Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Time-Evolution of Gaussian Wavepackets in Non-Hermitian Systems

Simulation and analysis of multidimensional dynamics of a quantum non-Hmeritian system is a challenging problem. Gaussian wavepacket dynamics has proven to be an intuitive semiclassical approach to approximately solving the dynamics of quantum systems. A Gaussian wavepacket approach is proposed for a continuous space extension to the Hatano-Nelson model that enables transparent analysis of the dynamics in terms of complex classical trajectories. We demonstrate certain cases where the configuration space trajectory can be made fully real by transforming the initial conditions to account for the non-Hermiticity appropriately through the momentum coordinates. However, in general the complex phase space is unavoidable. For the cases where the trajectory is real, the effective force can be decomposed into that due to the potential energy surface and that due to the imaginary vector potential. The impact of the vector potential on the trajectory of the wavepacket is directly proportional to both the strength of the vector potential and the width of the wavepacket.
ACCESS: Prompt Engineering for Automated Web Accessibility Violation Corrections

With the increasing need for inclusive and user-friendly technology, web accessibility is crucial to ensuring equal access to online content for individuals with disabilities, including visual, auditory, cognitive, or motor impairments. Despite the existence of accessibility guidelines and standards such as Web Content Accessibility Guidelines (WCAG) and the Web Accessibility Initiative (W3C), over 90% of websites still fail to meet the necessary accessibility requirements. For web users with disabilities, there exists a need for a tool to automatically fix web page accessibility errors. While research has demonstrated methods to find and target accessibility errors, no research has focused on effectively correcting such violations. This paper presents a novel approach to correcting accessibility violations on the web by modifying the document object model (DOM) in real time with foundation models. Leveraging accessibility error information, large language models (LLMs), and prompt engineering techniques, we achieved greater than a 51% reduction in accessibility violation errors after corrections on our novel benchmark: ACCESS. Our work demonstrates a valuable approach toward the direction of inclusive web content, and provides directions for future research to explore advanced methods to automate web accessibility.
Embedding Elites: Examining the Use of Tweets Embedded in Online News Articles across Reliable and Fringe Outlets

This study examines the use of embedded tweets in online news media. In particular, we add to the previous literature by exploring embedded tweets across reliable and unreliable news outlets. We use a mixed-method analysis to examine how the function and frequency of embedded tweets change across outlet reliability and news topic. We find that, no matter the outlet reliability, embedded tweets are most often used to relay the opinions of elites, to syndicate information from another news source, or to self-cite information an outlet previously produced. Our results also show some notable differences between reliable media and fringe media's use of tweets. Namely, fringe media embed tweets more and use those tweets as the source of news more than reliable media. Our work adds to the literature on hybrid media systems and the normalization of social media in journalism.
KFVM-WENO: A high-order accurate kernel-based finite volume method for compressible hydrodynamics

This paper presents a fully multidimensional kernel-based reconstruction scheme for finite volume methods applied to systems of hyperbolic conservation laws, with a particular emphasis on the compressible Euler equations. Non-oscillatory reconstruction is achieved through an adaptive order weighted essentially non-oscillatory (WENO-AO) method cast into a form suited to multidimensional reconstruction. A kernel-based approach inspired by radial basis functions (RBF) and Gaussian process (GP) modeling, which we call KFVM-WENO, is presented here. This approach allows the creation of a scheme of arbitrary order of accuracy with simply defined multidimensional stencils and substencils. Furthermore, the fully multidimensional nature of the reconstruction allows for a more straightforward extension to higher spatial dimensions and removes the need for complicated boundary conditions on intermediate quantities in modified dimension-by-dimension methods. In addition, a new simple-yet-effective set of reconstruction variables is introduced, which could be useful in existing schemes with little modification. The proposed scheme is applied to a suite of stringent and informative benchmark problems to demonstrate its efficacy and utility. A highly parallel multi-GPU implementation using Kokkos and the message passing interface (MPI) is also provided.
Search for the non-linearities of gravitational wave background in NANOGrav 15-year data set

