Search

Irradiation by energetic ions, electrons, and UV photons induces sputtering and chemical processes (radiolysis) in the surfaces of icy moons, comets, and icy grains. Laboratory experiments, both of ideal surfaces and of more complex and realistic analog samples, are crucial to understand the interaction of surfaces of icy moons and comets with their space environment. This study shows the first results of mass spectrometry measurements from porous water ice regolith samples irradiated with electrons as a representative analogy to water-ice rich surfaces in the solar system. Previous studies have shown that most electron-induced H2O radiolysis products leave the ice as H2 and O2 and that O2 can be trapped under certain conditions in the irradiated ice. Our new laboratory experiments confirm these findings. Moreover, they quantify residence times and saturation levels of O2 in originally pure water ice. H2O may also be released from the water ice by irradiation, but the quantification of the released H2O is more difficult and the total amount is sensitive to the electron flux and energy.

Online hate speech poses a serious threat to individual well-being and societal cohesion. A promising solution to curb online hate speech is counterspeech. Counterspeech is aimed at encouraging users to reconsider hateful posts by direct replies. However, current methods lack scalability due to the need for human intervention or fail to adapt to the specific context of the post. A potential remedy is the use of generative AI, specifically large language models (LLMs), to write tailored counterspeech messages. In this paper, we analyze whether contextualized counterspeech generated by state-of-the-art LLMs is effective in curbing online hate speech. To do so, we conducted a large-scale, pre-registered field experiment (N=2,664) on the social media platform Twitter/X. Our experiment followed a 2x2 between-subjects design and, additionally, a control condition with no counterspeech. On the one hand, users posting hateful content on Twitter/X were randomly assigned to receive either (a) contextualized counterspeech or (b) non-contextualized counterspeech. Here, the former is generated through LLMs, while the latter relies on predefined, generic messages. On the other hand, we tested two counterspeech strategies: (a) promoting empathy and (b) warning about the consequences of online misbehavior. We then measured whether users deleted their initial hateful posts and whether their behavior changed after the counterspeech intervention (e.g., whether users adopted a less toxic language). We find that non-contextualized counterspeech employing a warning-of-consequence strategy significantly reduces online hate speech. However, contextualized counterspeech generated by LLMs proves ineffective and may even backfire.

The string landscape statistical draw to large scalar soft masses leads to a mixed quasi-degeneracy/decoupling solution to the SUSY flavor and CP problems where first/second generation matter scalars lie in the 20-40 TeV range. With increasing first/second generation scalars, SUSY models actually become more natural due to two-loop RG effects which suppress the corresponding third generation soft masses. This can also lead to substantial parameter space regions which are forbidden by the presence of charge and/or color breaking (CCB) minima of the scalar potential. We outline the allowed SUSY parameter space for the gravity-mediated three extra-parameter-non-universal Higgs model NUHM3. The natural regions with m_h~ 125 GeV, \Delta_{EW}<~ 30 and decoupled first/second generation scalar are characterized by rather heavy gluinos and EW gauginos, but with rather small \mu and top-squarks not far beyond LHC Run 2 limits. This scenario also explains why SUSY has so far eluded discovery at LHC in that the parameter space with small scalar and gaugino masses is all excluded by the presence of CCB minima., Comment: 18 pages with 11 .png figures

In this study, we perform detailed spectroscopic modeling to analyze the interaction of circumstellar material (CSM) with ejecta in both hydrogen-rich and hydrogen-poor superluminous supernovae (SLSNe), systematically varying properties such as CSM density, composition, and geometry to explore their effects on spectral lines and light curve evolution. Using advanced radiative transfer simulations with the new, open-source SuperLite code to generate synthetic spectra, we identify key spectroscopic indicators of CSM characteristics. Our findings demonstrate that spectral lines of hydrogen and helium exhibit significant variations due to differences in CSM mass and composition. In hydrogen-rich SLSN- II, we observe pronounced hydrogen emission lines that correlate strongly with dense, extended CSM, suggesting massive, eruptive mass-loss histories. Conversely, in hydrogen-poor SLSNe, we recover mostly featureless spectra at early times, with weak hydrogen lines appearing only in the very early phases of the explosion, highlighting the quick ionization of traces of hydrogen present in the CSM. We analyze the properties of the resulting emission lines, particularly $\rm H_{\alpha}$ and $\rm H_{\beta}$, for our models using sophisticated statistical methods. This analysis reveals how variations in the supernova progenitor and CSM properties can lead to distinct spectroscopic evolutions over time. These temporal changes provide crucial insights into the underlying physics driving the explosion and the subsequent interaction with the CSM. By linking these spectroscopic observations to the initial properties of the progenitor and its surrounding ma, Comment: Accepted for publication in the ApJ

