Miracle-dz/UniFAR-FTUs huggingface dataset:
Bounding the dimension of bipartite quantum systems

Let us consider the set of joint quantum correlations arising from two-outcome local measurements on a bipartite quantum system. We prove that no finite dimension is sufficient to generate all these sets. We approach the problem in two different ways by constructing explicit examples for every dimension d, which demonstrates that there exist bipartite correlations that necessitate d-dimensional local quantum systems in order to generate them. We also show that at least 10 two-outcome measurements must be carried out by the two parties altogether so as to generate bipartite joint correlations not achievable by two-dimensional local systems. The smallest explicit example we found involves 11 settings.
AccelUPF: accelerating the 5G user plane using programmable hardware

The latest generation of 5G telecommunication networks are expected to provide high throughput and low latency while catering to diverse applications like mobile broadband, dense IoT, and self-driving cars. A high performance User Plane Function (UPF), the main element in the 5G user plane, is critical to achieving these performance goals. This paper presents AccelUPF, a 5G UPF that offloads functionality to programmable dataplane hardware for performance acceleration. While prior work has proposed accelerating the UPF by offloading its data forwarding functionality to programmable hardware, the Packet Forwarding Control Protocol (PFCP) messages from the control plane that configure the hardware data forwarding rules were still processed in software. We show that only offloading data forwarding and not PFCP message processing leads to suboptimal performance in the UPF for applications like IoT that have a much higher ratio of PFCP messages to data traffic, due to a bottleneck at the software control plane that configures the hardware packet forwarding rules. In contrast to prior work, AccelUPF offloads both PFCP message processing as well as data forwarding to programmable hardware. AccelUPF overcomes several technical challenges pertaining to the processing of the complex variable-sized PFCP messages within the memory and compute constraints of programmable hardware platforms. Our evaluation of AccelUPF implemented over a Netronome programmable NIC and an Intel Tofino programmable switch demonstrates performance gains over the state-of-the-art UPFs for real-world traffic scenarios.
Synthesizing state machines for data planes

The emergence of programmable switches such as the Intel Tofino has made it possible, in theory, to implement many network monitoring applications directly in the network data plane. In practice, however, such implementations are often more challenging than expected. A key difficulty is that such applications often depend, in part, on recognizing traffic patterns that are easy to specify as a deterministic finite state automaton (a DFA) but hard to implement thanks to stringent hardware constraints: to maximize throughput and avoid race conditions, state machine updates must be completed in a single Tofino pipeline stage, but the limited computational resources make finding an implementation a challenging puzzle. This paper presents a solution to such puzzles---a general framework for synthesizing DFA implementations automatically. A key insight is that such a synthesis system is free to renumber state machine states and implement transitions using any available arithmetic or logical operations over that renumbering, provided the resulting implementation is semantically equivalent to the input specification. To produce such a synthesizer, we model the required state machine semantics and the available single-stage switch operations using SMT constraints. An off-the-shelf SMT solver finds a solution to the constraints, and this solution is then translated to P4 code. We evaluate the effectiveness of our methods by synthesizing state machines for a variety of useful applications, including those that monitor TCP handshakes and video conference streams.
RELATIVISTIC JET DYNAMICS AND CALORIMETRY OF GAMMA-RAY BURSTS

We present numerical solutions of the two-dimensional relativistic hydrodynamics equations describing the deceleration and expansion of highly relativistic conical jets, of opening angles 0.05 ⩽ θ0 ⩽ 0.2, propagating into a medium of uniform density. Jet evolution is followed from a collimated relativistic outflow to the quasi-spherical non-relativistic phase. We show that relativistic sideways expansion becomes significant beyond the radius rθ at which the expansion Lorentz factor drops to θ−10. This is consistent with simple analytic estimates, which predict faster sideways expansion than has been claimed based on earlier numerical modeling. For t > ts = rθ/c the emission of radiation from the jet blast wave is similar to that of a spherical blast wave carrying the same energy (significant deviations at t ∼ ts occur only for well off-axis observers, θobs ∼ 1 ≫ θ0). Thus, the total (calorimetric) energy of gamma-ray burst blast waves may be estimated with only a small fractional error based on t > ts observations.
Accessible Communication App for Teaching Technical Writing: Teaching Technical Writing with Accessibly Designed App for Hybrid and Hyflex Classrooms

This project addresses the need for accessible classroom learning technologies that replicate the tools students have used in online learning during the COVID-19 pandemic. My research offers a concept for a communication app designed for use in in-person, hybrid, and hyflex classrooms. This app focuses on user experience (UX) research and universal design for learning (UDL) and is grounded in disability studies and principles of accessibility in higher education.
A study of rotating globular clusters. The case of the old, metal-poor globular cluster NGC 4372