The recently reported signal of common red noise between pulsars by several pulsar timing array collaborations has been thought as evidence of the stochastic gravitational wave background (SGWB) due to the Helling-Downs correlation. In this study, we will search for the non-linearities of SGWB through its effect on the overlap reduction function in NANOGrav 15-year data set. In particular, we focus on a folded non-Gaussian component to SGWB whose amplitude is quantified with a single parameter $\alpha$ in the unpolarized case. The results reveal that such a non-Gaussianity of SGWB is favored, and $\alpha=0$ (the Gaussian SGWB) is excluded at about $3\sigma$ level.
Quantum Circuit Mapping for Universal and Scalable Computing in MZI-based Integrated Photonics

Linear optical quantum computing (LOQC) offers a quantum computation paradigm based on well-established and robust technology and flexible environmental conditions following DiVincenzo's criteria. Within this framework, integrated photonics can be utilized to achieve gate-based quantum computing, defining qubits by path-encoding, quantum gates through the use of Mach-Zehnder interferometers (MZIs) as fundamental building blocks, and measurements through single-photon detectors. In particular, universal two-qubit gates can be achieved by suitable structures of MZIs together with post-selection or heralding. The most resource-efficient choice is given by the post-selected CZ gate. However, this implementation is characterized by a design which has a non-regular structure and cannot be cascaded. This limits the implementation of large-scale LOQC. Starting from these issues, we suggest an approach to move toward a universal and scalable LOQC on the integrated photonic platform. First of all, choosing the post-selected CZ as universal two-qubit gate, we extend the path-encoded dual-rail qubit to a triplet of waveguides, composed of an auxiliary waveguide and the pair of waveguides corresponding to the qubit basis states. Additionally, we introduce a swap photonic network that maps the regularly-labeled structure of the new path-encoded qubits to the structure needed for the post-selected CZ. We also discuss the optical swap gate that allows the connection of non-nearest neighbor path-encoded qubits. In this way, we can deterministically exchange the locations of the qubits and execute controlled quantum gates between any path-encoded qubits. Next, by truncating the auxiliary waveguides after any post-selected CZ, we find that it is possible to cascade this optical gate when it acts on different pairs that share only one qubit.
Towards Optimal Grammars for RNA Structures

In past work (Onokpasa, Wild, Wong, DCC 2023), we showed that (a) for joint compression of RNA sequence and structure, stochastic context-free grammars are the best known compressors and (b) that grammars which have better compression ability also show better performance in ab initio structure prediction. Previous grammars were manually curated by human experts. In this work, we develop a framework for automatic and systematic search algorithms for stochastic grammars with better compression (and prediction) ability for RNA. We perform an exhaustive search of small grammars and identify grammars that surpass the performance of human-expert grammars.
The Binary Broadening Function

We propose an extended formalism for the spectral broadening function (BF) based on the multiplication rule of block matrices. The formalism, which we named the binary broadening function (BBF), can produce decomposed BFs for individual components of a binary star system by using two spectral templates. The decomposed BFs can be used to derive precise rotational profiles and radial velocities for individual components. We test the BBF on simulated spectra and actual observational spectra to show that the method is feasible on spectroscopic binaries, even when the spectral lines of two stellar components are heavily blended. To demonstrate the capability of the method, we conduct a simulation of `sketching' (imaging) a transiting circumbinary exoplanet using the BBF. We also discuss issues of implementation such as the variation of BBF with biased templates, the pros and cons of BBF, and cases when the method is not applicable.
Pick and Place Planning is Better Than Pick Planning Then Place Planning

