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The recent discovery of superconductivity in infinite-layer (IL, ABO$_2$) nickelates has opened a new avenue to deepen the understanding of high-temperature superconductivity. However, progress in this field is slowed by significant challenges in material synthesis and the scarcity of research groups capable of producing high quality superconducting samples. IL nickelates are obtained from a reduction of the perovskite ABO$_3$ phase, typically achieved by annealing using CaH$_2$ as a reducing agent. Here, we present a new method to synthesize superconducting infinite-layer nickelate Pr$_{0.8}$Sr$_{0.2}$NiO$_2$ thin films using an aluminum overlayer deposited by sputtering as a reducing agent. We systematically optimized the aluminum deposition parameters and obtained superconducting samples reduced either in situ or ex situ (after air exposure of the precursor ABO$_3$ films). A comparison of their crystalline quality and transport properties shows that in situ Al reduction enhances the quality of the superconducting Pr$_{0.8}$Sr$_{0.2}$NiO$_2$ thin films, achieving a maximum superconducting transition temperature $T_{c}^{onset}$ of 17 K, in agreement with the optimum value reported for this compound. This simple synthesis route, much more accessible than existing methods, offers better control and reproducibility over the topotactic transformation, opening new opportunities to gain insights into the physics of superconductivity in nickelates.

With the arrival of the next generation of ultra-deep optical imaging surveys reaching $\mu_V$$\sim$30 mag/arcsec$^2$ (3$\sigma$; 10"$\times$10"), the removal of scattered light due to the point spread function (PSF) effect remains a critical step for the scientific exploitation of the low surface brightness information contained in these data. Because virtually all pixels in the ground-based images are affected by an unwanted screen of light with a brightness greater than $\mu_V$$\sim$29 mag/arcsec$^2$, the characterization of the extended PSF (R$>$5 arcmin) is mandatory. We describe the procedure used to construct the extended PSFs of the LIGHTS survey in the g- and r-band images taken with the Large Binocular Cameras (LBCs) of the Large Binocular Telescope (LBT). We produce PSFs with a radial extension of 7.5 arcmins. These are later extended to 30 arcmins following an empirically motivated power-law extrapolation of their behaviour in their outermost regions. As an example of the application of our methodology, we subtract the scattered light around the galaxy NGC3198. The result of this subtraction clearly shows the outermost parts of the galaxy's disc, which have been obscured by the influence of nearby bright stars. Following a reproducible science philosophy, we make all the PSF models and the scripts used to do the analysis of this paper publicly available., Comment: 13 pages, 14 figures, 1 tables

Several reports about infinite-layer nickelate thin films suggest that the superconducting critical temperature versus chemical doping phase diagram has a dome-like shape, similar to cuprates. Here, we demonstrate a highly reproducible superconducting state in undoped PrNiO$_2$ thin films grown onto SrTiO$_3$. Scanning transmission electron microscopy measurements demonstrate coherent and defect-free infinite-layer phase, a high structural quality with no unintentional chemical doping and a total absence of interstitial oxygen. X-ray absorption measurements show very sharp features at the Ni L$_{3,2}$-edges with a large linear dichroism, indicating the preferential hole-occupation of Ni$^{1+}$-3d$_{x^2-y^2}$ orbitals in a square planar geometry. Resonant inelastic X-ray scattering measurements reveal sharp magnon excitations of 200\,meV energy at magnetic Brillouin zone boundary, highly resonant at the Ni$^{1+}$ absorption peak. The results indicate that, when properly stabilized, infinite-layer nickelate thin films are superconducting without chemical doping.

We introduce the $L$-series of weakly holomorphic modular forms using Laplace transforms and give their functional equations. We then determine converse theorems for vector-valued harmonic weak Maass forms, Jacobi forms, and elliptic modular forms of half-integer weight in Kohnen plus space.

