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We consider a quantum bosonic channel that couples the input mode via a beam splitter or two-mode squeezer to an environmental mode that is prepared in an arbitrary state. We investigate the classical capacity of this channel, which we call a non-Gaussian attenuator or amplifier channel. If the environment state is thermal, we of course recover a Gaussian phase-covariant channel whose classical capacity is well known. Otherwise, we derive both a lower and an upper bound to the classical capacity of the channel, drawing inspiration from the classical treatment of the capacity of non-Gaussian additive-noise channels. We show that the lower bound to the capacity is always achievable and give examples where the non-Gaussianity of the channel can be exploited so that the communication rate beats the capacity of the Gaussian-equivalent channel (i.e., the channel where the environment state is replaced by a Gaussian state with the same covariance matrix). Finally, our upper bound leads us to formulate and investigate conjectures on the input state that minimizes the output entropy of non-Gaussian attenuator or amplifier channels. Solving these conjectures would be a main step towards accessing the capacity of a large class of non-Gaussian bosonic channels., Comment: 17 pages + 1 page of Appendix, 9 figures

Yttrium iron garnet (YIG) magnonics has sparked extensive research interests toward harnessing magnons (quasiparticles of collective spin excitation) for signal processing. In particular, YIG magnonics-based hybrid systems exhibit great potentials for quantum information science because of their wide frequency tunability and excellent compatibility with other platforms. However, the broad application and scalability of thin-film YIG devices in the quantum regime has been severely limited due to the substantial microwave loss in the host substrate for YIG, gadolinium gallium garnet (GGG), at cryogenic temperatures. In this study, we demonstrate that substrate-free YIG thin films can be obtained by introducing the controlled spalling and layer transfer technology to YIG/GGG samples. Our approach is validated by measuring a hybrid device consisting of a superconducting resonator and a spalled YIG film, which gives a strong coupling feature indicating the good coherence of our system. This advancement paves the way for enhanced on-chip integration and the scalability of YIG-based quantum devices., Comment: 10 pages, 8 figures

We study the task of $(\epsilon, \delta)$-differentially private online convex optimization (OCO). In the online setting, the release of each distinct decision or iterate carries with it the potential for privacy loss. This problem has a long history of research starting with Jain et al. [2012] and the best known results for the regime of {\epsilon} not being very small are presented in Agarwal et al. [2023]. In this paper we improve upon the results of Agarwal et al. [2023] in terms of the dimension factors as well as removing the requirement of smoothness. Our results are now the best known rates for DP-OCO in this regime. Our algorithms builds upon the work of [Asi et al., 2023] which introduced the idea of explicitly limiting the number of switches via rejection sampling. The main innovation in our algorithm is the use of sampling from a strongly log-concave density which allows us to trade-off the dimension factors better leading to improved results.

Comparative meta-analyses of groups of subjects by integrating multiple observational studies rely on estimated propensity scores (PSs) to mitigate covariate imbalances. However, PS estimation grapples with the theoretical and practical challenges posed by high-dimensional covariates. Motivated by an integrative analysis of breast cancer patients across seven medical centers, this paper tackles the challenges associated with integrating multiple observational datasets. The proposed inferential technique, called Bayesian Motif Submatrices for Covariates (B-MSC), addresses the curse of dimensionality by a hybrid of Bayesian and frequentist approaches. B-MSC uses nonparametric Bayesian "Chinese restaurant" processes to eliminate redundancy in the high-dimensional covariates and discover latent motifs or lower-dimensional structure. With these motifs as potential predictors, standard regression techniques can be utilized to accurately infer the PSs and facilitate covariate-balanced group comparisons. Simulations and meta-analysis of the motivating cancer investigation demonstrate the efficacy of the B-MSC approach to accurately estimate the propensity scores and efficiently address covariate imbalance when integrating observational health studies with high-dimensional covariates.

A significant amount of research is focused on developing and evaluating large language models for a variety of code synthesis tasks. These include synthesizing code from natural language, synthesizing tests from code, and synthesizing explanations of code. In contrast, the behavior of instructional code editing with LLMs is understudied. These are tasks in which the model is provided a block of code and an instruction to modify the code. The editing instruction may ask for a feature to be added or removed, describe a bug and ask for a fix, or ask for a different kind of solution. We introduce a carefully crafted benchmark of code editing tasks and use it to evaluate several cutting edge LLMs. Our evaluation exposes a significant gap between the capabilities of state-of-the-art open and closed models. For example, even GPT-3.5-Turbo is better than the best open model at code editing tasks. We also introduce a new, carefully curated, permissively licensed training dataset of code editing tasks coupled with natural language instructions. Using this training dataset, we show that we can fine-tune open Code LLMs to significantly improve their code editing capabilities, closing the gap between open and closed models. All code, data, and models are available at https://github.com/nuprl/CanItEdit.

