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High-performance DUV spectroscopy drives advancements in biomedical research, clinical diagnosis, and material science. Existing DUV resonant nanostructures face instability and photoluminescent noise challenges. We propose robust Si metasurfaces leveraging polaritonic resonances, a unique property driven by interband transitions, for enhanced nanophotonic sensing. Our polaritonic Kerker-type void metasurface enables double-resonance Raman scattering to analyze 2D semiconductors, improves biomolecule autofluorescence, and offers superior stability. This scalable platform unlocks versatile applications in interdisciplinary DUV spectroscopy and emerging nanomaterials research., Comment: in press, the DOI will be DOI: 10.1002/adfm.202420439

In recent years, there has been a lot of interest in converting C code to Rust, to benefit from the memory and thread safety guarantees of Rust. C2Rust is a rule-based system that can automatically convert C code to functionally identical Rust, but the Rust code that it produces is non-idiomatic, i.e., makes extensive use of unsafe Rust, a subset of the language that doesn't have memory or thread safety guarantees. At the other end of the spectrum are LLMs, which produce idiomatic Rust code, but these have the potential to make mistakes and are constrained in the length of code they can process. In this paper, we present C2SaferRust, a novel approach to translate C to Rust that combines the strengths of C2Rust and LLMs. We first use C2Rust to convert C code to non-idiomatic, unsafe Rust. We then decompose the unsafe Rust code into slices that can be individually translated to safer Rust by an LLM. After processing each slice, we run end-to-end test cases to verify that the code still functions as expected. We also contribute a benchmark of 7 real-world programs, translated from C to unsafe Rust using C2Rust. Each of these programs also comes with end-to-end test cases. On this benchmark, we are able to reduce the number of raw pointers by up to 38%, and reduce the amount of unsafe code by up to 28%, indicating an increase in safety. The resulting programs still pass all test cases. C2SaferRust also shows convincing gains in performance against two previous techniques for making Rust code safer.

External pressure suppresses the ferromagnetism of localized Cr 3d electron moments in the van der Waals insulator CrBr3, which cannot be explained without considering a dramatic pressure-induced crystal or electronic structure change. We addressed this issue by conducting a parallel experimental investigation of single crystals magnetic and structural properties using magnetization, X-ray diffraction, and Raman spectroscopy measurements. Ab initio DFT calculations of electronic structure and atomistic simulations of finite-temperature magnetism supported the analysis and interpretation of experimental results. The magnetization measurements at high pressures provided the first direct experimental evidence of the pressure-induced suppression of ferromagnetism. We observed a gradual decrease of the bulk magnetic moment and Curie temperature with increasing pressure, which accelerates at pressures above 3 GPa, leading to loss of ferromagnetism at 6.5 GPa. By increasing pressure, the ambient pressure phase gradually breaks down and is accompanied by the generation of layer stacking that favor the antiferromagnetic coupling of Cr moments. As a result, the appearing antiparallel pairs of moments disturb the ferromagnetic structure and reduce the bulk magnetic moment and Curie temperature. This scenario, which is well corroborated by the results of our theoretical calculations, suggests an antiferromagnetic phase emerging with increasing pressure beyond the critical value when the new single trigonal P-3m1 phase becomes stable, characterized by the "antiferromagnetic" A-A layer stacking, becomes stable. The weak coupling between adjacent magnetic layers in van der Waals materials allows variations in layering due to sufficient external forces. We hope our comprehensive study's results can help other researchers resolve frequently appearing issues of similar origin in this class of materials.

Highly-irradiated gas giant exoplanets are predicted to show circulation patterns dominated by day-to-night heat transport and a spatial distribution of clouds that is driven by advection and local heating. Hot-Jupiters have been extensively studied from broadband phase-curve observations at infrared and optical wavelengths, but spectroscopic observations in the reflected light are rare and the regime of smaller and higher-metallicity ultra-hot planets, such as hot-Neptunes, remains largely unexplored to date. Here we present the phase-resolved reflected-light and thermal-emission spectroscopy of the ultra-hot Neptune LTT 9779b, obtained through observing its full phase-curve from 0.6 to 2.8 $\mu$m with JWST NIRISS/SOSS. We detect an asymmetric dayside in reflected light (3.1$\sigma$ significance) with highly-reflective white clouds on the western dayside (A = 0.79$\pm$0.15) and a much lower-albedo eastern dayside (A = 0.41$\pm$0.10), resulting in an overall dayside albedo of A = 0.50$\pm$0.07. The thermal phase curve is symmetric about the substellar point, with a dayside effective temperature of T$_\mathrm{eff,day}$ = 2,260$^{+40}_{-50}$ K and a cold nightside (T$_\mathrm{eff,night}$ <1,330 K at 3-$\sigma$ confidence), indicative of short radiative timescales. We propose an atmospheric circulation and cloud distribution regime in which heat is transported eastward from the dayside towards the cold nightside by an equatorial jet, leading to a colder western dayside where temperatures are sufficiently low for the condensation of silicate clouds., Comment: Accepted for publication

Let $\Gamma$ be an undirected and simple graph. A set $ S $ of vertices in $\Gamma$ is called a {cyclic vertex cutset} of $\Gamma$ if $\Gamma - S$ is disconnected and has at least two components containing cycles. If $\Gamma$ has a cyclic vertex cutset, then it is said to be {cyclically separable}. The {cyclic vertex connectivity} of $\Gamma$ is the minimum of cardinalities of the cyclic vertex cutsets of $\Gamma$. For any finite group $G$, the order supergraph $\mathcal{S}(G)$ is the simple and undirected graph whose vertices are elements of $G$, and two vertices are adjacent if the order of one divides that of the other. In this paper, we characterize the finite nilpotent groups and various non-nilpotent groups whose order super graphs are cyclically separable.

Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while simultaneously learning multiple language-conditioned tasks. To address this, we introduce a novel method: the compositionally-enabled reinforcement learning language agent (CERLLA). Our method reduces the sample complexity of tasks specified with language by leveraging compositional policy representations and a semantic parser trained using reinforcement learning and in-context learning. We evaluate our approach in an environment requiring function approximation and demonstrate compositional generalization to novel tasks. Our method significantly outperforms the previous best non-compositional baseline in terms of sample complexity on 162 tasks designed to test compositional generalization. Our model attains a higher success rate and learns in fewer steps than the non-compositional baseline. It reaches a success rate equal to an oracle policy's upper-bound performance of 92%. With the same number of environment steps, the baseline only reaches a success rate of 80%., Comment: TMLR 2024

We combine the deepest X-ray survey from the Chandra Deep Field-South (CDF-S) `7-Ms' survey with the deepest mid-infrared (5.6$ \mu m$) image from the JWST/MIRI Deep Imaging Survey (MIDIS) in the Hubble Ultra-Deep Field (HUDF) to study the infrared counterparts and point-source emission of 31 X-ray sources with a median, intrinsic, rest-frame X-ray luminosity of $\log_{10}(L_{\rm Xc}^{\rm 0.5-7keV})$=42.04$\pm$0.22 erg $\rm s^{-1}$. The sample includes 24 AGN with a redshift range, as set by the X-ray detectability, of $z \simeq 0.5-3$. Through a multi-wavelength morphological decomposition, employing three separate classifications (visual, parametric and non-parametric) we separate (where present) the luminosity of the point-like AGN component from the remainder of the host-galaxy emission. The unprecedented mid-infrared sensitivity and imaging resolution of MIRI allows, in many cases, the direct characterisation of point-like (i.e. unresolved) components in the galaxies' emission. We establish a broad agreement between the three morphological classifications. At least 70% of the X-ray sources, including some classified as galaxies, show unresolved emission in the MIRI images, with the unresolved-to-total flux fraction at rest-frame 2$\mu m$ ranging from $\sim$0.2 to $\sim$0.9. At high X-ray luminosities ($\log_{10}(L_{\rm Xc}$)>43 erg $\rm s^{-1}$) we derive a consistent rest-frame near-infrared 2$ \mu m$ point-source luminosity to that derived for local AGN, whilst at lower X-ray luminosity we identify an excess in the 2$ \mu m$ emission compared to pre-JWST studies. We speculate this offset may be driven by a combination of Compton-thick AGN components and nuclear starburst, merger driven activity. Our observations highlight the complex nature of X-ray sources in the distant Universe and demonstrate the power of JWST/MIRI in quantifying their nuclear infrared emission. (Abridged), Comment: 21 pages, 7 figures. Submitted to A&A

Previous works have demonstrated the effectiveness of Chain-of-Thought (COT) prompts and verifiers in guiding Large Language Models (LLMs) through the space of reasoning. However, most such studies either use a fine-tuned verifier or rely on manually handcrafted few-shot examples. In contrast, in this paper, we focus on LLM-based self-verification of self-generated reasoning steps via COT prompts in a completely zero-shot regime. To explore this setting, we design a new zero-shot prompt, which we call COT STEP, to aid zero-shot decomposition of reasoning steps and design two new zero-shot prompts for LLM-based verifiers. We evaluate the verifiers' ability to classify the correctness of reasoning chains and explore different ways to use verifier scores in guiding reasoning for various mathematical and commonsense reasoning tasks with different LLMs.

Using Chen-Stein method in combination with size-biased couplings, we obtain the multivariate Poisson approximation in terms of the Wasserstein distance. As applications, we study the multivariate Poisson approximation of the distribution of joint subgraph counts in an Erd\H{o}s-R\'enyi random graph and the multivariate hypergeometric distribution giving explicit convergence rates.

Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks. While identifying incorrect code is often straightforward, detecting vulnerabilities in functionally correct code is more challenging, especially for developers with limited security knowledge, which poses considerable security risks of using LLM-generated code and underscores the need for robust evaluation benchmarks that assess both functional correctness and security. Current benchmarks like CyberSecEval and SecurityEval attempt to solve it but are hindered by unclear and impractical specifications, failing to assess both functionality and security accurately. To tackle these deficiencies, we introduce CWEval, a novel outcome-driven evaluation framework designed to enhance the evaluation of secure code generation by LLMs. This framework not only assesses code functionality but also its security simultaneously with high-quality task specifications and outcome-driven test oracles which provides high accuracy. Coupled with CWEval-bench, a multilingual, security-critical coding benchmark, CWEval provides a rigorous empirical security evaluation on LLM-generated code, overcoming previous benchmarks' shortcomings. Through our evaluations, CWEval reveals a notable portion of functional but insecure code produced by LLMs, and shows a serious inaccuracy of previous evaluations, ultimately contributing significantly to the field of secure code generation. We open-source our artifact at: https://github.com/Co1lin/CWEval ., Comment: to be published in LLM4Code 2025

Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional digital architectures, particularly when stochastic decisions are involved. Recently, analog computing architectures for accelerating stochastic optimization solvers have been presented, but they were limited to academic problems in quadratic polynomial format. Here we present KLIMA, a k-Local In-Memory Accelerator with resistive Content Addressable Memories (CAMs) and Dot-Product Engines (DPEs) to accelerate the solution of high-order industry-relevant optimization problems, in particular Boolean Satisfiability. By co-designing the optimization heuristics and circuit architecture we improve the speed and energy to solution up to 182x compared to the digital state of the art.

