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We explore the possibility of using ultra-peripheral proton-lead collisions at the LHC to study inclusive vector-quarkonium photoproduction, that occurs when a quasi-real photon emitted by a fully stripped lead ion breaks a proton to produce a vector quarkonium. Owing to the extremely large energies of the colliding hadrons circulating in the LHC, the range of accessible photon-nucleon centre-of-mass energies, W_gamma+p, largely exceeds what has been and will be studied at lepton-hadron colliders, HERA and the EIC. We perform a tune to HERA photoproduction data, use this tune to predict the yields of photoproduced J/psi, and estimate the corresponding transverse-momentum reach at LHC experiments. We also model the hadroproduction background and demonstrate that inclusive photoproduction can be isolated at the LHC from such background by imposing constraints on the hadronic activity in the lead-going direction at mid, forward, or far-forward rapidities depending on the capability of the detector under consideration. We find that the resulting cross sections are large enough to be measured by ALICE, ATLAS, CMS, and LHCb. We estimate the background-to-signal ratio after isolation to be of the order of 0.001 and 0.1 in the low and large transverse-momentum regions, respectively. In addition, we propose and assess the Jacquet-Blondel method to reconstruct the photon-nucleon centre-of-mass energy and the fractional energy of the quarkonium with respect to the photon., Comment: LaTeX, 34 pages, 26 figures, 9 tables uses svjour3.cls, svglov3.clo and svepjc3.clo (provided)

We study conditions under which the addition of variables to a regression equation can turn a previously statistically insignificant result into a significant one. Specifically, we characterize the minimum strength of association required for these variables--both with the dependent and independent variables, or with the dependent variable alone--to elevate the observed t-statistic above a specified significance threshold. Interestingly, we show that it is considerably difficult to overturn a statistically insignificant result solely by reducing the standard error. Instead, included variables must also alter the point estimate to achieve such reversals in practice. Our results can be used for sensitivity analysis and for bounding the extent of p-hacking, and may also offer algebraic explanations for patterns of reversals seen in empirical research, such as those documented by Lenz and Sahn (2021).

We investigate the dynamical and physical structures of bright-rimmed clouds (BRCs) in a nearby HII region. We focused on carbon- and oxygen-bearing species that trace photon-dominated regions (PDRs) and warm molecular cloud surfaces in order to understand the effect of UV radiation from the exciting stars on the cloud structure. We mapped four regions around the most prominent BRCs at scales of 4--10 arcmin in the HII region IC 1396 in [CII] 158 micron with (up)GREAT on board SOFIA. IC 1396 is predominantly excited by an O6.5V star. Toward IC 1396A, we also observed [OI] 63 micron and 145 micron. We combined these observations with JCMT archive data, which provide the low-J transitions of CO, $^{13}$CO, and C$^{18}$O. All spectra are velocity-resolved. The line profiles show a variety of velocity structures, which we investigated in detail for all observed emission lines. We find no clear sign of photoevaporating flows in the [CII] spectra, although the uncertainty in the location of the BRCs along the line of sight makes this interpretation inconclusive. Our analysis of the [$^{13}$CII] emission in IC 1396A suggests that the [CII] is likely mostly optically thin. The heating efficiency, measured by the ([CII]+[OI] 63 micron)/far-infrared intensity ratio, is higher in the northern part of IC 1396A than in the southern part, which may indicate a difference in the dust properties of the two areas. The complex velocity structures identified in the BRCs of IC 1396, which is apparently a relatively simple HII region, highlight the importance of velocity-resolved data for disentangling different components along the line of sight and thus facilitating a detailed study of the dynamics of the cloud. We also demonstrate that the optically thin [$^{13}$CII] and [OI] 145 micron emission lines are essential for a conclusive interpretation of the [CII] 158 micron and [OI] 63 micron line profiles., Comment: Accepted for publication in A&A, 26 pages including Appendices

Recent attention has focused on the development of methods for post-selection inference. However, the connections between these methods, and the extent to which one might be preferred to another, remain unclear. In this paper, we classify existing methods for post-selection inference into one of two frameworks: infer-and-widen or split-and-condition. The infer-and-widen framework produces confidence intervals whose midpoints are biased due to selection, and must be wide enough to account for this bias. By contrast, split-and-condition directly adjusts the intervals' midpoints to account for selection. We compare the two frameworks in three vignettes: the winner's curse, maximal contrasts, and inference after the lasso. Our results are striking: in each of these examples, a split-and-condition strategy leads to confidence intervals that are much narrower than the state-of-the-art infer-and-widen proposal, when methods are tuned to yield identical selection events. Furthermore, even an ``oracle" infer-and-widen confidence interval -- the narrowest possible interval that could be theoretically attained via infer-and-widen -- is not necessarily narrower than a feasible split-and-condition method. Taken together, these results point to split-and-condition as the most promising framework for post-selection inference in real-world settings.