The present paper evaluates the learning behaviour of a transformer-based neural network with regard to an irregular inflectional paradigm. We apply the paradigm cell filling problem to irregular patterns. We approach this problem using the morphological reinflection task and model it as a character sequence-to-sequence learning problem. The test case under investigation are irregular verbs in Spanish. Besides many regular verbs in Spanish L-shaped verbs the first person singular indicative stem irregularly matches the subjunctive paradigm, while other indicative forms remain unaltered. We examine the role of frequency during learning and compare models under differing input frequency conditions. We train the model on a corpus of Spanish with a realistic distribution of regular and irregular verbs to compare it with models trained on input with augmented distributions of (ir)regular words. We explore how the neural models learn this L-shaped pattern using post-hoc analyses. Our experiments show that, across frequency conditions, the models are surprisingly capable of learning the irregular pattern. Furthermore, our post-hoc analyses reveal the possible sources of errors. All code and data are available at \url{https://anonymous.4open.science/r/modeling_spanish_acl-7567/} under MIT license.

The study of magnetoresistance (MR) phenomena has been pivotal in advancing magnetic sensors and spintronic devices. Helimagnets present an intriguing avenue for spintronics research. Theoretical predictions suggest that MR magnitude in the helimagnetic (HM) regime surpasses that in the ferromagnetic (FM) regime by over an order of magnitude. However, in metallic helimagnets like manganese phosphide, MR in the HM phase remains modest (10%), limiting its application in MR devices. Here, a groundbreaking approach is presented to achieve a giant low field MR effect in nanostructured manganese phosphide films by leveraging confinement and strain effects along with spin helicity. Unlike the modest MR observed in bulk manganese phosphide single crystals and large grain polycrystalline films, which exhibit a small negative MR in the FM region (2%) increasing to 8% in the HM region across 10-300 K, a grain size-dependent giant positive MR (90%) is discovered near FM to HM transition temperature (110 K), followed by a rapid decline to a negative MR below 55 K in manganese phosphide nanocrystalline films. These findings illuminate a novel strain-mediated spin helicity phenomenon in nanostructured helimagnets, presenting a promising pathway for the development of high-performance MR sensors and spintronic devices through the strategic utilization of confinement and strain effects.

In this work, we study amodal video instance segmentation for automated driving. Previous works perform amodal video instance segmentation relying on methods trained on entirely labeled video data with techniques borrowed from standard video instance segmentation. Such amodally labeled video data is difficult and expensive to obtain and the resulting methods suffer from a trade-off between instance segmentation and tracking performance. To largely solve this issue, we propose to study the application of foundation models for this task. More precisely, we exploit the extensive knowledge of the Segment Anything Model (SAM), while fine-tuning it to the amodal instance segmentation task. Given an initial video instance segmentation, we sample points from the visible masks to prompt our amodal SAM. We use a point memory to store those points. If a previously observed instance is not predicted in a following frame, we retrieve its most recent points from the point memory and use a point tracking method to follow those points to the current frame, together with the corresponding last amodal instance mask. This way, while basing our method on an amodal instance segmentation, we nevertheless obtain video-level amodal instance segmentation results. Our resulting S-AModal method achieves state-of-the-art results in amodal video instance segmentation while resolving the need for amodal video-based labels. Code for S-AModal is available at https://github.com/ifnspaml/S-AModal., Comment: accepted at ECCV VCAD Workshop 2024

This work presents a proof-of-concept solution designed to improve the accuracy of radiographic feature characterisation in pre-surgical CT/MR volumes. The solution involves 3D co-location of 2D digital histology slides within ex-vivo, tumour tissue CT volumes. In the initial step, laboratory measurements obtained during histology dissection were used to seed the placement of the individual histology slices in corresponding tumour tissue CT volumes. The process was completed by aligning corresponding bone in histology images to bone in the CT using in-plane point-based registration. Six bisected canine humerus datasets of ex-vivo CT and corresponding measurements were used to validate dissection placements. Digital seeding exhibited a plane misalignment of 0.19 +- 1.8 mm. User input sensitivity caused 0.08 +- 0.2 mm in plane translation and between 0 and 1.6 degrees deviation. These are of similar magnitude to reported misalignment of 0.9-1.3 mm and 1.1-1.9 degrees in related prostate histology co-location [1]. Although this work only reported on animal femur sarcoma CT images and histology slide images, the solution can be generalised to various sarcoma geometries and presentation sites. Furthermore, the solution co-locates high-fidelity data to advance tissue characterisation with minimal disruption to existing clinical workflows. Improved tissue characterisation accuracy, supported by the resolution of histology images, can enhance surgical planning accuracy and patient outcomes by bringing the insights offered by histology closer to the start of the presentation-diagnosis-planning-surgery-recovery loop., Comment: 17 pages, 6 figures