Conventional human activity recognition (HAR) relies on classifiers trained to predict discrete activity classes, inherently limiting recognition to activities explicitly present in the training set. Such classifiers would invariably fail, putting zero likelihood, when encountering unseen activities. We propose Open Vocabulary HAR (OV-HAR), a framework that overcomes this limitation by first converting each activity into natural language and breaking it into a sequence of elementary motions. This descriptive text is then encoded into a fixed-size embedding. The model is trained to regress this embedding, which is subsequently decoded back into natural language using a pre-trained embedding inversion model. Unlike other works that rely on auto-regressive large language models (LLMs) at their core, OV-HAR achieves open vocabulary recognition without the computational overhead of such models. The generated text can be transformed into a single activity class using LLM prompt engineering. We have evaluated our approach on different modalities, including vision (pose), IMU, and pressure sensors, demonstrating robust generalization across unseen activities and modalities, offering a fundamentally different paradigm from contemporary classifiers.

Understanding the motivations underlying the human inclination to automate tasks is vital to developing truly helpful robots integrated into daily life. Accordingly, we ask: are individuals more inclined to automate chores based on the time they consume or the feelings experienced while performing them? This study explores these preferences and whether they vary across different social groups (i.e., gender category and income level). Leveraging data from the BEHAVIOR-1K dataset, the American Time-Use Survey, and the American Time-Use Survey Well-Being Module, we investigate the relationship between the desire for automation, time spent on daily activities, and their associated feelings - Happiness, Meaningfulness, Sadness, Painfulness, Stressfulness, or Tiredness. Our key findings show that, despite common assumptions, time spent does not strongly relate to the desire for automation for the general population. For the feelings analyzed, only happiness and pain are key indicators. Significant differences by gender and economic level also emerged: Women prefer to automate stressful activities, whereas men prefer to automate those that make them unhappy; mid-income individuals prioritize automating less enjoyable and meaningful activities, while low and high-income show no significant correlations. We hope our research helps motivate technologies to develop robots that match the priorities of potential users, moving domestic robotics toward more socially relevant solutions. We open-source all the data, including an online tool that enables the community to replicate our analysis and explore additional trends at https://hri1260.github.io/why-automate-this., Comment: 20 pages, 14 figures

Subsynchronous oscillations (SSOs) involving grid-following converters (GFLCs) connected to weak grids are a relatively new phenomena observed in modern power systems. SSOs are further exacerbated when grids become weaker because lines are disconnected due to maintenance or following faults. Such undesirable oscillations have also led to curtailment of inverter-based resource (IBR) outputs. In contrast to most literature addressing the issue by retuning/redesigning of standard IBR controllers, we propose a robust adaptive supplementary control for damping of such SSOs while keeping standard controls unaltered. As a result, uncertainty in system conditions can be handled without negatively impacting the nominal IBR performance. To that end, the adaptive control law is derived for a GFLC connected to the grid, where the grid is modeled by the Thevenin's equivalent representation with uncertainty and disturbances. The theoretical result provides dissipativity certificate for the closed-loop error dynamics with sufficient conditions for stability. The effectiveness of the developed controller is validated with several case studies conducted on a single-GFLC-infinite-bus test system, the IEEE $2$-area test system, wherein some of the synchronous generators are replaced by GFLCs, and a modified IEEE $5$-area test system with two GFLCs. The findings demonstrate that under very weak grid conditions, the proposed robust adaptive control performs well in stabilizing SSO modes, which a classical state-feedback control method fails to address., Comment: 14 pages, 19 figures, 3 tables, IEEE Transactions on Power Systems

We present the discovery of PSR J1947-1120, a new huntsman millisecond pulsar with a red giant companion star in a 10.3 d orbit. This pulsar was found via optical, X-ray, and radio follow-up of the previously unassociated gamma-ray source 4FGL J1947.6-1121. PSR J1947-1120 is the second confirmed pulsar in the huntsman class and establishes this as a bona fide subclass of millisecond pulsar. We use MESA models to show that huntsman pulsars can be naturally explained as neutron star binaries whose secondaries are currently in the "red bump" region of the red giant branch, temporarily underfilling their Roche lobes and hence halting mass transfer. Huntsman pulsars offer a new view of the formation of typical millisecond pulsars, allowing novel constraints on the efficiency of mass transfer and recycling at an intermediate stage in the process., Comment: ApJ in press

A relation between certain irreducible character values of the hyperoctahedral group $B_n$ ($\mathbb{Z}/2\mathbb{Z} \wr S_n$) and the symmetric group $S_{2n}$ was proved by F. L\"ubeck and D. Prasad in 2021. Their proof is algebraic in nature and uses Lie theory. Using combinatorial methods, R. Adin and Y. Roichman proved a similar relation between certain character values of $G\wr S_n$ and $S_{rn}$, where $G$ is an abelian group of order $r$ (generalizing the result of L\"ubeck-Prasad). Using their result, we prove yet another relation between certain irreducible character values of $G\wr S_n$ and $S_{rn}$, where $G$ is an abelian group of order $r$., Comment: 10 pages

Machine learning techniques have opened new avenues for real-time quantum state tomography (QST). In this work, we demonstrate the deployment of machine learning-based QST onto edge devices, specifically utilizing field programmable gate arrays (FPGAs). This implementation is realized using the {\it Vitis AI Integrated Development Environment} provided by AMD\textsuperscript \textregistered~Inc. Compared to the Graphics Processing Unit (GPU)-based machine learning QST, our FPGA-based one reduces the average inference time by an order of magnitude, from 38 ms to 2.94 ms, but only sacrifices the average fidelity about $1\% $ reduction (from 0.99 to 0.98). The FPGA-based QST offers a highly efficient and precise tool for diagnosing quantum states, marking a significant advancement in the practical applications for quantum information processing and quantum sensing., Comment: 6 pages, 5 figures