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.

This study unveils the elusive presence of criminal signatures in cyberspace, validating for the first time their existence through statistical evidence. By applying the A priori algorithm to the modus operandi of Advanced Persistent Threats, extracted from an extensive corpus of over 17,000 articles spanning 2007 to 2020, we highlight the enduring patterns leveraged by sophisticated cyber criminals. Our findings verify the existence of unique signatures associated with advanced cybercriminals, bridging a crucial gap in current understanding of human behavior in cyber-attacks. This pivotal research sets the foundation for an entirely new academic intersection in cybersecurity and computational criminology., Comment: Communications The 7th Conference for Information & Communication Technologies for Organization & Society

Cosmological hydrodynamical simulations, while the current state-of-the art methodology for generating theoretical predictions for the large scale structures of the Universe, are among the most expensive simulation tools, requiring upwards of 100 millions CPU hours per simulation. N-body simulations, which exclusively model dark matter and its purely gravitational interactions, represent a less resource-intensive alternative, however, they do not model galaxies, and as such cannot directly be compared to observations. In this study, we use conditional score-based models to learn a mapping from N-body to hydrodynamical simulations, specifically from dark matter density fields to the observable distribution of galaxies. We demonstrate that our model is capable of generating galaxy fields statistically consistent with hydrodynamical simulations at a fraction of the computational cost, and demonstrate our emulator is significantly more precise than traditional emulators over the scales 0.36 $h\ \text{Mpc}^{-1}$ $\leq$ k $\leq$ 3.88 $h\ \text{Mpc}^{-1}$., Comment: 7+4 pages, 3+1 figures, accepted at the ICML 2024 Workshop AI4Science

While hallucinations of large language models (LLMs) prevail as a major challenge, existing evaluation benchmarks on factuality do not cover the diverse domains of knowledge that the real-world users of LLMs seek information about. To bridge this gap, we introduce WildHallucinations, a benchmark that evaluates factuality. It does so by prompting LLMs to generate information about entities mined from user-chatbot conversations in the wild. These generations are then automatically fact-checked against a systematically curated knowledge source collected from web search. Notably, half of these real-world entities do not have associated Wikipedia pages. We evaluate 118,785 generations from 15 LLMs on 7,919 entities. We find that LLMs consistently hallucinate more on entities without Wikipedia pages and exhibit varying hallucination rates across different domains. Finally, given the same base models, adding a retrieval component only slightly reduces hallucinations but does not eliminate hallucinations.

Wearable on-body millimeter-wave (mmWave) radars can provide obstacle detection and guidance for visually impaired people. However, their everyday performance is hindered by the rigid form factor and limited scanning range. In this article, we propose a low-profile, fast-scanning leaky-wave antenna (LWA) operating in the unlicensed V-band (57-64 GHz) to be integrated for on-body applications such as lightweight portable frequency-modulated continuous wave (FMCW) radars. The proposed LWA consists of meandering microstrips that can conform to the human body curvatures while maintaining beam-forming and beam-scanning properties. Experimental results demonstrate that the planar LWA achieves a realized gain above 10 dB with a fan-beam steering range in the H-plane from -40{\deg} to 43{\deg} over the operating frequency band while the half power beam-width (HPBW) is within 20{\deg}. Since for the foreseen application the antenna is supposed to conform to the user's body, the performance is also analyzed for a bent condition. The beam steering range changes to -32{\deg} to 50{\deg} when placed on the knee (corresponding to 80 mm radius). Under bending conditions, the LWA exhibits a maximum degradation of 1.75 dB, while the HPBW increases to 25{\deg}. This shows that due to the small size of the antenna, the impact of bending is low and the beam-forming and beam-scanning property of the designed LWA remain intact. Furthermore, we enable 2-D spatial scanning by employing an array of twelve LWAs with phased excitation, extending the scanning range in the E-plane from -40{\deg} to 40{\deg}, while the HPBW remains below 20{\deg} across the operational frequency range.