We study transits of several ``hot Jupiter'' systems - including WASP-12 b, WASP-43 b, WASP-103 b, HAT-P-23 b, KELT-16 b, WD 1856+534 b, and WTS-2 b - with the goal of detecting tidal orbital decay and extending the baselines of transit times. We find no evidence of orbital decay in any of the observed systems except for that of the extensively studied WASP-12 b. Although the orbit of WASP-12 b is unequivocally decaying, we find no evidence for acceleration of said orbital decay, with measured $\ddot{P} = (-7 \pm 8) \times 10^{-14} \rm ~s^{-1}$, against the expected acceleration decay of $\ddot{P} \approx -10^{-23} \rm ~s^{-1}$. In the case of WD 1856+534 b, there is a tentative detection of orbital growth with $\dot{P} = (5.0 \pm 1.5) \times 10^{-10}$. While statistically significant, we err on the side of caution and wait for longer follow-up observations to consider the measured $\dot{P}$ real. For most systems, we provide a 95\%-confidence lower limit on the tidal quality factor, $Q_\star'$. The possibility of detecting orbital decay in hot Jupiters via long-term radial velocity (RV) measurements is also explored. We find that $\sim 1 \rm ~m~s^{-1}$ precision in RVs will be required to detect orbital decay of WASP-12 b with only 3 yr of observations. Currently available RV measurements and precision are unable to detect orbital decay in any of the systems studied here., Comment: 14 pages, 11 figures; Accepted in MNRAS on 2024 August 30. Received 2024 August 29; in original form 2024 February 13

Due to high-cadence automated surveys, we can now detect and classify supernovae (SNe) within a few days after explosion, if not earlier. Early-time spectra of young SNe directly probe the outermost layers of the ejecta, providing insights into the extent of stripping in the progenitor star and the explosion mechanism in the case of core-collapse supernovae. However, many SNe show overlapping observational characteristics at early time, complicating the early-time classification. In this paper, we focus on the study and classification of Type Ib supernovae (SNe Ib), which are a subclass of core-collapse supernovae that lack strong hydrogen lines but show helium lines in their spectra. Here we present a spectral dataset of 8 SNe Ib, chosen to have at least 3 pre-maximum spectra, which we call early spectra. Our dataset was obtained mainly by the the Las Cumbres Observatory (LCO) and consists of a total of 82 optical photospheric spectra, including 38 early spectra. This data set increases the number of published SNe Ib with at least three early spectra by ~60%. For our classification efforts, we use early spectra in addition to spectra taken around maximum light. We also convert our spectra into SN Identification (SNID) templates and make them available to the community for easier identification of young SNe Ib. Our data set increases the number of publicly available SNID templates of early spectra of SNe Ib by ~43%. Almost half of our sample has SN types that change over time or are different from what is listed on the Transient Name Server (TNS). We discuss the implications of our dataset and our findings for current and upcoming SN surveys and their classification efforts., Comment: 12 pages, 1 figure, submitting to A&A, comments are welcomed

The evolution of open quantum systems is a fundamental topic in various scientific fields. During time propagation, the environment occasionally makes measurements, forcing the system's wave function to collapse randomly. The von Neumann density matrix incorporates the statistics involved in these random processes, and its time development is often described by Markovian quantum master equations that incorporate a dissipator. For large systems, the complexity of the dissipator grows with the increasing number of possible measurements, posing conceptual and severe computational challenges. This article introduces a stochastic representation of the dissipator, using bundled measurement operators to address this complexity. Taking the Morse oscillator as an example, we demonstrate that small samples of bundled operators capture the system's dynamics. This stochastic bundling is different from the stochastic unraveling and the jump operator formalism and offers a new way of understanding quantum dissipation and decoherence.

Recently, BigVGAN has emerged as high-performance speech vocoder. Its sequence-to-sequence-based synthesis, however, prohibits usage in low-latency conversational applications. Our work addresses this shortcoming in three steps. First, we introduce low latency into BigVGAN via implementing causal convolutions, yielding decreased performance. Second, to regain performance, we propose a teacher-student transfer learning scheme to distill the high-delay non-causal BigVGAN into our low-latency causal vocoder. Third, taking advantage of a self-supervised learning (SSL) model, in our case wav2vec 2.0, we align its encoder speech representations extracted from our low-latency causal vocoder to the ground truth ones. In speaker-independent settings, both proposed training schemes notably elevate the performance of our low-latency vocoder, closing up to the original high-delay BigVGAN. At only 21% higher complexity, our best small causal vocoder achieves 3.96 PESQ and 1.25 MCD, excelling even the original small non-causal BigVGAN (3.64 PESQ) by 0.32 PESQ and 0.1 MCD points, respectively., Comment: Accepted at IWAENC 2024

