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We seek a systematic tightening method to represent the monogamy relation for some measure in multipartite quantum systems. By introducing a family of parametrized bounds, we obtain tighter lowering bounds for the monogamy relation compared with the most recently discovered relations. We provide detailed examples to illustrate why our bounds are better., Comment: 17pp

Imitation learning (IL) aims to mimic the behavior of an expert in a sequential decision making task by learning from demonstrations, and has been widely applied to robotics, autonomous driving, and autoregressive text generation. The simplest approach to IL, behavior cloning (BC), is thought to incur sample complexity with unfavorable quadratic dependence on the problem horizon, motivating a variety of different online algorithms that attain improved linear horizon dependence under stronger assumptions on the data and the learner's access to the expert. We revisit the apparent gap between offline and online IL from a learning-theoretic perspective, with a focus on general policy classes up to and including deep neural networks. Through a new analysis of behavior cloning with the logarithmic loss, we show that it is possible to achieve horizon-independent sample complexity in offline IL whenever (i) the range of the cumulative payoffs is controlled, and (ii) an appropriate notion of supervised learning complexity for the policy class is controlled. Specializing our results to deterministic, stationary policies, we show that the gap between offline and online IL is not fundamental: (i) it is possible to achieve linear dependence on horizon in offline IL under dense rewards (matching what was previously only known to be achievable in online IL); and (ii) without further assumptions on the policy class, online IL cannot improve over offline IL with the logarithmic loss, even in benign MDPs. We complement our theoretical results with experiments on standard RL tasks and autoregressive language generation to validate the practical relevance of our findings.

General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training corpora. Our audit of 14,000 web domains provides an expansive view of crawlable web data and how codified data use preferences are changing over time. We observe a proliferation of AI-specific clauses to limit use, acute differences in restrictions on AI developers, as well as general inconsistencies between websites' expressed intentions in their Terms of Service and their robots.txt. We diagnose these as symptoms of ineffective web protocols, not designed to cope with the widespread re-purposing of the internet for AI. Our longitudinal analyses show that in a single year (2023-2024) there has been a rapid crescendo of data restrictions from web sources, rendering ~5%+ of all tokens in C4, or 28%+ of the most actively maintained, critical sources in C4, fully restricted from use. For Terms of Service crawling restrictions, a full 45% of C4 is now restricted. If respected or enforced, these restrictions are rapidly biasing the diversity, freshness, and scaling laws for general-purpose AI systems. We hope to illustrate the emerging crises in data consent, for both developers and creators. The foreclosure of much of the open web will impact not only commercial AI, but also non-commercial AI and academic research., Comment: 41 pages (13 main), 5 figures, 9 tables

This paper presents a unified differentiable boolean operator for implicit solid shape modeling using Constructive Solid Geometry (CSG). Traditional CSG relies on min, max operators to perform boolean operations on implicit shapes. But because these boolean operators are discontinuous and discrete in the choice of operations, this makes optimization over the CSG representation challenging. Drawing inspiration from fuzzy logic, we present a unified boolean operator that outputs a continuous function and is differentiable with respect to operator types. This enables optimization of both the primitives and the boolean operations employed in CSG with continuous optimization techniques, such as gradient descent. We further demonstrate that such a continuous boolean operator allows modeling of both sharp mechanical objects and smooth organic shapes with the same framework. Our proposed boolean operator opens up new possibilities for future research toward fully continuous CSG optimization., Comment: SIGGRAPH'24

The companion to PSR J1622-0315, one of the most compact known redback millisecond pulsars, shows extremely low irradiation despite its short orbital period. We model this system to determine the binary parameters, combining optical observations from NTT in 2017 and NOT in 2022 with the binary modeling code ICARUS. We find a best-fit neutron star mass of $2.3 \pm 0.4\,\text{M}_\odot $, and a companion mass of $0.15 \pm 0.02\,\text{M}_\odot$. We detect for the first time low-level irradiation from asymmetry in the minima as well as a change in the asymmetry of the maxima of its light curves over five years. Using star spot models, we find better fits than those from symmetric direct heating models, with consistent orbital parameters. We discuss an alternative scenario where the changing asymmetry is produced by a variable intrabinary shock. In summary, we find that PSR J1622-0315 combines low irradiation with variable light curve asymmetry, and a relatively high neutron star mass., Comment: 18 pages, 11 figures

In this article, we calculate the $S$-matrix elements for the process $e^{+} e^{-}\rightarrow t \bar{t}$ mediated by SM photon, $Z$ boson and an additional $Z^{'}$ boson indicating the contribution from new physics. We calculate the amplitude square using two component spinor formalism and four component spinor formalism and show the equivalence of the results using the two formalisms. We also establish the relations between the couplings of $Z^{'}$ boson to fermions in the two component spinor formalism and four component spinor formalism.

Quantum computing is an emerging field on the multidisciplinary interface between physics, engineering, and computer science with the potential to make a large impact on computational intelligence (CI). The aim of this paper is to introduce quantum approximate optimization methods to the CI community because of direct relevance to solving combinatorial problems. We introduce quantum computing and variational quantum algorithms (VQAs). VQAs are an effective method for the near-term implementation of quantum solutions on noisy intermediate-scale quantum (NISQ) devices with less reliable qubits and early-stage error correction. Then, we explain Farhi et al.'s quantum approximate optimization algorithm (Farhi's QAOA, to prevent confusion). This VQA is generalized by Hadfield et al. to the quantum alternating operator ansatz (QAOA), which is a nature-inspired (particularly, adiabatic) quantum metaheuristic for approximately solving combinatorial optimization problems on gate-based quantum computers. We discuss connections of QAOA to relevant domains, such as computational learning theory and genetic algorithms, discussing current techniques and known results regarding hybrid quantum-classical intelligence systems. We present a schematic of how QAOA is constructed, and also discuss how CI techniques can be used to improve QAOA. We conclude with QAOA implementations for the well-known maximum cut, maximum bisection, and traveling salesperson problems, which can serve as templates for CI practitioners interested in using QAOA., Comment: 15 pages, 10 figures

