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Let $k$ be a field of characteristic zero. Let $m$ and $\alpha$ be positive integers. For $n\geq 2$, let $R_n=k[x_1,x_2,\dots,x_n]$ with the $k$-derivation $d_n$ given by $d_n=(1-x_1x_2^{\alpha})\partial_{x_1}+x_1^m\partial_{x_2}+x_2\partial_{x_3}+\dots+x_{n-1}\partial_{x_n}$. We prove that for all odd $\alpha \geq 1$ and all odd $m\geq 3$, $d_n$ is a simple derivation on $R_n$ and $d_n(R_n)$ contains no units. This generalizes a result of D. A. Jordan. We also show that the isotropy group of $d_n$ is conjugate to a subgroup of translations., Comment: 13 pages; Comments are welcome

We develop a method to evaluate integrals of non-holomorphic modular functions over the fundamental domain of the torus with modular parameter $\tau$ analytically. It proceeds in two steps: first the integral is transformed to a Lorentzian contour by the same strategy that leads to the Lorentzian inversion formula in CFT, and then we apply a two-dimensional version of the Rademacher expansion. This computes the integral in terms of an expansion sensitive to the singular behaviour of the integrand near all the Lorentzian cusps $\tau \to i \infty$, $\bar{\tau} \to x \in \mathbb{Q}$. We apply this technique to a variety of examples such as the evaluation of string one-loop partition functions, where it leads to the first analytic formula for the cosmological constants of the bosonic string and the $\mathrm{SO}(16) \times \mathrm{SO}(16)$ string., Comment: 40 pages + appendices

The colliding-wind binary system $\eta$ Carinae has been identified as a source of high-energy (HE, below $\sim$100\,GeV) and very-high-energy (VHE, above $\sim$100\,GeV) gamma rays in the last decade, making it unique among these systems. With its eccentric 5.5-year-long orbit, the periastron passage, during which the stars are separated by only $1-2$\,au, is an intriguing time interval to probe particle acceleration processes within the system. In this work, we report on an extensive VHE observation campaign that for the first time covers the full periastron passage carried out with the High Energy Stereoscopic System (H.E.S.S.) in its 5-telescope configuration with upgraded cameras. VHE gamma-ray emission from $\eta$ Carinae was detected during the periastron passage with a steep spectrum with spectral index $\Gamma= 3.3 \pm 0.2_{\mathrm{stat}} \, \pm 0.1_{\mathrm{syst}}$. Together with previous and follow-up observations, we derive a long-term light curve sampling one full orbit, showing hints of an increase of the VHE flux towards periastron, but no hint of variability during the passage itself. An analysis of contemporaneous Fermi-LAT data shows that the VHE spectrum represents a smooth continuation of the HE spectrum. From modelling the combined spectrum we conclude that the gamma-ray emission region is located at distances of ${\sim}10 - 20$\,au from the centre of mass of the system and that protons are accelerated up to energies of at least several TeV inside the system in this phase., Comment: Accepted at A&A; 11 pages, 9 figures

Bluebottles (\textit{Physalia} spp.) are marine stingers resembling jellyfish, whose presence on Australian beaches poses a significant public risk due to their venomous nature. Understanding the environmental factors driving bluebottles ashore is crucial for mitigating their impact, and machine learning tools are to date relatively unexplored. We use bluebottle marine stinger presence/absence data from beaches in Eastern Sydney, Australia, and compare machine learning models (Multilayer Perceptron, Random Forest, and XGBoost) to identify factors influencing their presence. We address challenges such as class imbalance, class overlap, and unreliable absence data by employing data augmentation techniques, including the Synthetic Minority Oversampling Technique (SMOTE), Random Undersampling, and Synthetic Negative Approach that excludes the negative class. Our results show that SMOTE failed to resolve class overlap, but the presence-focused approach effectively handled imbalance, class overlap, and ambiguous absence data. The data attributes such as the wind direction, which is a circular variable, emerged as a key factor influencing bluebottle presence, confirming previous inference studies. However, in the absence of population dynamics, biological behaviours, and life cycles, the best predictive model appears to be Random Forests combined with Synthetic Negative Approach. This research contributes to mitigating the risks posed by bluebottles to beachgoers and provides insights into handling class overlap and unreliable negative class in environmental modelling.

Over the past decades, there has been an increasing concern about the prevalence of abusive and violent content in Hollywood movies. This study uses Large Language Models (LLMs) to explore the longitudinal abuse and sentiment analysis of Hollywood Oscar and blockbuster movie dialogues from 1950 to 2024. By employing fine-tuned LLMs, we analyze subtitles for over a thousand movies categorised into four genres to examine the trends and shifts in emotional and abusive content over the past seven decades. Our findings reveal significant temporal changes in movie dialogues, which reflect broader social and cultural influences. Overall, the emotional tendencies in the films are diverse, and the detection of abusive content also exhibits significant fluctuations. The results show a gradual rise in abusive content in recent decades, reflecting social norms and regulatory policy changes. Genres such as thrillers still present a higher frequency of abusive content that emphasises the ongoing narrative role of violence and conflict. At the same time, underlying positive emotions such as humour and optimism remain prevalent in most of the movies. Furthermore, the gradual increase of abusive content in movie dialogues has been significant over the last two decades, where Oscar-nominated movies overtook the top ten blockbusters.

In this paper, we present a magnetohydrodynamics simulation of NOAA active region 11166 to understand the origin of a confined X-class flare that peaked at 23:23 UT on 2011 March 9. The simulation is initiated with a magnetic field extrapolated from the corresponding photospheric magnetogram using a non-force-free-field extrapolation technique. Importantly, the initial magnetic configuration identifies three-dimensional (3D) magnetic nulls and quasi-separatrix layers (QSLs), which nearly agree with the bright structures appeared in multi-wavelength observations. The Lorentz force associated with the extrapolated field self-consistently generates the dynamics that leads to the magnetic reconnections at the 3D nulls and the QSLs. These reconnections are found to contribute to the pre-flare activities and, ultimately, lead to the development of the flare ribbons. Notably, the anchored spine of the 3D null and the complete absence of flux rope in the flaring region are congruent with the confined nature of the flare. Furthermore, the simulation also suggests the role of reconnections at the 3D null with an open spine in the onset of a jet away from the flaring site., Comment: 22 pages, Accepted for Publication in Solar Physics

Debugging software, i.e., the localization of faults and their repair, is a main activity in software engineering. Therefore, effective and efficient debugging is one of the core skills a software engineer must develop. However, the teaching of debugging techniques is usually very limited or only taught in indirect ways, e.g., during software projects. As a result, most Computer Science (CS) students learn debugging only in an ad-hoc and unstructured way. In this work, we present our approach called Simulated Interactive Debugging that interactively guides students along the debugging process. The guidance aims to empower the students to repair their solutions and have a proper "learning" experience. We envision that such guided debugging techniques can be integrated into programming courses early in the CS education curriculum. To perform an initial evaluation, we developed a prototypical implementation using traditional fault localization techniques and large language models. Students can use features like the automated setting of breakpoints or an interactive chatbot. We designed and executed a controlled experiment that included this IDE-integrated tooling with eight undergraduate CS students. Based on the responses, we conclude that the participants liked the systematic guidance by the assisted debugger. In particular, they rated the automated setting of breakpoints as the most effective, followed by the interactive debugging and chatting, and the explanations for how breakpoints were set. In our future work, we will improve our concept and implementation, add new features, and perform more intensive user studies.