While the minimal anomaly-mediated SUSY breaking model (mAMSB) seems ruled out by constraints on Higgs mass, naturalness and wino dark matter, a slightly generalized version dubbed natural AMSB (nAMSB) remains both viable and compelling. Like mAMSB, nAMSB features winos as the lightest gauginos, but unlike mAMSB, nAMSB allows a small mu parameter so that higgsinos are the lightest of electroweakinos (EWinos). nAMSB spectra depend on the input value of gravitino mass m_{3/2}, where the lower range of m_{3/2} is excluded by LHC gluino pair searches while a higher m_{3/2} band is excluded by LHC limits on wino pair production followed by boosted hadronic wino decays. A remaining intermediate gap in m_{3/2} values remains allowed by present LHC searches, but appears to be completely explorable by high luminosity ugrades of LHC (HL-LHC). We explore a variety of compelling discovery channels that may allow one to close the intermediate gap in m_{3/2} values: 1. same-sign diboson +MET (SSdB) production arising from wino pair production, leading to same-sign dileptons plus MET, 2. trilepton production arising from wino pair production and 3. soft dilepton plus jet events from higgsino pair production, 4. top-squark pair production. From our signal-to-background analysis along a nAMSB model line, we expect HL-LHC to either discover or rule out the nAMSB model with 3000 fb^{-1} of integrated luminosity., Comment: 25 pages with 10 figures

Natural supersymmetry with light higgsinos is most likely to emerge from the string landscape since the volume of scan parameter space shrinks to tiny volumes for electroweak unnatural models. Rather general arguments favor a landscape selection of soft SUSY breaking terms tilted to large values, but tempered by the atomic principle: that the derived value of the weak scale in each pocket universe lie not too far from its measured value in our universe. But that leaves (at least) three different paradigms for gaugino masses in natural SUSY models: unified (as in nonuniversal Higgs models), anomaly-mediation form (as in natural AMSB) and mirage mediation form (with comparable moduli- and anomaly-mediated contributions). We perform landscape scans for each of these, and show they populate different, but overlapping, positions in m(\ell\bar{\ell}) and m(wino) space. The first of these may be directly measurable at high-lumi LHC via the soft opposite-sign dilepton plus jets plus MET signature arising from higgsino pair production while the second of these could be extracted from direct wino pair production leading to same-sign diboson production., Comment: 17 pages with 3 .png figures

For closed connected Riemannian spin manifolds an upper estimate of the smallest eigenvalue of the Dirac operator in terms of the hyperspherical radius is proved. When combined with known lower Dirac eigenvalue estimates, this has a number of geometric consequences. Some are known and include Llarull's scalar curvature rigidity of the standard metric on the sphere, Geroch's conjecture on the impossibility of positive scalar curvature on tori and a mean curvature estimate for spin fill-ins with nonnegative scalar curvature due to Gromov, including its rigidity statement recently proved by Cecchini, Hirsch and Zeidler. New applications provide a comparison of the hyperspherical radius with the Yamabe constant and improved estimates of the hyperspherical radius for K\"ahler manifolds, K\"ahler-Einstein manifolds, quaternionic K\"ahler manifolds and manifolds with a harmonic 1-form of constant length., Comment: Appendix and some references added

Type Ibn supernovae (SNe Ibn) are rare stellar explosions powered primarily by interaction between the SN ejecta and H-poor, He-rich material lost by their progenitor stars. Multi-wavelength observations, particularly in the X-rays, of SNe Ibn constrain their poorly-understood progenitor channels and mass-loss mechanisms. Here we present Swift X-ray, ultraviolet, and ground-based optical observations of the Type Ibn SN 2022ablq -- only the second SN Ibn with X-ray detections to date. While similar to the prototypical Type Ibn SN 2006jc in the optical, SN 2022ablq is roughly an order of magnitude more luminous in the X-rays, reaching unabsorbed luminosities $L_X$ $\sim$ 3$\times$10$^{40}$ erg s$^{-1}$ between 0.2 - 10 keV. From these X-ray observations we infer time-varying mass-loss rates between 0.05 - 0.5 $M_\odot$ yr$^{-1}$ peaking 0.5 - 2 yr before explosion. This complex mass-loss history and circumstellar environment disfavor steady-state winds as the primary progenitor mass-loss mechanism. We also search for precursor emission from alternative mass-loss mechanisms, such as eruptive outbursts, in forced photometry during the two years before explosion. We find no statistically significant detections brighter than M $\approx$ -14 -- too shallow to rule out precursor events similar to those observed for other SNe Ibn. Finally, numerical models of the explosion of a $\sim$15 $M_\odot$ helium star that undergoes an eruptive outburst $\approx$1.8 years before explosion are consistent with the observed bolometric light curve. We conclude that our observations disfavor a Wolf-Rayet star progenitor losing He-rich material via stellar winds and instead favor lower-mass progenitor models, including Roche-lobe overflow in helium stars with compact binary companions or stars that undergo eruptive outbursts during late-stage nucleosynthesis stages., Comment: 23 pages, 8 figures, submitted to ApJ. Comments welcome

Akin to the traditional quasi-classical trajectory method for investigating the dynamics on a single adiabatic potential energy surface for an elementary chemical reaction, we carry out the dynamics on a 2-state ab initio potential energy surface including nonadiabatic coupling terms as friction terms for D+ + H2 collisions. It is shown that the resulting dynamics correctly accounts for nonreactive charge transfer, reactive non charge transfer and reactive charge transfer processes. In addition, it leads to the formation of triatomic DH2+ species as well., Comment: 31 pages, 7 figures