Ingestible sensor devices, which are increasingly used for internal health monitoring, rely on antennas to perform sensing functions and simultaneously to communicate with external devices. Despite the development of various ingestible antennas, there has been no comprehensive comparison of their performance as biosensors. This paper addresses this gap by examining and comparing the suitability of three common types of ingestible antennas -- dipole, patch, and loop -- as biosensors for distinguishing gastrointestinal tissues (stomach, small intestine, and large intestine) based on their electromagnetic properties. The antennas studied in this work conform to the inner surface of biocompatible polylactic acid capsules with varying shell thicknesses and operate in the 433 MHz Industrial, Scientific, and Medical band. The comparison is performed in gastrointestinal tissues using several antenna parameters: 1) Sensing Capability: Changes in the phase of the reflection coefficient in the tissues are selected as the sensing parameter. 2) Robustness: The frequency interval (f_i) in which the antennas are matched (|S11| < -10 dB) in all the tissues and the maximum change in the center frequency (f_c) in different tissues are examined. 3) Radiation Performance: The gain and radiation efficiency of the antennas are examined. The effect of shell thickness on gain and radiation efficiency at 434 MHz is presented. Additionally, the radiation efficiency at various frequencies allocated for medical communications is compared with the theoretical maximum achievable efficiencies. These comprehensive data provide valuable information for making engineering decisions when designing multiplexed biosensor antennas for ingestible applications.

Young associations record complete histories of star forming events through their demographics and dynamics, and Gaia has greatly expanded our knowledge of these associations. We present the first structural and dynamical overview study of the Cep-Her Complex, which has recently emerged as the largest stellar population within 500 pc that still lacks substantial follow-up. We reveal that Cep-Her is not a singular association, but rather an amalgam of four, consisting of the older ($\tau > 100$ Myr) open cluster Roslund 6, in addition to three dynamically coherent and highly substructured young associations that we focus on: Orpheus (25-40 Myr), Cinyras (28-43 Myr), and Cupavo (54-80 Myr). With $9552 \pm 960$ stars in Orpheus, $3872 \pm 455$ stars in Cinyras, and $8794 \pm 1827$ stars in Cupavo, all three are among the largest young associations within 500 pc, rivalling major associations like Sco-Cen. Our novel view of the ages and dynamics of these associations reveals evidence for sequential star formation in Cinyras, in addition to a multi-origin pattern of stellar dispersal in Orpheus that may hint to the presence of multiple clouds at formation. Dynamical simulations indicate that while some embedded open clusters and central components of these associations are converging, they likely lack the mass necessary to capture one another and undergo hierarchical cluster assembly. Our results provide our first view of the complex star-forming environments that gave rise to the associations of Cep-Her, which will serve as important laboratories for future studies of star and planet formation., Comment: Accepted to ApJ. 39 Pages, 21 Figures, 5 Tables in AASTEX63 format

Gravitational lensing is the deflection of light rays due to the gravity of intervening masses. This phenomenon is observed in a variety of scales and configurations, involving any non-uniform mass such as planets, stars, galaxies, clusters of galaxies, and even the large scale structure of the universe. Strong lensing occurs when the distortions are significant and multiple images of the background source are observed. The lens objects must align on the sky of order ~1 arcsecond for galaxy-galaxy lensing, or 10's of arcseonds for cluster-galaxy lensing. As the discovery of lens systems has grown to the low thousands, these systems have become pivotal for precision measurements and addressing critical questions in astrophysics. Notably, they facilitate the measurement of the Universe's expansion rate, dark matter, supernovae, quasars, and the first stars among other topics. With future surveys expected to discover hundreds of thousands of lensing systems, the modelling and simulation of such systems must occur at orders of magnitude larger scale then ever before. Here we present `caustics`, a Python package designed to handle the extensive computational demands of modeling such a vast number of lensing systems., Comment: 13 pages, 3 figures, submitted to JOSS

Data assimilation is a central problem in many geophysical applications, such as weather forecasting. It aims to estimate the state of a potentially large system, such as the atmosphere, from sparse observations, supplemented by prior physical knowledge. The size of the systems involved and the complexity of the underlying physical equations make it a challenging task from a computational point of view. Neural networks represent a promising method of emulating the physics at low cost, and therefore have the potential to considerably improve and accelerate data assimilation. In this work, we introduce a deep learning approach where the physical system is modeled as a sequence of coarse-to-fine Gaussian prior distributions parametrized by a neural network. This allows us to define an assimilation operator, which is trained in an end-to-end fashion to minimize the reconstruction error on a dataset with different observation processes. We illustrate our approach on chaotic dynamical physical systems with sparse observations, and compare it to traditional variational data assimilation methods.

