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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

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.

Cryogenic fluids have extensive applications as fuel for launch vehicles in space applications and research. The physics of cryogenic flows are highly complex due to the sensitive nature of phase transformation from liquid to bubbly liquid and vapor, eventually resulting in cavitating flows at the ambient temperature owing to the very low boiling point of cryogenic fluids, which asserts us to classify such flows under multi-phase flow physics regime. This work elucidates the modeling of bubbly flow for cryogenic fluids such as liquid nitrogen in a converging-diverging venturi-like flow device known as cavitating venturi, a passive flow control metering device. The numerical works in literature are usually limited to modeling iso-thermal bubbly flows such as water devoid of involving energy equations because there is no occurrence of interface heat transfer as latent heat of vaporization of water is higher, unlike cryogenic fluids which are sensitive to phase change at ambient conditions. So, to realize an appropriate model for modeling cryogenic bubbly flows such as liquid nitrogen flow, the effect of heat transfer at the interface and convective heat transfer from the surrounding liquid to the traversing bubble needs to be included. Numerical modeling using an in-house code involving a finite-difference method The numerical results showed the importance of including the heat transport equation due to convection and at the interface of bubble-fluid as a significant source term for the bubble dynamics. The work is supported by computational simulation using a commercial CFD package for 2-dimensional simulations to predict a characterizing parameter, namely cavitation length. A limited flow visualization experiment using a high-speed camera is performed to study the cavitating zone length.

Large language models (LLMs) are notoriously memory-intensive during training, particularly with the popular AdamW optimizer. This memory burden necessitates using more or higher-end GPUs or reducing batch sizes, limiting training scalability and throughput. To address this, various memory-efficient optimizers have been proposed to reduce optimizer memory usage. However, they face critical challenges: (i) reliance on costly SVD operations; (ii) significant performance trade-offs compared to AdamW; and (iii) still substantial optimizer memory overhead to maintain competitive performance. In this work, we identify that AdamW's learning rate adaptation rule can be effectively coarsened as a structured learning rate update. Based on this insight, we propose Approximated Gradient Scaling for Memory-Efficient LLM Optimization (APOLLO), which approximates learning rate scaling using an auxiliary low-rank optimizer state based on pure random projection. This structured learning rate update rule makes APOLLO highly tolerant to further memory reductions while delivering comparable pre-training performance. Even its rank-1 variant, APOLLO-Mini, achieves superior pre-training performance compared to AdamW with SGD-level memory costs. Extensive experiments demonstrate that the APOLLO series performs on-par with or better than AdamW, while achieving greater memory savings by nearly eliminating the optimization states of AdamW. These savings provide significant system-level benefits: (1) Enhanced Throughput: 3x throughput on an 8xA100-80GB setup compared to AdamW by supporting 4x larger batch sizes. (2) Improved Model Scalability: Pre-training LLaMA-13B with naive DDP on A100-80GB GPUs without system-level optimizations. (3) Low-End GPU Friendly Pre-training: Pre-training LLaMA-7B on a single GPU using less than 12 GB of memory with weight quantization., Comment: Preprint

Robots in densely populated real-world environments frequently encounter constrained and cluttered situations such as passing through narrow doorways, hallways, and corridor intersections, where conflicts over limited space result in collisions or deadlocks among the robots. Current decentralized state-of-the-art optimization- and neural network-based approaches (i) are predominantly designed for general open spaces, and (ii) are overly conservative, either guaranteeing safety, or liveness, but not both. While some solutions rely on centralized conflict resolution, their highly invasive trajectories make them impractical for real-world deployment. This paper introduces LiveNet, a fully decentralized and robust neural network controller that enables human-like yielding and passing, resulting in agile, non-conservative, deadlock-free, and safe, navigation in congested, conflict-prone spaces. LiveNet is minimally invasive, without requiring inter-agent communication or cooperative behavior. The key insight behind LiveNet is a unified CBF formulation for simultaneous safety and liveness, which we integrate within a neural network for robustness. We evaluated LiveNet in simulation and found that general multi-robot optimization- and learning-based navigation methods fail to even reach the goal, and while methods designed specially for such environments do succeed, they are 10-20 times slower, 4-5 times more invasive, and much less robust to variations in the scenario configuration such as changes in the start states and goal states, among others. We open-source the LiveNet code at https://github.com/srikarg89/LiveNet{https://github.com/srikarg89/LiveNet.