Context. NGC 4372 is a poorly studied old, very metal-poor globular cluster (GC) located in the inner Milky Way halo. Aims. We present the first in-depth study of the kinematic properties and derive the structural parameters of NGC 4372 based on the fit of a Plummer profile and a rotating, physical model. We explore the link between internal rotation to different cluster properties and together with similar studies of more GCs, we put these in the context of globular cluster formation and evolution. Methods. We present radial velocities for 131 cluster member stars measured from high-resolution FLAMES/GIRAFFE observations. Their membership to the GC is additionally confirmed from precise metallicity estimates. We build a velocity dispersion profile and a systemic rotation curve using this kinematic data set. Additionally, we obtain an elliptical number density profile of NGC 4372 based on optical images using a Markov chain Monte Carlo fitting algorithm. From this, we derive the cluster’s half-light radius and ellipticity as rh = 3.44 � ± 0.04 � and � = 0.08 ± 0.01. Finally, we give a physical interpretation of the observed morphological and kinematic properties of this GC by fitting an axisymmetric, differentially rotating, dynamical model. Results. Our results show that NGC 4372 has an unusually high ratio of rotation amplitude to velocity dispersion (1. 2v s. 4. 5k m s −1 ) for its metallicity. This puts it in line, however, with two other exceptional, very metal-poor GCs: M 15 and NGC 4590. We also find a mild flattening of NGC 4372 in the direction of its rotation. Given its old age, this suggests that the flattening is indeed caused by the systemic rotation rather than tidal interactions with the Galaxy. Additionally, we estimate the dynamical mass of the GC
Brain tumor segmentation based on CBAM-TransUNet

Brain tumor is one of the most serious brain diseases, and accurate brain tumor segmentation is crucial in clinical planning treatment and evaluating treatment outcomes in brain tumor patients. In this paper, we propose a 3D visual transducer model (CBAM-TransUNet) that incorporates an attention mechanism for 3D multimodal brain tumor edge detection and segmentation, to improve the accuracy of brain tumor segmentation. In our proposed model based on the framework of the U-Net model (Ronneberger O et al., 2015), Swin Transformer module (LIU Z et al., 2021) is introduced in the process of the encoder and decoder of the model, and the convolution block attention module (WOOS et al., 2018) is applied at the bottleneck layer. Comprehensive experiments are implemented on the BraTS 2021 dataset and it shows that the proposed model obtains competitive results: the Dice coefficients of whole tumor, core tumor and enhanced tumor segmentation are 93.08%, 91.49% and 87.76%, respectively, and the other 95% Hausdorff distances are 2.93mm, 4.20mm, 4.91mm. The proposed CBAM-TransUNet model can effectively improve the accuracy of brain tumor segmentation.
BI-GAN: batch inversion membership inference attack on federated learning

Federated Learning is a growing advanced collaborative machine learning framework that aims to preserve user-privacy data. However, multiple researchers have investigated attack methods from the server side via gradient inversion techniques or Generative Adversarial Networks (GAN) to reconstruct the raw data distributions from users. In this paper, we propose Batch Inversion GAN (BI-GAN), a novel membership inference attack that can recover user-level batch images from local updates, utilizing both gradient inversion techniques and GAN. Our attack is more stealthy since it only requires access to gradients and does not interfere with the global model performance and is more robust in terms of image batch recovery and victim classification. The experiments show that our attack recovers higher quality images of the victim with higher accuracy compared to other attacks.
MAPS: a dataset for semantic profiling and analysis of Android applications

The vulnerability of smartphones to cyber attacks has been a serious concern for users emerging from the integrity of installed mobile applications (apps). These applications are fundamentally developed to provide legitimate and diversified on-the-go services; however, harmful and dangerous ones have found the perfect door to get into smartphones performing malicious behaviors. More effective and sophisticated application analysis is a practical strategy to reveal malicious activities and provide more insights into the application behavior. In this paper, we present the Malware Analysis and Profiling on Smartphones (MAPS) dataset for the research community that provides a very in-depth and detailed deep analysis of applications for extracting an enhanced set of features. The MAPS dataset relies on a diverse dataset collected from different repositories, containing more than 153000 applications (>2TB in size), and outputs more than 40000 features for analysis and training of deep neural network models.
Traffic engineering: from ISP to cloud wide area networks

We conduct a systematic review of two decades of work on wide-area network traffic engineering (TE). We summarize the contributions of important TE algorithms and systems developed for Internet Service Provider and cloud wide-area networks. We study the evolution of the goals of TE (from performance to reliability), TE system design (from decentralized to fully-centralized to partly-centralized) and the technology used in deploying these systems in large commercial networks (from vendor-specific protocols to software-defined implementations). We define a taxonomy of TE systems to categorize developments in TE research. We identify trends at the forefront of TE research and practice to motivate an agenda for future work in this area. Finally, to aid future work, we are releasing our summaries and implementations of several recent TE algorithms.1.
NetGVT: offloading global virtual time computation to programmable switches

