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We present a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration (PARTNR) designed to study human-robot coordination in household activities. PARTNR tasks exhibit characteristics of everyday tasks, such as spatial, temporal, and heterogeneous agent capability constraints. We employ a semi-automated task generation pipeline using Large Language Models (LLMs), incorporating simulation in the loop for grounding and verification. PARTNR stands as the largest benchmark of its kind, comprising 100,000 natural language tasks, spanning 60 houses and 5,819 unique objects. We analyze state-of-the-art LLMs on PARTNR tasks, across the axes of planning, perception and skill execution. The analysis reveals significant limitations in SoTA models, such as poor coordination and failures in task tracking and recovery from errors. When LLMs are paired with real humans, they require 1.5x as many steps as two humans collaborating and 1.1x more steps than a single human, underscoring the potential for improvement in these models. We further show that fine-tuning smaller LLMs with planning data can achieve performance on par with models 9 times larger, while being 8.6x faster at inference. Overall, PARTNR highlights significant challenges facing collaborative embodied agents and aims to drive research in this direction., Comment: Alphabetical author order

Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data. Understanding these networks is crucial for uncovering disease mechanisms and identifying therapeutic targets. In this work, we investigate the potential of large language models (LLMs) for GRN discovery, leveraging their learned biological knowledge alone or in combination with traditional statistical methods. We develop a task-based evaluation strategy to address the challenge of unavailable ground truth causal graphs. Specifically, we use the GRNs suggested by LLMs to guide causal synthetic data generation and compare the resulting data against the original dataset. Our statistical and biological assessments show that LLMs can support statistical modeling and data synthesis for biological research.

Predicting future human behavior is an increasingly popular topic in computer vision, driven by the interest in applications such as autonomous vehicles, digital assistants and human-robot interactions. The literature on behavior prediction spans various tasks, including action anticipation, activity forecasting, intent prediction, goal prediction, and so on. Our survey aims to tie together this fragmented literature, covering recent technical innovations as well as the development of new large-scale datasets for model training and evaluation. We also summarize the widely-used metrics for different tasks and provide a comprehensive performance comparison of existing approaches on eleven action anticipation datasets. This survey serves as not only a reference for contemporary methodologies in action anticipation, but also a guideline for future research direction of this evolving landscape., Comment: 30 pages, 9 figures, 12 tables

We present the first multi-frequency analysis of the candidate ultra-steep spectrum radio halo in the galaxy cluster PLCKESZ G171.94$-$40.65, using the upgraded Giant Metrewave Radio telescope (uGMRT; 400 MHz), and Karl G. Jansky Very Large Array (JVLA; 1-2 GHz) observations. Our radio data have been complemented with archival \textit{Chandra} X-ray observations to provide a crucial insight into the complex intracluster medium (ICM) physics, happening at large scales. We detect the radio halo emission to the extent of $\sim$ 1.5 Mpc at 400 MHz, significantly larger than previously reported, along with five tailed galaxies in the central region. We also report the discovery of an unknown diffuse source 'U', at the cluster periphery, with an extent of 300 kpc. Using the available observations, we have found that the radio spectrum of the halo is well-fitted with a single power law, having a spectral index of $-1.36 \pm 0.05$, indicating that it is not an ultra-steep spectrum radio halo. Our low-resolution (25$''$) resolved spectral map shows an overall uniform spectral index, with some patches of fluctuations. The X-ray and radio surface brightness are morphologically co-spatial, with a slight extension along the northwest-southeast direction, seen in both maps. The radio and X-ray surface brightness indicates strong positive correlations, with sub-linear correlation slopes ($\sim$ 0.71). Multiple tailed galaxies and the radio halo indicate a high dynamical activity at the cluster central region., Comment: 16 pages, 12 Figures, 6 Tables, Accepted for publication in ApJ

It is usually assumed that the memory of any thermo-mechanical protocol applied to a glass can be erased by heating the material in the supercooled liquid. While this is true for thermally treated amorphous solids, we show that hydrostatic compression can irreversibly modify the atomic motion, thermodynamic state and structure of a prototypical metallic glass-former, in a way which depends on the degree of ergodicity reached by the material during compression. While enhanced kinetic and thermodynamic stability can be obtained by quenching the dense liquid, high-pressure annealing in the glass leads to thermal rejuvenation and complex structural rearrangements at the level of the short and medium range order. When heated above their glass transition temperature, these compressed glasses do not convert into the pristine supercooled liquid but rather transform into different systems, challenging the generally accepted idea of an equilibrium recovery controlled solely by the microscopic $\alpha$-relaxation process.