The "Fall 2023 Snapshot on International Student Enrollment" findings are based on responses from over 630 U.S. higher education institutions on current patterns in international student enrollment as of fall 2023. The report focuses on international students studying at U.S. higher education institutions in person or online (in the United States or from abroad). The report, conducted by the Institute of International Education (IIE) and ten partner higher education associations, complements the "Open Doors® Report on International Educational Exchange." The findings from the "Fall 2023 Snapshot" reflect the resilience of the U.S. higher education system and provide an understanding of the current state of U.S. higher education institutions' commitment to international student mobility. With increases noted across all academic levels for the second consecutive year, the findings point to the continued growth of international student totals in the 2023/24 academic year. The full picture of the 2023/24 academic year will be available in November 2024 with the release of "Open Doors 2024." [This report was co-produced with the Association of International Enrollment Management (AIRC).]

Program similarity has become an increasingly popular area of research with various security applications such as plagiarism detection, author identification, and malware analysis. However, program similarity research faces a few unique dataset quality problems in evaluating the effectiveness of novel approaches. First, few high-quality datasets for binary program similarity exist and are widely used in this domain. Second, there are potentially many different, disparate definitions of what makes one program similar to another and in many cases there is often a large semantic gap between the labels provided by a dataset and any useful notion of behavioral or semantic similarity. In this paper, we present HELIX - a framework for generating large, synthetic program similarity datasets. We also introduce Blind HELIX, a tool built on top of HELIX for extracting HELIX components from library code automatically using program slicing. We evaluate HELIX and Blind HELIX by comparing the performance of program similarity tools on a HELIX dataset to a hand-crafted dataset built from multiple, disparate notions of program similarity. Using Blind HELIX, we show that HELIX can generate realistic and useful datasets of virtually infinite size for program similarity research with ground truth labels that embody practical notions of program similarity. Finally, we discuss the results and reason about relative tool ranking.

At present, the Standard Model (SM) agrees with almost all collider data. Yet, three finetuning issues -- the Higgs mass problem, the strong CP problem and the cosmological constant problem -- all call for new physics. The most plausible solutions at present are weak scale SUSY, the PQWW axion and the string landscape. A re-evaluation of EW finetuning in SUSY allows for a higgsino-like LSP and naturalness upper bounds well beyond LHC limits. Rather general arguments from string theory allow for statistical predictions that m_h~ 125 GeV with sparticles beyond present LHC limits. The most lucrative LHC search channel may be for light higgsino pair production. Dark matter turns out to be a SUSY DFSZ axion along with a diminished abundance of higgsino-like WIMPs., Comment: 6 pages with 2 figures; contribution to the 2024 QCD session of the 58th Rencontres de Moriond

Stochastic orbital techniques offer reduced computational scaling and memory requirements to describe ground and excited states at the cost of introducing controlled statistical errors. Such techniques often rely on two basic operations, stochastic trace estimation and stochastic resolution of identity, both of which lead to statistical errors that scale with the number of stochastic realizations ($N_{\xi}$) as $\sqrt{N_{\xi}^{-1}}$. Reducing the statistical errors without significantly increasing $N_{\xi}$ has been challenging and is central to the development of efficient and accurate stochastic algorithms. In this work, we build upon recent progress made to improve stochastic trace estimation based on the ubiquitous Hutchinson's algorithm and propose a two-step approach for the stochastic resolution of identity, in the spirit of the Hutch++ method. Our approach is based on employing a randomized low-rank approximation followed by a residual calculation, resulting in statistical errors that scale much better than $\sqrt{N_{\xi}^{-1}}$. We implement the approach within the second-order Born approximation for the self-energy in the computation of neutral excitations and discuss three different low-rank approximations for the two-body Coulomb integrals. Tests on a series of hydrogen dimer chains with varying lengths demonstrate that the Hutch++-like approximations are computationally more efficient than both deterministic and purely stochastic (Hutchinson) approaches for low error thresholds and intermediate system sizes. Notably, for arbitrarily large systems, the Hutchinson-like approximation outperforms both deterministic and Hutch++-like methods.

We compute the $\mathbb{C}$-motivic Adams spectral sequence for $\mathit{mmf}/\tau$. Up to reindexing, this spectral sequence is isomorphic to the algebraic Novikov spectral sequence for topological modular forms. We give a full analysis of the inclusion and projection maps which occur in the long exact sequence induced by multiplication by $\tau$ on Adams $E_2$-pages. Our computation is totally algebraic and can be used to obtain the $E_2$-page of the Adams-Novikov spectral sequence for topological modular forms in a way that is independent from previous computations using the elliptic curves Hopf algebroid., Comment: 27 pages, 5 figures. Comments welcome!