The ability to differentiate between different models of inflation through the imprint of primordial non-Gaussianity (PNG) requires a tight constraint on the local PNG parameter $f_{\text{NL}}^{\text{loc}}$. Future large scale structure surveys like \textit{Euclid}, Vera C. Rubin Observatory, and the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) will play a crucial role in advancing our understanding of the inflationary epoch. In this light, we present forecasts on PNG with tomographic angular power spectrum from simulations of SPHEREx. We put forward the effects of redshift bin mismatch of galaxies as a source of systematic error when estimating $f_{\text{NL}}^{\text{loc}}$ and galaxy linear halo bias. We %use \texttt{GLASS} to simulate $500$ SPHEREx-like galaxy density fields, and divide the galaxies into $13$ redshift bins assuming Gaussian photometric redshift errors. We show that the misclassification of galaxies in redshift bins can result in strong apparent tensions on $f_{\text{NL}}^{\text{loc}}$ up to $\sim 3\sigma$ and up to $\sim 9\sigma$ on galaxy bias. We propose a scattering matrix formalism to mitigate bin mismatch of galaxies and to obtain unbiased estimates of cosmological parameters from tomographic angular clustering measurements., Comment: Submitted to PASA. 11 Pages, 10 figures, 1 table

Video and audio are closely correlated modalities that humans naturally perceive together. While recent advancements have enabled the generation of audio or video from text, producing both modalities simultaneously still typically relies on either a cascaded process or multi-modal contrastive encoders. These approaches, however, often lead to suboptimal results due to inherent information losses during inference and conditioning. In this paper, we introduce SyncFlow, a system that is capable of simultaneously generating temporally synchronized audio and video from text. The core of SyncFlow is the proposed dual-diffusion-transformer (d-DiT) architecture, which enables joint video and audio modelling with proper information fusion. To efficiently manage the computational cost of joint audio and video modelling, SyncFlow utilizes a multi-stage training strategy that separates video and audio learning before joint fine-tuning. Our empirical evaluations demonstrate that SyncFlow produces audio and video outputs that are more correlated than baseline methods with significantly enhanced audio quality and audio-visual correspondence. Moreover, we demonstrate strong zero-shot capabilities of SyncFlow, including zero-shot video-to-audio generation and adaptation to novel video resolutions without further training.

We report integral field spectroscopy observations with the Near-Infrared Spectrograph on board JWST targeting the 60 kpc environment surrounding the most luminous quasar known at $z=4.6$. We detect ionized gas filaments on 40 kpc scales connecting a network of merging galaxies likely to form a cluster. We find regions of low ionization consistent with large-scale shock excitation surrounding the central dust-obscured quasar, out to distances nearly eight times the effective stellar radius of the quasar host galaxy. In the nuclear region, we find an ionized outflow driven by the quasar with velocities reaching 13,000 km s$^{-1}$, one of the fastest discovered to date with an outflow rate of 2000 M$_\odot$ yr$^{-1}$ and a kinetic luminosity of 6$\times10^{46}$ erg s$^{-1}$ resulting in coupling efficiency between the bolometric luminosity of the quasar and the outflow of 5%. The kinetic luminosity of the outflow is sufficient to power the turbulent motion of the gas on galactic and circumgalactic scales and is likely the primary driver of the radiative shocks on interstellar medium and circumgalactic medium scales. This provides compelling evidence supporting long-standing theoretical predictions that powerful quasar outflows are a main driver in regulating the heating and accretion rate of gas onto massive central cluster galaxies., Comment: 16 pages, 9 figures, 1 table, submitted to ApJ