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables

For Site Reliability Engineers, alerts are typically the first and often the primary indications that a system may not be performing as expected. Once alerts are triggered, Site Reliability Engineers delve into detailed data across various modalities such as metrics, logs, and traces - to diagnose system issues. However, defining an optimal set of alerts is increasingly challenging due to the sheer volume of multi-modal observability data points in large cloud-native systems. Typically, alerts are manually curated, primarily defined on the metrics modality, and heavily reliant on subject matter experts manually navigating through the large state-space of intricate relationships in multi-modal observability data. Such a process renders defining alerts prone to insufficient coverage, potentially missing critical events. Defining alerts is even more challenging with the shift from traditional monolithic architectures to microservice based architectures due to the intricate interplay between microservices governed by the application topology in an ever stochastic environment. To tackle this issue, we take a data driven approach wherein we propose KIMetrix, a system that relies only on historical metric data and lightweight microservice traces to identify microservice metric criticality. KIMetrix significantly aids Subject Matter Experts by identifying a critical set of metrics to define alerts, averting the necessity of weaving through the vast multi-modal observability sphere. KIMetrix delves deep into the metric-trace coupling and leverages information theoretic measures to recommend microservice-metric mappings in a microservice topology-aware manner. Experimental evaluation on state-of-the-art microservice based applications demonstrates the effectiveness of our approach.

Videoconferencing is now a frequent mode of communication in both professional and informal settings, yet it often lacks the fluidity and enjoyment of in-person conversation. This study leverages multimodal machine learning to predict moments of negative experience in videoconferencing. We sampled thousands of short clips from the RoomReader corpus, extracting audio embeddings, facial actions, and body motion features to train models for identifying low conversational fluidity, low enjoyment, and classifying conversational events (backchanneling, interruption, or gap). Our best models achieved an ROC-AUC of up to 0.87 on hold-out videoconference sessions, with domain-general audio features proving most critical. This work demonstrates that multimodal audio-video signals can effectively predict high-level subjective conversational outcomes. In addition, this is a contribution to research on videoconferencing user experience by showing that multimodal machine learning can be used to identify rare moments of negative user experience for further study or mitigation., Comment: ICASSP 2025

Molecular Dynamics (MD) simulations are essential for accurately predicting the physical and chemical properties of large molecular systems across various pressure and temperature ensembles. However, the high computational costs associated with All-Atom (AA) MD simulations have led to the development of Coarse-Grained Molecular Dynamics (CGMD), providing a lower-dimensional compression of the AA structure into representative CG beads, offering reduced computational expense at the cost of predictive accuracy. Existing CGMD methods, such as CG-Martini (calibrated against experimental data), aim to generate an embedding of a topology that sufficiently generalizes across a range of structures. Detrimentally, in attempting to specify parameterization with applicability across molecular classes, it is unable to specialize to domain-specific applications, where sufficient accuracy and computational speed are critical. This work presents a novel approach to optimize derived results from CGMD simulations by refining the general-purpose Martini3 topologies for domain-specific applications using Bayesian Optimization methodologies. We have developed and validated a CG potential applicable to any degree of polymerization, representing a significant advancement in the field. Our optimized CG potential, based on the Martini3 framework, aims to achieve accuracy comparable to AAMD while maintaining the computational efficiency of CGMD. This approach bridges the gap between efficiency and accuracy in multiscale molecular simulations, potentially enabling more rapid and cost-effective molecular discovery across various scientific and technological domains.

The total deuterium abundance [D/H] in the universe is set by just two processes: the creation of deuterium in Big Bang Nucleosynthesis at an abundance of [D/H]$=2.58\pm0.13\times10^{-5}$, and its destruction within stellar interiors. Measurements of the total [D/H] abundance can potentially provide a probe of Galactic chemical evolution, however, most measurements of [D/H] are only sensitive to the gas-phase deuterium, and the amount of deuterium sequestered in carbonaceous dust grains is debated. With the launch of JWST, it is now possible to measure the gas-phase [D/H] at unprecedented sensitivity and distances through observation of mid-IR lines of H$_2$ and HD. We employ data from the JWST Observations of Young protoStars (JOYS) program to measure the gas-phase [D/H] abundance with a rotation diagram analysis towards 5 nearby low-mass and 5 distant high-mass protostellar outflows. The gas-phase [D/H] varies between low-mass sources by up to a factor of $\sim4$, despite these sources likely having formed in a region of the Galactic disk that would be expected to have nearly constant total [D/H]. Most measurements of gas-phase [D/H] from our work or previous studies produce [D/H] $\lesssim 1.0\times10^{-5}$, a factor of $2-4$ lower than found from local UV absorption lines and as expected from Galactic chemical evolution models. The variations in [D/H] between our low-mass sources and the low [D/H] with respect to Galactic chemical evolution models suggest that our observations are not sensitive to the total [D/H]. Significant depletion of deuterium onto carbonaceous dust grains is a possible explanation, and tentative evidence of enhanced [D/H] towards shock positions with higher gas-phase Fe abundance is seen in the HH 211 outflow. Deeper observations of HD and H$_2$ in shocked environments and modelling of dust-grain destruction are warranted to test for the effects of depletion., Comment: 30 Pages, 20 Figures. Accepted for publication in Astronomy and Astrophysics

