. In this work, we develop an innovative, low-cost, and CMOS-compatible method for fabricating large surface polaritonic devices. This is achieved by direct patterning of a halide-perovskite thin film via thermal nanoimprint. As a result, we observe highly homogeneous polaritonic modes of quality factor $Q\approx 300$ at room temperature across a centimetric scale. Impressively, the process provides high reproducibility and fidelity, as the same mold can be reused more than 10 times to imprint the perovskite layer on different types of substrates. Our results could pave the way for the production of low-cost integrated polaritonic devices operating at room temperature.

A vector dark matter candidate, also known as dark photon, would induce an oscillating electric field through kinetic mixing. One detection strategy uses a spherical reflector to focus the induced emission at its center of curvature. On one hand, we investigate the effects of diffraction in this type of experiment from an analytical standpoint, making use of the Kirchhoff integral theorem in the low-curvature dish limit. On the other hand, we estimate the impact of mode-matching, in the case of detection by a pyramidal horn antenna. We show that the expected signal intensity can be significantly reduced compared to usual estimates. Our method is applied to the re-interpretation of the SHUKET experiment data, the results of which are shown to be degraded by a factor of $\sim$~50 due to both diffraction and mode-matching. The analytical method allows optimizing some experimental parameters to gain sensitivity in future runs. Our results can be applied to any dish antenna experiment using a low curvature reflector., Comment: 12+3 pages, 6 figures

CeNiSn is a Kondo semimetal where a gap opens at low temperatures due to hybridization between 4$f$ and conduction electrons, but a full insulating state fails to develop. Upon the insertion of hydrogen, long range magnetic order is induced. Here we report zero-field muon-spin relaxation and inelastic neutron scattering measurements of polycrystalline samples of the deuterides CeNiSnD$_x$ ($x$=1.0, 1.8). The muon-spin relaxation results confirm magnetic ordering in the whole sample of CeNiSnD below around 4.7 K, while inelastic neutron scattering reveals two well-defined crystalline-electric field (CEF) excitations at around 13 meV and 34 meV in CeNiSnD, and 5 meV and 27 meV for CeNiSnD$_{1.8}$. These results suggest that hydrogenation leads to the localization of the Ce-4$f$ electrons, giving rise to long-range magnetic order. We propose CEF level schemes for both systems, which predict a ground state moment of 0.96$\mu_{\rm B}$/Ce within the $ab$-plane for CeNiSnD$_{1.8}$ and a saturated moment of 1.26$\mu_{\rm B}$/Ce along the easy $c$ axis for CeNiSnD, that account for the observed magnetic properties., Comment: 8 pages, 5 figures

NLP has recently made exciting progress toward training language models (LMs) with strong scientific problem-solving skills. However, model development has not focused on real-life use-cases of LMs for science, including applications in education that require processing long scientific documents. To address this, we introduce TutorEval and TutorChat. TutorEval is a diverse question-answering benchmark consisting of questions about long chapters from STEM textbooks, written by experts. TutorEval helps measure real-life usability of LMs as scientific assistants, and it is the first benchmark combining long contexts, free-form generation, and multi-disciplinary scientific knowledge. Moreover, we show that fine-tuning base models with existing dialogue datasets leads to poor performance on TutorEval. Therefore, we create TutorChat, a dataset of 80,000 long synthetic dialogues about textbooks. We use TutorChat to fine-tune Llemma models with 7B and 34B parameters. These LM tutors specialized in math have a 32K-token context window, and they excel at TutorEval while performing strongly on GSM8K and MATH. Our datasets build on open-source materials, and we release our models, data, and evaluations., Comment: 8 pages without bibliography and appendix, 26 pages total

