Miracle-dz/UniFAR-FTUs huggingface dataset:
Comment on"Extending the Laws of Thermodynamics for Arbitrary Autonomous Quantum Systems"

Recently, Elouard and Lombard Latune [PRX Quantum 4, 020309 (2023)] claimed to extend the laws of thermodynamics to"arbitrary quantum systems"valid"at any scale"using"consistent"definitions allowing them to"recover known results"from the literature. I show that their definitions are in conflict with textbook thermodynamics and over- or underestimate the real entropy production by orders of magnitude. The cause of this problem is traced back to problematic definitions of entropy and temperature, the latter, for instance, violates the zeroth law. It is pointed out that another framework presented in PRX Quantum 2, 030202 (2021) does not suffer from these problems, while Elouard and Lombard Latune falsely claim that it only provides a positive entropy production for a smaller class of initial states. A simple way to unify both approaches is also presented.
Quantum Ising model on two dimensional anti-de Sitter space

This paper investigates the transverse Ising model on a discretization of two-dimensional anti-de Sitter space. We use classical and quantum algorithms to simulate real-time evolution and measure out-of-time-ordered correlators (OTOC). The latter can probe thermalization and scrambling of quantum information under time evolution. We compared tensor network-based methods both with simulation on gated-based superconducting quantum devices and analog quantum simulation using Rydberg arrays. While studying this system's thermalization properties, we observed different regimes depending on the radius of curvature of the space. In particular, we find a region of parameter space where the thermalization time depends only logarithmically on the number of degrees of freedom.
Riemannian Langevin Monte Carlo schemes for sampling PSD matrices with fixed rank

This paper introduces two explicit schemes to sample matrices from Gibbs distributions on $\mathcal S^{n,p}_+$, the manifold of real positive semi-definite (PSD) matrices of size $n\times n$ and rank $p$. Given an energy function $\mathcal E:\mathcal S^{n,p}_+\to \mathbb{R}$ and certain Riemannian metrics $g$ on $\mathcal S^{n,p}_+$, these schemes rely on an Euler-Maruyama discretization of the Riemannian Langevin equation (RLE) with Brownian motion on the manifold. We present numerical schemes for RLE under two fundamental metrics on $\mathcal S^{n,p}_+$: (a) the metric obtained from the embedding of $\mathcal S^{n,p}_+ \subset \mathbb{R}^{n\times n} $; and (b) the Bures-Wasserstein metric corresponding to quotient geometry. We also provide examples of energy functions with explicit Gibbs distributions that allow numerical validation of these schemes.
HITA: An Architecture for System-level Testing of Healthcare IoT Applications

System-level testing of healthcare Internet of Things (IoT) applications requires creating a test infrastructure with integrated medical devices and third-party applications. A significant challenge in creating such test infrastructure is that healthcare IoT applications evolve continuously with the addition of new medical devices from different vendors and new services offered by different third-party organizations following different architectures. Moreover, creating test infrastructure with a large number of different types of medical devices is time-consuming, financially expensive, and practically infeasible. Oslo City's healthcare department faced these challenges while working with various healthcare IoT applications. To address these challenges, this paper presents a real-world test infrastructure software architecture (HITA) designed for healthcare IoT applications. We evaluated HITA's digital twin (DT) generation component implemented using model-based and machine learning (ML) approaches in terms of DT fidelity, scalability, and time cost of generating DTs. Results show that the fidelity of DTs created using model-based and ML approaches reach 94% and 95%, respectively. Results from operating 100 DTs concurrently show that the DT generation component is scalable and ML-based DTs have a higher time cost.
Estimation of photon number distribution and derivative characteristics of photon-pair sources

The evaluation of a photon-pair source employs metrics like photon-pair generation rate, heralding efficiency, and second-order correlation function, all of which are determined by the photon number distribution of the source. These metrics, however, can be altered due to spectral or spatial filtering and optical losses, leading to changes in the metric characteristics. In this paper, we theoretically describe these changes in the photon number distribution and the effect of noise counts. We also review the previous methods used for estimating these characteristics and the photon number distribution. Moreover, we introduce an improved methodology for estimating the photon number distribution, focusing on photon-pair sources, and discuss the accuracy of the calculated characteristics from the estimated (or reconstructed) photon number distribution through simulations and experiments.
CXLMemUring: A Hardware Software Co-design Paradigm for Asynchronous and Flexible Parallel CXL Memory Pool Access