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

Advancements in theoretical simulations of mass gap objects, particularly those resulting from neutron star mergers and massive pulsars, play a crucial role in addressing the challenges of measuring neutron star radii. In the light of this, we have conducted a comprehensive investigation of compact objects (CSs), revealing that while the distribution of black hole masses varies based on formation mechanisms, they frequently cluster around specific values. For instance, the masses observed in GW190814 $(23.2^{+1.1}_{-1.0} \, M_{\odot})$ and GW200210 $(24.1^{+7.5}_{-4.6} M_{\odot})$ exemplify this clustering. We employed the gravitational decoupling approach within the framework of standard general relativity and thus focusing on the strange star model. This model highlights the effects of deformation adjusted by the decoupling constant and the bag function. By analyzing the mass-radius limits of mass gap objects from neutron star mergers and massive pulsars, we can effectively constrain the free parameters in our model, allowing us to predict the radii and moments of inertia for these objects. The mass-radius ($M-R$) and mass-inertia ($M-I$) profiles demonstrate the robustness of our models. It is shown that as the decoupling constant $\beta$ increases from 0 to 0.1 and the bag constant $\mathcal{B}_g$ decreases from 70 $MeV/fm^3$ to 55 $MeV/fm^3$, the maximum mass reaches $M_{max} = 2.87 \, M_\odot$ with a radius of 11.20 km. In contrast, for $\beta = 0$, the maximum mass is $M_{max} = 2.48 \, M_\odot$ with a radius of 10.69 km. Similarly, it has been exhibited that as $\beta$ decreases to 0, the maximum mass peaks at $M_{max} = 2.95 M_\odot$ for $\mathcal{B}_g = 55 MeV/fm^3$ with a radius of 11.32 km. These results not only exceed the observed masses of CSs but also correlate with recent findings from gravitational wave events like GW190814 and GW200210., Comment: pages 27, figures 12 and Tables 5

Understanding human-to-human interactions, especially in contexts like public security surveillance, is critical for monitoring and maintaining safety. Traditional activity recognition systems are limited by fixed vocabularies, predefined labels, and rigid interaction categories that often rely on choreographed videos and overlook concurrent interactive groups. These limitations make such systems less adaptable to real-world scenarios, where interactions are diverse and unpredictable. In this paper, we propose an open vocabulary human-to-human interaction recognition (OV-HHIR) framework that leverages large language models to generate open-ended textual descriptions of both seen and unseen human interactions in open-world settings without being confined to a fixed vocabulary. Additionally, we create a comprehensive, large-scale human-to-human interaction dataset by standardizing and combining existing public human interaction datasets into a unified benchmark. Extensive experiments demonstrate that our method outperforms traditional fixed-vocabulary classification systems and existing cross-modal language models for video understanding, setting the stage for more intelligent and adaptable visual understanding systems in surveillance and beyond., Comment: Accepted in IEEE ICASSP 2025

Visitors to cultural heritage sites often encounter official information, while local people's unofficial stories remain invisible. To explore expression of local narratives, we conducted a workshop with 20 participants utilizing Generative AI (GenAI) to support visual narratives, asking them to use Stable Diffusion to create images of familiar cultural heritage sites, as well as images of unfamiliar ones for comparison. The results revealed three narrative strategies and highlighted GenAI's strengths in illuminating, amplifying, and reinterpreting personal narratives. However, GenAI showed limitations in meeting detailed requirements, portraying cultural features, and avoiding bias, which were particularly pronounced with unfamiliar sites due to participants' lack of local knowledge. To address these challenges, we recommend providing detailed explanations, prompt engineering, and fine-tuning AI models to reduce uncertainties, using objective references to mitigate inaccuracies from participants' inability to recognize errors or misconceptions, and curating datasets to train AI models capable of accurately portraying cultural features., Comment: Accepted for publication in the 28th ACM SIGCHI Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2025)

We explore, through a combination of x-ray diffraction and elastoresistivity measurements, the effect of disorder on the strain-tuned charge density wave and associated multicriticality in Pd$_x$ErTe$_3$ (x = 0, 0.01, 0.02 and 0.026). We focus particularly on the behavior near the strain-tuned bicritical point that occurs in pristine ErTe$_3$ (x=0). Our study reveals that while Pd intercalation somewhat broadens the signatures of the CDW phase transitions, the line of first-order transitions at which the CDW reorients as a function of applied strain persists in the presence of disorder and still seemingly terminates at a critical point. The critical point occurs at a lower temperature and a lower strain compared to pristine ErTe$_3$. Similarly, the nematic elastoresistance of Pd$_x$ErTe$_3$, though suppressed in magnitude and broadened relative to that of ErTe$_3$, has a markedly more symmetric response around the critical point. These observations point to disorder driving a reduction in the system's electronic orthorhombicity even while the material remains irrevocably orthorhombic due to the presence of a glide plane in the crystal structure. Disorder, it would appear, reinforces the emergence of a "pseudo-tetragonal" electronic response in this fundamentally orthorhombic material.

In this study, we conducted a comprehensive review and analysis of the thermoelectric responses exhibited by monolayer pristine graphene in response to temperature variations. Employing the Boltzmann transport theory, we rigorously examined and evaluated various thermoelectric coefficients, with particular emphasis on elucidating their behavior under different scattering mechanisms. We derived the analytical expressions for electrical conductivity, thermopower, and thermal conductivity at low temperatures by Sommerfeld expansion of the Fermi integral. We demonstrated that our numerically obtained values are consistent with the analytical calculations at low temperatures and hence obeying Mott and Wiedeman Franz law. However, the deviation was observed at higher temperatures. Furthermore, we performed theoretical calculations of chemical potential at both low and high temperatures and compared them with our numerically evaluated results at all temperatures. Through extensive calculations and meticulous evaluation, our study contributes to a deeper understanding of the intricate thermoelectric properties inherent in monolayer pristine graphene., Comment: 15 pages, 7 figures, Selected Articles from the 2nd International Conference on Advanced Nanomaterials and Applications. ICANA 2024

Neural Machine Translation (NMT) systems built on multilingual sequence-to-sequence Language Models (msLMs) fail to deliver expected results when the amount of parallel data for a language, as well as the language's representation in the model are limited. This restricts the capabilities of domain-specific NMT systems for low-resource languages (LRLs). As a solution, parallel data from auxiliary domains can be used either to fine-tune or to further pre-train the msLM. We present an evaluation of the effectiveness of these two techniques in the context of domain-specific LRL-NMT. We also explore the impact of domain divergence on NMT model performance. We recommend several strategies for utilizing auxiliary parallel data in building domain-specific NMT models for LRLs.