We introduce WildBench, an automated evaluation framework designed to benchmark large language models (LLMs) using challenging, real-world user queries. WildBench consists of 1,024 tasks carefully selected from over one million human-chatbot conversation logs. For automated evaluation with WildBench, we have developed two metrics, WB-Reward and WB-Score, which are computable using advanced LLMs such as GPT-4-turbo. WildBench evaluation uses task-specific checklists to evaluate model outputs systematically and provides structured explanations that justify the scores and comparisons, resulting in more reliable and interpretable automatic judgments. WB-Reward employs fine-grained pairwise comparisons between model responses, generating five potential outcomes: much better, slightly better, slightly worse, much worse, or a tie. Unlike previous evaluations that employed a single baseline model, we selected three baseline models at varying performance levels to ensure a comprehensive pairwise evaluation. Additionally, we propose a simple method to mitigate length bias, by converting outcomes of ``slightly better/worse'' to ``tie'' if the winner response exceeds the loser one by more than $K$ characters. WB-Score evaluates the quality of model outputs individually, making it a fast and cost-efficient evaluation metric. WildBench results demonstrate a strong correlation with the human-voted Elo ratings from Chatbot Arena on hard tasks. Specifically, WB-Reward achieves a Pearson correlation of 0.98 with top-ranking models. Additionally, WB-Score reaches 0.95, surpassing both ArenaHard's 0.91 and AlpacaEval2.0's 0.89 for length-controlled win rates, as well as the 0.87 for regular win rates., Comment: Link: https://hf.co/spaces/allenai/WildBench

Language models (LMs) have long been used to improve results of automatic speech recognition (ASR) systems, but they are unaware of the errors that ASR systems make. Error correction models are designed to fix ASR errors, however, they showed little improvement over traditional LMs mainly due to the lack of supervised training data. In this paper, we present Denoising LM (DLM), which is a $\textit{scaled}$ error correction model trained with vast amounts of synthetic data, significantly exceeding prior attempts meanwhile achieving new state-of-the-art ASR performance. We use text-to-speech (TTS) systems to synthesize audio, which is fed into an ASR system to produce noisy hypotheses, which are then paired with the original texts to train the DLM. DLM has several $\textit{key ingredients}$: (i) up-scaled model and data; (ii) usage of multi-speaker TTS systems; (iii) combination of multiple noise augmentation strategies; and (iv) new decoding techniques. With a Transformer-CTC ASR, DLM achieves 1.5% word error rate (WER) on $\textit{test-clean}$ and 3.3% WER on $\textit{test-other}$ on Librispeech, which to our knowledge are the best reported numbers in the setting where no external audio data are used and even match self-supervised methods which use external audio data. Furthermore, a single DLM is applicable to different ASRs, and greatly surpassing the performance of conventional LM based beam-search rescoring. These results indicate that properly investigated error correction models have the potential to replace conventional LMs, holding the key to a new level of accuracy in ASR systems., Comment: under review

QCD gives rise to a rich spectrum of excited baryon states. Understanding their internal structure is important for many areas of nuclear physics, such as nuclear forces, dense matter, and neutrino-nucleus interactions. Generalized parton distributions (GPDs) are an established tool for characterizing the QCD structure of the ground-state nucleon. They are used to create 3D tomographic images of the quark/gluon structure and quantify the mechanical properties such as the distribution of mass, angular momentum and forces in the system. Transition GPDs extend these concepts to $N \rightarrow N^\ast$ transitions and can be used to characterize the 3D structure and mechanical properties of baryon resonances. They can be probed in high-momentum-transfer exclusive electroproduction processes with resonance transitions $e + N \rightarrow e' + M + N^\ast$, such as deeply-virtual Compton scattering ($M = \gamma$) or meson production ($M = \pi, K$, $etc.$), and in related photon/hadron-induced processes. This White Paper describes a research program aiming to explore baryon resonance structure with transition GPDs. This includes the properties and interpretation of the transition GPDs, theoretical methods for structures and processes, first experimental results from JLab 12 GeV, future measurements with existing and planned facilities (JLab detector and energy upgrades, COMPASS/AMBER, EIC, EicC, J-PARC, LHC ultraperihperal collisions), and the theoretical and experimental developments needed to realize this program.