In the generalized matter-geometry coupling theory, we investigate the physical characteristics and causality of some new cosmological models for a flat, homogeneous, and isotropic spacetime filled with stiff, radiation, dust, and curvature fluid sources. We obtain a particular cosmological model corresponding to each source fluid, called Models I, II, III, and IV, respectively. We make observational constraints on each model using the joint analysis of $31$ Cosmic Chronometer (CC) Hubble dataset and $1048$ Pantheon datasets to estimate the current values of model parameters. Using these statistical results, we have analyzed the information criteria, effective EoS parameter, causality of the models, and viability of this generalized gravity theory. Subsequently, we investigate the effective equation of state and deceleration parameter for each model. We found that all models in the late-time universe exhibit transit-phase acceleration, and Models I and II show both the early as well as late-time accelerating phase of the expanding universe. We found the current values of the deceleration parameter in the range $-0.886\le q_{0}\le-0.54$ with transition redshift $0.5137\le z_{t}\le0.6466$ and the effective EoS parameter in the range $-0.924\le\omega_{eff}\le-0.6933$. We analyzed the square sound speed condition $c_{s}^{2}\le c^{2}$ for each model., Comment: 23 pages, 8 figures

Large language model (LLM) serving is becoming an increasingly important workload for cloud providers. Based on performance SLO requirements, LLM inference requests can be divided into (a) interactive requests that have tight SLOs in the order of seconds, and (b) batch requests that have relaxed SLO in the order of minutes to hours. These SLOs can degrade based on the arrival rates, multiplexing, and configuration parameters, thus necessitating the use of resource autoscaling on serving instances and their batch sizes. However, previous autoscalers for LLM serving do not consider request SLOs leading to unnecessary scaling and resource under-utilization. To address these limitations, we introduce Chiron, an autoscaler that uses the idea of hierarchical backpressure estimated using queue size, utilization, and SLOs. Our experiments show that Chiron achieves up to 90% higher SLO attainment and improves GPU efficiency by up to 70% compared to existing solutions.

The rapid rise in inverter-based renewable resources has heightened concerns over subsynchronous resonance and oscillations, thereby challenging grid stability. This paper reviews approaches to identify and mitigate these issues, focusing on frequency scanning methods for stability assessment. It categorizes white-, black-, and gray-box modeling techniques, compares positive-sequence, dq-frame, and alpha-beta domain scanning, and examines perturbation shapes like step, ramp, and chirp. A comparative study highlights their strengths, limitations, and suitability for specific scenarios. By summarizing past events and surveying available tools, this work guides operators and researchers toward more effective, reliable stability analysis methods in grids with high renewable penetration.

We study the saturation of three-dimensional unstable perturbations on a fast rotating turbulent flow using direct numerical simulations (DNSs). Under the effect of Kolmogorov forcing, a transition between states dominated by coherent two-dimensional modes to states with three-dimensional variations (quasi-two-dimensional) is observed as we change the global rotation rate. We find this akin to a critical phenomenon, wherein the order parameter scales with the distance to the critical point raised to an exponent. The exponent itself deviates from the predicted mean field value. Also, the nature of the fluctuations of the order parameter near the critical point indicate the presence of on-off intermittency. The critical rotation rate at which the transition occurs exhibits a linear scaling behaviour with the forcing wave number. A reduced model based on linear stability analysis is used to find the linear threshold estimates; we find these to be in good agreement with the 3D nonlinear DNS results.

Agent-based program repair offers to automatically resolve complex bugs end-to-end by combining the planning, tool use, and code generation abilities of modern LLMs. Recent work has explored the use of agent-based repair approaches on the popular open-source SWE-Bench, a collection of bugs from highly-rated GitHub Python projects. In addition, various agentic approaches such as SWE-Agent have been proposed to solve bugs in this benchmark. This paper explores the viability of using an agentic approach to address bugs in an enterprise context. To investigate this, we curate an evaluation set of 178 bugs drawn from Google's issue tracking system. This dataset spans both human-reported (78) and machine-reported bugs (100). To establish a repair performance baseline on this benchmark, we implement Passerine, an agent similar in spirit to SWE-Agent that can work within Google's development environment. We show that with 20 trajectory samples and Gemini 1.5 Pro, Passerine can produce a patch that passes bug tests (i.e., plausible) for 73% of machine-reported and 25.6% of human-reported bugs in our evaluation set. After manual examination, we found that 43% of machine-reported bugs and 17.9% of human-reported bugs have at least one patch that is semantically equivalent to the ground-truth patch. These results establish a baseline on an industrially relevant benchmark, which as we show, contains bugs drawn from a different distribution -- in terms of language diversity, size, and spread of changes, etc. -- compared to those in the popular SWE-Bench dataset.

This paper presents the design and analysis of a Hybrid High-Order (HHO) approximation for a distributed optimal control problem governed by the Poisson equation. We propose three distinct schemes to address unconstrained control problems and two schemes for constrained control problems. For the unconstrained control problem, while standard finite elements achieve a convergence rate of \( k+1 \) (with \( k \) representing the polynomial degree), our approach enhances this rate to \( k+2 \) by selecting the control from a carefully constructed reconstruction space. For the box-constrained problem, we demonstrate that using lowest-order elements (\( \mathbb{P}_0 \)) yields linear convergence, in contrast to finite element methods (FEM) that require linear elements to achieve comparable results. Furthermore, we derive a cubic convergence rate for control in the variational discretization scheme. Numerical experiments are provided to validate the theoretical findings., Comment: 34 pages

The time-variable emission from the accretion flow of Sgr A*, the supermassive black hole at the Galactic Center, has long been examined in the radio-to-mm, near-infrared (NIR), and X-ray regimes of the electromagnetic spectrum. However, until now, sensitivity and angular resolution have been insufficient in the crucial mid-infrared (MIR) regime. The MIRI instrument on JWST has changed that, and we report the first MIR detection of Sgr A*. The detection was during a flare that lasted about 40 minutes, a duration similar to NIR and X-ray flares, and the source's spectral index steepened as the flare ended. The steepening suggests synchrotron cooling is an important process for Sgr A*'s variability and implies magnetic field strengths $\sim$40--70 Gauss in the emission zone. Observations at $1.3~\mathrm{mm}$ with the Submillimeter Array revealed a counterpart flare lagging the MIR flare by $\approx$10 minutes. The observations can be self-consistently explained as synchrotron radiation from a single population of gradually cooling high-energy electrons accelerated through (a combination of) magnetic reconnection and/or magnetized turbulence., Comment: Accepted for publication ApJL