Across a broad range of qubits, a pervasive trade-off becomes obvious: increased coherence seems to be only possible at the cost of qubit speed. This is consistent with the notion that protecting a qubit from its noisy surroundings also limits the control over it. Indeed, from ions to atoms, to superconductors and spins, the leading qubits share a similar Q-factor - the product of speed and coherence time - even though the speed and coherence of various qubits can differ by up to 8 orders of magnitude. This is the qubit speed-coherence dilemma: qubits are either coherent but slow or fast but short-lived. Here, we demonstrate a qubit for which we can triple the speed while simultaneously quadrupling the Hahn-echo coherence time when tuning a local electric field. In this way, the qubit speed and coherence scale together without compromise on either quantity, boosting the Q-factor by over an order of magnitude. Our qubit is a hole spin in a Ge/Si core/shell nanowire providing strong 1D confinement, resulting in the direct Rashba spin-orbit interaction. Due to Heavy-hole light-hole mixing a maximum of the spin-orbit strength is reached at finite electrical field. At the local maximum, charge fluctuations are decoupled from the qubit and coherence is enhanced, yet the drive speed becomes maximal. Our proof-of-concept experiment shows that a properly engineered qubit can be made faster and simultaneously more coherent, removing an important roadblock. Further, it demonstrates that through all-electrical control a qubit can be sped up, without coupling more strongly to the electrical noise environment. As charge fluctuators are unavoidable in semiconductors and all-electrical control is highly scalable, our results improve the prospects for quantum computing in Si and Ge., Comment: Main: 6 pages with 3 display items plus 2 pages for references and methods Supplementary: 12 pages with 7 display items

Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a real 'needle in the haystack' problem, which, until now, has resisted complete automation due to device variability and fabrication imperfections. In this study, we present the first fully autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a clear indication of successful qubit operation. We demonstrate this automation, achieved without human intervention, in a Ge/Si core/shell nanowire device. Our approach integrates deep learning, Bayesian optimization, and computer vision techniques. We expect this automation algorithm to apply to a wide range of semiconductor qubit devices, allowing for statistical studies of qubit quality metrics. As a demonstration of the potential of full automation, we characterise how the Rabi frequency and g-factor depend on barrier gate voltages for one of the qubits found by the algorithm. Twenty years after the initial demonstrations of spin qubit operation, this significant advancement is poised to finally catalyze the operation of large, previously unexplored quantum circuits.

We present initial results from a JWST survey of the youngest Galactic core-collapse supernova remnant Cassiopeia A (Cas A), made up of NIRCam and MIRI imaging mosaics that map emission from the main shell, interior, and surrounding circumstellar/interstellar material (CSM/ISM). We also present four exploratory positions of MIRI/MRS IFU spectroscopy that sample ejecta, CSM, and associated dust from representative shocked and unshocked regions. Surprising discoveries include: 1) a web-like network of unshocked ejecta filaments resolved to 0.01 pc scales exhibiting an overall morphology consistent with turbulent mixing of cool, low-entropy matter from the progenitor's oxygen layer with hot, high-entropy matter heated by neutrino interactions and radioactivity, 2) a thick sheet of dust-dominated emission from shocked CSM seen in projection toward the remnant's interior pockmarked with small (approximately one arcsecond) round holes formed by knots of high-velocity ejecta that have pierced through the CSM and driven expanding tangential shocks, 3) dozens of light echoes with angular sizes between 0.1 arcsecond to 1 arcminute reflecting previously unseen fine-scale structure in the ISM. NIRCam observations place new upper limits on infrared emission from the neutron star in Cas A's center and tightly constrain scenarios involving a possible fallback disk. These JWST survey data and initial findings help address unresolved questions about massive star explosions that have broad implications for the formation and evolution of stellar populations, the metal and dust enrichment of galaxies, and the origin of compact remnant objects., Comment: 27 pages, 10 figures, now published in ApJL

By leveraging the no-cloning principle of quantum mechanics, unclonable cryptography enables us to achieve novel cryptographic protocols that are otherwise impossible classically. Two most notable examples of unclonable cryptography are copy-protection (CP) and unclonable encryption (UE). Most known constructions rely on the QROM (as opposed to the plain model). Despite receiving a lot of attention in recent years, two important open questions still remain: CP for point functions in the plain model, which is usually considered as feasibility demonstration, and UE with unclonable indistinguishability security in the plain model. A core ingredient of these protocols is the so-called monogamy-of-entanglement (MoE) property. Such games allow quantifying the correlations between the outcomes of multiple non-communicating parties sharing entanglement in a particular context. Specifically, we define the games between a challenger and three players in which the first player is asked to split and share a quantum state between the two others, who are then simultaneously asked a question and need to output the correct answer. In this work, by relying on previous works [CLLZ21, CV22], we establish a new MoE property for subspace coset states, which allows us to progress towards the aforementioned goals. However, it is not sufficient on its own, and we present two conjectures that would allow first to show that CP of point functions exists in the plain model, with different challenge distributions, and then that UE with unclonable indistinguishability security exists in the plain model. We believe that our new MoE to be of independent interest, and it could be useful in other applications as well. To highlight this last point, we leverage our new MoE property to show the existence of a tokenized signature scheme with a new security definition, called unclonable unforgeability., Comment: Added unclonable unforgeability for tokenized signatures