From the perspective of majorization theory, we study how to enhance the entanglement of a two-mode squeezed vacuum (TMSV) state by using local filtration operations. We present several schemes achieving entanglement enhancement with photon addition and subtraction, and then consider filtration as a general probabilistic procedure consisting in acting with local (non-unitary) operators on each mode. From this, we identify a sufficient set of two conditions on filtration operators for successfully enhancing the entanglement of a TMSV state, namely the operators must be Fock-orthogonal (i.e., preserving the orthogonality of Fock states) and Fock-amplifying (i.e., giving larger amplitudes to larger Fock states). Our results notably prove that ideal photon addition, subtraction, and any concatenation thereof always enhance the entanglement of a TMSV state in the sense of majorization theory. We further investigate the case of realistic photon addition (subtraction) and are able to upper bound the distance between a realistic photon-added (-subtracted) TMSV state and a nearby state that is provably more entangled than the TMSV, thus extending entanglement enhancement to practical schemes via the use of a notion of approximate majorization. Finally, we consider the state resulting from $k$-photon addition (on each of the two modes) on a TMSV state. We prove analytically that the state corresponding to $k=1$ majorizes any state corresponding to $2\leq k \leq 8$ and we conjecture the validity of the statement for all $k\geq 9$., Comment: 11 pages + 3 pages of Appendix, 7 figures

We study quasinormal $f-$mode oscillations in neutron star(NS) interiors within the linearized General Relativistic formalism. We utilize approximately 9000 nuclear Equations of State (EOS) using spectral representation techniques, incorporating constraints on nuclear saturation properties, chiral Effective Field Theory ($\chi$EFT) for pure neutron matter, and perturbative Quantum Chromodynamics (pQCD) for densities pertinent to NS cores. The median values of f-mode frequency, $\nu_f$ (damping time, $\tau_f$) for NS with masses ranging from 1.4 - 2.0 $M_\odot$ lie between 1.80 - 2.20 kHz (0.13 - 0.22 s) for our entire EOS set. Our study reveals a weak correlation between $f-$mode frequencies and individual nuclear saturation properties, prompting the necessity for more intricate methodologies to unveil multi-parameter relationships. We observe a robust linear relationship between the radii and $f-$mode frequencies for different NS masses. Leveraging this correlation alongside NICER observations of PSR J0740+6620 and PSR J0030+0451, we establish constraints that exhibit partial and minimal overlap for observational data from Riley et al. and Miller et al. respectively with our nucleonic EOS dataset. Moreover, NICER data aligns closely with radius and frequency values for a few hadron-quark hybrid EOS models. This indicates the need to consider additional exotic particles such as deconfined quarks at suprasaturation densities. We conclude that future observations of the radius or $f-$mode frequency for more than one NS mass, particularly at the extremes of viable NS mass scale, would either rule out nucleon-only EOS or provide definitive evidence in its favour., Comment: 21 pages, 10 figures, 4 tables, Accepted in Astrophysical Journal

Stabilizer states along with Clifford manipulations (unitary transformations and measurements) thereof -- despite being efficiently simulable on a classical computer -- are an important tool in quantum information processing, with applications to quantum computing, error correction and networking. Cluster states, defined on a graph, are a special class of stabilizer states that are central to measurement based quantum computing, all-photonic quantum repeaters, distributed quantum computing, and entanglement distribution in a network. All cluster states are local-Clifford equivalent to a stabilizer state. In this paper, we review the stabilizer framework, and extend it, by: incorporating general stabilizer measurements such as multi-qubit fusions, and providing an explicit procedure -- using Karnaugh maps from Boolean algebra -- for converting arbitrary stabilizer gates into tableau operations of the CHP formalism for efficient stabilizer manipulations. Using these tools, we develop a graphical rule-book and a MATLAB simulator with a graphical user interface for arbitrary stabilizer manipulations of cluster states, a user of which, e.g., for research in quantum networks, will not require any background in quantum information or the stabilizer framework. We extend our graphical rule-book to include dual-rail photonic-qubit cluster state manipulations with probabilistically-heralded linear-optical circuits for various rotated Bell measurements, i.e., fusions (including new `Type-I' fusions we propose, where only one of the two fused qubits is destructively measured), by incorporating graphical rules for their success and failure modes. Finally, we show how stabilizer descriptions of multi-qubit fusions can be mapped to linear optical circuits.