On top of Segment Anything Model (SAM), SAM 2 further extends its capability from image to video inputs through a memory bank mechanism and obtains a remarkable performance compared with previous methods, making it a foundation model for video segmentation task. In this paper, we aim at making SAM 2 much more efficient so that it even runs on mobile devices while maintaining a comparable performance. Despite several works optimizing SAM for better efficiency, we find they are not sufficient for SAM 2 because they all focus on compressing the image encoder, while our benchmark shows that the newly introduced memory attention blocks are also the latency bottleneck. Given this observation, we propose EdgeTAM, which leverages a novel 2D Spatial Perceiver to reduce the computational cost. In particular, the proposed 2D Spatial Perceiver encodes the densely stored frame-level memories with a lightweight Transformer that contains a fixed set of learnable queries. Given that video segmentation is a dense prediction task, we find preserving the spatial structure of the memories is essential so that the queries are split into global-level and patch-level groups. We also propose a distillation pipeline that further improves the performance without inference overhead. As a result, EdgeTAM achieves 87.7, 70.0, 72.3, and 71.7 J&F on DAVIS 2017, MOSE, SA-V val, and SA-V test, while running at 16 FPS on iPhone 15 Pro Max., Comment: Code will be released at https://github.com/facebookresearch/EdgeTAM

In recent years, there has been a tremendous interest in using generative AI, and particularly large language models (LLMs) in software engineering; indeed there are now several commercially available tools, and many large companies also have created proprietary ML-based tools for their own software engineers. While the use of ML for common tasks such as code completion is available in commodity tools, there is a growing interest in application of LLMs for more bespoke purposes. One such purpose is code migration. This article is an experience report on using LLMs for code migrations at Google. It is not a research study, in the sense that we do not carry out comparisons against other approaches or evaluate research questions/hypotheses. Rather, we share our experiences in applying LLM-based code migration in an enterprise context across a range of migration cases, in the hope that other industry practitioners will find our insights useful. Many of these learnings apply to any application of ML in software engineering. We see evidence that the use of LLMs can reduce the time needed for migrations significantly, and can reduce barriers to get started and complete migration programs.

Bayesian Neural Networks (BNNs) offer robust uncertainty quantification in model predictions, but training them presents a significant computational challenge. This is mainly due to the problem of sampling multimodal posterior distributions using Markov Chain Monte Carlo (MCMC) sampling and variational inference algorithms. Moreover, the number of model parameters scales exponentially with additional hidden layers, neurons, and features in the dataset. Typically, a significant portion of these densely connected parameters are redundant and pruning a neural network not only improves portability but also has the potential for better generalisation capabilities. In this study, we address some of the challenges by leveraging MCMC sampling with network pruning to obtain compact probabilistic models having removed redundant parameters. We sample the posterior distribution of model parameters (weights and biases) and prune weights with low importance, resulting in a compact model. We ensure that the compact BNN retains its ability to estimate uncertainty via the posterior distribution while retaining the model training and generalisation performance accuracy by adapting post-pruning resampling. We evaluate the effectiveness of our MCMC pruning strategy on selected benchmark datasets for regression and classification problems through empirical result analysis. We also consider two coral reef drill-core lithology classification datasets to test the robustness of the pruning model in complex real-world datasets. We further investigate if refining compact BNN can retain any loss of performance. Our results demonstrate the feasibility of training and pruning BNNs using MCMC whilst retaining generalisation performance with over 75% reduction in network size. This paves the way for developing compact BNN models that provide uncertainty estimates for real-world applications., Comment: 22 pages, 11 figures

We propose an elementary method to show non-Gaussianity of invariant measures of parabolic stochastic partial differential equations with polynomial non-linearities in the Da Prato--Debussche regime. The approach is essentially algebraic and involves using the generator equation of the SPDE at stationarity. Our results in particular cover the $\Phi^4_\delta$ measures in dimensions $\delta<\frac{14}{5}$, which includes cases where the invariant measure is singular with respect to the invariant measure of the linear solution.

This mentoring resource is a guide to establishing and running near-peer mentorship programs. It is based on the working knowledge and best practices developed by the Access Network, a collection of nine student-led communities at universities across the country working towards a vision of a more diverse, equitable, inclusive, and accessible STEM environment. Many of these communities, also referred to as sites, include a near-peer mentoring program that is developed to best support their local context. The format of these programs vary, ranging from structured classes with peer mentoring groups to student clubs supporting 1-on-1 relationships. To further support program participants as both students and as whole people, sites often run additional events such as lecture series, workshops, and social activities guided tailored to each student community's needs. Through this process, student leaders have generated and honed best practices for all aspects of running their sites. This guide is an attempt to synthesize those efforts, offering practical advice for student leaders setting up near-peer mentorship programs in their own departments. It has been written through the lens of undergraduate near-peer mentorship programs, although our framework could easily be extended to other demographics (e.g. high schoolers, graduate students, etc.). Our experience is with STEM mentorship specifically, though these practices can extend to any discipline. In this document, we outline best practices for designing, running, and sustaining near-peer mentorship programs. We provide template resources to assist with this work, and lesson plans to run mentor and mentee training sessions. We hope you find this guide useful in designing, implementing, and re-evaluating community oriented near-peer mentoring programs.

Van der Waals (vdW) heterostructures subjected to interlayer twists or heterostrains demonstrate structural superlubricity, leading to their potential use as superlubricants in micro- and nano-electro-mechanical devices. However, quantifying superlubricity across the vast four-dimensional heterodeformation space using experiments or atomic-scale simulations is a challenging task. In this work, we develop an atomically informed dynamic Frenkel--Kontorova (DFK) model for predicting the interface friction drag coefficient of an arbitrarily heterodeformed bilayer graphene (BG) system. The model is motivated by MD simulations of friction in heterodeformed BG. In particular, we note that interface dislocations formed during structural relaxation translate in unison when a heterodeformed BG is subjected to shear traction, leading us to the hypothesis that the kinetic properties of interface dislocations determine the friction drag coefficient of the interface. The constitutive law of the DFK model comprises the generalized stacking fault energy of the AB stacking, a scalar displacement drag coefficient, and the elastic properties of graphene, which are all obtained from atomistic simulations. Simulations of the DFK model confirm our hypothesis since a single choice of the displacement drag coefficient, fit to the kinetic property of an individual dislocation in an atomistic simulation, predicts interface friction in any heterodeformed BG. By bridging the gap between dislocation kinetics at the microscale to interface friction at the macroscale, the DFK model enables a high-throughput investigation of strain-engineered vdW heterostructures.