CXL has been the emerging technology for expanding memory for both the host CPU and device accelerators with load/store interface. Extending memory coherency to the PCIe root complex makes the codesign more flexible in that you can access the memory with coherency using your near-device computability. Since the capacity demand with tolerable latency and bandwidth is growing, we need to come up with a new hardware-software codesign way to offload the synthesized memory operations to the CXL endpoint, CXL switch or near CXL root complex cores like Intel DSA to fetch data; the CPU or accelerators can calculate other stuff in the backend. On CXL done loading, the data will be put into L1 if capacity fits, and the in-core ROB will be notified by mailbox and resume the calculation on the previous hardware context. Since the distance(timing window) of the load instruction sequence is unknown, a profiling-guided way of codegening and adaptively updating offloaded code will be required for a long-running job. We propose to evaluate CXLMemUring the modified BOOMv3 with added in-core-logic and CXL endpoint access simulation using CHI, and we will add a weaker RISCV Core near endpoint for code offloading, and the codegening will be based on program analysis with traditional profiling guided way.
Subregion Complementarity in AdS/CFT

We examine the bulk reconstruction in the AdS/CFT correspondence. We demonstrate that the subregion duality fails to hold, highlighting discrepancies between operators in causal wedge reconstruction and those in global reconstruction at the leading order in the large $N$ limit. We argue the invalidity of the entanglement wedge reconstruction, attributing it to non-perturbative finite $N$ effects or quantum gravity effects due to the trans-Planckian modes near the horizon. Nevertheless, we propose the subregion complementarity, illustrating that different CFT operators can describe the same bulk correlators restricted in a bulk subregion. While we expect that this complementarity is valid outside the horizon in general eternal black holes, it is inapplicable for single-sided black holes where a semi-classical description at the stretched horizon is absent.
BOLA360: Near-optimal View and Bitrate Adaptation for 360-degree Video Streaming

Recent advances in omnidirectional cameras and AR/VR headsets have spurred the adoption of 360-degree videos that are widely believed to be the future of online video streaming. 360-degree videos allow users to wear a head-mounted display (HMD) and experience the video as if they are physically present in the scene. Streaming high-quality 360-degree videos at scale is an unsolved problem that is more challenging than traditional (2D) video delivery. The data rate required to stream 360-degree videos is an order of magnitude more than traditional videos. Further, the penalty for rebuffering events where the video freezes or displays a blank screen is more severe as it may cause cybersickness. We propose an online adaptive bitrate (ABR) algorithm for 360-degree videos called BOLA360 that runs inside the client's video player and orchestrates the download of video segments from the server so as to maximize the quality-of-experience (QoE) of the user. BOLA360 conserves bandwidth by downloading only those video segments that are likely to fall within the field-of-view (FOV) of the user. In addition, BOLA360 continually adapts the bitrate of the downloaded video segments so as to enable a smooth playback without rebuffering. We prove that BOLA360 is near-optimal with respect to an optimal offline algorithm that maximizes QoE. Further, we evaluate BOLA360 on a wide range of network and user head movement profiles and show that it provides $13.6\%$ to $372.5\%$ more QoE than state-of-the-art algorithms. While ABR algorithms for traditional (2D) videos have been well-studied over the last decade, our work is the first ABR algorithm for 360-degree videos with both theoretical and empirical guarantees on its performance.
A 2-Approximation Algorithm for Data-Distributed Metric k-Center

In a metric space, a set of point sets of roughly the same size and an integer $k\geq 1$ are given as the input and the goal of data-distributed $k$-center is to find a subset of size $k$ of the input points as the set of centers to minimize the maximum distance from the input points to their closest centers. Metric $k$-center is known to be NP-hard which carries to the data-distributed setting. We give a $2$-approximation algorithm of $k$-center for sublinear $k$ in the data-distributed setting, which is tight. This algorithm works in several models, including the massively parallel computation model (MPC).
Light-Front Holographic QCD from a Coherent State in String Theory

It is shown that Light-Front Holographic Quantum Chromodynamics (LFHQCD) can be embedded in type II string theory based on the coherent state $e^{-\frac{1}{\lambda} L_{-1}}|h\rangle$. Salient features of hadron spectroscopy as known from LFHQCD carry over to string theory, notably the linear Regge trajectories of mesons and baryons in the chiral limit where $\lambda$ defines the QCD scale. From the LFHQCD perspective, this is an avenue to implement hadron spin. From the string perspective, string theory remains as a TOE with excited states generally at the Planck scale. Yet due to the unique properties of the coherent state, the mass scale of the tower of massive states on it is broken down from the Planck scale to the QCD scale, thereby reproducing properties of hadronic spectra. Furthermore, connection exists to gravitationally dressed excited states in AdS3.
Towards Practical Capture of High-Fidelity Relightable Avatars