Transition metal dichalcogenides (TMDs) exhibit remarkable optical properties due to the diverse number of strongly bound excitons, which can be fine-tuned by alloying. Despite a flurry of research activity in characterizing these excitons, a comprehensive and profound understanding of their behavior with temperature is lacking. Here, we report the rich spectrum of excitonic features within bulk van der Waals alloy Mo$_{0.5}$W$_{0.5}$S$_2$ and Mo$_{0.5}$W$_{0.5}$Se$_2$ single crystals through temperature-dependent reflectance spectroscopy and first-principle calculations. We observed Rydberg excitons and interlayer excitons in both the single crystals. Notably, we provide the first experimental evidence of highly energetic A$^\prime$ and B$^\prime$ excitons in Mo$_{0.5}$W$_{0.5}$S$_2$ at room temperature. The strong carrier-phonon scattering significantly broadens the A$^\prime$, B$^\prime$ and interlayer excitons at room temperature in bulk Mo$_{0.5}$W$_{0.5}$S$_2$ single crystal compared to its selenide. Our findings, supported by density functional theory and Bethe-Salpeter equation calculations, signify the crucial role of carrier-phonon interactions. These results open pathways for next-generation optoelectronic devices and quantum technologies operating at high temperature.

In many machine learning for healthcare tasks, standard datasets are constructed by amassing data across many, often fundamentally dissimilar, sources. But when does adding more data help, and when does it hinder progress on desired model outcomes in real-world settings? We identify this situation as the \textit{Data Addition Dilemma}, demonstrating that adding training data in this multi-source scaling context can at times result in reduced overall accuracy, uncertain fairness outcomes, and reduced worst-subgroup performance. We find that this possibly arises from an empirically observed trade-off between model performance improvements due to data scaling and model deterioration from distribution shift. We thus establish baseline strategies for navigating this dilemma, introducing distribution shift heuristics to guide decision-making on which data sources to add in data scaling, in order to yield the expected model performance improvements. We conclude with a discussion of the required considerations for data collection and suggestions for studying data composition and scale in the age of increasingly larger models., Comment: Machine Learning For Health Care 2024 (MLHC)

Despite the availability of international prize-money competitions, scaled vehicles, and simulation environments, research on autonomous racing and the control of sports cars operating close to the limit of handling has been limited by the high costs of vehicle acquisition and management, as well as the limited physics accuracy of open-source simulators. In this paper, we propose a racing simulation platform based on the simulator Assetto Corsa to test, validate, and benchmark autonomous driving algorithms, including reinforcement learning (RL) and classical Model Predictive Control (MPC), in realistic and challenging scenarios. Our contributions include the development of this simulation platform, several state-of-the-art algorithms tailored to the racing environment, and a comprehensive dataset collected from human drivers. Additionally, we evaluate algorithms in the offline RL setting. All the necessary code (including environment and benchmarks), working examples, datasets, and videos are publicly released and can be found at: https://assetto-corsa-gym.github.io, Comment: Project page and code can be found at: \url{https://assetto-corsa-gym.github.io/}

YOLO is a deep neural network (DNN) model presented for robust real-time object detection following the one-stage inference approach. It outperforms other real-time object detectors in terms of speed and accuracy by a wide margin. Nevertheless, since YOLO is developed upon a DNN backbone with numerous parameters, it will cause excessive memory load, thereby deploying it on memory-constrained devices is a severe challenge in practice. To overcome this limitation, model compression techniques, such as quantizing parameters to lower-precision values, can be adopted. As the most recent version of YOLO, YOLOv7 achieves such state-of-the-art performance in speed and accuracy in the range of 5 FPS to 160 FPS that it surpasses all former versions of YOLO and other existing models in this regard. So far, the robustness of several quantization schemes has been evaluated on older versions of YOLO. These methods may not necessarily yield similar results for YOLOv7 as it utilizes a different architecture. In this paper, we conduct in-depth research on the effectiveness of a variety of quantization schemes on the pre-trained weights of the state-of-the-art YOLOv7 model. Experimental results demonstrate that using 4-bit quantization coupled with the combination of different granularities results in ~3.92x and ~3.86x memory-saving for uniform and non-uniform quantization, respectively, with only 2.5% and 1% accuracy loss compared to the full-precision baseline model., Comment: Presented at the "2023 28th International Computer Conference, Computer Society of Iran (CSICC)" and indexed in IEEE