Distributed discrete-event simulation is an essential method for analyzing large-scale models, including weather forecast and network simulations. A distributed simulation often requires synchronizing state among the different parts of the model according to a global virtual time (GVT). However, existing approaches require multiple round-trip times to a server to compute a new GVT value. In this paper, we propose NetGVT, a system that computes GVT using programmable switches, thereby avoiding the round-trip latency of a server-based solution. In particular, our design is concerned with two main constraints of the switch programming model: the limited number of arithmetic and logic operations and the limited memory available on the device. We aggregate computations and unroll them across different pipeline stages in a hierarchical manner to address the former. Then, we adopt compression mechanisms to store a short representation of virtual clocks in the on-chip registers to tackle the memory limitations. We implemented a prototype of NetGVT and evaluated its performance with a synthetic lock-step simulation in a Tofino switch. Our results demonstrate that NetGVT outperforms techniques that do not rely on in-network computing by 40% in terms of distributed simulations completion time.
From the molecular-cloud- to the embedded-cluster-mass function with a density threshold for star formation

The mass function dN ∝ m -β0 dm of molecular clouds and clumps is shallower than the mass function dN ∝ m -β ★ dm of young star clusters, gas-embedded and gas-free alike, as their respective mass function indices are β 0 ≃ 1.7 and β ★ ≃ 2. We demonstrate that such a difference can arise from different mass-radius relations for the embedded clusters and the molecular clouds (clumps) hosting them. In particular, the formation of star clusters with a constant mean volume density in the central regions of molecular clouds of constant mean surface density steepens the mass function from clouds to embedded clusters. This model is observationally supported since the mean surface density of molecular clouds is approximately constant, while there is a growing body of evidence, in both Galactic and extragalactic environments, that efficient star formation requires a hydrogen molecule number density threshold of n th ≃ 10 4-5 cm -3 . Adopting power-law volume density profiles of index p for spherically symmetric molecular clouds (clumps), we define two zones within each cloud (clump): a central cluster-forming region, actively forming stars by virtue of a local number density higher than n th , and an outer envelope inert in terms of star formation. We map how much the slope of the cluster-forming region mass function differs from that of their host clouds (clumps) as a function of their respective mass-radius relations and of the cloud (clump) density index. We find that for constant surface density clouds with density index p ≃ 1.9, a cloud mass function of index β 0 = 1.7 gives rise to a cluster-forming region mass function of index β≃2. Our model equates with defining two distinct star formation efficiencies (SFEs): a global mass-varying SFE averaged over the whole cloud (clump), and a local mass-independent SFE measured over the central cluster-forming region. While the global SFE relates the mass function of clouds to that of embedded clusters, the local SFE rules cluster evolution after residual star-forming gas expulsion. That the cluster mass function slope does not change through early cluster evolution implies a mass-independent local SFE and, thus, the same mass function index for cluster-forming regions and embedded clusters, that is β=β ★ . Our model can therefore reproduce the observed cluster mass function index β ★ ≃2. For the same model parameters, the radius distribution also steepens from clouds (clumps) to embedded clusters, which contributes to explaining observed cluster radius distributions.
Photoluminescence from gas-suspended SiOx nanoparticles synthesized by laser ablation

Time-resolved photoluminescence (PL) spectra are reported for gas-suspended 1–10 nm diameter SiOx particles formed by laser ablation of Si into 1–10 Torr He and Ar. Three spectral bands (1.8, 2.5 and 3.2 eV) similar to PL from oxidized porous silicon were measured, but with a pronounced vibronic structure. Particle size and composition were determined with Z-contrast transmission electron microscopy imaging and high resolution electron energy loss spectroscopy linescans of individual nanoparticles. Maximized violet (3.2 eV) PL from the gas-suspended nanoparticles was correlated with an ex situ SiO1.4 overall particle stoichiometry. Cryogenically-collected gas-suspended nanoparticles produced web-like-aggregate films exhibiting very weak PL. Standard anneals restored strong PL bands without vibronic structure, but otherwise in agreement with the PL measured from the gas-suspended nanoparticles.
Nonlinear parameter optimization method for high-resolution monitoring of marine environment