Recent advancements in audio deepfake detection have leveraged graph neural networks (GNNs) to model frequency and temporal interdependencies in audio data, effectively identifying deepfake artifacts. However, the reliance of GNN-based methods on substantial labeled data for graph construction and robust performance limits their applicability in scenarios with limited labeled data. Although vast amounts of audio data exist, the process of labeling samples as genuine or fake remains labor-intensive and costly. To address this challenge, we propose SIGNL (Spatio-temporal vIsion Graph Non-contrastive Learning), a novel framework that maintains high GNN performance in low-label settings. SIGNL constructs spatio-temporal graphs by representing patches from the audio's visual spectrogram as nodes. These graph structures are modeled using vision graph convolutional (GC) encoders pre-trained through graph non-contrastive learning, a label-free that maximizes the similarity between positive pairs. The pre-trained encoders are then fine-tuned for audio deepfake detection, reducing reliance on labeled data. Experiments demonstrate that SIGNL outperforms state-of-the-art baselines across multiple audio deepfake detection datasets, achieving the lowest Equal Error Rate (EER) with as little as 5% labeled data. Additionally, SIGNL exhibits strong cross-domain generalization, achieving the lowest EER in evaluations involving diverse attack types and languages in the In-The-Wild dataset.

During the COVID-19 pandemic, community tensions intensified, fuelling Hinduphobic sentiments and discrimination against individuals of Hindu descent within India and worldwide. Large language models (LLMs) have become prominent in natural language processing (NLP) tasks and social media analysis, enabling longitudinal studies of platforms like X (formerly Twitter) for specific issues during COVID-19. We present an abuse detection and sentiment analysis framework that offers a longitudinal analysis of Hinduphobia on X (Twitter) during and after the COVID-19 pandemic. This framework assesses the prevalence and intensity of Hinduphobic discourse, capturing elements such as derogatory jokes and racist remarks through sentiment analysis and abuse detection from pre-trained and fine-tuned LLMs. Additionally, we curate and publish a "Hinduphobic COVID-19 X (Twitter) Dataset" of 8,000 tweets annotated for Hinduphobic abuse detection, which is used to fine-tune a BERT model, resulting in the development of the Hinduphobic BERT (HP-BERT) model. We then further fine-tune HP-BERT using the SenWave dataset for multi-label sentiment analysis. Our study encompasses approximately 27.4 million tweets from six countries, including Australia, Brazil, India, Indonesia, Japan, and the United Kingdom. Our findings reveal a strong correlation between spikes in COVID-19 cases and surges in Hinduphobic rhetoric, highlighting how political narratives, misinformation, and targeted jokes contributed to communal polarisation. These insights provide valuable guidance for developing strategies to mitigate communal tensions in future crises, both locally and globally. We advocate implementing automated monitoring and removal of such content on social media to curb divisive discourse.

We present broadband X-ray observations of the High Mass X-ray Binary (HMXB) pulsar SMC X-2, using concurrent NuSTAR and NICER observations during its 2022 outburst. The source is found to be spinning with a period of 2.37281(3) s. We confirm the existence of cyclotron resonant scattering feature (CRSF) at 31 keV in addition to the iron emission line in the X-ray continuum of the source. Spectral analysis performed with the physical bulk and thermal Comptonization model indicates that the bulk Comptonization dominates the thermal Comptonization. Using phase-resolved spectroscopy, we have investigated the variations of the spectral parameters relative to pulse phase that may be due to the complex structure of magnetic field of the pulsar or the impact of the emission geometry. It is observed that the spectral parameters exhibit significant variabilities relative to the pulsed phase. Time-resolved spectroscopy is employed to examine the evolution of the continuum and changes in the spectral characteristics. Measurements of luminosity along with variations in cyclotron line energy and photon index suggest that the source may be accreting in the super-critical regime., Comment: Accepted for Publication in New Astronomy

Per- and polyfluoroalkyl substances (PFAS), often referred as "forever chemicals," are pervasive environmental pollutants due to their resistance to degradation. Among these, perfluorooctanoic acid (PFOA) poses significant threats to human health, contaminating water sources globally. Here, we have demonstrated the potential of a novel electrochemical sensor based on two-dimensional (2D) aluminium-based multicomponent quasicrystals (2D-Al QC) for the ultrasensitive sub-picomolar level detection of PFOA. The 2D-Al QC-inked electrode was employed here to detect PFOA by differential pulse voltammetry (DPV). The limit of detection (LoD) achieved is 0.59 +/- 0.05 pM. The sensor was evaluated for selectivity with other interfering compounds, repeatability of cycles, and reproducibility for five similar electrodes with a deviation of 0.8 %. The stability of the sensor has also been analysed after ninety days ,which shows a minimal variation of 15%. Spectroscopic techniques and theoretical calculations were further utilized to understand the interaction between the 2D-Al QC and PFOA. The results demonstrate that the 2D-Al QC offers a promising platform for the rapid and sensitive detection of PFOA, potentially addressing current environmental monitoring challenges.

We present multi-wavelength analysis of ZTF23abelseb (AT 2023sva), an optically discovered fast-fading ($\Delta m_r = 2.2$ mag in $\Delta t = 0.74 $ days), luminous ($M_r \sim -30.0$ mag) and red ($g-r = 0.50$ mag) transient at $z = 2.28$ with accompanying luminous radio emission. AT 2023sva does not possess a $\gamma$-ray burst (GRB) counterpart to an isotropic equivalent energy limit of $E_{\rm{\gamma, \, iso}} < 1.6 \times 10^{52}$ erg, determined through searching $\gamma$-ray satellite archives between the last non-detection and first detection, making it the sixth example of an optically-discovered afterglow with a redshift measurement and no detected GRB counterpart. We analyze AT 2023sva's optical, radio, and X-ray observations to characterize the source. From radio analyses, we find the clear presence of strong interstellar scintillation (ISS) 72 days after the initial explosion, allowing us to place constraints on the source's angular size and bulk Lorentz factor. When comparing the source sizes derived from ISS of orphan events to those of the classical GRB population, we find orphan events have statistically smaller source sizes. We also utilize Bayesian techniques to model the multi-wavelength afterglow. Within this framework, we find evidence that AT 2023sva possesses a shallow power-law structured jet viewed slightly off-axis ($\theta_{\rm{v}} = 0.07 \pm 0.02$) just outside of the jet's core opening angle ($\theta_{\rm{c}} = 0.06 \pm 0.02$). We determine this is likely the reason for the lack of a detected GRB counterpart, but also investigate other scenarios. AT 2023sva's evidence for possessing a structured jet stresses the importance of broadening orphan afterglow search strategies to a diverse range of GRB jet angular energy profiles, to maximize the return of future optical surveys., Comment: 22 pages, 14 Figures, Submitted to MNRAS