Stereochemically active lone-pair (SCALP) cations are attractive units for realizing optical anisotropy. Antimony (III) chloride perovskites with SCALP have remained largely unknown till date. We synthesized vacancy ordered Cs3Sb2Cl9 perovskite single crystals with SbCl6 octahedral linkage containing SCALP. Remarkably, Cs3Sb2Cl9 single crystals exhibit an exceptional birefringence of 0.12 +(-) 0.01 at 550 nm, which is the largest among pristine all-inorganic halide perovskites. The SCALP brings a large local structural distortion of the SbCl6 octahedra promoting birefringence optical responses in Cs3Sb2Cl9 single crystals. Theoretical calculations reveal that the considerable hybridization of Sb 5s with Sb 5p and Cl 3p states largely contribute to the SCALP. Furthermore, the change in the Sb-Cl-Sb bond angle creates distortion in the SbCl6 octahedral arrangement in the apical and equatorial directions within the crystal structure incorporating the required anisotropy for the birefringence. This work explores pristine inorganic halide perovskite single crystals as a potential birefringent material with prospects in integrated optical devices.

Diffusion models have revolutionized image generation in recent years, yet they are still limited to a few sizes and aspect ratios. We propose ElasticDiffusion, a novel training-free decoding method that enables pretrained text-to-image diffusion models to generate images with various sizes. ElasticDiffusion attempts to decouple the generation trajectory of a pretrained model into local and global signals. The local signal controls low-level pixel information and can be estimated on local patches, while the global signal is used to maintain overall structural consistency and is estimated with a reference image. We test our method on CelebA-HQ (faces) and LAION-COCO (objects/indoor/outdoor scenes). Our experiments and qualitative results show superior image coherence quality across aspect ratios compared to MultiDiffusion and the standard decoding strategy of Stable Diffusion. Project page: https://elasticdiffusion.github.io/, Comment: Accepted at CVPR 2024. Project Page: https://elasticdiffusion.github.io/

Recent advancements in large language models (LLMs) have spurred interest in using them for generating robot programs from natural language, with promising initial results. We investigate the use of LLMs to generate programs for service mobile robots leveraging mobility, perception, and human interaction skills, and where accurate sequencing and ordering of actions is crucial for success. We contribute CodeBotler, an open-source robot-agnostic tool to program service mobile robots from natural language, and RoboEval, a benchmark for evaluating LLMs' capabilities of generating programs to complete service robot tasks. CodeBotler performs program generation via few-shot prompting of LLMs with an embedded domain-specific language (eDSL) in Python, and leverages skill abstractions to deploy generated programs on any general-purpose mobile robot. RoboEval evaluates the correctness of generated programs by checking execution traces starting with multiple initial states, and checking whether the traces satisfy temporal logic properties that encode correctness for each task. RoboEval also includes multiple prompts per task to test for the robustness of program generation. We evaluate several popular state-of-the-art LLMs with the RoboEval benchmark, and perform a thorough analysis of the modes of failures, resulting in a taxonomy that highlights common pitfalls of LLMs at generating robot programs. We release our code and benchmark at https://amrl.cs.utexas.edu/codebotler/., Comment: 8 pages, Accepted at IEEE Robotics and Automation Letters (RA-L)

Coherent imaging systems, such as medical ultrasound and synthetic aperture radar (SAR), are subject to corruption from speckle due to sub-resolution scatterers. Since speckle is multiplicative in nature, the constituent image regions become corrupted to different extents. The task of denoising such images requires algorithms specifically designed for removing signal-dependent noise. This paper proposes a novel image denoising algorithm for removing signal-dependent multiplicative noise with diffusion models, called Speckle Denoising Diffusion Probabilistic Models (SDDPM). We derive the mathematical formulations for the forward process, the reverse process, and the training objective. In the forward process, we apply multiplicative noise to a given image and prove that the forward process is Gaussian. We show that the reverse process is also Gaussian and the final training objective can be expressed as the Kullback Leibler (KL) divergence between the forward and reverse processes. As derived in the paper, the final denoising task is a single step process, thereby reducing the denoising time significantly. We have trained our model with natural land-use images and ultrasound images for different noise levels. Extensive experiments centered around two different applications show that SDDPM is robust and performs significantly better than the comparative models even when the images are severely corrupted., Comment: 12 pages, 12 figures

A data language is a set of finite words defined on an infinite alphabet. Data languages are used to express properties associated with data values (domain defined over a countably infinite set). In this paper, we introduce set augmented finite automata (SAFA), a new class of automata for expressing data languages. We investigate the decision problems, closure properties, and expressiveness of SAFA. We also study the deterministic variant of these automata., Comment: This is a full version of a paper with the same name accepted in DLT 2023. Other than the full proofs, this paper contains several new results concerning more closure properties, universality problem, comparison of expressiveness with register automata and class counter automata, and more results on deterministic SAFA