Classification of 3D point clouds is a challenging machine learning (ML) task with important real-world applications in a spectrum from autonomous driving and robot-assisted surgery to earth observation from low orbit. As with other ML tasks, classification models are notoriously brittle in the presence of adversarial attacks. These are rooted in imperceptible changes to inputs with the effect that a seemingly well-trained model ends up misclassifying the input. This paper adds to the understanding of adversarial attacks by presenting Eidos, a framework providing Efficient Imperceptible aDversarial attacks on 3D pOint cloudS. Eidos supports a diverse set of imperceptibility metrics. It employs an iterative, two-step procedure to identify optimal adversarial examples, thereby enabling a runtime-imperceptibility trade-off. We provide empirical evidence relative to several popular 3D point cloud classification models and several established 3D attack methods, showing Eidos' superiority with respect to efficiency as well as imperceptibility., Comment: Preprint

It seems that in the current age, computers, computation, and data have an increasingly important role to play in scientific research and discovery. This is reflected in part by the rise of machine learning and artificial intelligence, which have become great areas of interest not just for computer science but also for many other fields of study. More generally, there have been trends moving towards the use of bigger, more complex and higher capacity models. It also seems that stochastic models, and stochastic variants of existing deterministic models, have become important research directions in various fields. For all of these types of models, gradient-based optimization remains as the dominant paradigm for model fitting, control, and more. This dissertation considers unconstrained, nonlinear optimization problems, with a focus on the gradient itself, that key quantity which enables the solution of such problems. In chapter 1, we introduce the notion of reverse differentiation, a term which describes the body of techniques which enables the efficient computation of gradients. We cover relevant techniques both in the deterministic and stochastic cases. We present a new framework for calculating the gradient of problems which involve both deterministic and stochastic elements. In chapter 2, we analyze the properties of the gradient estimator, with a focus on those properties which are typically assumed in convergence proofs of optimization algorithms. Chapter 3 gives various examples of applying our new gradient estimator. We further explore the idea of working with piecewise continuous models, that is, models with distinct branches and if statements which define what specific branch to use., Comment: cornell university dissertation

Folding can transform mundane objects such as napkins into stunning works of art. However, finding new folding transformations for sheet materials is a challenging problem that requires expertise and real-world experimentation. In this paper, we present Modal Folding -- an automated approach for discovering energetically optimal folding transformations, i.e., large deformations that require little mechanical work. For small deformations, minimizing internal energy for fixed displacement magnitudes leads to the well-known elastic eigenmodes. While linear modes provide promising directions for bending, they cannot capture the rotational motion required for folding. To overcome this limitation, we introduce strain-space modes -- nonlinear analogues of elastic eigenmodes that operate on per-element curvatures instead of vertices. Using strain-space modes to determine target curvatures for bending elements, we can generate complex nonlinear folding motions by simply minimizing the sheet's internal energy. Our modal folding approach offers a systematic and automated way to create complex designs. We demonstrate the effectiveness of our method with simulation results for a range of shapes and materials, and validate our designs with physical prototypes., Comment: 9 pages, SIGGRAPH 2024 Conference

This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module. This system learns and detects parts in the images that are discriminative among categories, without the need for fine-tuning the CNN, making it more scalable than other part-based models. While part-based approaches naturally offer interpretable representations, we propose explanations at image and category levels and introduce specific constraints on the part learning process to make them more discrimative., Comment: IEEE ICIP 2023

This paper studies interpretability of convolutional networks by means of saliency maps. Most approaches based on Class Activation Maps (CAM) combine information from fully connected layers and gradient through variants of backpropagation. However, it is well understood that gradients are noisy and alternatives like guided backpropagation have been proposed to obtain better visualization at inference. In this work, we present a novel training approach to improve the quality of gradients for interpretability. In particular, we introduce a regularization loss such that the gradient with respect to the input image obtained by standard backpropagation is similar to the gradient obtained by guided backpropagation. We find that the resulting gradient is qualitatively less noisy and improves quantitatively the interpretability properties of different networks, using several interpretability methods., Comment: VISAPP 2024

Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map. Additionally, attention-based pooling serves as a form of masking the in feature space. Motivated by this observation, we design an attention-based pooling mechanism intended to replace Global Average Pooling (GAP) at inference. This mechanism, called Cross-Attention Stream (CA-Stream), comprises a stream of cross attention blocks interacting with features at different network depths. CA-Stream enhances interpretability in models, while preserving recognition performance., Comment: CVPR XAI4CV workshop 2024

Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors undermine trust in LLMs and pose significant hurdles to their adoption in real-world applications, such as legal assistance and medical diagnosis, where precision, reliability, and ethical considerations are paramount. These could also lead to user dissatisfaction, which is currently inadequately assessed and captured. Therefore, to effectively and transparently assess users' satisfaction and trust in their interactions with LLMs, we design and develop LLMChain, a decentralized blockchain-based reputation system that combines automatic evaluation with human feedback to assign contextual reputation scores that accurately reflect LLM's behavior. LLMChain not only helps users and entities identify the most trustworthy LLM for their specific needs, but also provides LLM developers with valuable information to refine and improve their models. To our knowledge, this is the first time that a blockchain-based distributed framework for sharing and evaluating LLMs has been introduced. Implemented using emerging tools, LLMChain is evaluated across two benchmark datasets, showcasing its effectiveness and scalability in assessing seven different LLMs., Comment: Paper accepted at IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) IEEE, Osaka, Japan (2024)

CAM-based methods are widely-used post-hoc interpretability method that produce a saliency map to explain the decision of an image classification model. The saliency map highlights the important areas of the image relevant to the prediction. In this paper, we show that most of these methods can incorrectly attribute an important score to parts of the image that the model cannot see. We show that this phenomenon occurs both theoretically and experimentally. On the theory side, we analyze the behavior of GradCAM on a simple masked CNN model at initialization. Experimentally, we train a VGG-like model constrained to not use the lower part of the image and nevertheless observe positive scores in the unseen part of the image. This behavior is evaluated quantitatively on two new datasets. We believe that this is problematic, potentially leading to mis-interpretation of the model's behavior., Comment: Accepted for publication at ECML 2024 (28 pages, 9 figures)

Bromochloro alkanes (BCAs) have been manufactured for use as flame retardants for decades and preliminary environmental risk screening suggests they are likely to behave similarly to polychlorinated alkanes (PCAs), subclasses of which are restricted as Stockholm Convention Persistent Organic Pollutants (POPs). BCAs have rarely been studied in the environment, though some evidence suggests they may migrate from treated-consumer materials into indoor dust, resulting in human exposure via inadvertent ingestion. In this study, BCA-C14 mixture standards were synthesized and used to validate an analytical method. This method relies on chloride-enhanced liquid chromatography-electrospray ionization-Orbitrap-high resolution mass spectrometry (LC-ESI-Orbitrap-HRMS) and a novel CP-Seeker integration software package for homologue detection and integration. Dust sample preparation via ultrasonic extraction, acidified silica clean-up and fractionation on neutral silica cartridges was found to be suitable for BCAs, with absolute recovery of individual homologues averaging 66 to 78% and coefficients of variation $\le$10% in replicated spiking experiments (n=3). In addition, a total of 59 indoor dust samples from six countries including Australia (n=10), Belgium (n=10), Colombia (n=10), Japan (n=10), Thailand (n=10) and the United States of America (n=9) were analysed for BCAs. BCAs were detected in seven samples from the USA, with carbon chain lengths of C8, C10, C12, C14, C16, C18, C24 to C28, C30 and C31 observed overall, though not detected in samples from any other countries. Bromination of detected homologues in the indoor dust samples ranged from Br1-4 as well as Br7, while chlorine numbers ranged from Cl2-11. BCA-C18 were the most frequently detected, observed in each of the USA samples, while the most prevalent halogenation degrees were homologues of Br2 and Cl4-5. Broad estimations of BCA concentrations in the dust samples indicated that levels may approach those of other flame retardants in at least some instances. These findings suggest that development of quantification strategies and further investigation of environmental occurrence and health implications are needed.

Motivated by a problem from incompressible fluid mechanics of Brenier (JAMS 1989), and its recent entropic relaxation by Arnaudo, Cruizero, L\'eonard & Zambrini (AIHP PS 2020), we study a problem of entropic minimization on the path space when the reference measure is a generic Feller semimartingale. We show that, under some regularity condition, our problem connects naturally with a, possibly non-local, version of the Hamilton-Jacobi-Bellman equation. Additionally, we study existence of minimizers when the reference measure in a Ornstein-Uhlenbeck process., Comment: 13 pages, comments welcome!