Abstract In this article, marine environment detection has been studied for improving the high resolution of the environment. The problem of low resolution of marine environment detection is caused by data synthesis defects. The supply chain management (SCM) technology is used to optimize related data to improve the resolution. The main procedure is to first preprocess the obtained hydrological data and eliminate the unreasonable amount represented by extreme values, and then the SCM method was used to estimate the results. Finally, the accuracy of the estimation is evaluated by the cross-validation algorithm. In the example verification, the comparison between the SCM method and the traditional optimal interpolation (OI) method in data integration accuracy has been done. This article compares mean square error, mean absolute error (MAE), root-mean-square error (RMSE), and R 2 parameters. SCM provides better results than OI. Mean error (ME) = 0.6°C/month, MEA = 1.6°C/month, RMSE = 42.3°C/month, and ME and MAE values are lower in summer. It shows that it is sensitive to the lack of data and has a better ability to provide high-resolution and accurate marine environmental data in real time.
Decision and Coordination of an O2O Supply Chain With Market Segmentation and Showrooming Effect

This paper investigates impacts of market segmentation and showrooming effect on the decision-making of an O2O supply chain, and puts forwards a contract to coordinate the O2O supply chain. Results show that, the showrooming effect is beneficial to the manufacturer, retailer and the supply chain, and the retailer will offer offline showrooming service. Under the influence of market segmentation, O2O supply chain is not necessarily better than single-channel supply chain structure. But adopting advertising and other means to improve consumers’ online channel acceptance, it can realize transformation from single-channel to O2O structure. The benefits of showrooming effect can eliminate the disadvantage of market segmentation. Moreover, a service cost sharing contract is put forward, which can perfectly coordinate the O2O supply chain with market segmentation and showrooming effect. These findings help managers to understand which channel structure is optimal by considering market segmentation and showrooming effect and identify possible pathways for them to perfectly cooperation.
Performance of the coupled-cluster singles and doubles method applied to two-dimensional quantum dots

Many-Body Perturbation Theory is put to test as a method for reliable calculations of the electron-electron interaction in two-dimensional quantum dots. We show that second order correlation gives qualitative agreement with experiments on a level which was not found within the Hartree-Fock description. For weaker confinements, the second order correction is shown to be insufficient and higher order contributions must be taken into account. We demonstrate that all order Many-Body Perturbation Theory in the form of the Coupled Cluster Singles and Doubles method yields very reliable results for confinements close to those estimated from experimental data. The possibility to use very large basis sets is shown to be a major advantage compared to Full Configuration Interaction approaches, especially for more than five confined electrons. Also, the possibility to utilize two-electron correlation in combination with tailor made potentials to achieve useful properties is explored. In the case of a two-dimensional quantum dot molecule we vary the interdot distance, and in the case of a two-dimensional quantum ring we vary the ring radius, in order to alter the spectra. In the latter case we demonstrate that correlation in combination with electromagnetic pulses can be used for the realization of quantum logical gates.
Effect of Big Data Analytics in Reverse Supply Chain: An Indian Context

The main purpose of this paper is to know about the recent status of big data analytics (BDA) on various manufacturing and reverse supply chain levels (RSCL) in Indian industries. In particular, it emphasises on understanding of BDA concept in Indian industries and proposes a structure to examine industries’ development in executing BDA extends in reverse supply chain management (RSCM). A survey was conducted through questionnaires on RSCM levels of 330 industries. Of the 330 surveys that were mailed, 125 completed surveys were returned, corresponding to a response rate of 37.87 percent, which was slightly greater than previous studies (Queiroz and Telles, 2018). The information of Indian industries with respect to BDA, the hurdles with boundaries to BDA-venture reception, and the connection with reverse supply chain levels and BDA learning were recognized.
Unusual band renormalization in the simplest iron-based superconductor FeSe 1 − x

The electronic structure of the iron chalcogenide superconductor FeSe_{1-x} was investigated by high- resolution angle-resolved photoemission spectroscopy (ARPES). The results were compared to DFT calculations showing some significant differences between the experimental electronic structure of FeSe_{1-x}, DFT calculations and existing data on FeSe_{x}Te_{1-x}. The bands undergo a pronounced orbital dependent renormalization, different from what was observed for FeSe_{x}Te_{1-x} and any other pnictides.
Rational Bézier Guarding

We present a reliable method to generate planar meshes of nonlinear rational triangular elements. The elements are guaranteed to be valid, i.e. defined by injective rational functions. The mesh is guaranteed to conform exactly, without geometric error, to arbitrary rational domain boundary and feature curves. The method generalizes the recent Bézier Guarding technique, which is applicable only to polynomial curves and elements. This generalization enables the accurate handling of practically important cases involving, for instance, circular or elliptic arcs and NURBS curves, which cannot be matched by polynomial elements. Furthermore, although many practical scenarios are concerned with rational functions of quadratic and cubic degree only, our method is fully general and supports arbitrary degree. We demonstrate the method on a variety of test cases.