Robotic pick and place stands at the heart of autonomous manipulation. When conducted in cluttered or complex environments robots must jointly reason about the selected grasp and desired placement locations to ensure success. While several works have examined this joint pick-and-place problem, none have fully leveraged recent learning-based approaches for multi-fingered grasp planning. We present a modular algorithm for joint pick and place planning that can make use of state of the art grasp classifiers for planning multi-fingered grasps for novel objects from partial view point clouds. We demonstrate our joint pick and place formulation with several costs associated with different placement tasks. Experiments on pick and place tasks with cluttered scenes using a physical robot show that our joint inference method is more successful than a sequential pick then place approach, while also achieving better placement configurations.
Noise-induced phase transitions in hybrid quantum circuits

The presence of quantum noises inherent to real physical systems can strongly impact the physics in quantum hybrid circuits with local random unitaries and mid-circuit measurements. For example, an infinitesimal size-independent noise probability can lead to the disappearance of measurement-induced entanglement phase transition and the emergence of a single area-law phase. In this Letter, we investigate the effects of quantum noises with size-dependent probabilities $q=p/L^{\alpha}$ where $\alpha$ represents the scaling exponent. We have identified a noise-induced entanglement phase transition from a volume law to a power (area) law in the presence (absence) of measurements as $p$ increases when $\alpha=1.0$. With the help of an effective statistical model, we find that this transition is a first-order phase transition and shares the same analytical understanding as the noise-induced coding transition. We also discuss the differences between the effect of size-dependent noise and the boundary noise in the phase transitions. We validate our analytical predictions with extensive numerical results from stabilizer circuit simulations.
BAR Nash Equilibrium and Application to Blockchain Design

This paper presents a novel solution concept, called BAR Nash Equilibrium (BARNE) and apply it to analyse the Verifier's dilemma, a fundamental problem in blockchain. Our solution concept adapts the Nash equilibrium (NE) to accommodate interactions among Byzantine, altruistic and rational agents, which became known as the BAR setting in the literature. We prove the existence of BARNE in a large class of games and introduce two natural refinements, global and local stability. Using this equilibrium and its refinement, we analyse the free-rider problem in the context of byzantine consensus. We demonstrate that by incorporating fines and forced errors into a standard quorum-based blockchain protocol, we can effectively reestablish honest behavior as a globally stable BARNE.
New Centralized MSR Codes With Small Sub-packetization

Centralized repair refers to repairing $h\geq 2$ node failures using $d$ helper nodes in a centralized way, where the repair bandwidth is counted by the total amount of data downloaded from the helper nodes. A centralized MSR code is an MDS array code with $(h,d)$-optimal repair for some $h$ and $d$. In this paper, we present several classes of centralized MSR codes with small sub-packetization. At first, we construct an alternative MSR code with $(1,d_i)$-optimal repair for multiple repair degrees $d_i$ simultaneously. Based on the code structure, we are able to construct a centralized MSR code with $(h_i,d_i)$-optimal repair property for all possible $(h_i,d_i)$ with $h_i\mid (d_i-k)$ simultaneously. The sub-packetization is no more than ${\rm lcm}(1,2,\ldots,n-k)(n-k)^n$, which is much smaller than a previous work given by Ye and Barg ($({\rm lcm}(1,2,\ldots,n-k))^n$). Moreover, for general parameters $2\leq h\leq n-k$ and $k\leq d\leq n-h$, we further give a centralized MSR code enabling $(h,d)$-optimal repair with sub-packetization smaller than all previous works.
FaKnow: A Unified Library for Fake News Detection

Over the past years, a large number of fake news detection algorithms based on deep learning have emerged. However, they are often developed under different frameworks, each mandating distinct utilization methodologies, consequently hindering reproducibility. Additionally, a substantial amount of redundancy characterizes the code development of such fake news detection models. To address these concerns, we propose FaKnow, a unified and comprehensive fake news detection algorithm library. It encompasses a variety of widely used fake news detection models, categorized as content-based and social context-based approaches. This library covers the full spectrum of the model training and evaluation process, effectively organizing the data, models, and training procedures within a unified framework. Furthermore, it furnishes a series of auxiliary functionalities and tools, including visualization, and logging. Our work contributes to the standardization and unification of fake news detection research, concurrently facilitating the endeavors of researchers in this field. The open-source code and documentation can be accessed at https://github.com/NPURG/FaKnow and https://faknow.readthedocs. io, respectively.
Effective velocity and $L^\infty$-based well-posedness for incompressible fluids with odd viscosity