Understanding light-induced hidden orders is relevant for nonequilibrium materials control and future ultrafast technologies. Hidden superconducting order, in particular, has been a focus of recent experimental and theoretical efforts. In this study, we investigate the stability of light-induced $\eta$ pairing. Using a memory truncated implementation of nonequilibrium dynamical mean field theory (DMFT) and entropy cooling techniques, we study the long-time dynamics of the photoinduced superconducting state. In the presence of coupling to a cold phonon bath, the photodoped system reaches a quasi-steady state, which can be sustained over a long period of time in large-gap Mott insulators. We show that this long-lived prethermalized state is well described by the nonequilibrium steady state implementation of DMFT.

There are two main approaches to building locally flat embedded surfaces in 4-manifolds: direct methods which geometrically manipulate a given map of a surface, and more indirect methods using surgery theory. Both methods rely on Freedman--Quinn's disc embedding theorem. These are the lecture notes for a minicourse giving an introduction to both methods, by sketching the proofs of the following results: every primitive second homology class in a closed, simply connected 4-manifold is represented by a locally flat torus (Lee--Wilczy\'{n}ski); and every Alexander polynomial one knot in $S^3$ is topologically slice (Freedman--Quinn)., Comment: 33 pages, 15 figures. Comments welcome!

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds., Comment: published in the ApJ Letters

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

As part of the guaranteed time observations program Mid-Infrared Characterization Of Nearby Iconic galaxy Centers (MICONIC), we used the medium-resolution spectrometer (MRS) of the Mid-Infrared Instrument (MIRI) on board of the JWST to study the nearby merger NGC6240. We aim to characterise the dual active galactic nuclei (AGN), the ionised gas outflows and the main properties of the interstellar medium over a mapped area of 6.6"x7.7". We obtained integral field spectroscopic mid-infrared data of NGC6240, resolving both nuclei for the first time in the full 5-28{\mu}m spectral range. We modelled the emission lines through a kinematic decomposition, finding that the fine-structure lines in the southern (S) nucleus are broader than for the northern (N) nucleus (full width at half maximum of $\geq$1500 vs ~700 km s^{-1} on average). High excitation lines, such as [NeV], [NeVI], and [MgV], are clearly detected in the N nucleus. In the S nucleus, the same lines can be detected but only after a decomposition of the PAH features in the integrated spectrum, due to a combination of a strong mid-IR continuum, the broad emission lines, and the intense star formation (SF). The SF is distributed all over the mapped FoV of 3.5kpc x 4.1kpc (projected), with the maximum located around the S nucleus. Both nuclear regions appear to be connected by a bridge region detected with all the emission lines. Based on the observed MRS line ratios and the high velocity dispersion ({\sigma}~600 km s^{-1}), shocks are also dominating the emission in this system. We detected the presence of outflows as a bubble north-west from the N nucleus and at the S nucleus. We estimated a ionised mass outflow rate of 1.4$\pm$0.3 M yr^{-1} and 1.8$\pm$0.2 M yr^{-1}, respectively. Given the derived kinetic power of these outflows, both the AGN and the starburst could have triggered them. [Abridged], Comment: 23 pages, 10 figures. Accepted for publication in A&A on 26/11/2024

Kubernetes offers a powerful orchestration platform for machine learning training, but memory management can be challenging due to specialized needs and resource constraints. This paper outlines how Kubernetes handles memory requests, limits, Quality of Service classes, and eviction policies for ML workloads, with special focus on GPU memory and ephemeral storage. Common pitfalls such as overcommitment, memory leaks, and ephemeral volume exhaustion are examined. We then provide best practices for stable, scalable memory utilization to help ML practitioners prevent out-of-memory events and ensure high-performance ML training pipelines., Comment: 4 pages

Despite their remarkable success, large language models (LLMs) have shown limited ability on applied tasks such as vulnerability detection. We investigate various prompting strategies for vulnerability detection and, as part of this exploration, propose a prompting strategy that integrates natural language descriptions of vulnerabilities with a contrastive chain-of-thought reasoning approach, augmented using contrastive samples from a synthetic dataset. Our study highlights the potential of LLMs to detect vulnerabilities by integrating natural language descriptions, contrastive reasoning, and synthetic examples into a comprehensive prompting framework. Our results show that this approach can enhance LLM understanding of vulnerabilities. On a high-quality vulnerability detection dataset such as SVEN, our prompting strategies can improve accuracies, F1-scores, and pairwise accuracies by 23%, 11%, and 14%, respectively.