Online social media platforms such as YouTube have a wide, global reach. However, little is known about the experience of low-resourced language speakers on such platforms; especially in how they experience and navigate harmful content. To better understand this, we (1) conducted semi-structured interviews (n=15) and (2) analyzed search results (n=9313), recommendations (n=3336), channels (n=120) and comments (n=406) of policy-violating sexual content on YouTube focusing on the Amharic language. Our findings reveal that -- although Amharic-speaking YouTube users find the platform crucial for several aspects of their lives -- participants reported unplanned exposure to policy-violating sexual content when searching for benign, popular queries. Furthermore, malicious content creators seem to exploit under-performing language technologies and content moderation to further target vulnerable groups of speakers, including migrant domestic workers, diaspora, and local Ethiopians. Overall, our study sheds light on how failures in low-resourced language technology may lead to exposure to harmful content and suggests implications for stakeholders in minimizing harm. Content Warning: This paper includes discussions of NSFW topics and harmful content (hate, abuse, sexual harassment, self-harm, misinformation). The authors do not support the creation or distribution of harmful content., Comment: To appear in ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) 2024

The sideslip angle, crucial for vehicle safety and stability, is determined using both longitudinal and lateral velocities. However, measuring the lateral component often necessitates costly sensors, leading to its common estimation, a topic thoroughly explored in existing literature. This paper introduces LOP-UKF, a novel method for estimating vehicle lateral velocity by integrating Lidar Odometry with the Pacejka tire model predictions, resulting in a robust estimation via an Unscendent Kalman Filter (UKF). This combination represents a distinct alternative to more traditional methodologies, resulting in a reliable solution also in edge cases. We present experimental results obtained using the Dallara AV-21 across diverse circuits and track conditions, demonstrating the effectiveness of our method., Comment: Accepted to 35th IEEE Intelligent Vehicles Symposium - IEEE IV 2024

After decades of research, superconductivity was finally found in nickel-based analogs of superconducting cuprates, with infinite-layer (IL) structure. These results are so far restricted to thin films in the case of IL-nickelates. Therefore, the nature of the interface with the substrate, and how it couples with the thin film properties is still an open question. Here, using scanning transmission electron microscopy (STEM)- electron energy loss spectroscopy (EELS) and four-dimensional (4D)-STEM, a novel chemically sharp p-type interface is observed in a series of superconducting IL-praseodymium nickelate samples, and a comparative study is carried out with the previously reported n-type interface obtained in other samples. Both interfaces have strong differences, with the p-type interface being highly polar. In combination with ab-initio calculations, we find that the influence of the interface on the electronic structure is local, and does not extend beyond 2-3 unit cells into the thin film. This decouples the direct influence of the interface in driving the superconductivity, and indicates that the IL-nickelate thin films do not have a universal interface model. Insights into the spatial hole-distribution in SC samples, provided by monochromated EELS and total reflection-hard x-ray photoemission spectroscopy, suggest that this particular distribution might be directly influencing superconductivity.

In this research, we investigated the efficacy of Metformin, the most commonly administered type-2 diabetes drug for triple negative breast cancer (TNBC) treatment, due to its various anticancer properties. It is a plant-based bio-compound, synthesized as a novel biguanide, called dimethyl biguanide or metformin. One of the ways it operates is by hindering electron transport chain-complex I, in mitochondria, which causes a drop-in energy (ATP) generation. This eventually builds energetic stress and a decline in energy. Therefore, the natural cellular processes and proliferating tumor cells are obstructed. Here, we used electroporation, where, the MDA-MB-231, human TNBC cells were subjected to high intensity, short-duration electrical pulses (EP) in the presence of Metformin. The cell viability results indicate lower cell viability of 43.45% as compared to 85.20% with drug alone at 5mM concentration. This indicates that Metformin, the most common diabetes drug could also be explored for cancer treatment.