XB 1254-690 is a neutron star low-mass X-ray binary with an orbital period of 3.88 hrs, and it exhibits energy-dependent intensity dips, thermonuclear bursts, and flares. We present the results of an analysis of a long observation of this source using the AstroSat satellite. The X-ray light curve gradually changed from a high-intensity flaring state to a low-intensity one with a few dips. The hardness intensity diagram showed that the source is in a high-intensity banana state with a gradually changing flux. Based on this, we divide the observation into four flux levels for a flux-resolved spectral study. The X-ray spectra can be explained by a model consisting of absorption, thermal emission from the disc and non-thermal emission from the corona. From our studies, we detect a correlation between the temperature of the thermal component and the flux and we examine the implications of our results for the accretion disc geometry of this source., Comment: Accepted for publication in MNRAS; 11 pages, 12 figures

Speaker diarization systems segment a conversation recording based on the speakers' identity. Such systems can misclassify the speaker of a portion of audio due to a variety of factors, such as speech pattern variation, background noise, and overlapping speech. These errors propagate to, and can adversely affect, downstream systems that rely on the speaker's identity, such as speaker-adapted speech recognition. One of the ways to mitigate these errors is to provide segment-level diarization confidence scores to downstream systems. In this work, we investigate multiple methods for generating diarization confidence scores, including those derived from the original diarization system and those derived from an external model. Our experiments across multiple datasets and diarization systems demonstrate that the most competitive confidence score methods can isolate ~30% of the diarization errors within segments with the lowest ~10% of confidence scores., Comment: Accepted in INTERSPEECH 2024

GRBs produced by the collapse of massive stars are usually found near the most prominent star-forming regions of star-forming galaxies. GRB 171205A happened in the outskirts of a spiral galaxy, a peculiar location in an atypical GRB host. In this paper we present a highly-resolved study of the molecular gas of this host, with CO(1-0) observations from ALMA. We compare with GMRT atomic HI observations, and with data at other wavelengths to provide a broad-band view of the galaxy. The ALMA observations have a spatial resolution of 0.2" and a spectral resolution of 10 km/s, observed when the afterglow had a flux density of ~53 mJy. This allowed a molecular study both in emission and absorption. The HI observations allowed to study the host galaxy and its extended environment. The CO emission shows an undisturbed spiral structure with a central bar, and no significant emission at the location of the GRB. Our CO spectrum does not reveal any CO absorption, with a column density limit of < 10^15 cm^-2. This argues against the progenitor forming in a massive molecular cloud. The molecular gas traces the galaxy arms with higher concentration in the regions dominated by dust. The HI gas does not follow the stellar light or the molecular gas and is concentrated in two blobs, with no emission towards the centre of the galaxy, and is slightly displaced towards the southwest of the galaxy, where the GRB exploded. Within the extended neighbourhood of the host galaxy, we identify another prominent HI source at the same redshift, at a projected distance of 188 kpc. Our observations show that the progenitor of this GRB is not associated to a massive molecular cloud, but more likely related to low-metallicity atomic gas. The distortion in the HI gas field is indicator of an odd environment that could have triggered star formation and could be linked to a past interaction with the companion galaxy., Comment: 13 pages, 10 figures, 8 tables, A&A submitted after 1st referee review

The design of a 637-nm wavelength, photonic-integrated-circuit-based external cavity laser (PIC-based ECL) aimed at quantum technology applications is presented together with first experimental results. The PIC is designed to provide relaxed alignment tolerance for coupling to a reflective semiconductor optical amplifier (RSOA) gain chip. This is achieved by using a multi-mode edge coupler (MMEC) in place of the usually employed single-mode coupling schemes. A 1-dB-penalty misalignment tolerance of up to +/- 2.4 um can be achieved in the plane of the chip, creating a path towards reliable flip-chip integration at short wavelengths. The power coupled to the PIC is fed to a Sagnac-loop reflector, filtered by a pair of ring resonators operated in Vernier configuration for providing the required frequency selective optical feedback. The ring resonators are designed to have different loaded Q-factors and they are asymmetrically coupled to bus and drop waveguides with suitably engineered directional couplers to provide single output waveguide emission. Moreover, requirements for high output power and narrow linewidths are balanced. Finally, preliminary measurements strongly suggest lasing in the fabricated devices, with further performance optimization being currently carried out.

We study the modulation of fast magnetosonic waves (MSWs) in rotating inhomogeneous low-$\beta$ magnetoplasmas with the effects of gravitation and the Coriolis force. By employing the standard multiple-scale reductive perturbation technique (RPT), we derive a nonlinear Schr\"{o}dinger (NLS) equation that governs the evolution of slowly varying MSW envelopes. The fast MSW becomes dispersive by the effects of the Coriolis force in the fluid motion, and the magnetic field and density inhomogeneity effects favor the Jeans instability in self-gravitating plasmas in a larger domain of the wave number ($k$, below the Jeans critical wave number, $k_J$) than homogeneous plasmas. The relative influence of the Jeans frequency ($\omega_J$, associated with the gravitational force) and the angular frequency ($\Omega_0$, relating to the Coriolis force) on the Jeans carrier MSW mode and the modulational instability (MI) of the MSW envelope is studied. We show that the MSW envelope (corresponding to the unstable carrier Jeans mode with $\omega_J>2\Omega_0$ and $k2\Omega_0$ but $k>k_J$ manifests either modulational stability or MI having a finite growth rate before being cut off. We find an enhancement of the MI growth rate by the influence of magnetic field or density inhomogeneity. The case with constant gravity force (other than the self-gravity) perpendicular to the magnetic field is also briefly discussed to show that the fast magnetosonic carrier mode is always unstable, giving MI of slowly varying envelopes with no cut-offs for the growth rates. Possible applications of MI in solar plasmas, such as those in the X-ray corona, are also briefly discussed., Comment: 14 Pages, 7 figures