Bilayer crystals, built from monolayers of two-dimensional (2D) crystals with different lattice orientations, generate interlayer potentials leading to unique excitonic properties. However, the ability to tune the interlayer potential is limited by the fixed covalent 2D lattice geometries. Synthetically substituting one of the layers with an atomically thin molecular crystal could substantially expand the design space for tuning the excitonic response, enabled by explicit control of chemical and electronic structures of the molecular building blocks. Here we report the large-scale synthesis of four-atom-thick hybrid bilayer crystals (HBCs), comprising perylene-based molecular and transition metal dichalcogenide (TMD) monolayer single crystals, which show precise tuning of the HBC lattices at the molecular level. We observe near-unity anisotropic photoluminescence, which is directly tuned by specific molecular geometry and HBC composition. Our ab initio GW and Bethe-Salpeter equation calculations show that the anisotropic emission originates from delocalized and anisotropic hybrid excitons. These excitons inherit characteristics from states associated with both the TMD and molecular layers, resulting from a hybridized bilayer band structure of the HBC. By introducing HBCs as a scalable solid-state platform, our work paves the way for tunable interlayer energy landscapes achievable through direct synthesis, opening up new frontiers in molecule-based quantum materials.

The first JWST/MIRI photometric observations of TRAPPIST-1 b allowed for the detection of the thermal emission of the planet at 15 $\mu m$, suggesting that the planet could be a bare rock with a zero albedo and no redistribution of heat. These observations at 15 $\mu m$ were acquired as part of GTO time that included a twin program at 12.8 $\mu m$ in order to have a measurement in and outside the CO$_2$ absorption band. Here we present five new occultations of TRAPPIST-1 b observed with MIRI in an additional photometric band at 12.8 $\mu m$. We perform a global fit of the 10 eclipses and derive a planet-to-star flux ratio and 1-$\sigma$ error of 452 $\pm$ 86 ppm and 775 $\pm$ 90 ppm at 12.8 $\mu m$ and 15 $\mu m$, respectively. We find that two main scenarios emerge. An airless planet model with an unweathered (fresh) ultramafic surface, that could be indicative of relatively recent geological processes fits well the data. Alternatively, a thick, pure-CO2 atmosphere with photochemical hazes that create a temperature inversion and result in the CO2 feature being seen in emission also works, although with some caveats. Our results highlight the challenges in accurately determining a planet's atmospheric or surface nature solely from broadband filter measurements of its emission, but also point towards two very interesting scenarios that will be further investigated with the forthcoming phase curve of TRAPPIST-1 b., Comment: 49 pages, 3 main text figure, 2 extended figures, 10 supplementary figures, accepted for publication in Nature Astronomy on October 29, 2024

Event detection in text streams is a crucial task for the analysis of online media and social networks. One of the current challenges in this field is establishing a performance standard while maintaining an acceptable level of computational complexity. In our study, we use an incremental clustering algorithm combined with recent advancements in sentence embeddings. Our objective is to compare our findings with previous studies, specifically those by Cao et al. (2024) and Mazoyer et al. (2020). Our results demonstrate significant improvements and could serve as a relevant baseline for future research in this area.

Matched-filtering searches for gravitational-wave signals from compact binary mergers employ template banks which are a collection of modeled waveforms described by unique intrinsic parameters. We present two banks designed for low-latency and archive sub-solar mass (SSM) searches in data from the fourth observing run of LIGO-Virgo-KAGRA, and demonstrate the efficacy of the banks via simulated signals. Further, we introduce a set of modifications to the geometric, manifold algorithm that allow the method to work for exceedingly low component masses necessary for SSM bank production. The archive search bank contains a total of $3,452,006$ templates, and covers a mass parameter space of $0.2$ to $10\ M_\odot$ in the larger component and $0.2$ to $1.0\ M_\odot$ in the smaller component, the spin parameter space of $-0.9$ to $0.9$ for masses above $0.5$ $M_\odot$ and $-0.1$ to $0.1$ for masses below $0.5$ $M_\odot$, and the mass ratio parameter space of $1$ to $10$. The PSD used was from a week of the first half of the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA, and the low frequency cutoff was set to $45$ Hz with a maximum waveform duration of $128$ seconds. The bank simulations performed using SBank have shown that the banks presented in this paper have sufficient efficacy for use in their respective searches.

We propose a nested quantum dot structure for improved control of entanglement induced by the Heisenberg exchange between an electron and a qubit with relative motion. The entanglement is quantified by the mutual information (MI). The electron, initially prepared in the ground state, generally produces greater entanglement when excited to the scattering state compared to remaining in the bound state. In the bound state, the final entanglement oscillates as a function of the qubit speed and can be tuned accordingly. In the case of long-range interaction, the normalized exchange distribution leads to substantial final entanglement, independent of the qubit moving direction, indicating that even very weak but prolonged exchange can still generate significant entanglement. In the case of short-range interaction, different moving directions lead to varying MI values. We also consider the scenario without the nested dot and find that the same maximum (among all times) MI is pre-determined solely by the initial angle between the spins. In this case, the entanglement exhibits different growth characteristics during different phases. The saturation of the MI mimics that of a strict zero-dimensional quantum dot, where exchange and time are combined into a single parameter, the amount of interaction., Comment: 7 pages, 3 figures

RAG (Retrieval Augmented Generation) allows LLMs (large language models) to generate better responses with external knowledge, but using more external knowledge often improves generation quality at the expense of response delay. Prior work either reduces the response delay (through better scheduling of RAG queries) or strives to maximize quality (which involves tuning the RAG workflow), but they fall short in optimizing the tradeoff between the delay and quality of RAG responses. This paper presents RAGServe, the first RAG system that jointly schedules queries and adapts the key RAG configurations of each query, such as the number of retrieved text chunks and synthesis methods, in order to balance quality optimization and response delay reduction. Using 4 popular RAG-QA datasets, we show that compared with the state-of-the-art RAG optimization schemes, RAGServe reduces the generation latency by $1.64-2.54\times$ without sacrificing generation quality., Comment: 17 pages, 18 figures