The present paper is concerned with the well-posedness theory for non-homogeneous incompressible fluids exhibiting odd (non-dissipative) viscosity effects. Differently from previous works, we consider here the full odd viscosity tensor. Similarly to the work of Bresch and Desjardins in compressible fluid mechanics, we identify the presence of an effective velocity in the system, linking the velocity field of the fluid and the gradient of a suitable function of the density. By use of this effective velocity, we propose a new formulation of the original system of equations, thus highlighting a strong similarity with the equations of the ideal magnetohydrodynamics. By taking advantage of the new formulation of the equations, we establish a local in time well-posedness theory in Besov spaces based on $L^\infty$ and prove a lower bound for the lifespan of the solutions implying ``asymptotically global'' existence: in the regime of small initial density variations, $\rho_0-1= O(\varepsilon)$ for small $\varepsilon>0$, the corresponding solution is defined up to some time $T_\varepsilon>0$ satisfying the property $T_\varepsilon\,\longrightarrow\,+\infty$ when $\varepsilon\to0^+$.
Characterization of Magnetic Labyrinthine Structures through Junctions and Terminals Detection using Template Matching and CNN

In material sciences, characterizing faults in periodic structures is vital for understanding material properties. To characterize magnetic labyrinthine patterns, it is necessary to accurately identify junctions and terminals, often featuring over a thousand closely packed defects per image. This study introduces a new technique called TM-CNN (Template Matching - Convolutional Neural Network) designed to detect a multitude of small objects in images, such as defects in magnetic labyrinthine patterns. TM-CNN was used to identify these structures in 444 experimental images, and the results were explored to deepen the understanding of magnetic materials. It employs a two-stage detection approach combining template matching, used in initial detection, with a convolutional neural network, used to eliminate incorrect identifications. To train a CNN classifier, it is necessary to create a large number of training images. This difficulty prevents the use of CNN in many practical applications. TM-CNN significantly reduces the manual workload for creating training images by automatically making most of the annotations and leaving only a small number of corrections to human reviewers. In testing, TM-CNN achieved an impressive F1 score of 0.988, far outperforming traditional template matching and CNN-based object detection algorithms.
Quantitative Precipitation Estimation Integrated by Poisson’s Equation Using Radar Mosaic, Satellite, and Rain Gauge Network

AbstractHigh-resolution quantitative precipitation estimation (QPE) from radar and satellite combined with rain gauges is one of the most important guides for hydrological forecasts. Whereas rain gauges provide accurate measurement at a point, remote sensing helps to retrieve the spatial pattern. An algorithm, named Siprec, has been used to blend rain gauges, radar mosaic data, and satellite Eumetsat/MPE estimates by using Poisson’s equation over two basins in Brazil. The results indicated that Siprec decreased the root mean square error (RMSE) when compared to radar and satellite estimates as well as improved the correlation. Most of the errors were related to precipitation above 10 mm h−1, due to large spatial variability, typical of deep convection. The solution of Poisson’s equation acts directly on the data received at a certain time, converging the amplitude to the rain gauge values and keeping the spatial distribution of the radar or satellite measurement without a priori adjustments. This is an i.
A Quantitative Analysis and Guidelines of Data Streaming Accelerator in Modern Intel Xeon Scalable Processors