We present the results of a nation-wide baseline survey, conducted by us, for the status of Astronomy education among secondary school students in India. The survey was administered in 10 different languages to over 2000 students from diverse backgrounds, and it explored multiple facets of their perspectives on astronomy. The topics included students' views on the incorporation of astronomy in curricula, their grasp of fundamental astronomical concepts, access to educational resources, cultural connections to astronomy, and their levels of interest and aspirations in the subject. We find notable deficiencies in students' knowledge of basic astronomical principles, with only a minority demonstrating proficiency in key areas such as celestial sizes, distances, and lunar phases. Furthermore, access to resources such as telescopes and planetariums remain limited across the country. Despite these challenges, a significant majority of students expressed a keen interest in astronomy. We further analyze the data along socioeconomic and gender lines. Particularly striking were the socioeconomic disparities, with students from resource-poor backgrounds often having lower levels of access and proficiency. Some differences were observed between genders, although not very pronounced. The insights gleaned from this study hold valuable implications for the development of a more robust astronomy curriculum and the design of effective teacher training programs in the future., Comment: 15 pages, 19 figures

We report results from a detailed study of the neutron star X-ray binary, 4U 1608-52 using observations with {\it AstroSat} (LAXPC/SXT) and {\it NICER} during its 2016 and 2020 outbursts. The 0.7--20.0 keV spectra could be well described with the disk blackbody and thermal Comptonization model. The best-fitting inner disk temperature is $\sim$ 1 keV and radius { $\sim$ 22.17$^{+2.57}_{-2.38}$--27.19$^{+2.03}_{-1.85}$} km and no significant evolution was observed in the disk radius after performing flux and time-resolved spectroscopy. We used a multi-Lorentzian approach to fit the power density spectra and obtained broad-band noise variability. We estimated the energy-dependent fractional root mean square and time-lag of the broad-band noise, and these variations are quantitatively modelled as being due to the coherent variation of the disk emission and the coronal heating rate. Thus, the rapid temporal modeling is consistent with the longer term spectral evolution where the inner disk radius does not vary, and instead the variations can be attributed to accretion rate variations which changes the inner disk temperature and the coronal heating rate., Comment: 17 pages, 13 figures, The manuscript has been accepted for publication in The Astrophysical Journal

The momentum of human spaceflight initiatives continues to build toward Mars, and technological advances may eventually enable the potential for permanent space settlement. Aspirations for sustaining human life in space must be predicated on human factors, rather than technological constraints alone, and advances in models of governance and ethics are necessary as human civilization becomes a spacefaring species. This paper presents an idealistic but feasible model for economic freedom on Mars, which is situated within a framework in which Mars has been designated as a sovereign juridical peer to Earth. Under such conditions, Mars could maintain monetary stability through full reserve banking and a restriction on exchange with any fractional reserve Earth currencies, with a volume of circulating currency that changes based on the total population within fixed capacity infrastructure. Mars could maintain long-term political stability by diffusing the ownership of capital on Mars, which would allow all citizens of Mars to draw sufficient wealth from a combination of capital ownership and labor to live a good life. This model could also support limited tourism on Mars, in which real goods are exchanged for services but currency transactions between planets are prohibited. This model demonstrates the potential for a viable and sustainable economy on Mars that could conceivably be implemented, including on a sovereign Mars but also in other scenarios of space settlement. More broadly, this model illustrates that ideas such as diffuse capital ownership and limited government can enable freedom in space, and numerous models beyond a centralized world space agency should be explored to ensure the optimal governance of the emerging space economy., Comment: Submitted to Space Policy

We study almost-envy-freeness in house allocation, where $m$ houses are to be allocated among $n$ agents so that every agent receives exactly one house. An envy-free allocation need not exist, and therefore we may have to settle for relaxations of envy-freeness. But typical relaxations such as envy-free up to one good do not make sense for house allocation, as every agent is required to receive exactly one house. Hence we turn to different aggregate measures of envy as markers of fairness. In particular, we define the amount of envy experienced by an agent $a$ w.r.t. an allocation to be the number of agents that agent $a$ envies under that allocation. We quantify the envy generated by an allocation using three different metrics: 1) the number of agents who are envious; 2) the maximum amount of envy experienced by any agent; and 3) the total amount of envy experienced by all agents, and look for allocations that minimize one of the three metrics. We thus study three computational problems corresponding to each of the three metrics and prove a host of algorithmic and hardness results. We also suggest practical approaches for these problems via integer linear program (ILP) formulations and report the findings of our experimental evaluation of ILPs. Finally, we study the price of fairness (PoF), which quantifies the loss of welfare we must suffer due to the fairness requirements, and we prove a number of results on PoF, including tight bounds as well as algorithms that simultaneously optimize both welfare and fairness., Comment: 49 pages, 5 tables; A shorter version of this paper appeared at AAMAS 2023

As JWST begins to return observations, it is more important than ever that exoplanet climate models can consistently and correctly predict the observability of exoplanets, retrieval of their data, and interpretation of planetary environments from that data. Model intercomparisons play a crucial role in this context, especially now when few data are available to validate model predictions. The CUISINES Working Group of NASA's Nexus for Exoplanet System Science (NExSS) supports a systematic approach to evaluating the performance of exoplanet models, and provides here a framework for conducting community-organized exoplanet Model Intercomparison Projects (exoMIPs). The CUISINES framework adapts Earth climate community practices specifically for the needs of exoplanet researchers, encompassing a range of model types, planetary targets, and parameter space studies. It is intended to help researchers to work collectively, equitably, and openly toward common goals. The CUISINES framework rests on five principles: 1) Define in advance what research question(s) the exoMIP is intended to address. 2) Create an experimental design that maximizes community participation, and advertise it widely. 3) Plan a project timeline that allows all exoMIP members to participate fully. 4) Generate data products from model output for direct comparison to observations. 5) Create a data management plan that is workable in the present and scalable for the future. Within the first years of its existence, CUISINES is already providing logistical support to 10 exoMIPs, and will continue to host annual workshops for further community feedback and presentation of new exoMIP ideas., Comment: 14 pages, 2 figures