We report spectroscopic identification of the host galaxies of 18 ultra-strong MgII systems (USMgII) at $0.6 \leq z \leq 0.8$. We created the largest sample by merging these with 20 host galaxies from our previous survey within $0.4 \leq z \leq 0.6$. Using this sample, we confirm that the measured impact parameters ($\rm 6.3\leq D[kpc] \leq 120$ with a median of 19 kpc) are much larger than expected, and the USMgII host galaxies do not follow the canonical $\rm W_{2796}-D$ anti-correlation. We show that the presence and significance of this anti-correlation may depend on the sample selection. The $\rm W_{2796}-D$ anti-correlation seen for the general MgII absorbers show a mild evolution at low $\rm W_{2796}$ end over the redshift range $0.4 \leq z \leq 1.5$ with an increase of the impact parameters. Compared to the host galaxies of normal MgII absorbers, USMgII host galaxies are brighter and more massive for a given impact parameter. While the USMgII systems preferentially pick star-forming galaxies, they exhibit slightly lower ongoing star-forming rates compared to main sequence galaxies with the same stellar mass, suggesting a transition from star-forming to quiescent states. For a limiting magnitude of $m_r < 23.6$, at least $29\%$ of the USMgII host galaxies are isolated, and the width of the MgII absorption in these cases may originate from gas flows (infall/outflow) in isolated halos of massive star-forming but not starbursting galaxies. We associate more than one galaxy with the absorber in $\ge 21\%$ cases where interactions may cause wide velocity spread., Comment: 19(+7 in appendix) pages and 12(+3 in appendix) figures. Accepted for publication in MNRAS

Quantum nonlocality, pioneered in Bell's seminal work and subsequently verified through a series of experiments, has drawn substantial attention due to its practical applications in various protocols. Evaluating and comparing the extent of nonlocality within distinct quantum correlations holds significant practical relevance. Within the resource theoretic framework this can be achieved by assessing the inter-conversion rate among different nonlocal correlations under free local operations and shared randomness. In this study we, however, present instances of quantum nonlocal correlations that are incomparable in the strongest sense. Specifically, when starting with an arbitrary many copies of one nonlocal correlation, it becomes impossible to obtain even a single copy of the other correlation, and this incomparability holds in both directions. Remarkably, these incomparable quantum correlations can be obtained even in the simplest Bell scenario, which involves two parties, each having two dichotomic measurements setups. Notably, there exist an uncountable number of such incomparable correlations. Our result challenges the notion of a 'unique gold coin', often referred to as the 'maximally resourceful state', within the framework of the resource theory of quantum nonlocality, which has nontrivial implications in the study of nonlocality distillation., Comment: New results have been added. Accepted in Physical Review Letters

In response to the pressing need for advanced clinical problem-solving tools in healthcare, we introduce BooksMed, a novel framework based on a Large Language Model (LLM). BooksMed uniquely emulates human cognitive processes to deliver evidence-based and reliable responses, utilizing the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework to effectively quantify evidence strength. For clinical decision-making to be appropriately assessed, an evaluation metric that is clinically aligned and validated is required. As a solution, we present ExpertMedQA, a multispecialty clinical benchmark comprised of open-ended, expert-level clinical questions, and validated by a diverse group of medical professionals. By demanding an in-depth understanding and critical appraisal of up-to-date clinical literature, ExpertMedQA rigorously evaluates LLM performance. BooksMed outperforms existing state-of-the-art models Med-PaLM 2, Almanac, and ChatGPT in a variety of medical scenarios. Therefore, a framework that mimics human cognitive stages could be a useful tool for providing reliable and evidence-based responses to clinical inquiries.

We present a predictive and general approach to investigate near-field energy transfer processes between localized defects in semiconductors, which couples first principle electronic structure calculations and a nonrelativistic quantum electrodynamics description of photons in the weak-coupling regime. We apply our approach to investigate an exemplar point defect in an oxide, the F center in MgO, and we show that the energy transfer from a magnetic source, e.g., a rare earth impurity, to the vacancy can lead to spin non conserving long-lived excitation that are dominant processes in the near field, at distances relevant to the design of photonic devices and ultra-high dense memories. We also define a descriptor for coherent energy transfer to predict geometrical configurations of emitters to enable long-lived excitations, that are useful to design optical memories in semiconductor and insulators., Comment: 41 pages, 11 figures

Achieving the ultimate Holevo limit of optical communication capacity requires a joint-detection receiver which makes a collective quantum measurement over multiple modulated symbols. Such superadditivity -- a higher communication rate than that achievable by symbol-by-symbol optical detection -- is a special case of the well-known nonlocality without entanglement and has yet to be demonstrated. In this article, we propose and demonstrate a design of joint-detection receivers, the Green Machine, that can achieve superadditivity. We build this receiver and show that its capacity surpasses any symbol-by-symbol receivers in the photon-starved regime with binary-phase-shift-keying (BPSK). Our Green Machine receiver can also significantly reduce the transmitter peak power requirement compared with the pulse-position modulation (the conventional modulation format used for deep space laser communication). We further show that the self-referenced phase makes it immune to phase noise, e.g., atmospheric turbulence or platform vibrations., Comment: 23 pages, 9 figures