The discovery of the kagome metal CsV$_3$Sb$_5$ sparked broad interest, due to the coexistence of a charge density wave (CDW) phase and possible unconventional superconductivity in the material. In this study, we use low-energy electron diffraction (LEED) with a $\mu$m-sized electron beam to explore the periodic lattice distortion at the antimony-terminated surface in the CDW phase. We recorded high-quality backscattering diffraction patterns in ultrahigh vacuum from multiple cleaved samples. Unexpectedly, we did not find superstructure reflexes at intensity levels predicted from dynamical LEED calculations for the reported $2 \times 2 \times 2$ bulk structure. Our results suggest that in CsV$_3$Sb$_5$ the periodic lattice distortion accompanying the CDW is less pronounced at Sb-terminated surfaces than in the bulk., Comment: 9 pages, 6 figures

Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce, making it difficult to understand where improvement comes from and what bottlenecks exist. We propose to evaluate algorithms via the makeup of synthetic data generated by each algorithm in terms of data quality, diversity, and complexity. We choose these three characteristics for their significance in open-ended processes and the impact each has on the capabilities of downstream models. We find quality to be essential for in-distribution model generalization, diversity to be essential for out-of-distribution generalization, and complexity to be beneficial for both. Further, we emphasize the existence of Quality-Diversity trade-offs in training data and the downstream effects on model performance. We then examine the effect of various components in the synthetic data pipeline on each data characteristic. This examination allows us to taxonomize and compare synthetic data generation algorithms through the components they utilize and the resulting effects on data QDC composition. This analysis extends into a discussion on the importance of balancing QDC in synthetic data for efficient reinforcement learning and self-improvement algorithms. Analogous to the QD trade-offs in training data, often there exist trade-offs between model output quality and output diversity which impact the composition of synthetic data. We observe that many models are currently evaluated and optimized only for output quality, thereby limiting output diversity and the potential for self-improvement. We argue that balancing these trade-offs is essential to the development of future self-improvement algorithms and highlight a number of works making progress in this direction.

The recent advances in quantum information processing, sensing and communications are surveyed with the objective of identifying the associated knowledge gaps and formulating a roadmap for their future evolution. Since the operation of quantum systems is prone to the deleterious effects of decoherence, which manifests itself in terms of bit-flips, phase-flips or both, the pivotal subject of quantum error mitigation is reviewed both in the presence and absence of quantum coding. The state-of-the-art, knowledge gaps and future evolution of quantum machine learning are also discussed, followed by a discourse on quantum radar systems and briefly hypothesizing about the feasibility of integrated sensing and communications in the quantum domain. Finally, we conclude with a set of promising future research ideas in the field of ultimately secure quantum communications with the objective of harnessing ideas from the classical communications field.

Achieving quantum advantage in energy storage and power extraction is a primary objective in the design of quantum-based batteries. We explore how long-range (LR) interactions in conjunction with Floquet driving can improve the performance of quantum batteries, particularly when the battery is initialized in a fully polarized state. In particular, we exhibit that by optimizing the driving frequency, the maximum average power scales super extensively with system-size which is not achievable through next-nearest neighbor interactions or traditional unitary charging, thereby gaining genuine quantum advantage. We illustrate that the inclusion of either two-body or many-body interaction terms in the LR charging Hamiltonian leads to a scaling benefit. Furthermore, we discover that a super-linear scaling in power results from increasing the strength of interaction compared to the transverse magnetic field and the range of interaction with low fall-off rate, highlighting the advantageous role of long-range interactions in optimizing quantum battery charging., Comment: 10 pages, 8 figures

We consider the problem of identifying the defectives from a population of items via a non-adaptive group testing framework with a random pooling-matrix design. We analyze the sufficient number of tests needed for approximate set identification, i.e., for identifying almost all the defective and non-defective items with high confidence. To this end, we view the group testing problem as a function learning problem and develop our analysis using the probably approximately correct (PAC) framework. Using this formulation, we derive sufficiency bounds on the number of tests for three popular binary group testing algorithms: column matching, combinatorial basis pursuit, and definite defectives. We compare the derived bounds with the existing ones in the literature for exact recovery theoretically and using simulations. Finally, we contrast the three group testing algorithms under consideration in terms of the sufficient testing rate surface and the sufficient number of tests contours across the range of the approximation and confidence levels.