The new generation of strained silicon nitride resonators harbors great promise for scanning force microscopy, especially when combined with the extensive toolbox of cavity optomechanics. However, accessing a mechanical resonator inside an optical cavity with a scanning tip is challenging. Here, we experimentally demonstrate a cavity-based scanning force microscope based on a silicon nitride membrane sensor. We overcome geometric constraints by making use of the extended nature of the mechanical resonator normal modes, which allows us to spatially separate the scanning and readout sites of the membrane. Our microscope is geared towards low-temperature applications in the zeptonewton regime, such as nanoscale nuclear spin detection and imaging., Comment: 10 pages, 7 figures

Aggression is a two-player game of troop placement and attack played on a map (modeled as a graph). Players take turns deploying troops on a territory (a vertex on the graph) until they run out. Once all troops are placed, players take turns attacking enemy territories. A territory can be attacked if it has $k$ troops and there are more than $k$ enemy troops on adjacent territories. At the end of the game, the player who controls the most territories wins. In the case of a tie, the player with more surviving troops wins. The first player to exhaust their troops in the placement phase leads the attack phase. We study the complexity of the game when the graph along with an assignment of troops and the sequence of attacks planned by the second player. Even in this restrained setting, we show that the problem of determining an optimal sequence of first player moves is NP-complete. We then analyze the game for when the input graph is a matching or a cycle., Comment: 19 pages, 4 figures; a shorter version was accepted for presentation at COCOON 2025

The emergence of quantum computing technology over the last decade indicates the potential for a transformational impact in the study of quantum mechanical systems. It is natural to presume that such computing technologies would be valuable to large scientific institutions, such as United States national laboratories. However, detailed descriptions of what these institutions would like to use these computers for are limited. To help provide some initial insights into this topic, this report develops detailed use cases of how quantum computing technology could be utilized to enhance a variety of quantum physics research activities at Los Alamos National Laboratory, including quantum magnetic materials, high-temperature superconductivity and nuclear astrophysics simulations. The report discusses how current high-performance computers are used for scientific discovery today and develops detailed descriptions of the types of quantum physics simulations that Los Alamos National Laboratory scientists would like to conduct, if a sufficient computing technology became available. While the report strives to highlight the breadth of potential application areas for quantum computation, this investigation has also indicated that many more use cases exist at Los Alamos National Laboratory, which could be documented in similar detail with sufficient time and effort.

Classification of gamma-ray bursts (GRBs) has been a long-standing puzzle in high-energy astrophysics. Recent observations challenge the traditional short vs. long viewpoint, where long GRBs are thought to originate from the collapse of massive stars and short GRBs from compact binary mergers. Machine learning (ML) algorithms have been instrumental in addressing this problem, revealing five distinct GRB groups within the Swift/BAT light curve data, two of which are associated with kilonovae (KNe). We corroborate these five classes by extending this analysis to the Fermi/GBM data using unsupervised ML techniques. These five clusters are well separated in fluence-duration plane, hinting at a potential link between fluence, duration and complexities (or structures) in the light curves of GRBs. Further, we confirm two distinct classes of KN-associated GRBs. The presence of GRB 170817A in one of the two KNe-associated clusters lends evidence to the hypothesis that this class of GRBs could potentially be produced by binary neutron star (BNS) mergers. The second KN-associated GRB cluster could potentially originate from NS-BH mergers. Future multimessenger observations of compact binaries in gravitational waves (GWs) and electromagnetic waves can be paramount in understanding these clusters better., Comment: 9 pages, 4 figures

Nonlinear optics plays an important role in many areas of science and technology. The advance of nonlinear optics is empowered by the discovery and utilization of materials with growing optical nonlinearity. Here we demonstrate an indium gallium phosphide (InGaP) integrated photonics platform for broadband, ultra-efficient second-order nonlinear optics. The InGaP nanophotonic waveguide enables second-harmonic generation with a normalized efficiency of $128,000\%$/W/cm$^2$ at 1.55 $\mu$m pump wavelength, nearly two orders of magnitude higher than the state of the art in the telecommunication C band. Further, we realize an ultra-bright, broadband time-energy entangled photon source with a pair generation rate of 97 GHz/mW and a bandwidth of 115 nm centered at the telecommunication C band. The InGaP entangled photon source shows high coincidence-to-accidental counts ratio CAR $>10^4$ and two-photon interference visibility $>98\%$. The InGaP second-order nonlinear photonics platform will have wide-ranging implications for non-classical light generation, optical signal processing, and quantum networking.

Diffusion models (DMs) have shown remarkable capabilities in generating realistic high-quality images, audios, and videos. They benefit significantly from extensive pre-training on large-scale datasets, including web-crawled data with paired data and conditions, such as image-text and image-class pairs. Despite rigorous filtering, these pre-training datasets often inevitably contain corrupted pairs where conditions do not accurately describe the data. This paper presents the first comprehensive study on the impact of such corruption in pre-training data of DMs. We synthetically corrupt ImageNet-1K and CC3M to pre-train and evaluate over 50 conditional DMs. Our empirical findings reveal that various types of slight corruption in pre-training can significantly enhance the quality, diversity, and fidelity of the generated images across different DMs, both during pre-training and downstream adaptation stages. Theoretically, we consider a Gaussian mixture model and prove that slight corruption in the condition leads to higher entropy and a reduced 2-Wasserstein distance to the ground truth of the data distribution generated by the corruptly trained DMs. Inspired by our analysis, we propose a simple method to improve the training of DMs on practical datasets by adding condition embedding perturbations (CEP). CEP significantly improves the performance of various DMs in both pre-training and downstream tasks. We hope that our study provides new insights into understanding the data and pre-training processes of DMs., Comment: 50 pages, 33 figures, 4 tables

The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages information across patients to impute missing data associated with each patient of interest. We focus on two types of missing data: (i) unrecorded outcomes from discontinuing the assigned treatments and (ii) unobserved outcomes associated with unassigned treatments. Data imputation in the former powers and de-biases RCTs, while data imputation in the latter simulates "synthetic RCTs" to predict the outcomes for each patient under every treatment. The SNN estimator is interpretable, transparent, and causally justified under a broad class of missing data scenarios. Relative to several standard methods, we empirically find that SNN performs well for the above two applications using Phase 3 clinical trial data on patients with Alzheimer's Disease. Our findings directly suggest that SNN can tackle a current pain point within the clinical trial workflow on patient dropouts and serve as a new tool towards the development of precision medicine. Building on our insights, we discuss how SNN can further generalize to real-world applications.

The SWMR atomic register is a fundamental building block in shared memory distributed systems and implementing it from SWSR atomic registers is an important problem. While this problem has been solved in crash-prone systems, it has received less attention in Byzantine systems. Recently, Hu and Toueg gave such an implementation of the SWMR register from SWSR registers. While their definition of register linearizability is consistent with the definition of Byzantine linearizability of a concurrent history of Cohen and Keidar, it has these drawbacks. (1) If the writer is Byzantine, the register is linearizable no matter what values the correct readers return. (2) It ignores values written consistently by a Byzantine writer. We need a stronger notion of a {\em correct write operation}. (3) It allows a value written to just one or a few readers' SWSR registers to be returned, thereby not validating the intention of the writer to write that value honestly. (4) Its notion of a ``current'' value returned by a correct reader is not related to the most recent value written by a correct write operation of a Byzantine writer. We need a more up to date version of the value that can be returned by a correct reader. In this paper, we give a stronger definition of a Byzantine linearizable register that overcomes the above drawbacks. Then we give a construction of a Byzantine linearizable SWMR atomic register from SWSR registers that meets our stronger definition. The construction is correct when $n>3f$, where $n$ is the number of readers, $f$ is the maximum number of Byzantine readers, and the writer can also be Byzantine. The construction relies on a public-key infrastructure., Comment: 18 pages

In this article, we investigate the stability of syzygy bundles corresponding to ample and globally generated vector bundles on smooth irreducible projective surfaces.

We present Link Density (LD) computed from the Recurrence Network (RN) of a time series data as an effective measure that can detect dynamical transitions in a system. We illustrate its use using time series from the standard Rossler system in the period doubling transitions and the transition to chaos. Moreover, we find that the standard deviation of LD can be more effective in highlighting the transition points. We also consider the variations in data when the parameter of the system is varying due to internal or intrinsic perturbations but at a time scale much slower than that of the dynamics. In this case also, the measure LD and its standard deviation correctly detect transition points in the underlying dynamics of the system. The computation of LD requires minimal computing resources and time, and works well with short data sets. Hence, we propose this measure as a tool to track transitions in dynamics from data, facilitating quicker and more effective analysis of large number of data sets., Comment: 7n pages, 3 figures

This paper highlights methods from geostatistics that are relevant to the interpretation, intercomparison, and synthesis of atmospheric model data, with a specific application to exoplanet atmospheric modeling. Climate models are increasingly used to study theoretical and observational properties of exoplanets, which include a hierarchy of models ranging from fast and idealized models to those that are slower but more comprehensive. Exploring large parameter spaces with computationally-expensive models can be accomplished with sparse sampling techniques, but analyzing such sparse samples can pose challenges for conventional interpolation functions. Ordinary kriging is a statistical method for describing the spatial distribution of a data set in terms of the variogram function, which can be used to interpolate sparse samples across any number of dimensions. Variograms themselves may also be useful diagnostic tools for describing the spatial distribution of model data in exoplanet atmospheric model intercomparison projects. Universal kriging is another method that can synthesize data calculated by models of different complexity, which can be used to combine sparse samples of data from slow models with larger samples of data from fast models. Ordinary and universal kriging can also provide a way to synthesize model predictions with sparse samples of exoplanet observations and may have other applications in exoplanet science., Comment: Accepted for publication in the Planetary Science Journal

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every learner and a teaching assistant for every teacher. The full extent of this dream, however, has not yet materialised. We argue that this is primarily due to the difficulties with verbalising pedagogical intuitions into gen AI prompts and the lack of good evaluation practices, reinforced by the challenges in defining excellent pedagogy. Here we present our work collaborating with learners and educators to translate high level principles from learning science into a pragmatic set of seven diverse educational benchmarks, spanning quantitative, qualitative, automatic and human evaluations; and to develop a new set of fine-tuning datasets to improve the pedagogical capabilities of Gemini, introducing LearnLM-Tutor. Our evaluations show that LearnLM-Tutor is consistently preferred over a prompt tuned Gemini by educators and learners on a number of pedagogical dimensions. We hope that this work can serve as a first step towards developing a comprehensive educational evaluation framework, and that this can enable rapid progress within the AI and EdTech communities towards maximising the positive impact of gen AI in education.

We study the oblique propagation of weakly nonlinear dust-ion-acoustic (DIA) solitary waves (SWs) and shocks in collisional magnetized nonthermal dusty plasmas that are relevant in laboratory and space (Saturn's E-ring) environments. We consider plasmas to be composed of $q$-nonextensive hot electrons, thermal positive ions, and immobile negatively charged dust grains immersed in a static magnetic field and take into account the effects of ion creation (source), and ion loss (sink), ion-neutral and ion-dust collisions, anisotropic ion pressure and dust-charge fluctuations on the evolution of small-amplitude SWs and shocks. The ion-neutral collision enhancement equilibrium dust-charge number is self-consistently determined using Newton's Raphson method. We found that in laboratory dusty plasmas with adiabatic dust-charge variation [i.e., when the dust charging frequency ($\nu_{\rm{ch}}$) is much higher than the dust-plasma oscillation frequency ($\omega_{\rm{pd}}$)], the DIA solitary waves (DIASWs) get damped by the effects of the ion-dust and ion-neutral collisions, whereas the ion creation and ion loss leads to the amplification of solitary waves, and they appear as only compressive types with positive potential. On the other hand, in Saturn's E-ring plasmas, where the collisional and ion creation or ion loss effects are insignificant, the non-adiabaticity of dust-charge variation can give rise to the evolution of either damped DIASWs or DIA shocks, depending on the smallness of the ratios $\nu_{\rm{ch}}/\omega_{\rm{pd}}$ or $\omega_{\rm{pd}}/\nu_{\rm{ch}}$, respectively. Furthermore, two critical values of the nonextensive parameter $q$ exist, below (or above) which, the DIASWs and shocks can appear as rarefactive (or compressive) types. The characteristics of DIASWs and shocks are also analyzed numerically for parameters relevant to the laboratory and Saturn's E-ring plasmas., Comment: Total 19 pages and 21 Figures with some Figure number has subplots

Atmospheric pollutants such as CFCs and NO$_{2}$ have been proposed as potential remotely detectable atmospheric technosignature gases. Here we investigate the potential for artificial greenhouse gases including CF$_{4}$, C$_{2}$F$_{6}$, C$_{3}$F$_{8}$, SF$_{6}$, and NF$_{3}$ to generate detectable atmospheric signatures. In contrast to passive incidental byproducts of industrial processes, artificial greenhouse gases would represent an intentional effort to change the climate of a planet with long-lived, low toxicity gases and would possess low false positive potential. An extraterrestrial civilization may be motivated to undertake such an effort to arrest a predicted snowball state on their home world or to terraform an otherwise uninhabitable terrestrial planet within their system. Because artificial greenhouse gases strongly absorb in the thermal mid-infrared window of temperate atmospheres, a terraformed planet will logically possess strong absorption features from these gases at mid-IR wavelengths ($\sim$8-12 $\mu$m), possibly accompanied by diagnostic features in the near-IR. As a proof of concept, we calculate the needed observation time to detect 1 [10](100) ppm of C$_{2}$F$_{6}$/C$_{3}$F$_{8}$/SF$_{6}$ on TRAPPIST-1f with JWST MIRI/LRS and NIRSpec. We find that a combination of 1[10](100) ppm each of C$_{2}$F$_{6}$, C$_{3}$F$_{8}$, and SF$_{6}$ can be detected with an S/N $\geq$ 5 in as few as 25[10](5) transits with MIRI/LRS. We further explore mid-infrared direct-imaging scenarios with the LIFE mission concept and find these gases are more detectable than standard biosignatures at these concentrations. Consequently, artificial greenhouse gases can be readily detected (or excluded) during normal planetary characterization observations with no additional overhead., Comment: 18 pages, 8 figures, 6 tables; ApJ, 969, 20

Unmanned Aerial Vehicles (UAVs) have gained popularity in data harvesting (DH) and coverage path planning (CPP) to survey a given area efficiently and collect data from aerial perspectives, while data harvesting aims to gather information from various Internet of Things (IoT) sensor devices, coverage path planning guarantees that every location within the designated area is visited with minimal redundancy and maximum efficiency. We propose the ARDDQN (Attention-based Recurrent Double Deep Q Network), which integrates double deep Q-networks (DDQN) with recurrent neural networks (RNNs) and an attention mechanism to generate path coverage choices that maximize data collection from IoT devices and to learn a control scheme for the UAV that generalizes energy restrictions. We employ a structured environment map comprising a compressed global environment map and a local map showing the UAV agent's locate efficiently scaling to large environments. We have compared Long short-term memory (LSTM), Bi-directional long short-term memory (Bi-LSTM), Gated recurrent unit (GRU) and Bidirectional gated recurrent unit (Bi-GRU) as recurrent neural networks (RNN) to the result without RNN We propose integrating the LSTM with the Attention mechanism to the existing DDQN model, which works best on evolution parameters, i.e., data collection, landing, and coverage ratios for the CPP and data harvesting scenarios.

We present the results from a long term X-ray analysis of Mrk 279 during the period 2018-2020. We use data from multiple missions - AstroSat, NuSTAR and XMM-Newton, for the purpose. The X-ray spectrum can be modelled as a double Comptonisation along with the presence of neutral Fe K${\alpha}$ line emission, at all epochs. We determined the source's X-ray flux and luminosity at these different epochs. We find significant variations in the source's flux state. We also investigated the variations in the source's spectral components during the observation period. We find that the photon index and hence the spectral shape follow the variations only over longer time periods. We probe the correlations between fluxes of different bands and their photon indices, and found no significant correlations between the parameters., Comment: Accepted for publication in Research in Astronomy and Astrophysics

In this article, we investigate Serrano's conjecture for strictly nef divisors on projective bundles over higher dimensional smooth projective varieties.

Named Entity Recognition (NER) is a useful component in Natural Language Processing (NLP) applications. It is used in various tasks such as Machine Translation, Summarization, Information Retrieval, and Question-Answering systems. The research on NER is centered around English and some other major languages, whereas limited attention has been given to Indian languages. We analyze the challenges and propose techniques that can be tailored for Multilingual Named Entity Recognition for Indian Languages. We present a human annotated named entity corpora of 40K sentences for 4 Indian languages from two of the major Indian language families. Additionally,we present a multilingual model fine-tuned on our dataset, which achieves an F1 score of 0.80 on our dataset on average. We achieve comparable performance on completely unseen benchmark datasets for Indian languages which affirms the usability of our model., Comment: 8 pages, accepted in NAACL-SRW, 2024

In this work, we assess the potential detectability of solar panels made of silicon on an Earth-like exoplanet as a potential technosignature. Silicon-based photovoltaic cells have high reflectance in the UV-VIS and in the near-IR, within the wavelength range of a space-based flagship mission concept like the Habitable Worlds Observatory (HWO). Assuming that only solar energy is used to provide the 2022 human energy needs with a land cover of ~2.4%, and projecting the future energy demand assuming various growth-rate scenarios, we assess the detectability with an 8 m HWO-like telescope. Assuming the most favorable viewing orientation, and focusing on the strong absorption edge in the ultraviolet-to-visible (0.34 - 0.52 um), we find that several 100s of hours of observation time is needed to reach a SNR of 5 for an Earth-like planet around a Sun-like star at 10pc, even with a solar panel coverage of ~23% land coverage of a future Earth. We discuss the necessity of concepts like Kardeshev Type I/II civilizations and Dyson spheres, which would aim to harness vast amounts of energy. Even with much larger populations than today, the total energy use of human civilization would be orders of magnitude below the threshold for causing direct thermal heating or reaching the scale of a Kardashev Type I civilization. Any extraterrrestrial civilization that likewise achieves sustainable population levels may also find a limit on its need to expand, which suggests that a galaxy-spanning civilization as imagined in the Fermi paradox may not exist., Comment: Accepted to The Astrophysical Journal

We study the interaction of a dark exciton Bose-Einstein condensate with the nuclei in GaAs/AlGaAs coupled quantum wells and find clear evidence for nuclear polarization buildup that accompanies the appearance of the condensate. We show that the nuclei are polarized throughout the mesa area, extending to regions which are far away from the photoexcitation area, and persisting for seconds after the excitation is switched off. Photoluminescence measurements in the presence of RF radiation reveal that the hyperfine interaction between the nuclear and electron spins is enhanced by two orders of magnitude. We suggest that this large enhancement manifests the collective nature of the N-excitons condensate, which amplifies the interaction by a factor of sqrt{N}.