The "Spring 2023 Snapshot on International Educational Exchange" continues the commitment of the Institute of International Education (IIE) to map the current state of international educational exchange to and from the United States. The report presents data from 527 U.S. higher education institutions in two sections: (1) current trends in international students studying at U.S. institutions in spring 2023 and recruitment patterns for prospective students and (2) the realities of U.S. study abroad in summer 2023 and academic year 2023/24. Each section is supplemented by special analyses on underrepresented populations participating in international educational exchange. On inbound mobility, we present data on non-degree international students and refugees and displaced students. The study abroad section includes insights on data collection of underrepresented populations in study abroad, including students with high financial need, first generation students, and other populations.

Natural supersymmetry (SUSY) with light higgsinos is perhaps the most plausible of all weak scale SUSY models while a variety of motivations point to (right) tau sleptons as the lightest of all the sleptons. We examine a SUSY model line with rather light right-staus embedded within natural SUSY. For light stau_1 of a few hundred GeV, then the decays stau_1 -> \tau\tchi_{1,2}^0 and \nu_\tau\tchi_1^- occur at comparable rates where the (higgsino-like) \tchi_1^\pm and \tchi_2^0 release only small visible energy: in this case, the expected \tau^+\tau^- +\eslt signature is diminished from usual expectations due to the presence of the nearly invisible decay mode \ttau_1 -> \nu_\tau\tchi_1^-. However, once m_{\ttau_1}> ~m(bino), then decays to binos such as \ttau_1 -> \tau\tchi_3^0 open up where \tchi_3^0 decays to higgsinos plus W^\pm, Z^0 and h at comparable rates. For these heavier staus, then stau pair production gives rise to diboson+\eslt events which may contain 0, 1 or 2 additional hard \tau leptons. From these considerations, we examine the potential for future discovery of tau-slepton pair production at high-luminosity LHC. While we do not find a 5\sigma HL-LHC discovery reach for 3000 fb^{-1}, we do find a 95\% CL exclusion reach, ranging between m_{\ttau_1}:100-450 GeV for m_{\tchi_1^0}~ 100 GeV. This latter reach disappears for m_{\tchi_1^0}>~ 200 GeV., Comment: 20 pages including 7 .png figures; version 2 should coincide with published version and fixes several typos

Solar cells based on inorganic perovskite CsPbI3 are promising candidates to resolve the challenge of operational stability in the field of perovskite photovoltaics. For stable operation, however, it is crucial to thoroughly understand the extractive and recombinative processes occurring at the interfaces of perovskite and the charge-selective layers. In this study, we focus on the electronic properties of (doped) TiO2 as an electron-selective contact. We show via KPFM that co-doping of TiO2 with Nb(V) and Sn(IV) reduces the materials work function by 270 meV, giving it stronger n-type characteristics compared to Nb(V) mono-doped TiO2. The altered electronic alignment with CsPbI3 translates to enhanced electron extraction, as demonstrated with ssPL, trPL and trSPV in triad. Importantly, we extract crucial parameters, such as the concentration of extracted electrons and the interface hole recombination velocity, from the SPV transients via 2D drift-diffusion simulations. When implementing the co-doped TiO2 into full n-i-p solar cells, the operational stability is enhanced to 32000 h of projected TS80 lifetime. This study provides fundamental understanding of interfacial charge extraction and its correlation with operational stability of perovskite solar cells, which can be transferred to other charge-selective contacts., Comment: 20 pages, 2 figures

Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks. Currently, frozen features are not modified during training. On the other hand, when networks are trained directly on images, data augmentation is a standard recipe that improves performance with no substantial overhead. In this paper, we conduct an extensive pilot study on few-shot image classification that explores applying data augmentations in the frozen feature space, dubbed 'frozen feature augmentation (FroFA)', covering twenty augmentations in total. Our study demonstrates that adopting a deceptively simple pointwise FroFA, such as brightness, can improve few-shot performance consistently across three network architectures, three large pretraining datasets, and eight transfer datasets., Comment: CVPR 2024 (18 pages, main paper + supplementary material)

Active fluids, such as suspensions of microswimmers, are known to self-organize into complex spatio-temporal flow patterns. An intriguing example is mesoscale turbulence, a state of dynamic vortex structures exhibiting a characteristic length scale. Here, we employ a minimal model for the effective microswimmer velocity field to explore how the turbulent state develops from regular vortex patterns when the strength of activity resp. related parameters such as nonlinear advection or polar alignment strength - is increased. First, we demonstrate analytically that the system, without any spatial constraints, develops a stationary square vortex lattice in the absence of nonlinear advection. Subsequently, we perform an extended stability analysis of this nonuniform "ground state" and uncover a linear instability, which follows from the mutual excitement and simultaneous growth of multiple perturbative modes. This extended analysis is based on linearization around an approximation of the analytical vortex lattice solution and allows us to calculate critical activity parameters. Above these critical values, the vortex lattice develops into mesoscale turbulence in numerical simulations. Utilizing the numerical approach, we uncover an extended region of hysteresis where both patterns are possible depending on the initial condition. Here, we find that turbulence persists below the instability of the vortex lattice. We further determine the stability of square vortex patterns as a function of their wavenumber and represent the results analogous to the well-known Busse balloons known from classical pattern-forming systems. Here, the region of stable periodic patterns shrinks and eventually disappears with increasing activity parameters. Our results show that the strength of activity plays a similar role for active turbulence as the Reynolds number does in driven flow exhibiting inertial turbulence.