As semiconductor power density is no longer constant with the technology process scaling down, modern CPUs are integrating capable data accelerators on chip, aiming to improve performance and efficiency for a wide range of applications and usages. One such accelerator is the Intel Data Streaming Accelerator (DSA) introduced in Intel 4th Generation Xeon Scalable CPUs (Sapphire Rapids). DSA targets data movement operations in memory that are common sources of overhead in datacenter workloads and infrastructure. In addition, it becomes much more versatile by supporting a wider range of operations on streaming data, such as CRC32 calculations, delta record creation/merging, and data integrity field (DIF) operations. This paper sets out to introduce the latest features supported by DSA, deep-dive into its versatility, and analyze its throughput benefits through a comprehensive evaluation. Along with the analysis of its characteristics, and the rich software ecosystem of DSA, we summarize several insights and guidelines for the programmer to make the most out of DSA, and use an in-depth case study of DPDK Vhost to demonstrate how these guidelines benefit a real application.
FBM: Freestanding bilayer microscope for single-molecule imaging of membrane proteins

Integral membrane proteins (IMPs) constitute a large fraction of organismal proteomes, playing fundamental roles in physiology and disease. Despite their importance, the mechanisms underlying dynamic features of IMPs, such as anomalous diffusion, protein-protein interactions, and protein clustering, remain largely unknown due to the high complexity of cell membrane environments. Available methods for in vitro studies are insufficient to study IMP dynamics systematically. This publication introduces the Freestanding-Bilayer Microscope (FBM), which combines the advantages of freestanding bilayers with single-particle tracking. The FBM, based on planar lipid bilayers, enables the study of IMP dynamics with single-molecule resolution and unconstrained diffusion. This paper benchmarks the FBM against total internal reflection fluorescence (TIRF) imaging on supported bilayers and is used here to estimate ion channel open probability and to examine the diffusion behavior of an ion channel in phase- separated bilayers. The FBM emerges as a powerful tool to examine membrane protein/lipid organization and dynamics to understand cell membrane processes.
Reduced Proactive and Reactive Cognitive Flexibility in Older Adults Underlies Performance Costs During Dual-Task Walking: A Mobile Brain/Body Imaging (MoBI) Study

Age-related reductions in cognitive flexibility may limit modulation of control processes during systematic increases to cognitive-motor demands, exacerbating dual-task costs. In this study, behavioral and neurophysiologic changes to proactive and reactive control during progressive cognitive-motor demands were compared across older and younger adults to explore the basis for age-differences in cognitive-motor interference (CMI). 19 younger (19 - 29 years old, mean age = 22.84 +/- 2.75 years, 6 male, 13 female) and 18 older (60 - 77 years old, mean age = 67.89 +/- 4.60 years, 9 male, 9 female) healthy adults completed cued task-switching while alternating between sitting and walking on a treadmill. Gait kinematics, task performance measures, and brain activity were recorded using electroencephalography (EEG) based Mobile Brain/Body Imaging (MoBI). Response accuracy on easier trial types improved in younger, but not older adults when they walked while performing the cognitive task. As difficulty increased, walking provoked accuracy costs in older, but not younger adults. Both groups registered faster responses and reduced gait variability during dual-task walking. Older adults exhibited lower amplitude modulations of proactive and reactive neural activity as cognitive-motor demands systematically increased, which may reflect reduced flexibility for progressive preparatory and reactive adjustments over behavioral control.
From Brownian to deterministic motor movement in a DNA-based molecular rotor

Molecular devices that have an anisotropic, periodic potential landscape can be operated as Brownian motors. When the potential landscape is cyclically switched with a chemical reaction or an external force, such devices can harness random Brownian fluctuations to generate directed motion. Recently, directed Brownian motor-like rotatory movement was demonstrated with an electrically switched DNA origami rotor with designed, ratchet-like obstacles. Here, we demonstrate that also the intrinsic anisotropy of DNA origami rotors that originally were not designed as Brownian motor devices is sufficient to result in motor movement. We show that for low amplitudes of an external switching field such devices operate as Brownian motors, while at higher amplitudes the movement is better described by the deterministic motion of an overdamped electrical motor. We characterize the amplitude and frequency dependence of the movements in both regimes, showing that after an initial steep rise the angular speed peaks and drops for excessive driving amplitudes and frequencies. The characteristics of the rotor movement are well described by a simple stochastic model of the system.