In this paper, we propose a novel framework, Tracking-free Relightable Avatar (TRAvatar), for capturing and reconstructing high-fidelity 3D avatars. Compared to previous methods, TRAvatar works in a more practical and efficient setting. Specifically, TRAvatar is trained with dynamic image sequences captured in a Light Stage under varying lighting conditions, enabling realistic relighting and real-time animation for avatars in diverse scenes. Additionally, TRAvatar allows for tracking-free avatar capture and obviates the need for accurate surface tracking under varying illumination conditions. Our contributions are two-fold: First, we propose a novel network architecture that explicitly builds on and ensures the satisfaction of the linear nature of lighting. Trained on simple group light captures, TRAvatar can predict the appearance in real-time with a single forward pass, achieving high-quality relighting effects under illuminations of arbitrary environment maps. Second, we jointly optimize the facial geometry and relightable appearance from scratch based on image sequences, where the tracking is implicitly learned. This tracking-free approach brings robustness for establishing temporal correspondences between frames under different lighting conditions. Extensive qualitative and quantitative experiments demonstrate that our framework achieves superior performance for photorealistic avatar animation and relighting.
New tool for 21-cm cosmology. I. Probing 
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 and beyond

In this work we present 21cmFirstCLASS, a modified version of 21cmFAST, the most popular code in the literature for computing the anisotropies of the 21-cm signal. Our code uses the public cosmic microwave background (CMB) Boltzmann code CLASS, to establish consistent initial conditions at recombination for any set of cosmological parameters and evolves them throughout the dark ages, cosmic dawn, the epoch of heating and reionization. We account for inhomogeneity in the temperature and ionization fields throughout the evolution, crucial for a robust calculation of both the global 21-cm signal and its fluctuations. We demonstrate how future measurements of the CMB and the 21-cm signal can be combined and analyzed with 21cmFirstCLASS to obtain constraints on both cosmological and astrophysical parameters and examine degeneracies between them. As an example application, we show how 21cmFirstCLASS can be used to study cosmological models that exhibit non-linearities already at the dark ages, such as scattering dark matter (SDM). For the first time, we present self-consistent calculations of the 21-cm power spectrum in the presence of SDM during the non-linear epoch of cosmic dawn.
Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration

Whole brain segmentation with magnetic resonance imaging (MRI) enables the non-invasive measurement of brain regions, including total intracranial volume (TICV) and posterior fossa volume (PFV). Enhancing the existing whole brain segmentation methodology to incorporate intracranial measurements offers a heightened level of comprehensiveness in the analysis of brain structures. Despite its potential, the task of generalizing deep learning techniques for intracranial measurements faces data availability constraints due to limited manually annotated atlases encompassing whole brain and TICV/PFV labels. In this paper, we enhancing the hierarchical transformer UNesT for whole brain segmentation to achieve segmenting whole brain with 133 classes and TICV/PFV simultaneously. To address the problem of data scarcity, the model is first pretrained on 4859 T1-weighted (T1w) 3D volumes sourced from 8 different sites. These volumes are processed through a multi-atlas segmentation pipeline for label generation, while TICV/PFV labels are unavailable. Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available. We evaluate our method with Dice similarity coefficients(DSC). We show that our model is able to conduct precise TICV/PFV estimation while maintaining the 132 brain regions performance at a comparable level. Code and trained model are available at: https://github.com/MASILab/UNesT/wholebrainSeg.
On the Relationship Between Control Barrier Functions and Projected Dynamical Systems

In this paper, we study the relationship between systems controlled via Control Barrier Function (CBF) approaches and a class of discontinuous dynamical systems, called Projected Dynamical Systems (PDSs). In particular, under appropriate assumptions, we show that the vector field of CBF-controlled systems is a Krasovskii-like perturbation of the set-valued map of a differential inclusion, that abstracts PDSs. This result provides a novel perspective to analyze and design CBF-based controllers. Specifically, we show how, in certain cases, it can be employed for designing CBF-based controllers that, while imposing safety, preserve asymptotic stability and do not introduce undesired equilibria or limit cycles. Finally, we briefly discuss about how it enables continuous implementations of certain projection-based controllers, that are gaining increasing popularity.
Continuum asymptotics for tree growth models

We classify the forward dynamics of all (plane) tree-valued Markov chains $(T_n,n \geq 1)$ with uniform backward dynamics. Every such Markov chain is classified by a decorated planar real tree. We also show that under an inhomogeneous rescaling after trimming leaves $(T_n, n\geq 1)$ converges to a random real tree in the Gromov--Prokhorov metric. This generalises and sheds some new light on work by Evans, Gr\"ubel and Wakolbinger (2017) on the binary special case.
Effect of electron-phonon interactions on three-level QD-based spaser: linear and quadratic potentials