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

Using the ground states of the Bose- and Fermi-Hubbard model as the battery's initial state, we demonstrate that using the Wannier-Stark (WS) field for charging in addition to onsite interactions can increase the maximum power of the battery. Although the benefit is not ubiquitous, bosonic batteries are more affected by the WS field than fermionic ones. In particular, there exists a critical WS field strength above which the power gets increased in the battery. Further, we determine a closed form expression of the stored work when the battery is in the ground state of the Bose- and Fermi-Hubbard model with only hopping term and the charging is carried out with onsite interactions and WS field irrespective of lattice-size of the battery. Moreover, we exhibit that it is possible to extract work in the fermionic batteries even without charging when the edge sites are attached to two local thermal baths having high temperatures -- this process we refer to as {\it environment-assisted ergotropy}. Note, however, that the bosonic batteries are able to exhibit such an environmental benefit in the transient regime when the lattice-size is increased and when Wannier-Stark field is present. Nonetheless, if the onsite interaction or WS potential with a critical strength is utilized as a charger, energy can be stored and extracted from both bosonic and fermionic batteries in the presence of the thermal baths., Comment: 10 pages, 8 figures

The room temperature growth, characterization, and electrical transport properties of magnetron sputtered superconducting Ti40V60 alloy thin films are presented. The films exhibit low surface roughness and tunable transport properties. As the sputtering current increases, the superconducting transition move towards higher temperatures. Rietveld refinement of two dimensional XRD (2D XRD) pattern reveals the presence of stress in the films, which shifts from tensile to compressive as the sputtering current increases. Additionally, the crystallite size of the films increases with higher sputtering currents. The films exhibit a strong preferential orientation, contributing to their texturing. The crystallite size and texturing are found to be correlated with the superconducting transition temperature (TC) of the films. As the crystallite size and texturing increase, the TC of the films also rises.

Robust matrix completion (RMC) is a widely used machine learning tool that simultaneously tackles two critical issues in low-rank data analysis: missing data entries and extreme outliers. This paper proposes a novel scalable and learnable non-convex approach, coined Learned Robust Matrix Completion (LRMC), for large-scale RMC problems. LRMC enjoys low computational complexity with linear convergence. Motivated by the proposed theorem, the free parameters of LRMC can be effectively learned via deep unfolding to achieve optimum performance. Furthermore, this paper proposes a flexible feedforward-recurrent-mixed neural network framework that extends deep unfolding from fix-number iterations to infinite iterations. The superior empirical performance of LRMC is verified with extensive experiments against state-of-the-art on synthetic datasets and real applications, including video background subtraction, ultrasound imaging, face modeling, and cloud removal from satellite imagery., Comment: arXiv admin note: substantial text overlap with arXiv:2110.05649

Autonomous vehicle (AV) technology is transforming the landscape of transportation bypromising safer, more efficient, and sustainable mobilitysolutions. In recent years, significant advancements in AI, machine learning, sensor fusion, and vehicle-to-everything(V2X)communicationhavepropelledthedevelopmentoffullyautonomous vehicles. This paper explores the cutting-edge technologies driving the evolution of AVs,thechallengesfacedintheirdeployment,andthepotentialsocietal,economic,and regulatory impacts. It highlights the key innovations in perception systems, decision-making algorithms, and infrastructure integration, as well as the emerging trends towards Level 4 and Level 5 autonomy. The paper also discusses future directions, including ethical considerations and the roadmap to mass adoption of autonomous mobility. Ultimately, the integrationofautonomousvehicles into globaltransportation systems is expected to revolutionize urban planning, reduce traffic accidents, and significantlyloweremissions,pavingthewayforasmarterandmoresustainablefuture.

The global shift towards electric vehicles (EVs) as a sustainable alternative to traditional gasoline-powered cars has triggered a significant rise in the demand for lithium-ion batteries. However, as the adoption of EVs grows, the issue of battery disposal and recycling has emerged as a critical challenge. The recycling of EV batteries is essential not only for reducing the environmental impact of battery waste but also for ensuring the sustainable supply of critical raw materials such as lithium, cobalt, and nickel. This paper explores recent innovations in the field of electric vehicle battery recycling, examining advanced techniques such as direct recycling, hydrometallurgical processes, and sustainable battery design. It also highlights the role of policy and industry collaboration in improving recycling infrastructure and addressing the economic and environmental challenges associated with battery waste. By focusing on both the technical and regulatory aspects of EV battery recycling, this paper aims to provide a comprehensive overview of the state of the industry and the future outlook for recycling technologies, ultimately paving the way for a cleaner, more sustainable future in transportation.

Most existing traffic video datasets including Waymo are structured, focusing predominantly on Western traffic, which hinders global applicability. Specifically, most Asian scenarios are far more complex, involving numerous objects with distinct motions and behaviors. Addressing this gap, we present a new dataset, DAVE, designed for evaluating perception methods with high representation of Vulnerable Road Users (VRUs: e.g. pedestrians, animals, motorbikes, and bicycles) in complex and unpredictable environments. DAVE is a manually annotated dataset encompassing 16 diverse actor categories (spanning animals, humans, vehicles, etc.) and 16 action types (complex and rare cases like cut-ins, zigzag movement, U-turn, etc.), which require high reasoning ability. DAVE densely annotates over 13 million bounding boxes (bboxes) actors with identification, and more than 1.6 million boxes are annotated with both actor identification and action/behavior details. The videos within DAVE are collected based on a broad spectrum of factors, such as weather conditions, the time of day, road scenarios, and traffic density. DAVE can benchmark video tasks like Tracking, Detection, Spatiotemporal Action Localization, Language-Visual Moment retrieval, and Multi-label Video Action Recognition. Given the critical importance of accurately identifying VRUs to prevent accidents and ensure road safety, in DAVE, vulnerable road users constitute 41.13% of instances, compared to 23.71% in Waymo. DAVE provides an invaluable resource for the development of more sensitive and accurate visual perception algorithms in the complex real world. Our experiments show that existing methods suffer degradation in performance when evaluated on DAVE, highlighting its benefit for future video recognition research.

Understanding how physical systems are influenced by disorder is a fundamental challenge in quantum science. Addressing its effects often involves numerical averaging over a large number of samples, and it is not always easy to gain an analytical handle on exploring the effect of disorder. In this work, we derive exact solutions for disorder-averaged dynamics generated by any Hamiltonian that is a periodic matrix (potentially with non-trivial base, a property also called ($p,q$)-potency). Notably, this approach is independent of the initial state, exact for arbitrary evolution times, and it holds for Hermitian as well as non-Hermitian systems. The ensemble behavior resembles that of an open quantum system, whose decoherence function or rates are determined by the disorder distribution and the periodicity of the Hamiltonian. Depending on the underlying distribution, the dynamics can display non-Markovian characteristics detectable through non-Markovian witnesses. We illustrate the scheme for qubit and qudit systems described by (products of) spin $1/2$, spin $1$, and clock operators. Our methodology offers a framework to leverage disorder-averaged exact dynamics for a range of applications in quantum-information processing and beyond., Comment: 9+8 pages, 5 figures

Models predict that the well-studied mass-radius relation of white dwarf stars depends on the temperature of the star, with hotter white dwarfs having larger masses at a given radius than cooler stars. In this paper, we use a catalog of 26,041 DA white dwarfs observed in Sloan Digital Sky Survey Data Releases 1-19. We measure the radial velocity, effective temperature, surface gravity, and radius for each object. By binning this catalog in radius or surface gravity, we average out the random motion component of the radial velocities for nearby white dwarfs to isolate the gravitational redshifts for these objects and use them to directly measure the mass-radius relation. For gravitational redshifts measured from binning in either radius or surface gravity, we find strong evidence for a temperature-dependent mass-radius relation, with warmer white dwarfs consistently having greater gravitational redshifts than cool objects at a fixed radius or surface gravity. For warm white dwarfs, we find that their mean radius is larger and mean surface gravity is smaller than those of cool white dwarfs at 5.2{\sigma} and 6.0{\sigma} significance, respectively. Selecting white dwarfs with similar radii or surface gravities, the significance of the difference in mean gravitational redshifts between the warm and cool samples is >6.1{\sigma} and >3.6{\sigma} for measurements binned in radius and surface gravity, respectively, in the direction predicted by theory. This is an improvement over previous implicit detections, and our technique can be expanded to precisely test the white dwarf mass-radius relation with future surveys.

Accurate vehicle trajectory prediction is crucial for ensuring safe and efficient autonomous driving. This work explores the integration of Transformer based model with Long Short-Term Memory (LSTM) based technique to enhance spatial and temporal feature learning in vehicle trajectory prediction. Here, a hybrid model that combines LSTMs for temporal encoding with a Transformer encoder for capturing complex interactions between vehicles is proposed. Spatial trajectory features of the neighboring vehicles are processed and goes through a masked scatter mechanism in a grid based environment, which is then combined with temporal trajectory of the vehicles. This combined trajectory data are learned by sequential LSTM encoding and Transformer based attention layers. The proposed model is benchmarked against predecessor LSTM based methods, including STA-LSTM, SA-LSTM, CS-LSTM, and NaiveLSTM. Our results, while not outperforming it's predecessor, demonstrate the potential of integrating Transformers with LSTM based technique to build interpretable trajectory prediction model. Future work will explore alternative architectures using Transformer applications to further enhance performance. This study provides a promising direction for improving trajectory prediction models by leveraging transformer based architectures, paving the way for more robust and interpretable vehicle trajectory prediction system.

We present simulation results examining the presence and behavior of standing shocks in zero-energy low angular momentum advective accretion flows and explore their (in)stabilities properties taking into account various specific angular momentum, $\lambda_0$. Within the range $10-50R_g$ (where $R_g$ denotes the Schwarzschild radius), shocks are discernible for $\lambda_0\geq 1.75$. In the special relativistic hydrodynamic (RHD) simulation when $\lambda_0 = 1.80$, we find the merger of two shocks resulted in a dramatic increase in luminosity. We present the impact of external and internal flow collisions from the funnel region on luminosity. Notably, oscillatory behavior characterizes shocks within $1.70 \leq \lambda_0 \leq 1.80$. Using free-free emission as a proxy for analysis, we shows that the luminosity oscillations between frequencies of $0.1-10$ Hz for $\lambda_0$ range $1.7 \leq \lambda_0 \leq 1.80$. These findings offer insights into quasi-periodic oscillations emissions from certain black hole X-ray binaries, exemplified by GX 339-4. Furthermore, for the supermassive black hole at the Milky Way's center, Sgr A*, oscillation frequencies between $10^{-6}$ and $10^{-5}$ Hz were observed. This frequency range, translating to one cycle every few days, aligns with observational data from the X-ray telescopes such as Chandra, Swift, and XMM-Newton., Comment: 22 pages, 13 figures, Accepted for publication in the Research in Astronomy and Astrophysics (RAA) journal

Parameter estimation of gravitational wave data is often computationally expensive, requiring simplifying assumptions such as circularisation of binary orbits. Although, if included, the sub-dominant effects like orbital eccentricity may provide crucial insights into the formation channels of compact binary mergers. To address these challenges, we present a pipeline strategy leveraging minimally modelled waveform reconstruction to identify the presence of eccentricity in real time. Using injected signals, we demonstrate that ignoring eccentricity ($e_{\rm 20Hz} \gtrsim 0.1$) leads to significant biases in parameter recovery, including chirp mass estimates falling outside the 90% credible interval. Waveform reconstruction shows inconsistencies increase with eccentricity, and this behaviour is consistent for different mass ratios. Our method enables low-latency inferences of binary properties supporting targeted follow-up analyses and can be applied to identify any physical effect of measurable strength.

$Ar^+$ ion has been used regularly for the cleaning of semiconductor, metal surfaces for epitaxial nanostructures growth. We have investigated the effect of low-energy $Ar^+$ ion sputtering and subsequent annealing on the $Si(111)-(7\times7)$ surfaces under ultrahigh vacuum (UHV) condition. Using $in-situ$ scanning tunnelling microscopy (STM) we have compared the morphological changes to the $Si(111)-(7\times7)$ surfaces before and after the sputtering process. Following $500~eV Ar^+$ ion sputtering, the atomically flat $Si(111)-(7\times7)$ surface becomes amorphous. The average root mean square (rms) surface roughness $({\sigma}_{avg})$ of the sputtered surface and that following post-annealing at different temperatures $(500^\circ-700^\circ)C$ under UHV have been measured as a function of STM scan size. While, annealing at $\sim 500^\circ C$ shows no detectable changes in the surface morphology, recrystallization process starts at $\sim 600^\circ C$. For the sputtered samples annealed at temperatures $\geq 600^\circ C, \,log~\sigma_{avg}$ varies linearly at lower length scales and approaches a saturation value of $\sim 0.6 nm$ for the higher length scales confirming the self-affine fractal nature. The correlation length increases with annealing temperature indicating gradual improvement in crystallinity. For the present experimental conditions, $650^\circ C$ is the optimal annealing temperature for recrystallization. The results offer a method to engineer the crystallinity of sputtered surface during nanofabrication process., Comment: 20 Pages, 4 Figures

Compact binary sources that emit gravitational waves (GW) are expected to be both spinning and have eccentric orbits. To date, there has been no closed-form expression for the phasing of GWs that contain information from both spin and eccentricity. The introduction of eccentricity can slow waveform generation, as obtaining closed-form expressions for the waveform phase is unattainable due to the complexity of the differential equations involved, often requiring slower numerical methods. However, closed-form expressions for the waveform phase can be obtained when eccentricity is treated as a small parameter, enabling quick waveform generation. In this paper, closed-form expressions for the GW phasing in the form of Taylor approximants up to the eighth power in initial eccentricity $(e_0)$ are obtained while also including aligned spins up to the third post-Newtonian order. The phasing is obtained in both time and frequency domains. The TaylorT2 phasing is also resummed for usage in scenarios where initial eccentricities are as high as 0.5. Finally, a waveform is constructed using the $e_{0}^2$ expanded TaylorF2 phasing for aligned-spin systems added to TaylorF2Ecc. We perform mismatch computation between this model and TaylorF2Ecc. The findings indicate that for eccentricities $\gtrsim 0.15$ (defined at 10 Hz) and small spins $(\sim 0.2 )$, the mismatches can be higher than 1%. This leads to an overall loss in signal-to-noise ratio and lower detection efficiency of GWs coming from eccentric spinning compact binary inspirals if the combined effects of eccentricity and aligned spins are neglected in the waveforms.

The second Hot QCD Matter 2024 conference at IIT Mandi focused on various ongoing topics in high-energy heavy-ion collisions, encompassing theoretical and experimental perspectives. This proceedings volume includes 19 contributions that collectively explore diverse aspects of the bulk properties of hot QCD matter. The topics encompass the dynamics of electromagnetic fields, transport properties, hadronic matter, spin hydrodynamics, and the role of conserved charges in high-energy environments. These studies significantly enhance our understanding of the complex dynamics of hot QCD matter, the quark-gluon plasma (QGP) formed in high-energy nuclear collisions. Advances in theoretical frameworks, including hydrodynamics, spin dynamics, and fluctuation studies, aim to improve theoretical calculations and refine our knowledge of the thermodynamic properties of strongly interacting matter. Experimental efforts, such as those conducted by the ALICE and STAR collaborations, play a vital role in validating these theoretical predictions and deepening our insight into the QCD phase diagram, collectivity in small systems, and the early-stage behavior of strongly interacting matter. Combining theoretical models with experimental observations offers a comprehensive understanding of the extreme conditions encountered in relativistic heavy-ion and proton-proton collisions., Comment: Compilation of the 19 contributions in Bulk Matter presented at the second 'Hot QCD Matter 2024 Conference' held from July 1-3, 2024, organized by IIT Mandi, Himachal Pradesh, India

Continual learning allows the system to learn and adapt to new tasks while retaining the knowledge acquired from previous tasks. However, deep learning models suffer from catastrophic forgetting of knowledge learned from earlier tasks while learning a new task. Moreover, retraining large models like transformers from scratch for every new task is costly. An effective approach to address continual learning is to use a large pre-trained model with task-specific adapters to adapt to the new tasks. Though this approach can mitigate catastrophic forgetting, they fail to transfer knowledge across tasks as each task is learning adapters separately. To address this, we propose a novel approach Linked Adapters that allows knowledge transfer through a weighted attention mechanism to other task-specific adapters. Linked adapters use a multi-layer perceptron (MLP) to model the attention weights, which overcomes the challenge of backward knowledge transfer in continual learning in addition to modeling the forward knowledge transfer. During inference, our proposed approach effectively leverages knowledge transfer through MLP-based attention weights across all the lateral task adapters. Through numerous experiments conducted on diverse image classification datasets, we effectively demonstrated the improvement in performance on the continual learning tasks using Linked Adapters., Comment: 13 Pages, 5 Figures

This paper addresses the problem of estimating the positions of points from distance measurements corrupted by sparse outliers. Specifically, we consider a setting with two types of nodes: anchor nodes, for which exact distances to each other are known, and target nodes, for which complete but corrupted distance measurements to the anchors are available. To tackle this problem, we propose a novel algorithm powered by Nystr\"om method and robust principal component analysis. Our method is computationally efficient as it processes only a localized subset of the distance matrix and does not require distance measurements between target nodes. Empirical evaluations on synthetic datasets, designed to mimic sensor localization, and on molecular experiments, demonstrate that our algorithm achieves accurate recovery with a modest number of anchors, even in the presence of high levels of sparse outliers.

As quantum computing rapidly advances, its near-term applications are becoming increasingly evident. However, the high cost and under-utilization of quantum resources are prompting a shift from single-user to multi-user access models. In a multi-tenant environment, where multiple users share one quantum computer, protecting user confidentiality becomes crucial. The varied uses of quantum computers increase the risk that sensitive data encoded by one user could be compromised by others, rendering the protection of data integrity and confidentiality essential. In the evolving quantum computing landscape, it is imperative to study these security challenges within the scope of realistic threat model assumptions, wherein an adversarial user can mount practical attacks without relying on any heightened privileges afforded by physical access to a quantum computer or rogue cloud services. In this paper, we demonstrate the potential of crosstalk as an attack vector for the first time on a Noisy Intermediate Scale Quantum (NISQ) machine, that an adversarial user can exploit within a multi-tenant quantum computing model. The proposed side-channel attack is conducted with minimal and realistic adversarial privileges, with the overarching aim of uncovering the quantum algorithm being executed by a victim. Crosstalk signatures are used to estimate the presence of CNOT gates in the victim circuit, and subsequently, this information is encoded and classified by a graph-based learning model to identify the victim quantum algorithm. When evaluated on up to 336 benchmark circuits, our attack framework is found to be able to unveil the victim's quantum algorithm with up to 85.7\% accuracy.

Protecting sensitive information on data streams is a critical challenge for modern systems. Current approaches to privacy in data streams follow two strategies. The first transforms the stream into a private sequence, enabling the use of non-private analyses but incurring high memory costs. The second uses compact data structures to create private summaries but restricts flexibility to predefined queries. To address these limitations, we propose $\textsf{PrivHP}$, a lightweight synthetic data generator that ensures differential privacy while being resource-efficient. $\textsf{PrivHP}$ generates private synthetic data that preserves the input stream's distribution, allowing flexible downstream analyses without additional privacy costs. It leverages a hierarchical decomposition of the domain, pruning low-frequency subdomains while preserving high-frequency ones in a privacy-preserving manner. To achieve memory efficiency in streaming contexts, $\textsf{PrivHP}$ uses private sketches to estimate subdomain frequencies without accessing the full dataset. $\textsf{PrivHP}$ is parameterized by a privacy budget $\varepsilon$, a pruning parameter $k$ and the sketch width $w$. It can process a dataset of size $n$ in $\mathcal{O}((w+k)\log (\varepsilon n))$ space, $\mathcal{O}(\log (\varepsilon n))$ update time, and outputs a private synthetic data generator in $\mathcal{O}(k\log k\log (\varepsilon n))$ time. Prior methods require $\Omega(n)$ space and construction time. Our evaluation uses the expected 1-Wasserstein distance between the sampler and the empirical distribution. Compared to state-of-the-art methods, we demonstrate that the additional cost in utility is inversely proportional to $k$ and $w$. This represents the first meaningful trade-off between performance and utility for private synthetic data generation., Comment: 28 Pages, 1 Table, 3 Figures, 4 Algorithms