In this article, we revisit the question of fluctuations of linear statistics of beta ensembles in the single cut and non-critical regime for general potentials $V$ under mild regularity and growth assumptions. Our main objective is to establish sharp quantitative Central Limit Theorems (CLT) for strong distances, such as the total variation distance, which to the best of our knowledge, is new for general potentials, even qualitatively. Namely, setting $\mu_V$ the equilibrium measure, for a test function $\xi \in \mathscr{C}^{14}$, we establish the convergence in total variation of $X_n=\sum_{i=1}^n \xi(\lambda_i)-n\langle \xi,\mu_V\rangle$ to an explicit Gaussian variable at the sharp speed $1/n$. Under the same assumptions, we also establish multivariate CLTs for vectors of linear statistics in $p-$Wasserstein distances for any $p\ge 1$, with the optimal rate $1/n$, a result which already in dimension one sharpens the speed of convergence established in the recent contribution [26] as well as the required regularity on the test functions. A second objective of this paper, in a more qualitative direction, is to establish the so-called super-convergence of linear statistics, that is to say the convergence of all derivatives of the densities of $X_n$ uniformly on $\mathbb{R}$, provided that $\xi\in\mathscr{C}^\infty(\mathbb{R})$ and is not too degenerated in some sense.

We present a framework that combines the adjoint state method together with reverse-time back-propagation to solve prohibitively large open-system quantum control problems. Our approach enables the optimization of arbitrary cost functions with fully general controls applied on large open quantum systems described by a Lindblad master equation. It is scalable, computationally efficient, and has a low memory footprint. We apply this framework to optimize two inherently dissipative operations in superconducting qubits which lag behind in terms of fidelity and duration compared to other unitary operations: the dispersive readout and all-microwave reset of a transmon qubit. Our results show that, given a fixed set of system parameters, shaping the control pulses can yield 2x improvements in the fidelity and duration for both of these operations compared to standard strategies. Our approach can readily be applied to optimize quantum controls in a vast range of applications such as reservoir engineering, autonomous quantum error correction, and leakage-reduction units., Comment: 5 pages, 3 figures

We determine the rational real forms of the complex Mori fiber spaces for which the identity component of the automorphism group is a maximal connected algebraic subgroup of $\mathrm{Bir}(\mathbb{P}_{\mathbb{C}}^{3})$. This yields a list of maximal connected algebraic subgroup of $\mathrm{Bir}(\mathbb{P}_{\mathbb{R}}^{3})$. We furthermore determine the equivariant Sarkisov links starting from these rational real forms. This article is the first step towards classifying all the maximal connected algebraic subgroups of $\mathrm{Bir}(\mathbb{P}_{\mathbb{R}}^{3})$., Comment: 48 pages, comments are welcome

Large Language Models (LLMs) have garnered considerable interest due to their impressive natural language capabilities, which in conjunction with various emergent properties make them versatile tools in workflows ranging from complex code generation to heuristic finding for combinatorial problems. In this paper we offer a perspective on their applicability to materials science research, arguing their ability to handle ambiguous requirements across a range of tasks and disciplines mean they could be a powerful tool to aid researchers. We qualitatively examine basic LLM theory, connecting it to relevant properties and techniques in the literature before providing two case studies that demonstrate their use in task automation and knowledge extraction at-scale. At their current stage of development, we argue LLMs should be viewed less as oracles of novel insight, and more as tireless workers that can accelerate and unify exploration across domains. It is our hope that this paper can familiarise material science researchers with the concepts needed to leverage these tools in their own research.

In $1996$, H.-D. Cao constructed a $U(n)$-invariant steady gradient K\"ahler-Ricci soliton on $\mathbb{C}^{n}$ and asked whether every steady gradient K\"ahler-Ricci soliton of positive curvature on $\mathbb{C}^{n}$ is necessarily $U(n)$-invariant (and hence unique up to scaling). Recently, Apostolov-Cifarelli answered this question in the negative for $n=2$. Here, we construct a family of $U(1)\times U(n-1)$-invariant, but not $U(n)$-invariant, complete steady gradient K\"ahler-Ricci solitons with strictly positive curvature operator on real $(1,\,1)$-forms (in particular, with strictly positive sectional curvature) on $\mathbb{C}^{n}$ for $n\geq3$, thereby answering Cao's question in the negative for $n\geq3$. This family of steady Ricci solitons interpolates between Cao's $U(n)$-invariant steady K\"ahler-Ricci soliton and the product of the cigar soliton and Cao's $U(n-1)$-invariant steady K\"ahler-Ricci soliton. This provides the K\"ahler analog of the Riemannian flying wings construction of Lai. In the process of the proof, we also demonstrate that the almost diameter rigidity of $\mathbb{P}^{n}$ endowed with the Fubini-Study metric does not hold even if the curvature operator is bounded below by $2$ on real $(1,\,1)$-forms., Comment: Appendix B added; more references added

Broken links in websites external resources pose a serious threat to cybersecurity and the credibility of websites. They can be hijacked to eavesdrop user traffic or to inject malicious software. In this paper, we present the first result of an ongoing research. We focus on the prevalence of broken links in external resources on home pages of the most visited websites in the world. The analysis was conducted on the top 88 000 homepages extracted from the Majestic Million rankings. 35,2% of them have at least one broken link. We also identify the common causes of these broken links and highlight improper implementation of testing phases to prevent such errors. We provide a formal model for the distribution of external links. At the next research step, we are exploring the potential impact on privacy of broken links by analyzing inherited traffic of purchasable expired domains., Comment: 4 pages, 3 tables, 1 figure

Principal component analysis (PCA) is a longstanding and well-studied approach for dimension reduction. It rests upon the assumption that the underlying signal in the data has low rank, and thus can be well-summarized using a small number of dimensions. The output of PCA is typically represented using a scree plot, which displays the proportion of variance explained (PVE) by each principal component. While the PVE is extensively reported in routine data analyses, to the best of our knowledge the notion of inference on the PVE remains unexplored. In this paper, we consider inference on the PVE. We first introduce a new population quantity for the PVE with respect to an unknown matrix mean. Critically, our interest lies in the PVE of the sample principal components (as opposed to unobserved population principal components); thus, the population PVE that we introduce is defined conditional on the sample singular vectors. We show that it is possible to conduct inference, in the sense of confidence intervals, p-values, and point estimates, on this population quantity. Furthermore, we can conduct valid inference on the PVE of a subset of the principal components, even when the subset is selected using a data-driven approach such as the elbow rule. We demonstrate the proposed approach in simulation and in an application to a gene expression dataset.

This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements for trustworthy AI. Topics addressed include the role of AI at various operational layers of AVs, the implications of the EU's AI Act on AVs, and the need for new testing methodologies for Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). The study also provides a detailed analysis on the importance of cybersecurity audits, the need for explainability in AI decision-making processes and protocols for assessing the robustness and ethical behaviour of predictive systems in AVs. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology, highlighting the need for multidisciplinary expertise., Comment: 44 pages, 8 figures, submitted to a peer-review journal

The spatio-temporal interpolation of large geophysical datasets has historically been adressed by Optimal Interpolation (OI) and more sophisticated model-based or data-driven DA techniques. In the last ten years, the link established between Stochastic Partial Differential Equations (SPDE) and Gaussian Markov Random Fields (GMRF) opened a new way of handling both large datasets and physically-induced covariance matrix in Optimal Interpolation. Recent advances in the deep learning community also enables to adress this problem as neural architecture embedding data assimilation variational framework. The reconstruction task is seen as a joint learning problem of the prior involved in the variational inner cost and the gradient-based minimization of the latter: both prior models and solvers are stated as neural networks with automatic differentiation which can be trained by minimizing a loss function, typically stated as the mean squared error between some ground truth and the reconstruction. In this work, we draw from the SPDE-based Gaussian Processes to estimate complex prior models able to handle non-stationary covariances in both space and time and provide a stochastic framework for interpretability and uncertainty quantification. Our neural variational scheme is modified to embed an augmented state formulation with both state and SPDE parametrization to estimate. Instead of a neural prior, we use a stochastic PDE as surrogate model along the data assimilation window. The training involves a loss function for both reconstruction task and SPDE prior model, where the likelihood of the SPDE parameters given the true states is involved in the training. Because the prior is stochastic, we can easily draw samples in the prior distribution before conditioning to provide a flexible way to estimate the posterior distribution based on thousands of members.