Sexual education aims to foster a healthy lifestyle in terms of emotional, mental and social well-being. In countries like India, where adolescents form the largest demographic group, they face significant vulnerabilities concerning sexual health. Unfortunately, sexual education is often stigmatized, creating barriers to providing essential counseling and information to this at-risk population. Consequently, issues such as early pregnancy, unsafe abortions, sexually transmitted infections, and sexual violence become prevalent. Our current proposal aims to provide a safe and trustworthy platform for sexual education to the vulnerable rural Indian population, thereby fostering the healthy and overall growth of the nation. In this regard, we strive towards designing SUKHSANDESH, a multi-staged AI-based Question Answering platform for sexual education tailored to rural India, adhering to safety guardrails and regional language support. By utilizing information retrieval techniques and large language models, SUKHSANDESH will deliver effective responses to user queries. We also propose to anonymise the dataset to mitigate safety measures and set AI guardrails against any harmful or unwanted response generation. Moreover, an innovative feature of our proposal involves integrating ``avatar therapy'' with SUKHSANDESH. This feature will convert AI-generated responses into real-time audio delivered by an animated avatar speaking regional Indian languages. This approach aims to foster empathy and connection, which is particularly beneficial for individuals with limited literacy skills. Partnering with Gram Vaani, an industry leader, we will deploy SUKHSANDESH to address sexual education needs in rural India.

We present the largest optical photometry compilation of Gamma-Ray Bursts (GRBs) with redshifts ($z$). We include 64813 observations of 535 events (including upper limits) from 28 February 1997 up to 18 August 2023. We also present a user-friendly web tool \textit{grbLC} which allows users the visualization of photometry, coordinates, redshift, host galaxy extinction, and spectral indices for each event in our database. Furthermore, we have added a Gamma Ray Coordinate Network (GCN) scraper that can be used to collect data by gathering magnitudes from the GCNs. The web tool also includes a package for uniformly investigating colour evolution. We compute the optical spectral indices for 138 GRBs for which we have at least 4 filters at the same epoch in our sample and craft a procedure to distinguish between GRBs with and without colour evolution. By providing a uniform format and repository for the optical catalogue, this web-based archive is the first step towards unifying several community efforts to gather the photometric information for all GRBs with known redshifts. This catalogue will enable population studies by providing light curves (LCs) with better coverage since we have gathered data from different ground-based locations. Consequently, these LCs can be used to train future LC reconstructions for an extended inference of the redshift. The data gathering also allows us to fill some of the orbital gaps from Swift in crucial points of the LCs, e.g., at the end of the plateau emission or where a jet break is identified., Comment: 20 pages, 16 figures, 2 tables. Submitted to MNRAS, this version matches the third revision. The Online Materials and data will be available after the publication

Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

Today, cloud workloads are essentially opaque to the cloud platform. Typically, the only information the platform receives is the virtual machine (VM) type and possibly a decoration to the type (e.g., the VM is evictable). Similarly, workloads receive little to no information from the platform; generally, workloads might receive telemetry from their VMs or exceptional signals (e.g., shortly before a VM is evicted). The narrow interface between workloads and platforms has several drawbacks: (1) a surge in VM types and decorations in public cloud platforms complicates customer selection; (2) essential workload characteristics (e.g., low availability requirements, high latency tolerance) are often unspecified, hindering platform customization for optimized resource usage and cost savings; and (3) workloads may be unaware of potential optimizations or lack sufficient time to react to platform events. In this paper, we propose a framework, called Workload Intelligence (WI), for dynamic bi-directional communication between cloud workloads and cloud platform. Via WI, workloads can programmatically adjust their key characteristics, requirements, and even dynamically adapt behaviors like VM priorities. In the other direction, WI allows the platform to programmatically inform workloads about upcoming events, opportunities for optimization, among other scenarios. Because of WI, the cloud platform can drastically simplify its offerings, reduce its costs without fear of violating any workload requirements, and reduce prices to its customers on average by 48.8%.

Quantum many-body systems serve as a suitable working medium for realizing quantum thermal machines (QTMs) by offering distinct advantages such as cooperative many-body effects, and performance boost at the quantum critical points. However, the bulk of the existing literature exploring the criticality of many-body systems in the context of QTMs involves models sans the electronic interactions, which are non-trivial to deal with and require sophisticated numerical techniques. Here we adopt the prototypical Hubbard model in two dimensions (2D) in the framework of the line graph Lieb-kagome lattice for the working medium of a multi-functional QTM. We resort to a non-perturbative, static path approximated (SPA) Monte Carlo technique to deal with the repulsive Hubbard model. We observe that in a Stirling cycle, in both the interacting and non-interacting limits, the heat engine function dominates and its performance gets better when the strain is induced from the kagome to the Lieb limit, while for the reverse the refrigeration action is preferred. Further, we show that the QTM performs better when the difference between the temperatures of the two baths is lower and the QTM reaches the Carnot limit in this regime. Further, we extensively study the performance of the QTM in the repulsive Hubbard interacting regime where the magnetic orders come into the picture. We explore the performance of the QTM along the quantum critical points and in the large interaction limit., Comment: Preliminary draft, comments welcomed