A theory of martingales for censoring is developed. The Doob-Meyer martingale is shown to be inadequate in general, and a repaired martingale is proposed with a non-predictable centering term. Associated martingale transforms, variation processes, and covariation processes are developed based on a measure of half-predictability that generalizes predictability. The development is applied to study the Kaplan Meier estimator.

Superstring flux compactifications can stabilize all moduli while leading to an enormous number of vacua solutions, each leading to different $4-d$ laws of physics. While the string landscape provides at present the only plausible explanation for the size of the cosmological constant, it may also predict the form of weak scale supersymmetry which is expected to emerge. Rather general arguments suggest a power-law draw to large soft terms, but these are subject to an anthropic selection of not-too-large a value for the weak scale. The combined selection allows one to compute relative probabilities for the emergence of supersymmetric models from the landscape. Models with weak scale naturalness appear most likely to emerge since they have the largest parameter space on the landscape. For finetuned models such as high scale SUSY or split SUSY, the required weak scale finetuning shrinks their parameter space to tiny volumes, making them much less likely to appear compared to natural models. Probability distributions for sparticle and Higgs masses from natural models show a preference for Higgs mass $m_h\sim 125$ GeV with sparticles typically beyond present LHC limits, in accord with data. From these considerations, we briefly describe how natural SUSY is expected to be revealed at future LHC upgrades. This article is a contribution to the Special Edition of the journal {\it Entropy} honoring Paul Frampton on his 80th birthday., Comment: 16 pages with 6 .png figures; submitted for 80th birthday volume of Entropy honoring Paul Frampton

We study the Adams-Novikov spectral sequence in $\mathbb{F}_p$-synthetic spectra, computing the synthetic analogs of $\mathrm{BP}$ and its cooperations to identify the synthetic Adams-Novikov $\mathrm{E}_2$-page, computed in a range with a synthetic algebraic Novikov spectral sequence. We then identify deformations associated to the Cartan-Eilenberg and algebraic Novikov spectral sequences in terms of stable comodule categories, categorifying an algebraic Novikov spectral sequence result of Gheorghe-Wang-Xu. We then apply Isaksen-Wang-Xu methods in $\mathbb{F}_p$-synthetic spectra to deduce differentials in the synthetic Adams-Novikov for the sphere, producing almost entirely algebraic computations through the 45-stem., Comment: 54 pages, 6 figures. Comments welcome!

Marginal structural models have been widely used in causal inference to estimate mean outcomes under either a static or a prespecified set of treatment decision rules. This approach requires imposing a working model for the mean outcome given a sequence of treatments and possibly baseline covariates. In this paper, we introduce a dynamic marginal structural model that can be used to estimate an optimal decision rule within a class of parametric rules. Specifically, we will estimate the mean outcome as a function of the parameters in the class of decision rules, referred to as a regimen-response curve. In general, misspecification of the working model may lead to a biased estimate with questionable causal interpretability. To mitigate this issue, we will leverage risk to assess "goodness-of-fit" of the imposed working model. We consider the counterfactual risk as our target parameter and derive inverse probability weighting and canonical gradients to map it to the observed data. We provide asymptotic properties of the resulting risk estimators, considering both fixed and data-dependent target parameters. We will show that the inverse probability weighting estimator can be efficient and asymptotic linear when the weight functions are estimated using a sieve-based estimator. The proposed method is implemented on the LS1 study to estimate a regimen-response curve for patients with Parkinson's disease., Comment: 25 pages, 2 figures, 2 tables

Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipating such extremities would allow beekeepers to take early preventive action. Thus, given sensor (temperature) time series data from beehives, how can we find patterns and do forecasting? Forecasting is crucial as it helps spot unexpected behavior and thus issue warnings to the beekeepers. In that case, what are the right models for forecasting? ARIMA, RNNs, or something else? We propose the EBV (Electronic Bee-Veterinarian) method, which has the following desirable properties: (i) principled: it is based on a) diffusion equations from physics and b) control theory for feedback-loop controllers; (ii) effective: it works well on multiple, real-world time sequences, (iii) explainable: it needs only a handful of parameters (e.g., bee strength) that beekeepers can easily understand and trust, and (iv) scalable: it performs linearly in time. We applied our method to multiple real-world time sequences, and found that it yields accurate forecasting (up to 49% improvement in RMSE compared to baselines), and segmentation. Specifically, discontinuities detected by EBV mostly coincide with domain expert's opinions, showcasing our approach's potential and practical feasibility. Moreover, EBV is scalable and fast, taking about 20 minutes on a stock laptop for reconstructing two months of sensor data., Comment: 9 pages, 7 figure, Accepted at 2024 SIAM International Conference on Data Mining (SDM'24)