Segment Anything Model 2 (SAM 2) has emerged as a powerful tool for video object segmentation and tracking anything. Key components of SAM 2 that drive the impressive video object segmentation performance include a large multistage image encoder for frame feature extraction and a memory mechanism that stores memory contexts from past frames to help current frame segmentation. The high computation complexity of multistage image encoder and memory module has limited its applications in real-world tasks, e.g., video object segmentation on mobile devices. To address this limitation, we propose EfficientTAMs, lightweight track anything models that produce high-quality results with low latency and model size. Our idea is based on revisiting the plain, nonhierarchical Vision Transformer (ViT) as an image encoder for video object segmentation, and introducing an efficient memory module, which reduces the complexity for both frame feature extraction and memory computation for current frame segmentation. We take vanilla lightweight ViTs and efficient memory module to build EfficientTAMs, and train the models on SA-1B and SA-V datasets for video object segmentation and track anything tasks. We evaluate on multiple video segmentation benchmarks including semi-supervised VOS and promptable video segmentation, and find that our proposed EfficientTAM with vanilla ViT perform comparably to SAM 2 model (HieraB+SAM 2) with ~2x speedup on A100 and ~2.4x parameter reduction. On segment anything image tasks, our EfficientTAMs also perform favorably over original SAM with ~20x speedup on A100 and ~20x parameter reduction. On mobile devices such as iPhone 15 Pro Max, our EfficientTAMs can run at ~10 FPS for performing video object segmentation with reasonable quality, highlighting the capability of small models for on-device video object segmentation applications.

This study integrates Transit Signal Priority (TSP) into multi-agent reinforcement learning (MARL) based traffic signal control. The first part of the study develops adaptive signal control based on MARL for a pair of coordinated intersections in a microscopic simulation environment. The two agents, one for each intersection, are centrally trained using a value decomposition network (VDN) architecture. The trained agents show slightly better performance compared to coordinated actuated signal control based on overall intersection delay at v/c of 0.95. In the second part of the study the trained signal control agents are used as background signal controllers while developing event-based TSP agents. In one variation, independent TSP agents are formulated and trained under a decentralized training and decentralized execution (DTDE) framework to implement TSP at each intersection. In the second variation, the two TSP agents are centrally trained under a centralized training and decentralized execution (CTDE) framework and VDN architecture to select and implement coordinated TSP strategies across the two intersections. In both cases the agents converge to the same bus delay value, but independent agents show high instability throughout the training process. For the test runs, the two independent agents reduce bus delay across the two intersections by 22% compared to the no TSP case while the coordinated TSP agents achieve 27% delay reduction. In both cases, there is only a slight increase in delay for a majority of the side street movements.

Spin relaxation of electrons in materials involve both reversible dephasing and irreversible decoherence processes. Their interplay leads to a complex dependence of spin relaxation times on the direction and magnitude of magnetic fields, relevant for spintronics and quantum information applications. Here, we use real-time first-principles density matrix dynamics simulations to directly simulate Hahn echo measurements, disentangle dephasing from decoherence, and predict T1, T2 and T2* spin lifetimes. We show that g-factor fluctuations lead to non-trivial magnetic field dependence of each of these lifetimes in inversion-symmetric crystals of CsPbBr3 and silicon, even when only intrinsic spin-phonon scattering is present. Most importantly, fluctuations in the off-diagonal components of the g-tensor lead to a strong magnetic field dependence of even the T1 lifetime in silicon. Our calculations elucidate the detailed role of anisotropic g-factors in determining the spin dynamics even in simple, low spin-orbit coupling materials such as silicon.