There is growing interest in understanding how interactions between system-wide objectives and local community decision-making will impact the clean energy transition. The concept of energysheds has gained traction in the areas of public policy and social science as a way to study these relationships. However, development of technical definitions of energysheds that permit system analysis are still largely missing. In this work, we propose a mathematical definition for energysheds, and introduce an analytical framework for studying energyshed concepts within the context of future electric power system operations. This framework is used to develop insights into the factors that impact a community's ability to achieve energyshed policy incentives within a larger connected power grid, as well as the tradeoffs associated with different spatial policy requirements. We also propose an optimization-based energyshed policy design problem, and show that it can be solved to global optimality within arbitrary precision by employing concepts from quasi-convex optimization. Finally, we investigate how interconnected energysheds can cooperatively achieve their objectives in bulk power system operations.

The use of Large Language Models (LLMs) for writing has sparked controversy both among readers and writers. On one hand, writers are concerned that LLMs will deprive them of agency and ownership, and readers are concerned about spending their time on text generated by soulless machines. On the other hand, AI-assistance can improve writing as long as writers can conform to publisher policies, and as long as readers can be assured that a text has been verified by a human. We argue that a system that captures the provenance of interaction with an LLM can help writers retain their agency, conform to policies, and communicate their use of AI to publishers and readers transparently. Thus we propose HaLLMark, a tool for visualizing the writer's interaction with the LLM. We evaluated HaLLMark with 13 creative writers, and found that it helped them retain a sense of control and ownership of the text.

Linear system solving is a key tool for computational power system studies, e.g., optimal power flow, transmission switching, or unit commitment. CPU-based linear system solver speeds, however, have saturated in recent years. Emerging research shows that GPU-based linear system solvers are beginning to achieve notable speedup over CPU-based alternatives in some applications. Due to the architecture of GPU memory access, numerical pivoting represents the new bottleneck which prevents GPU-based solvers from running even faster. Accordingly, this paper proposes a matrix perturbation-based method to induce static pivoting. Using this approach, a series of perturbed, well-conditioned, pivot-free linear systems are solved in parallel on GPUs. Matrix expansion routines are then used to linearly combine the results, and the true solution is recovered to an arbitrarily high degree of theoretical accuracy. We showcase the validity of our approach on distributed-slack AC power flow solve iterations associated with the PGLib 300-bus test case., Comment: submitted to PESGM 2024

Many-body forces, and specially three-body forces, are sometimes a relevant ingredient in various fields, such as atomic, nuclear or hadronic physics. As their precise structure is generally difficult to uncover or to implement, phenomenological effective forces are often used in practice. A form commonly used for a many-body variable is the square-root of the sum of two-body variables. Even in this case, the problem can be very difficult to treat numerically. But this kind of many-body forces can be handled at the same level of difficulty than two-body forces by the envelope theory. The envelope theory is a very efficient technique to compute approximate, but reliable, solutions of many-body systems, specially for identical particles. The quality of this technique is tested here for various three-body forces with non-relativistic systems composed of three identical particles. The energies, the eigenfunctions, and some observables are compared with the corresponding accurate results computed with a numerical variational method., Comment: 13 pages

We report Chandra-ACIS observations of SN 2023ixf in M101 on day 13 and 86 since the explosion. The X-rays in both epochs are characterized by high temperature plasma from the forward shocked region as a result of circumstellar interaction. We are able to constrain the absorption column density at both Chandra epochs, which is much larger than that due to the Galactic and host absorption column, and we attribute it to absorption by the circumstellar matter in the immediate vicinity of SN 2023ixf. Combining our column density measurements with the published measurement on day 4, we show that the column density declines as $t^{-2}$ between day 4 to day 13 and then evolves as $t^{-1}$. The unabsorbed $0.3-10$ keV luminosity evolves as $t^{-1}$ during the Chandra epochs. On day 13 Chandra observation we detect the Fe K$\alpha$ fluorescent line at 6.4 keV indicating presence of cold material in the vicinity of the SN. The line is absent on day 86, consistent with the decreased column density by a factor of 7 between the two epochs. Our analysis indicates that during 10 years to 1.5 years before explosion, the progenitor was evolving with a constant mass-loss rate of $5.6\times 10^{-4}$ M$_\odot$ yr$^{-1}$., Comment: Accepted for publication in ApJ Letters