The solar corona is highly structured by bunches of magnetic field lines forming either loops, or twisted flux ropes representing prominences/filaments, or very dynamic structures such as jets. The aim of this paper is to understand the interaction between filament channels and jets. We use high-resolution H$\alpha$ spectra obtained by the ground-based Telescope Heliographique pour lEtude du Magnetisme et des Instabilites Solaires (THEMIS) in Canary Islands, and data from Helioseismic Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO). In this paper we present a multi-wavelength study of the interaction of filaments and jets. They both consist of cool plasma embedded in magnetic structures. A jet is particularly well studied in all the AIA channels with a flow reaching 100-180 km s$^{-1}$. Its origin is linked to cancelling flux at the edge of the active region. Large Dopplershifts in H$\alpha$ are derived in a typical area for a short time (order of min). They correspond to flows around 140 km s$^{-1}$. In conclusion we conjecture that these flows correspond to some interchange of magnetic field lines between the filament channel and the jets leading to cool plasmoid ejections or reconnection jets perpendicularly to the jet trajectory., Comment: 13 Figures, 14 pages

Recent gain in popularity of AI conversational agents has led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences. Additionally, psychological risks have often been presented as a sub-category within broader AI-related risks in past taxonomy works, leading to under-representation of the impact of psychological risks of AI use. To address these challenges, our work presents a novel risk taxonomy focusing on psychological risks of using AI gathered through lived experience of individuals. We employed a mixed-method approach, involving a comprehensive survey with 283 individuals with lived mental health experience and workshops involving lived experience experts to develop a psychological risk taxonomy. Our taxonomy features 19 AI behaviors, 21 negative psychological impacts, and 15 contexts related to individuals. Additionally, we propose a novel multi-path vignette based framework for understanding the complex interplay between AI behaviors, psychological impacts, and individual user contexts. Finally, based on the feedback obtained from the workshop sessions, we present design recommendations for developing safer and more robust AI agents. Our work offers an in-depth understanding of the psychological risks associated with AI conversational agents and provides actionable recommendations for policymakers, researchers, and developers., Comment: 25 pages, 2 figures, 4 tables; Corrected typos

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

This paper proposes a novel loss function, called 'Tube Loss', for simultaneous estimation of bounds of a Prediction Interval (PI) in the regression setup, and also for generating probabilistic forecasts from time series data solving a single optimization problem. The PIs obtained by minimizing the empirical risk based on the Tube Loss are shown to be of better quality than the PIs obtained by the existing methods in the following sense. First, it yields intervals that attain the prespecified confidence level $t \in(0,1)$ asymptotically. A theoretical proof of this fact is given. Secondly, the user is allowed to move the interval up or down by controlling the value of a parameter. This helps the user to choose a PI capturing denser regions of the probability distribution of the response variable inside the interval, and thus, sharpening its width. This is shown to be especially useful when the conditional distribution of the response variable is skewed. Further, the Tube Loss based PI estimation method can trade-off between the coverage and the average width by solving a single optimization problem. It enables further reduction of the average width of PI through re-calibration. Also, unlike a few existing PI estimation methods the gradient descent (GD) method can be used for minimization of empirical risk. Finally, through extensive experimentation, we have shown the efficacy of the Tube Loss based PI estimation in kernel machines, neural networks and deep networks and also for probabilistic forecasting tasks. The codes of the experiments are available at https://github.com/ltpritamanand/Tube_loss

Sulfide-based photocatalysts (PC) are promising materials for efficiently producing hydrogen (H2). This chapter aims to provide a detailed survey of the recent advancements in sulfide-based photocatalysts and emphasize their enhanced performance and pathways to efficient H2 production. A detailed summary has been given, including several metal sulfides, such as cadmium sulfide (CdS), zinc sulfide (ZnS), molybdenum disulfide (MoS2), tungsten disulfide (WS2), lead sulfide (PbS), nickel sulfides (NiS/NiS2), iron disulfide (FeS2), copper sulfides (CuS/Cu2S), cobalt sulfides (CoS/CoS2), tin disulfide (SnS2), indium sulfide (In2S3), bismuth sulfide (Bi2S3), zinc cadmium sulfide (ZnxCd1-xS), manganese cadmium sulfide (MnxCd1-xS), zinc indium sulfide (ZnIn2S4), and cadmium indium sulfide (CdIn2S4). This chapter will focus on the latest advancements in metal-sulfide-based materials for photocatalytic hydrogen evolution reactions (HER), taking its accelerated growth and excellent research into account. After briefly outlining the basic properties, the chapter will showcase the cutting-edge strategies and recent research progress, including the construction of heterojunctions, defect engineering, co-catalyst loading, elemental doping, and single-atom engineering, which improve the electronic structure and charge separation capabilities of metal sulfides for photocatalytic hydrogen production. A future perspective and outlook have been proposed, focusing on some key points and a standard protocol. With this knowledge, we hope sulfide-based photocatalysts can be modified and engineered to improve their efficiency and stability in future research.

Although hydrogen generation by water electrolysis is the cheapest of all other available sources, water splitting still occurs with sluggish kinetics. It is a challenging barrier for H2 production on a large scale. Moreover, research is still underway to understand the oxygen evolution reaction (OER) and design the catalysts with improved OER performance. Herein, we report the synthesis, characterization, and OER performance of iron-doped copper oxide (CuO) as low-cost catalysts for water oxidation. The OER occurs at about 1.49 V versus the RHE with a Tafel slope of 69 mV/dec in a 1 M KOH solution. The overpotential of 338 mV at 10 mA/cm2 is among the lowest compared with other copper-based materials. The catalyst can deliver a stable current density of >10 mA/cm2 for more than 10 hours. Additionally, wastewater treatment, particularly synthetic dye wastewater, is vital for preventing water scarcity and adverse effects on human health and ecotoxicology. The as-synthesized catalysts are also utilized for Fenton-like photo-degradation under low-power visible household LED lights toward the most commonly industrially used simulated Methylene blue dye wastewater. Almost complete degradation of the MB dye has been achieved within 50 minutes of visible light irradiation with a first-order rate constant of 0.0973/min. This dual functionality feature can open new pathways as a non-noble, highly efficient, and robust catalyst for OER and wastewater treatments.