Compositing an object into an image involves multiple non-trivial sub-tasks such as object placement and scaling, color/lighting harmonization, viewpoint/geometry adjustment, and shadow/reflection generation. Recent generative image compositing methods leverage diffusion models to handle multiple sub-tasks at once. However, existing models face limitations due to their reliance on masking the original object during training, which constrains their generation to the input mask. Furthermore, obtaining an accurate input mask specifying the location and scale of the object in a new image can be highly challenging. To overcome such limitations, we define a novel problem of unconstrained generative object compositing, i.e., the generation is not bounded by the mask, and train a diffusion-based model on a synthesized paired dataset. Our first-of-its-kind model is able to generate object effects such as shadows and reflections that go beyond the mask, enhancing image realism. Additionally, if an empty mask is provided, our model automatically places the object in diverse natural locations and scales, accelerating the compositing workflow. Our model outperforms existing object placement and compositing models in various quality metrics and user studies.

Our research investigates the capability of modern multimodal reasoning models, powered by Large Language Models (LLMs), to facilitate vision-powered assistants for multi-step daily activities. Such assistants must be able to 1) encode relevant visual history from the assistant's sensors, e.g., camera, 2) forecast future actions for accomplishing the activity, and 3) replan based on the user in the loop. To evaluate the first two capabilities, grounding visual history and forecasting in short and long horizons, we conduct benchmarking of two prominent classes of multimodal LLM approaches -- Socratic Models and Vision Conditioned Language Models (VCLMs) on video-based action anticipation tasks using offline datasets. These offline benchmarks, however, do not allow us to close the loop with the user, which is essential to evaluate the replanning capabilities and measure successful activity completion in assistive scenarios. To that end, we conduct a first-of-its-kind user study, with 18 participants performing 3 different multi-step cooking activities while wearing an egocentric observation device called Aria and following assistance from multimodal LLMs. We find that the Socratic approach outperforms VCLMs in both offline and online settings. We further highlight how grounding long visual history, common in activity assistance, remains challenging in current models, especially for VCLMs, and demonstrate that offline metrics do not indicate online performance., Comment: 9 pages, 4 figures

With the growing prevalence of large language models, it is increasingly common to annotate datasets for machine learning using pools of crowd raters. However, these raters often work in isolation as individual crowdworkers. In this work, we regard annotation not merely as inexpensive, scalable labor, but rather as a nuanced interpretative effort to discern the meaning of what is being said in a text. We describe a novel, collaborative, and iterative annotator-in-the-loop methodology for annotation, resulting in a 'Bridging Benchmark Dataset' of comments relevant to bridging divides, annotated from 11,973 textual posts in the Civil Comments dataset. The methodology differs from popular anonymous crowd-rating annotation processes due to its use of an in-depth, iterative engagement with seven US-based raters to (1) collaboratively refine the definitions of the to-be-annotated concepts and then (2) iteratively annotate complex social concepts, with check-in meetings and discussions. This approach addresses some shortcomings of current anonymous crowd-based annotation work, and we present empirical evidence of the performance of our annotation process in the form of inter-rater reliability. Our findings indicate that collaborative engagement with annotators can enhance annotation methods, as opposed to relying solely on isolated work conducted remotely. We provide an overview of the input texts, attributes, and annotation process, along with the empirical results and the resulting benchmark dataset, categorized according to the following attributes: Alienation, Compassion, Reasoning, Curiosity, Moral Outrage, and Respect., Comment: Accepted Paper at AAAI/ACM Artificial Intelligence, Ethics, and Society (AIES)

Graphene is a privileged 2D platform for hosting confined light-matter excitations known as surface plasmon-polaritons (SPPs), as it possesses low intrinsic losses with a high degree of optical confinement. However, the inherently isotropic optical properties of graphene limit its ability to guide and focus SPPs, making it less suitable than anisotropic elliptical and hyperbolic materials as a platform for polaritonic lensing and canalization. Here, we present the graphene/CrSBr heterostructure as an engineered 2D interface that hosts highly anisotropic SPP propagation over a wide range of frequencies in the mid-infrared and terahertz. Using a combination of scanning tunneling microscopy (STM), scattering-type scanning near-field optical microscopy (s-SNOM), and first-principles calculations, we demonstrate mutual doping in excess of 10$^{13}$ cm$^{-2}$ holes/electrons between the interfacial layers of graphene/CrSBr heterostructures. SPPs in graphene activated by charge transfer interact with charge-induced anisotropic intra- and interband transitions in the interfacial doped CrSBr, leading to preferential SPP propagation along the quasi-1D chains that compose each CrSBr layer. This multifaceted proximity effect both creates SPPs and endows them with anisotropic transport and propagation lengths that differ by an order-of-magnitude between the two in-plane crystallographic axes of CrSBr.