We consider first-order elliptic differential operators acting on vector bundles over smooth manifolds with smooth boundary, which is permitted to be noncompact. Under very mild assumptions, we obtain a regularity theory for sections in the maximal domain. Under additional geometric assumptions, and assumptions on an adapted boundary operator, we obtain a trace theorem on the maximal domain. This allows us to systematically study both local and nonlocal boundary conditions. In particular, the Atiyah-Patodi-Singer boundary condition occurs as a special case. Furthermore, we study contexts which induce semi-Fredholm and Fredholm extensions., Comment: Reference added, condition (G1) made more precise, minor changes

Supersymmetric models with a strongly interacting superconformal hidden sector (HS) may drive soft SUSY breaking scalar masses, bilinear soft term B\mu and Higgs combinations m_{H_{u,d}}^2+\mu^2 to small values at some intermediate scale, leading to unique sparticle mass spectra along with possibly diminished finetuning in spite of a large superpotential $\mu$ parameter. We set up a computer code to calculate such spectra, which are then susceptible to a variety of constraints: 1. possible charge-or-color breaking (CCB) minima in the scalar potential, 2. unbounded from below (UFB) scalar potential, 3. improper electroweak symmetry breaking, 4. a charged or sneutrino lightest SUSY particle (LSP), 5. generating m_h~ 125 GeV, 6. consistency with LHC sparticle mass limits, and 7. naturalness. We find this bevy of constraints leaves little or no viable parameter space for the case where hidden sector dynamics dominates MSSM running, even for the case of non-universal gaugino masses. For the case with moderate HS running with comparable MSSM running, and with universal gaugino masses, then the finetuning is ameliorated, but nonetheless remains high. Viable spectra with moderate HS running and with low finetuning and large mu can be found for non-universal gaugino masses., Comment: 29 pages including 27 .png frames

Over recent decades, robotic (or highly automated) searches for supernovae (SNe) have discovered several thousand events, many of them in quite nearby galaxies (distances < 30 Mpc). Most of these SNe, including some of the best-studied events to date, were found before maximum brightness and have associated with them extensive follow-up photometry and spectroscopy. Some of these discoveries are so-called SN impostors, thought to be superoutbursts of luminous blue variable stars, although possibly a new, weak class of massive-star explosions. We conducted a Snapshot program with the Hubble Space Telescope(HST) and obtained images of the sites of 31 SNe and four impostors, to acquire late-time photometry through two filters. The primary aim of this project was to reveal the origin of any lingering energy for each event, whether it is the result of radioactive decay or, in some cases, ongoing late-time interaction of the SN shock with pre-existing circumstellar matter, or the presence of a light echo. Alternatively, lingering faint light at the SN position may arise from an underlying stellar population (e.g., a host star cluster, companion star, or a chance alignment). The results from this study complement and extend those from Snapshot programs by various investigators in previous HST cycles., Comment: Added details on paper: 37 pages, 42 figures, accepted for publication in ApJ

Abstract Simulating mixed-state evolution in open quantum systems is crucial for various chemical physics, quantum optics, and computer science applications. These simulations typically follow the Lindblad master equation dynamics. An alternative approach known as quantum state diffusion unraveling is based on the trajectories of pure states generated by random wave functions, which evolve according to a nonlinear It\^o-Schr\"odinger equation (ISE). This study introduces weak first- and second-order solvers for the ISE based on directly applying the It\^o-Taylor expansion with exact derivatives in the interaction picture. We tested the method on free and driven Morse oscillators coupled to a thermal environment and found that both orders allowed practical estimation with a few dozen iterations. The variance was relatively small compared to the linear unraveling and did not grow with time. The second-order solver delivers much higher accuracy and stability with bigger time steps than the first-order scheme, with a small additional workload. However, the second-order algorithm has quadratic complexity with the number of Lindblad operators as opposed to the linear complexity of the first-order algorithm.

This study overviews and extends a recently developed stochastic finite-temperature Kohn-Sham density functional theory to study warm dense matter using Langevin dynamics, specifically under periodic boundary conditions. The method's algorithmic complexity exhibits nearly linear scaling with system size and is inversely proportional to the temperature. Additionally, a novel linear-scaling stochastic approach is introduced to assess the Kubo-Greenwood conductivity, demonstrating exceptional stability for DC conductivity. Utilizing the developed tools, we investigate the equation of state, radial distribution, and electronic conductivity of Hydrogen at a temperature of 30,000K. As for the radial distribution functions, we reveal a transition of Hydrogen from gas-like to liquid-like behavior as its density exceeds $4 g/cm^3$. As for the electronic conductivity as a function of the density, we identified a remarkable isosbestic point at frequencies around 7eV, which may be an additional signature of a gas-liquid transition in Hydrogen at 30,000K.