We report an experimental study of the magnetic properties of infinite-layer Nd1-xSrxNiO2 thin films by x-ray magnetic circular dichroism (XMCD) at Ni L3,2 and Nd M5,4 edges. We show that at low temperatures the out-of-plane component of the Ni1+ spin-moment is characterized by a rapid increase for magnetic fields below 1T , followed by a slower linear increase reaching a spin-moment value of 0.25uB/Ni at 9T in the case of superconducting Nd0.8Sr0.2NiO2. On the other hand, the Nd M5,4 XMCD shows a clear paramagnetic behaviour, which make both Ni- and Nd-spin-sublattices fully uncorrelated.The magnetic field and temperature dependencies of the Ni L3,2 XMCD data can be explained by assuming an out-of-plane canting of the strongly in-plane anti-ferromagnetic ordered Ni1+ spins. A symmetry lowering of the NiO2 planes observed via four-dimensional scanning transmission electron microscopy, triggering a DMI, can be responsible of the proposed Ni1+ spin-canting at zero-field. The resulting out-of-plane weak-ferromagnetic coupling under magnetic field explains the relatively large spin-moment and its magnetic field and temperature dependence.

Technology-facilitated gender-based violence has become a global threat to women's political representation and democracy. Understanding how online hate affects its targets is thus paramount. We analyse 10 million tweets directed at female candidates in the Brazilian election in 2022 and examine their reactions to online misogyny. Using a self-trained machine learning classifier to detect Portuguese misogynistic tweets and a quantitative analysis of the candidates' tweeting behaviour, we investigate how the number of misogynistic attacks received alters the online activity of the female candidates. We find that young and left-wing candidates and candidates with higher visibility online received significantly more attacks. Furthermore, we find that an increase in misogynistic attacks in the previous week is associated with a decrease in female candidates' tweets in the following week. This potentially threatens their equal participation in public opinion building and silences women's voices in political discourse.

Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult. As a result, practitioners make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 390 tools, we map the current ecosystem of available AI audit tools. While there are many tools designed to assist practitioners with setting standards and evaluating AI systems, these tools often fell short of supporting the accountability goals of AI auditing in practice. We thus highlight areas for future tool development beyond evaluation -- from harms discovery to advocacy -- and outline challenges practitioners faced in their efforts to use AI audit tools. We conclude that resources are lacking to adequately support the full scope of needs for many AI audit practitioners and recommend that the field move beyond tools for just evaluation, towards more comprehensive infrastructure for AI accountability.

One of the most concrete measures to take towards meaningful AI accountability is to consequentially assess and report the systems' performance and impact. However, the practical nature of the "AI audit" ecosystem is muddled and imprecise, making it difficult to work through various concepts and map out the stakeholders involved in the practice. First, we taxonomize current AI audit practices as completed by regulators, law firms, civil society, journalism, academia, consulting agencies. Next, we assess the impact of audits done by stakeholders within each domain. We find that only a subset of AI audit studies translate to desired accountability outcomes. We thus assess and isolate practices necessary for effective AI audit results, articulating the observed connections between AI audit design, methodology and institutional context on its effectiveness as a meaningful mechanism for accountability., Comment: To appear in the proceedings of the 2nd IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) 2024

Infinite layer nickelates provide a new route beyond copper oxides to address outstanding questions in the field of unconventional superconductivity. However, their synthesis poses considerable challenges, largely hindering experimental research on this new class of oxide superconductors. That synthesis is achieved in a two-step process that yields the most thermodynamically stable perovskite phase first, then the infinite-layer phase by topotactic reduction, the quality of the starting phase playing a crucial role. Here, we report on reliable synthesis of superconducting infinite-layer nickelate films after successive topochemical reductions of a parent perovskite phase with nearly optimal stoichiometry. Careful analysis of the transport properties of the incompletely reduced films reveals an improvement of the strange metal behaviour of their normal state resistivity over subsequent topochemical reductions, offering insight into the reduction process., Comment: 26 pages; 25 figures. Results unchanged; supplemental figures added; references added