Traditional inference in relational event models from dynamic network data involves only dyadic and node-specific variables, as anything that is global, i.e. constant across dyads, drops out of the partial likelihood. We address this with the use of nested case-control sampling on a time-shifted version of the event process. This leads to a partial likelihood of a degenerate logistic additive model, enabling efficient estimation of global and non-global covariate effects. The method's effectiveness is demonstrated through a simulation study. An application to bike sharing data reveals significant influences of global covariates like weather and time of day on bike-sharing dynamics.

4$d$ transition metal oxide (TMO) offers an intriguing puzzle for their electronic and magnetic ground state. They are in the cross-over regime of strong spin orbit interaction (SOI) and electron-electron correlation ($U$) with quenched orbital angular momentum. Our work unravels the electronic and magnetic ground state of the less investigated 4$d^{3}$ double perovskite ruthenates A$_{2}$LaRuO$_6$ (A = Ca, Ba). The negligible effect of SOI is evident from the bulk magnetic, specific heat measurements and density functional theory (DFT) calculations, indicating a classical spin-only magnetic ground state (${S}$ = 3/2) for the materials. Magnetization measurements show that both materials have long range antiferromagnetic order with high degree of magnetic frustration ($f$ $\approx$13 -15). Interestingly, a near $T^2$- behavior is observed in low-$T$ magnetic heat capacity measurement, indicating the presence of low-dimensional spin-wave exciation and magnetic frustration in both materials. The temperature dependent resistivity measurements and electronic band structure calculations confirm a conventional Mott insulating ground state in these two systems. Moreover, our experimental investigation and DFT calculations highlight the reason for the nonexistence of Sr$_2$LaRuO$_6$., Comment: 11 pages, 12 figures, 5 tables

Correlated materials may exhibit unusually high resistivity increasing linearly in temperature, breaking through the Mott-Ioffe-Regel bound, above which coherent quasiparticles are destroyed. The fate of collective charge excitations, or plasmons, in these systems is a subject of debate. Several studies suggest plasmons are overdamped while others detect unrenormalized plasmons. Here, we present direct optical images of low-loss hyperbolic plasmon polaritons (HPPs) in the correlated van der Waals metal MoOCl2. HPPs are plasmon-photon modes that waveguide through extremely anisotropic media and are remarkably long-lived in MoOCl2. Many-body theory supported by photoemission results reveals that MoOCl2 is in an orbital-selective and highly incoherent Peierls phase. Different orbitals acquire markedly different bonding-antibonding character, producing a highly-anisotropic, isolated Fermi surface. The Fermi surface is further reconstructed and made partly incoherent by electronic interactions, renormalizing the plasma frequency. HPPs remain long-lived in spite of this, allowing us to uncover previously unseen imprints of electronic correlations on plasmonic collective modes., Comment: 32 pages, 16 figures

Assembling atomic layers of van der Waals materials (vdW) combines the physics of two materials, offering opportunities for novel functional devices. Realization of this has been possible because of advancements in nanofabrication processes which often involve chemical processing of the materials under study; this can be detrimental to device performance. To address this issue, we have developed a modified micro-manipulator setup for cryogenic exfoliation, pick up, and transfer of vdW materials to assemble heterostructures. We use the glass transition of a polymer PDMS to cleave a flake into two, followed by its pick-up and drop to form pristine twisted junctions. To demonstrate the potential of the technique, we fabricated twisted heterostructure of Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ (BSCCO), a van der Waals high-temperature cuprate superconductor. We also employed this method to re-exfoliate NbSe$_2$ and make twisted heterostructure. Transport measurements of the fabricated devices indicate the high quality of the artificial twisted interface. In addition, we extend this cryogenic exfoliation method for other vdW materials, offering an effective way of assembling heterostructures and twisted junctions with pristine interfaces.

Fair division is the problem of allocating a set of items among agents in a fair manner. One of the most sought-after fairness notions is envy-freeness (EF), requiring that no agent envies another's allocation. When items are indivisible, it ceases to exist, and envy-freeness up to any good (EFX) emerged as one of its strongest relaxations. The existence of EFX allocations is arguably the biggest open question within fair division. Recently, Christodoulou, Fiat, Koutsoupias, and Sgouritsa (EC 2023) showed that EFX allocations exist for the case of graphical valuations where an instance is represented by a graph: nodes are agents, edges are goods, and each agent values only her incident edges. On the other hand, they showed NP-hardness for checking the existence of EFX orientation where every edge is allocated to one of its incident vertices, and asked for a characterization of graphs that exhibit EFX orientation regardless of the assigned valuations. In this paper, we make significant progress toward answering their question. We introduce the notion of strongly EFX orientable graphs -- graphs that have EFX orientations regardless of how much agents value the edges. We show a surprising connection between this property and the chromatic number $\chi(G)$ of the graph $G$. In particular, we show that graphs with $\chi(G)\le 2$ are strongly EFX orientable, and those with $\chi(G)>3$ are not strongly EFX orientable. We provide examples of strongly EFX orientable and non-strongly EFX orientable graphs of $\chi(G)=3$ to prove tightness. Finally, we give a complete characterization of strong EFX orientability when restricted to binary valuations., Comment: 12 pages, 4 figures