Mitigating climate change requires transforming agriculture to minimize environ mental impact and build climate resilience. Regenerative agricultural practices enhance soil organic carbon (SOC) levels, thus improving soil health and sequestering carbon. A challenge to increasing regenerative agriculture practices is cheaply measuring SOC over time and understanding how SOC is affected by regenerative agricultural practices and other environmental factors and farm management practices. To address this challenge, we introduce an AI-driven Soil Organic Carbon Copilot that automates the ingestion of complex multi-resolution, multi-modal data to provide large-scale insights into soil health and regenerative practices. Our data includes extreme weather event data (e.g., drought and wildfire incidents), farm management data (e.g., cropland information and tillage predictions), and SOC predictions. We find that integrating public data and specialized models enables large-scale, localized analysis for sustainable agriculture. In comparisons of agricultural practices across California counties, we find evidence that diverse agricultural activity may mitigate the negative effects of tillage; and that while extreme weather conditions heavily affect SOC, composting may mitigate SOC loss. Finally, implementing role-specific personas empowers agronomists, farm consultants, policymakers, and other stakeholders to implement evidence-based strategies that promote sustainable agriculture and build climate resilience.

Addressing climate change calls for action to control CO$_2$ pollution. Direct air and ocean capture offer a solution to this challenge. Making carbon capture competitive with alternatives, such as forestation and mineralisation, requires fundamentally novel approaches and ideas. One such approach is electrosorption, which is currently limited by the availability of suitable electrosorbents. In this work, we introduce a metal-organic copper-2,3,6,7,10,11-hexahydroxytriphenylene (Cu$_3$(HHTP)$_2$) metal-organic framework (MOF) that can act as electrosorbent for CO$_2$ capture, thereby expanding the palette of materials that can be used for this process. Cu$_3$(HHTP)$_2$ is the first MOF to switch its ability to capture and release CO$_2$ in aqueous electrolytes. By using cyclic voltammetry (CV) and differential electrochemical mass spectrometry (DEMS), we demonstrate reversible CO$_2$ electrosorption. Based on density functional theory (DFT) calculations, we provide atomistic insights into the mechanism of electrosorption and conclude that efficient CO$_2$ capture is facilitated by a combination of redox-active copper and aromatic HHTP ligand within Cu3(HHTP)2. By showcasing the applicability of Cu$_3$(HHTP)$_2$ -- with a CO$_2$ capacity of 2 mmol g$^{-1}$ and an adsorption enthalpy of -20 kJ mol$^{-1}$ - this study encourages further exploration of conductive redox-active MOFs in the search for superior CO$_2$ electrosorbents., Comment: 17 pages, 4 figures, supporting information

Uncertainty quantification is crucial in time series prediction, and quantile regression offers a valuable mechanism for uncertainty quantification which is useful for extreme value forecasting. Although deep learning models have been prominent in multi-step ahead prediction, the development and evaluation of quantile deep learning models have been limited. We present a novel quantile regression deep learning framework for multi-step time series prediction. In this way, we elevate the capabilities of deep learning models by incorporating quantile regression, thus providing a more nuanced understanding of predictive values. We provide an implementation of prominent deep learning models for multi-step ahead time series prediction and evaluate their performance under high volatility and extreme conditions. We include multivariate and univariate modelling, strategies and provide a comparison with conventional deep learning models from the literature. Our models are tested on two cryptocurrencies: Bitcoin and Ethereum, using daily close-price data and selected benchmark time series datasets. The results show that integrating a quantile loss function with deep learning provides additional predictions for selected quantiles without a loss in the prediction accuracy when compared to the literature. Our quantile model has the ability to handle volatility more effectively and provides additional information for decision-making and uncertainty quantification through the use of quantiles when compared to conventional deep learning models.

The Magellanic Stream has long been known to contain multiple HI strands and corresponding stellar populations are beginning to be discovered. Combining an H3-selected sample with stars drawn from the Gaia catalog, we trace stars along a sub-dominant strand of the Magellanic Stream, as defined by gas content, across 30$^\circ$ on the sky. We find that the dominant strand is devoid of stars with Galactocentric distance $\lesssim 55$ kpc while the subdominant strand shows a close correspondence to such stars. We conclude that (1) the two Stream strands have different origins, (2) they are likely only close in projection, (3) the subdominant strand is tidal in origin, and (4) the subdominant strand is composed of disk material, likely drawn from the disk of the Small Magellanic Cloud., Comment: 8 pages, 8 figures