As AI continues to advance, there is a growing demand for systems that go beyond language-based assistance and move toward intelligent agents capable of performing real-world actions. This evolution requires the transition from traditional Large Language Models (LLMs), which excel at generating textual responses, to Large Action Models (LAMs), designed for action generation and execution within dynamic environments. Enabled by agent systems, LAMs hold the potential to transform AI from passive language understanding to active task completion, marking a significant milestone in the progression toward artificial general intelligence. In this paper, we present a comprehensive framework for developing LAMs, offering a systematic approach to their creation, from inception to deployment. We begin with an overview of LAMs, highlighting their unique characteristics and delineating their differences from LLMs. Using a Windows OS-based agent as a case study, we provide a detailed, step-by-step guide on the key stages of LAM development, including data collection, model training, environment integration, grounding, and evaluation. This generalizable workflow can serve as a blueprint for creating functional LAMs in various application domains. We conclude by identifying the current limitations of LAMs and discussing directions for future research and industrial deployment, emphasizing the challenges and opportunities that lie ahead in realizing the full potential of LAMs in real-world applications. The code for the data collection process utilized in this paper is publicly available at: https://github.com/microsoft/UFO/tree/main/dataflow, and comprehensive documentation can be found at https://microsoft.github.io/UFO/dataflow/overview/., Comment: 25pages,12 figures

Digital healthcare systems have revolutionized medical services, facilitating provider collaboration, enhancing diagnosis, and optimizing and improving treatments. They deliver superior quality, faster, reliable, and cost-effective services. Researchers are addressing pressing health challenges by integrating information technology, computing resources, and digital health records. However, digitizing healthcare introduces significant risks to patient data privacy and security, with the potential for unauthorized access to protected health information. Although patients can authorize data access through consent, there is a pressing need for mechanisms to ensure such given consent is informed and executed properly and timely. Patients deserve transparency and accountability regarding the access to their data: who access it, when, and under what circumstances. Current healthcare systems, often centralized, leave much to be desired in managing these concerns, leading to numerous security incidents. To address these issues, we propose a system based on blockchain and smart contracts for managing informed consent for accessing health records by the treatment team members, incorporating safeguards to verify that consent processes are correctly executed. Blockchain's inherent immutability ensures the integrity of consent. Smart contracts automatically execute agreements, enhancing accountability. They provide a robust framework for protecting patient privacy in the digital age. Experimental evaluations show that the proposed approach can be integrated easily with the existing healthcare systems without incurring financial and technological challenges.

Ensemble forecasts often outperform forecasts from individual standalone models, and have been used to support decision-making and policy planning in various fields. As collaborative forecasting efforts to create effective ensembles grow, so does interest in understanding individual models' relative importance in the ensemble. To this end, we propose two practical methods that measure the difference between ensemble performance when a given model is or is not included in the ensemble: a leave-one-model-out algorithm and a leave-all-subsets-of-models-out algorithm, which is based on the Shapley value. We explore the relationship between these metrics, forecast accuracy, and the similarity of errors, both analytically and through simulations. We illustrate this measure of the value a component model adds to an ensemble in the presence of other models using US COVID-19 death forecasts. This study offers valuable insight into individual models' unique features within an ensemble, which standard accuracy metrics alone cannot reveal., Comment: 28 pages, 8 figures in the main text; includes supplementary material

Spatial perception is a fundamental component of intelligence. While many studies highlight that large multimodal language models (MLMs) struggle to reason about space, they only test for static spatial reasoning, such as categorizing the relative positions of objects. Meanwhile, real-world deployment requires dynamic capabilities like perspective-taking and egocentric action recognition. As a roadmap to improving spatial intelligence, we introduce SAT, Spatial Aptitude Training, which goes beyond static relative object position questions to the more dynamic tasks. SAT contains 218K question-answer pairs for 22K synthetic scenes across a training and testing set. Generated using a photo-realistic physics engine, our dataset can be arbitrarily scaled and easily extended to new actions, scenes, and 3D assets. We find that even MLMs that perform relatively well on static questions struggle to accurately answer dynamic spatial questions. Further, we show that SAT instruction-tuning data improves not only dynamic spatial reasoning on SAT, but also zero-shot performance on existing real-image spatial benchmarks: $23\%$ on CVBench, $8\%$ on the harder BLINK benchmark, and $18\%$ on VSR. When instruction-tuned on SAT, our 13B model matches larger proprietary MLMs like GPT4-V and Gemini-3-1.0 in spatial reasoning. Our data/code is available at http://arijitray1993.github.io/SAT/ ., Comment: Project webpage: http://arijitray1993.github.io/SAT/

We study the distribution of ranks of elliptic curves in quadratic twist families using Iwasawa-theoretic methods, contributing to the understanding of Goldfeld's conjecture. Given an elliptic curve $ E/\mathbb{Q} $ with good ordinary reduction at $ 2 $ and $ \lambda_2(E/\mathbb{Q}) = 0 $, we use Matsuno's Kida-type formula to construct quadratic twists $ E^{(d)} $ such that $ \lambda_2(E^{(d)}/\mathbb{Q}) $ remains unchanged or increases by $ 2 $. When the root number of $E^{(d)}$ is $-1$ and the Tate-Shafarevich group $Sha(E^{(d)}/\mathbb{Q})[2^\infty] $ is finite, this yields quadratic twists with Mordell--Weil rank $ 1 $. These results support the conjectural expectation that, on average, half of the quadratic twists in a family have rank $ 0 $ and half have rank $ 1 $. In the cases we consider we obtain asymptotic lower bounds for the number of twists by squarefree numbers $d\leq X$ which match with the conjectured value up to an explicit power of $\log X$. They complement recent groundbreaking results of Smith on Goldfeld's conjecture., Comment: Version 2: 18 pages; introduction and references expanded. A few new results added. A short final section included to discuss asymptotics for prescribed lambda invariants