Preference learning has long been studied in Human-Robot Interaction (HRI) in order to adapt robot behavior to specific user needs and desires. Typically, human preferences are modeled as a scalar function; however, such a formulation confounds critical considerations on how the robot should behave for a given task, with desired -- but not required -- robot behavior. In this work, we distinguish between such required and desired robot behavior by leveraging a planning framework. Specifically, we propose a novel problem formulation for preference learning in HRI where various types of human preferences are encoded as soft planning constraints. Then, we explore a data-driven method to enable a robot to infer preferences by querying users, which we instantiate in rearrangement tasks in the Habitat 2.0 simulator. We show that the proposed approach is promising at inferring three types of preferences even under varying levels of noise in simulated user choices between potential robot behaviors. Our contributions open up doors to adaptable planning-based robot behavior in the future.

Modern detector technology and highly brilliant fourth-generation synchrotrons allow to improve the temporal resolution in time-resolved diffraction studies. Profiting from this, we applied temperature scanning X-ray photon correlation spectroscopy (XPCS) to probe the dynamics of a Pt-based metallic glass former in the glass, glass transition region, and supercooled liquid, covering up to six orders of magnitude in time scales. Our data demonstrates that the structural alpha-relaxation process is still observable in the glass, although it is partially masked by a faster source of decorrelation observed at atomic scale. We present an approach that interprets these findings as the superposition of heterogeneous liquid-like and stress-driven ballistic-like atomic motions. This work not only extends the dynamical range probed by standard isothermal XPCS, but also clarifies the fate of the alpha-relaxation across the glass transition and provides a new perception on the anomalous, compressed temporal decay of the density-density correlation functions observed in metallic glasses and many out-of-equilibrium soft materials.

We study the computation of competitive equilibrium for Fisher markets with $n$ agents and $m$ divisible chores. Prior work showed that competitive equilibria correspond to the nonzero KKT points of a non-convex analogue of the Eisenberg-Gale convex program. We introduce an analogue of the Eisenberg-Gale dual for chores: we show that all KKT points of this dual correspond to competitive equilibria, and while it is not a dual of the non-convex primal program in a formal sense, the objectives touch at all KKT points. Similar to the primal, the dual has problems from an optimization perspective: there are many feasible directions where the objective tends to positive infinity. We then derive a new constraint for the dual, which restricts optimization to a hyperplane that avoids all these directions. We show that restriction to this hyperplane retains all KKT points, and surprisingly, does not introduce any new ones. This allows, for the first time ever, application of iterative optimization methods over a convex region for computing competitive equilibria for chores. We next introduce a greedy Frank-Wolfe algorithm for optimization over our program and show a state-of-the-art convergence rate to competitive equilibrium. In the case of equal incomes, we show a $\mathcal{\tilde O}(n/\epsilon^2)$ rate of convergence, which improves over the two prior state-of-the-art rates of $\mathcal{\tilde O}(n^3/\epsilon^2)$ for an exterior-point method and $\mathcal{\tilde O}(nm/\epsilon^2)$ for a combinatorial method. Moreover, our method is significantly simpler: each iteration of our method only requires solving a simple linear program. We show through numerical experiments on simulated data and a paper review bidding dataset that our method is extremely practical. This is the first highly practical method for solving competitive equilibrium for Fisher markets with chores., Comment: 25 pages, 17 figures

A new experimental setup combining X-Ray Photon Correlation Spectroscopy (XPCS) in the hard x-ray regime and a high-pressure sample environment is developed to monitor the pressure dependence of the internal motion of complex systems down to the atomic scale in the multi-GPa range, from room temperature to 600K. The high flux of coherent high energy x-rays at 4th generation synchrotron source solves the problems caused by the absorption of the Diamond Anvil Cells used to generate the high pressure, enabling the measurement of the intermediate scattering function over 6 orders of magnitude in time, from $10^{-3}$ s to $10^{3}$s. The constraints posed by the high-pressure generation such as the preservation of the x-ray's coherence, as well as the sample, pressure and temperature stability are discussed, and the feasibility of high pressure XPCS is demonstrated through results obtained on metallic glasses.

Hybridization between the localized f-electrons and the delocalized conduction electrons together with the crystal electric field (CEF) play a determinant role in governing the many-body ground state of a correlated-electron system. Here, we investigate the low-energy CEF states in CeAg_2Ge_2, a prototype Kondo-lattice antiferromagnet where Kondo correlation is found to exist within the antiferromagnetic phase. Using time-domain THz reflection spectroscopy, we show the first direct evidence of two low-energy CEF transitions at 0.6 THz (2.5 meV) and 2.1 THz (8.7 meV). The presence of low-frequency infrared-active phonon modes further manifests as a Fano-modified lineshape of the 2.1 THz CEF conductivity peak. The temporal spectral weights obtained directly from the THz time traces, in addition, corroborate the corresponding CEF temperature scales of the compound., Comment: 10 pages, 6 figures

The paper reports the results of an experiment aimed at testing to what extent ChatGPT 3.5 and 4 is able to answer questions regarding privacy policies designed in the new format that we propose. In a world of human-only interpreters, there was a trade-off between comprehensiveness and comprehensibility of privacy policies, leading to the actual policies not containing enough information for users to learn anything meaningful. Having shown that GPT performs relatively well with the new format, we provide experimental evidence supporting our policy suggestion, namely that the law should require fully comprehensive privacy policies, even if this means they become less concise.

Glass forming liquids exhibit structural relaxation behaviors, reflecting underlying atomic rearrangements on a wide range of timescales. These behaviors play a crucial role in determining many material properties. However, the relaxation processes on the atomic scale are not well understood due to the experimental difficulties in directly characterizing the evolving correlations of atomic order in disordered systems. Here, taking the model system Ge15Te85, we demonstrate an experimental approach that probes the relaxation dynamics by scattering the coherent X-ray pulses with femtosecond duration produced by X-ray free electron lasers (XFELs). By collecting the summed speckle patterns from two rapidly successive, nearly identical X-ray pulses generated using a split-delay system, we can extract the contrast decay of speckle patterns originating from sample dynamics and observe the full decorrelation of local order on the sub-picosecond timescale. This provides the direct atomic-level evidence of fragile liquid behavior of Ge15Te85. Our results demonstrate the strategy for XFEL-based X-ray photon correlation spectroscopy (XPCS), attaining femtosecond temporal and atomic-scale spatial resolutions. This twelve orders of magnitude extension from the millisecond regime of synchrotron-based XPCS opens a new avenue of experimental studies of relaxation dynamics in liquids, glasses, and other highly disordered systems.

We study the problem of fairly dividing indivisible goods among a set of agents under the fairness notion of Any Price Share (APS). APS is known to dominate the widely studied Maximin share (MMS). Since an exact APS allocation may not exist, the focus has traditionally been on the computation of approximate APS allocations. Babaioff et al. studied the problem under additive valuations, and asked (i) how large can the APS value be compared to the MMS value? and (ii) what guarantees can one achieve beyond additive functions. We partly answer these questions by considering valuations beyond additive, namely submodular and XOS functions, with binary marginals. For the submodular functions with binary marginals, also known as matroid rank functions (MRFs), we show that APS is exactly equal to MMS. Consequently, we get that an exact APS allocation exists and can be computed efficiently while maximizing the social welfare. Complementing this result, we show that it is NP-hard to compute the APS value within a factor of 5/6 for submodular valuations with three distinct marginals of {0, 1/2, 1}. We then consider binary XOS functions, which are immediate generalizations of binary submodular functions in the complement free hierarchy. In contrast to the MRFs setting, MMS and APS values are not equal under this case. Nevertheless, we show that under binary XOS valuations, $MMS \leq APS \leq 2 \cdot MMS + 1$. Further, we show that this is almost the tightest bound we can get using MMS, by giving an instance where $APS \geq 2 \cdot MMS$. The upper bound on APS, implies a ~0.1222-approximation for APS under binary XOS valuations. And the lower bound implies the non-existence of better than 0.5-APS even when agents have identical valuations, which is in sharp contrast to the guaranteed existence of exact MMS allocation when agent valuations are identical.

We study fair distribution of a collection of m indivisible goods among a group of n agents, using the widely recognized fairness principles of Maximin Share (MMS) and Any Price Share (APS). These principles have undergone thorough investigation within the context of additive valuations. We explore these notions for valuations that extend beyond additivity. First, we study approximate MMS under the separable (piecewise-linear) concave (SPLC) valuations, an important class generalizing additive, where the best known factor was 1/3-MMS. We show that 1/2-MMS allocation exists and can be computed in polynomial time, significantly improving the state-of-the-art. We note that SPLC valuations introduce an elevated level of intricacy in contrast to additive. For instance, the MMS value of an agent can be as high as her value for the entire set of items. Further, the equilibrium computation problem, which is polynomial-time for additive valuations, becomes intractable for SPLC. We use a relax-and-round paradigm that goes through competitive equilibrium and LP relaxation. Our result extends to give (symmetric) 1/2-APS, a stronger guarantee than MMS. APS is a stronger notion that generalizes MMS by allowing agents with arbitrary entitlements. We study the approximation of APS under submodular valuation functions. We design and analyze a simple greedy algorithm using concave extensions of submodular functions. We prove that the algorithm gives a 1/3-APS allocation which matches the current best-known factor. Concave extensions are hard to compute in polynomial time and are, therefore, generally not used in approximation algorithms. Our approach shows a way to utilize it within analysis (while bypassing its computation), and might be of independent interest., Comment: To appear in AAAI Conference on Artificial Intelligence, 2024

We report the discovery of a radio relic in the northeastern periphery of the cluster Abell 2108 (A2108). A2108 is a part of the uGMRT LOw-MAss Galaxy Cluster Survey (GLOMACS), where our main aim is to search for diffuse radio emission signatures in very sparsely explored low-mass galaxy clusters using uGMRT band-3 (central frequency 400 MHz). We used our uGMRT band-3 data along with the existing archival band-3 uGMRT data to improve image sensitivity. Along with the previously reported southwestern relic, the discovery of the new relic makes A2108 one of the few low-mass clusters hosting double relics. The new relic spans over a region of 610 kpc $\times$ 310 kpc and, interestingly, differs considerably in size and morphology from the other relic. With XMM-Newton science archive data, we also report the tentative detection of a mildly supersonic shock of Mach number $\mathcal{M}_\mathrm{SB}=1.42$ and $\mathcal{M}_\mathrm{T} = 1.43$ from the surface brightness and temperature discontinuities, respectively near this newly found relic. Both the relics in A2108 are found to be significantly under-luminous compared to other double relic systems in the mass-luminosity plane. Though mild supersonic shocks resulting from an off-axis merger could have influenced their origin, we hypothesize that further local environments have played a crucial role in shaping their morphology., Comment: 7 pages, 5 figures, Accepted for publication in MNRAS

We present radio and X-ray studies of A3444 and MS1455.0+2232, two galaxy clusters with radio minihalos in their cool cores. A3444 is imaged using the Giant Metrewave Radio Telescope (GMRT) at 333, 607 and 1300 MHz and the Very Large Array at 1435 MHz. Most of the minihalo is contained within r<120 kpc, but a fainter extension, stretching out to 380 kpc South-West of the center, is detected at 607 MHz. Using Chandra, we detect four X-ray sloshing cold fronts: three in the cool core at r=60, 120 and 230 kpc, and a fourth one at r=400 kpc - in the region of the southwestern radio extension - suggesting that the intracluster medium (ICM) is sloshing on a cluster-wide scale. The radio emission is contained within the envelope defined by these fronts. We also analyzed archival 383 MHz GMRT and Chandra observations of MS1455.0+2232, which exhibits a known minihalo with its bright part delineated by cold fronts inside the cool core, but with a faint extension beyond the core. Similarly to A3444, we find a cold front at r~425 kpc, containing the radio emission. Thus the entire diffuse radio emission seen in these clusters appears to be related to large-scale sloshing of the ICM. The radio spectrum of the A3444 minihalo is a power law with a steep index $\alpha=1.0\pm0.1$. The spectrum steepens with increasing distance from the center, as expected if the minihalo originates from re-acceleration of relativistic particles by the sloshing-induced turbulence in the ICM., Comment: 29 pages, 15 figures, accepted for publication in ApJ

Purpose: To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Materials and Methods: Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian, radial, spiral) either from true multi-coil cardiac MR data (N=692) or from pseudo-MR data simulated from Inter4K natural videos (N=692). Real-time undersampled dynamic MR images were reconstructed using DL networks trained with cardiac data and natural videos, and compressed sensing (CS). Differences were assessed in simulations (N=104 datasets) in terms of MSE, PSNR, and SSIM and prospectively for cardiac (short axis, four chambers, N=20) and speech (N=10) data in terms of subjective image quality ranking, SNR and Edge sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were performed to assess statistical significance. Results: For all simulation metrics, DL networks trained with cardiac data outperformed DL networks trained with natural videos, which outperformed CS (p<0.05). However, in prospective experiments DL reconstructions using both training datasets were ranked similarly (and higher than CS) and presented no statistical differences in SNR and Edge Sharpness for most conditions. Additionally, high SSIM was measured between the DL methods with cardiac data and natural videos (SSIM>0.85). Conclusion: The developed pipeline enabled learning dynamic MR reconstruction from natural videos preserving DL reconstruction advantages such as high quality fast and ultra-fast reconstructions while overcoming some limitations (data scarcity or sharing). The natural video dataset, code and pre-trained networks are made readily available on github. Key Words: real-time; dynamic MRI; deep learning; image reconstruction; machine learning

We present the first detailed analysis of the ultra-steep spectrum radio halo in the merging galaxy cluster Abell 521, based on upgraded Giant Metrewave Radio telescope (uGMRT) observations. The combination of radio observations (300-850 MHz) and archival X-ray data provide a new window into the complex physics occurring in this system. When compared to all previous analyses, our sensitive radio images detected the centrally located radio halo emission to a greater extent of $\sim$ 1.3 Mpc. A faint extension of the southeastern radio relic has been discovered. We detected another relic, recently discovered by MeerKAT, and coincident with a possible shock front in the X-rays, at the northwest position of the center. We find that the integrated spectrum of the radio halo is well-fitted with a spectral index of $-1.86 \pm 0.12$. A spatially resolved spectral index map revealed the spectral index fluctuations, as well as an outward radial steepening of the average spectral index. The radio and X-ray surface brightness are well correlated for the entire and different sub-parts of the halo, with sub-linear correlation slopes (0.50$-$0.65). We also found a mild anti-correlation between the spectral index and X-ray surface brightness. Newly detected extensions of the SE relic and the counter relic are consistent with the merger in the plane of the sky., Comment: 31 pages (total), 35 figures, 7 tables, 2 appendix (12 figures, 2 tables). Accepted for publication in Astrophysical Journal (APJ)

LLMs have recently made impressive inroads on tasks whose output is structured, such as coding, robotic planning and querying databases. The vision of creating AI-powered personal assistants also involves creating structured outputs, such as a plan for one's day, or for an overseas trip. Here, since the plan is executed by a human, the output doesn't have to satisfy strict syntactic constraints. A useful assistant should also be able to incorporate vague constraints specified by the user in natural language. This makes LLMs an attractive option for planning. We consider the problem of planning one's day. We develop an LLM-based planner (LLMPlan) extended with the ability to self-reflect on its output and a symbolic planner (SymPlan) with the ability to translate text constraints into a symbolic representation. Despite no formal specification of constraints, we find that LLMPlan performs explicit constraint satisfaction akin to the traditional symbolic planners on average (2% performance difference), while retaining the reasoning of implicit requirements. Consequently, LLM-based planners outperform their symbolic counterparts in user satisfaction (70.5% vs. 40.4%) during interactive evaluation with 40 users.

Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled noise at the interface between the server that stores and processes the data, and the data consumers. Local differential privacy is a variant that allows data providers to apply the privatization mechanism themselves on their data individually. Therefore it provides protection also in contexts in which the server, or even the data collector, cannot be trusted. The introduction of noise, however, inevitably affects the utility of the data, particularly by distorting the correlations between individual data components. This distortion can prove detrimental to tasks such as causal discovery. In this paper, we consider various well-known locally differentially private mechanisms and compare the trade-off between the privacy they provide, and the accuracy of the causal structure produced by algorithms for causal learning when applied to data obfuscated by these mechanisms. Our analysis yields valuable insights for selecting appropriate local differentially private protocols for causal discovery tasks. We foresee that our findings will aid researchers and practitioners in conducting locally private causal discovery.

Relaxation dynamics, as a key to understand glass formation and glassy properties, remains an elusive and challenging issue in condensed matter physics. In this work, in situ high-pressure synchrotron high-energy x-ray photon correlation spectroscopy has been developed to probe the atomic-scale relaxation dynamics of a cerium-based metallic glass during compression. Although the sample density continuously increases, the collective atomic motion initially slows down as generally expected and then counter-intuitively accelerates with further compression (density increase), showing an unusual non-monotonic pressure-induced steady relaxation dynamics crossover at ~3 GPa. Furthermore, by combining in situ high-pressure synchrotron x-ray diffraction, the relaxation dynamics anomaly is evidenced to closely correlate with the dramatic changes in local atomic structures during compression, rather than monotonically scaling with either sample density or overall stress level. These findings could provide new insight into relaxation dynamics and their relationship with local atomic structures of glasses., Comment: 21 pages, 4 figures

Accurately measuring discrimination in machine learning-based automated decision systems is required to address the vital issue of fairness between subpopulations and/or individuals. Any bias in measuring discrimination can lead to either amplification or underestimation of the true value of discrimination. This paper focuses on a class of bias originating in the way training data is generated and/or collected. We call such class causal biases and use tools from the field of causality to formally define and analyze such biases. Four sources of bias are considered, namely, confounding, selection, measurement, and interaction. The main contribution of this paper is to provide, for each source of bias, a closed-form expression in terms of the model parameters. This makes it possible to analyze the behavior of each source of bias, in particular, in which cases they are absent and in which other cases they are maximized. We hope that the provided characterizations help the community better understand the sources of bias in machine learning applications.

We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in home environments. Habitat 3.0 offers contributions across three dimensions: (1) Accurate humanoid simulation: addressing challenges in modeling complex deformable bodies and diversity in appearance and motion, all while ensuring high simulation speed. (2) Human-in-the-loop infrastructure: enabling real human interaction with simulated robots via mouse/keyboard or a VR interface, facilitating evaluation of robot policies with human input. (3) Collaborative tasks: studying two collaborative tasks, Social Navigation and Social Rearrangement. Social Navigation investigates a robot's ability to locate and follow humanoid avatars in unseen environments, whereas Social Rearrangement addresses collaboration between a humanoid and robot while rearranging a scene. These contributions allow us to study end-to-end learned and heuristic baselines for human-robot collaboration in-depth, as well as evaluate them with humans in the loop. Our experiments demonstrate that learned robot policies lead to efficient task completion when collaborating with unseen humanoid agents and human partners that might exhibit behaviors that the robot has not seen before. Additionally, we observe emergent behaviors during collaborative task execution, such as the robot yielding space when obstructing a humanoid agent, thereby allowing the effective completion of the task by the humanoid agent. Furthermore, our experiments using the human-in-the-loop tool demonstrate that our automated evaluation with humanoids can provide an indication of the relative ordering of different policies when evaluated with real human collaborators. Habitat 3.0 unlocks interesting new features in simulators for Embodied AI, and we hope it paves the way for a new frontier of embodied human-AI interaction capabilities., Comment: Project page: http://aihabitat.org/habitat3

This paper discusses predictive performance and processes undertaken on flight pricing data utilizing r2(r-square) and RMSE that leverages a large dataset, originally from Expedia.com, consisting of approximately 20 million records or 4.68 gigabytes. The project aims to determine the best models usable in the real world to predict airline ticket fares for non-stop flights across the US. Therefore, good generalization capability and optimized processing times are important measures for the model. We will discover key business insights utilizing feature importance and discuss the process and tools used for our analysis. Four regression machine learning algorithms were utilized: Random Forest, Gradient Boost Tree, Decision Tree, and Factorization Machines utilizing Cross Validator and Training Validator functions for assessing performance and generalization capability., Comment: 4 pages, 13 figures, 1 table

A non-interactive ZK (NIZK) proof enables verification of NP statements without revealing secrets about them. However, an adversary that obtains a NIZK proof may be able to clone this proof and distribute arbitrarily many copies of it to various entities: this is inevitable for any proof that takes the form of a classical string. In this paper, we ask whether it is possible to rely on quantum information in order to build NIZK proof systems that are impossible to clone. We define and construct unclonable non-interactive zero-knowledge arguments (of knowledge) for NP, addressing a question first posed by Aaronson (CCC 2009). Besides satisfying the zero-knowledge and argument of knowledge properties, these proofs additionally satisfy unclonability. Very roughly, this ensures that no adversary can split an honestly generated proof of membership of an instance $x$ in an NP language $\mathcal{L}$ and distribute copies to multiple entities that all obtain accepting proofs of membership of $x$ in $\mathcal{L}$. Our result has applications to unclonable signatures of knowledge, which we define and construct in this work; these non-interactively prevent replay attacks., Comment: Clarified definitions and included applications

In topological materials, shielding of bulk and surface states by crystalline symmetries has provided hitherto unknown access to electronic states in condensed matter physics. Interestingly, photo-excited carriers relax on an ultrafast timescale, demonstrating large transient mobility that could be harnessed for the development of ultrafast optoelectronic devices. In addition, these devices are much more effective than topologically trivial systems because topological states are resilient to the corresponding symmetry-invariant perturbations. By using optical pump probe measurements, we systematically describe the relaxation dynamics of a topologically nontrivial chiral single crystal, PtAl. Based on the experimental data on transient reflectivity and electronic structures, it has been found that the carrier relaxation process involves both acoustic and optical phonons with oscillation frequencies of 0.06 and 2.94 THz, respectively, in picosecond time scale. PtAl with a space group of $P$$2_{1}$3 allows only one non-zero susceptibility element i.e. $d_{14}$, in second harmonic generation (SHG) with a large value of 468(1) pm/V, which is significantly higher than that observed in standard GaAs(111) and ZnTe(110) crystals. The intensity dependence of the SHG signal in PtAl reveals a non-perturbative origin. The present study on PtAl provides deeper insight into topological states which will be useful for ultrafast optoelectronic devices., Comment: 10 pages, 5 figures