We study interactive learning of LLM-based language agents based on user edits made to the agent's output. In a typical setting such as writing assistants, the user interacts with a language agent to generate a response given a context, and may optionally edit the agent response to personalize it based on their latent preference, in addition to improving the correctness. The edit feedback is naturally generated, making it a suitable candidate for improving the agent's alignment with the user's preference, and for reducing the cost of user edits over time. We propose a learning framework, PRELUDE that infers a description of the user's latent preference based on historic edit data. The inferred user preference descriptions are used to define prompts for generating responses in the future. This avoids fine-tuning the agent, which is costly, challenging to scale with the number of users, and may even degrade its performance on other tasks. Furthermore, learning descriptive preference improves interpretability, allowing the user to view and modify the learned preference. However, user preference can be complex, subtle, and vary based on context, making it challenging to learn. To address this, we propose a simple yet effective algorithm named CIPHER that leverages the LLM to infer the user preference for a given context based on user edits. In the future, CIPHER retrieves inferred preferences from the k-closest contexts in the history, and forms an aggregate preference for response generation. We introduce two interactive environments -- summarization and email writing, and use a GPT-4 simulated user for evaluation. On both tasks, CIPHER outperforms several baselines by achieving the lowest edit distance cost while only having a small overhead in LLM query cost. Our analysis reports that user preferences learned by CIPHER show significant similarity to the ground truth latent preferences.

Analog Quantum Computers are promising tools for improving performance on applications such as modeling behavior of quantum materials, providing fast heuristic solutions to optimization problems, and simulating quantum systems. Due to the challenges of simulating dynamic quantum systems, there are relatively few classical tools for modeling the behavior of these devices and verifying their performance. QuantumAnnealing.jl provides a toolkit for performing simulations of Analog Quantum Computers on classical hardware. This package includes functionality for simulation of the time evolution of the Transverse Field Ising Model, replicating annealing schedules used by real world annealing hardware, implementing custom annealing schedules, and more. This allows for rapid prototyping of models expected to display interesting behavior, verification of the performance of quantum devices, and easy comparison against the expected behavior of quantum devices against classical approaches for small systems. The software is provided as open-source and is available through Julia's package registry system., Comment: 10 pages, 15 figures

We report simultaneous detection of twin kHz and $\sim 40$ Hz quasi-periodic oscillations (QPOs) in the time-resolved analysis of the AstroSat/LAXPC observation of the neutron star low mass X-ray binary, 4U 1728-34. The frequencies of the multiple sets of triplets are correlated with each other and are consistent with their identification as the orbital, periastron and twice the nodal precessions frequencies. The observed relations, along with the known spin of the neutron star, put constraints on the mass and the ratio of moment of inertia to the mass of the neutron star to be $M^*_\odot = 1.92\pm 0.01$ and $I_{45}/M^*_\odot = 1.07\pm 0.01$ under the simplistic assumption that the metric is a Kerr one. We crudely estimate that the mass and moment of inertia values obtained may differ by about 1 % and 5 %, respectively, if a self-consistent metric is invoked. Using the TOV equations for computing the moment of inertia of a neutron star in slow rotation approximation, having different equations of state, we find that the predicted values of neutron star parameters favor stiffer equations of state. We expect more stringent constraints would be obtained using a more detailed treatment, where the EOS-dependent metric is used to compute the expected frequencies rather than the Kerr metric used here. The results provide insight into both the nature of these QPOs and the neutron star interior., Comment: 12 pages, 9 figures, accepted for publication in ApJ

The power flow equations are central to many problems in power system planning, analysis, and control. However, their inherent non-linearity and non-convexity present substantial challenges during problem-solving processes, especially for optimization problems. Accordingly, linear approximations are commonly employed to streamline computations, although this can often entail compromises in accuracy and feasibility. This paper proposes an approach termed Conservative Bias Linear Approximations (CBLA) for addressing these limitations. By minimizing approximation errors across a specified operating range while incorporating conservativeness (over- or under-estimating quantities of interest), CBLA strikes a balance between accuracy and tractability by maintaining linear constraints. By allowing users to design loss functions tailored to the specific approximated function, the bias approximation approach significantly enhances approximation accuracy. We illustrate the effectiveness of our proposed approach through several test cases.

We study interactive learning in a setting where the agent has to generate a response (e.g., an action or trajectory) given a context and an instruction. In contrast, to typical approaches that train the system using reward or expert supervision on response, we study learning with hindsight instruction where a teacher provides an instruction that is most suitable for the agent's generated response. This hindsight labeling of instruction is often easier to provide than providing expert supervision of the optimal response which may require expert knowledge or can be impractical to elicit. We initiate the theoretical analysis of interactive learning with hindsight labeling. We first provide a lower bound showing that in general, the regret of any algorithm must scale with the size of the agent's response space. We then study a specialized setting where the underlying instruction-response distribution can be decomposed as a low-rank matrix. We introduce an algorithm called LORIL for this setting and show that its regret scales as $\sqrt{T}$ where $T$ is the number of rounds and depends on the intrinsic rank but does not depend on the size of the agent's response space. We provide experiments in two domains showing that LORIL outperforms baselines even when the low-rank assumption is violated.

Reinforcement Learning (RL) from Human Preference-based feedback is a popular paradigm for fine-tuning generative models, which has produced impressive models such as GPT-4 and Claude3 Opus. This framework often consists of two steps: learning a reward model from an offline preference dataset followed by running online RL to optimize the learned reward model. In this work, leveraging the idea of reset, we propose a new RLHF algorithm with provable guarantees. Motivated by the fact that offline preference dataset provides informative states (i.e., data that is preferred by the labelers), our new algorithm, Dataset Reset Policy Optimization (DR-PO), integrates the existing offline preference dataset into the online policy training procedure via dataset reset: it directly resets the policy optimizer to the states in the offline dataset, instead of always starting from the initial state distribution. In theory, we show that DR-PO learns to perform at least as good as any policy that is covered by the offline dataset under general function approximation with finite sample complexity. In experiments, we demonstrate that on both the TL;DR summarization and the Anthropic Helpful Harmful (HH) dataset, the generation from DR-PO is better than that from Proximal Policy Optimization (PPO) and Direction Preference Optimization (DPO), under the metric of GPT4 win-rate. Code for this work can be found at https://github.com/Cornell-RL/drpo., Comment: 28 pages, 6 tables, 3 Figures, 3 Algorithms