In this paper, we present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. The model is used in a Model Predictive Controller in which we included a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation. The stability analysis of the model is presented, as well as a brief description of the overall planning and control scheme which includes an offline trajectory generation pipeline, an online local speed profile planner, and a low-level longitudinal controller. An improvement of the lateral path tracking is demonstrated in preliminary experimental results that have been produced on a Dallara AV-21 during the first Indy Autonomous Challenge event on the Monza F1 racetrack. Final adjustments and tuning have been performed in a high-fidelity simulator demonstrating the effectiveness of the solution when performing close to the tire limits., Comment: Presented at the 2023 IEEE 11th International Conference on Systems and Control, Sousse, Tunisia, December 18-20, 2023

As AI systems proliferate in society, the AI community is increasingly preoccupied with the concept of AI Safety, namely the prevention of failures due to accidents that arise from an unanticipated departure of a system's behavior from designer intent in AI deployment. We demonstrate through an analysis of real world cases of such incidents that although current vocabulary captures a range of the encountered issues of AI deployment, an expanded socio-technical framing will be required for a more complete understanding of how AI systems and implemented safety mechanisms fail and succeed in real life., Comment: Published at ICLR workshop on ML in the Real World, 2020

A class of graphs $\mathcal{G}$ is $\chi$-bounded if there exists a function $f$ such that $\chi(G) \leq f(\omega(G))$ for each graph $G \in \mathcal{G}$, where $\chi(G)$ and $\omega(G)$ are the chromatic and clique number of $G$, respectively. The square of a graph $G$, denoted as $G^2$, is the graph with the same vertex set as $G$ in which two vertices are adjacent when they are at a distance at most two in $G$. In this paper, we study the $\chi$-boundedness of squares of bipartite graphs and its subclasses. Note that the class of squares of graphs, in general, admit a quadratic $\chi$-binding function. Moreover there exist bipartite graphs $B$ for which $\chi\left(B^2\right)$ is $\Omega\left(\frac{\left(\omega\left(B^2\right)\right)^2 }{\log \omega\left(B^2\right)}\right)$. We first ask the following question: "What sub-classes of bipartite graphs have a linear $\chi$-binding function?" We focus on the class of convex bipartite graphs and prove the following result: for any convex bipartite graph $G$, $\chi\left(G^2\right) \leq \frac{3 \omega\left(G^2\right)}{2}$. Our proof also yields a polynomial-time $3/2$-approximation algorithm for coloring squares of convex bipartite graphs. We then introduce a notion called "partite testable properties" for the squares of bipartite graphs. We say that a graph property $P$ is partite testable for the squares of bipartite graphs if for a bipartite graph $G=(A,B,E)$, whenever the induced subgraphs $G^2[A]$ and $G^2[B]$ satisfies the property $P$ then $G^2$ also satisfies the property $P$. Here, we discuss whether some of the well-known graph properties like perfectness, chordality, (anti-hole)-freeness, etc. are partite testable or not. As a consequence, we prove that the squares of biconvex bipartite graphs are perfect., Comment: 22 pages, 5 figures

Calibration of pixel values is a fundamental step for accurate measurements in astronomical imaging. In astronomical jargon this is known as estimating zero point magnitude. Here, we introduce a newly added script in GNU Astronomy Utilities (Gnuastro) version 0.20 for the zero point magnitude estimation, named: astscript-zeropoint. The script offers numerous features, such as the flexibility to use either image(s) or a catalog as the reference dataset. Additionally, steps are parallelized to enhance efficiency for big data. Thanks to Gnuastro's minimal dependencies, the script is both flexible and portable. The figures of this research note are reproducible with Maneage, on the Git commit c89275e., Comment: Supplementary data on Zenodo (https://doi.org/10.5281/zenodo.10256845), project source on Codeberg (https://codeberg.org/gnuastro/papers/src/branch/zeropoint) and archived on Software Heritage (swh:1:dir:8b2d1f63be96de3de03aa3e2bb68fa7fa52df56f; see text for usage)