In this article, we study the effect of electron-phonon interaction on a spaser (surface plasmon amplification by stimulated emission of radiation) system consisting of a metal nanoparticle surrounded by a large number of quantum dots (QDs). Usually, the effect of electron-phonon interaction is neglected in the spaser related literature. But a gain media, in this case QDs, attributed by the large Raman scattering cross-section, exhibits stronger electron-phonon interaction. In the present work, we investigate the effects of electron-phonon interaction on a three-level QD-based spaser. We consider two types of interaction potentials, linear and quadratic, and analyze their effects individually. First, we focus on the linear electron-phonon interaction that perturbs the electrons present in the excited state. This yields a periodic steady-state number of localized surface plasmon (LSP). The accompanying analytic solution reveals that the population inversion of the gain medium depends on the linear potential strength (Frohlich constant) but does not affect the threshold of spaser considerably for the given numerical parameters. In addition to the LSP, phonons are generated during this process, the temporal dynamics of which are also presented here. Initially, the number of phonons exhibit decaying periodic oscillations, whose amplitude depends on the strength of the electron-phonon interaction. Under continuous pumping, at later times, the number of phonons reaches a steady-state value, which may find potential application in realization of continuous phonon nanolasers. Further, the effect of the quadratic potential is investigated phenomenologically by increasing the excited-state decay rate. This results in a large number of LSP and an intense spaser spectrum.
RST-style Discourse Parsing Guided by Document-level Content Structures

Rhetorical Structure Theory based Discourse Parsing (RST-DP) explores how clauses, sentences, and large text spans compose a whole discourse and presents the rhetorical structure as a hierarchical tree. Existing RST parsing pipelines construct rhetorical structures without the knowledge of document-level content structures, which causes relatively low performance when predicting the discourse relations for large text spans. Recognizing the value of high-level content-related information in facilitating discourse relation recognition, we propose a novel pipeline for RST-DP that incorporates structure-aware news content sentence representations derived from the task of News Discourse Profiling. By incorporating only a few additional layers, this enhanced pipeline exhibits promising performance across various RST parsing metrics.
Searching for new physics effects in future 
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 mass and 
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We investigate the phenomenology of the dark $Z$ boson, $Z_d$, which is associated with a new Abelian gauge symmetry and couples to the standard model particles via kinetic mixing $\varepsilon$ and mass mixing $\varepsilon_Z^{}$. We examine two cases: (i) $Z_d$ is lighter than the $Z$ boson, and (ii) $Z_d$ is heavier than that. In the first case, it is known that $Z_d$ causes a deviation in the weak mixing angle at low energies from the standard model prediction. We study the prediction in the model and compare it with the latest experimental data. In the second case, the $Z$-$Z_d$ mixing enhances the $W$ boson mass. We investigate the effect of $Z_d$ on various electroweak observables including the $W$ boson mass using the $S$, $T$, and $U$ parameters. We point out an interesting feature: in the limit $\varepsilon \to 0$, the equation $S = - U$ holds independently of the mass of $Z_d$ and the size of $\varepsilon_Z^{}$, while $|S|\gg |U|$ in many new physics models. We find that the dark $Z$ boson with a mass of $O(100)~\mathrm{GeV}$ with a relatively large mass mixing can reproduce the CDF result within $2\sigma$ while avoiding all other experimental constraints. Such dark $Z$ bosons are expected to be tested at future high-energy colliders.
Confidence in causal inference under structure uncertainty in linear causal models with equal variances

Abstract Inferring the effect of interventions within complex systems is a fundamental problem of statistics. A widely studied approach uses structural causal models that postulate noisy functional relations among a set of interacting variables. The underlying causal structure is then naturally represented by a directed graph whose edges indicate direct causal dependencies. In a recent line of work, additional assumptions on the causal models have been shown to render this causal graph identifiable from observational data alone. One example is the assumption of linear causal relations with equal error variances that we will take up in this work. When the graph structure is known, classical methods may be used for calculating estimates and confidence intervals for causal-effects. However, in many applications, expert knowledge that provides an a priori valid causal structure is not available. Lacking alternatives, a commonly used two-step approach first learns a graph and then treats the graph as known in inference. This, however, yields confidence intervals that are overly optimistic and fail to account for the data-driven model choice. We argue that to draw reliable conclusions, it is necessary to incorporate the remaining uncertainty about the underlying causal structure in confidence statements about causal-effects. To address this issue, we present a framework based on test inversion that allows us to give confidence regions for total causal-effects that capture both sources of uncertainty: causal structure and numerical size of non-zero effects.
Current Methods for Hyperpolarized [1-13C]pyruvate MRI Human Studies

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the “HP 13C MRI Consensus Group” as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality.