Miracle-dz/UniFAR-FTUs huggingface dataset:
Fermi-gas correlators of ADHM theory and triality symmetry

<jats:p>We analytically study the Fermi-gas formulation of sphere correlation functions of the Coulomb branch operators for 3d <jats:inline-formula><jats:alternatives><jats:tex-math>\mathcal{N}=4</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:mrow><mml:mstyle mathvariant="script"><mml:mi>𝒩</mml:mi></mml:mstyle><mml:mo>=</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:math></jats:alternatives></jats:inline-formula> ADHM theory with a gauge group <jats:inline-formula><jats:alternatives><jats:tex-math>U(N)</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>U</mml:mi><mml:mo stretchy="false" form="prefix">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false" form="postfix">)</mml:mo></mml:mrow></mml:math></jats:alternatives></jats:inline-formula>, an adjoint hypermultiplet and <jats:inline-formula><jats:alternatives><jats:tex-math>l</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:mi>l</mml:mi></mml:math></jats:alternatives></jats:inline-formula> hypermultiplets which can describe a stack of <jats:inline-formula><jats:alternatives><jats:tex-math>N</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:mi>N</mml:mi></mml:math></jats:alternatives></jats:inline-formula> M2-branes at <jats:inline-formula><jats:alternatives><jats:tex-math>A_{l-1}</jats:tex-math><mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>l</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math></jats:alternatives></jats:inline-formula> singularities. We find that the leading coefficients of the perturbative grand canonical correlation functions are invariant under a hidden triality symmetry conjectured from the twisted M-theory. The triality symmetry also helps us to fix the next-to-leading corrections analytically. </jats:p>
Micromotion minimization using Ramsey interferometry

We minimize the stray electric field in a linear Paul trap quickly and accurately, by applying interferometry pulse sequences to a trapped ion optical qubit. The interferometry sequences are sensitive to the change of ion equilibrium position when the trap stiffness is changed, and we use this to determine the stray electric field. The simplest pulse sequence is a two-pulse Ramsey sequence, and longer sequences with multiple pulses offer a higher precision. The methods allow the stray field strength to be minimized beyond state-of-the-art levels. Using a sequence of nine pulses we reduce the 2D stray field strength to (10.5 ± 0.8) mV m−1 in 11 s measurement time. The pulse sequences are easy to implement and automate, and they are robust against laser detuning and pulse area errors. We use interferometry sequences with different lengths and precisions to measure the stray field with an uncertainty below the standard quantum limit. This marks a real-world case in which quantum metrology offers a significant enhancement. Also, we minimize micromotion in 2D using a single probe laser, by using an interferometry method together with the resolved sideband method; this is useful for experiments with restricted optical access. Furthermore, a technique presented in this work is related to quantum protocols for synchronizing clocks; we demonstrate these protocols here.
A shock near the virial radius of the Perseus Cluster

Context. Previous X-ray studies of the Perseus Cluster, consisting of 85 Suzaku pointings along eight azimuthal directions, revealed a particularly steep decrease in the projected temperature profile near the virial radius (∼r200) towards the northwest (NW). Aims. To further explore this shock candidate, another 4 Suzaku observations on the NW edge of the Perseus Cluster have been obtained. These deeper data were designed to provide the best possible control of systematic uncertainties in the spectral analysis. Methods. Using the combined Suzaku observations, we have carefully investigated this interesting region by analyzing the spectra of various annuli and extracting projected thermodynamic profiles. Results. We find that the projected temperature profile shows a break near r200, indicating a shock with ℳ = 1.9 ± 0.3. Corresponding discontinuities are also found in the projected emission measure and the density profiles at the same location. This evidence of a shock front so far away from the cluster center is unprecedented, and may provide a first insight into the properties of large-scale virial shocks which shape the process of galaxy cluster growth.
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing

Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc. However, in domains such as content/product recommendation and risk management, where sequence of event data is the most used raw data form and experts derived features are more commonly used, deep learning models struggle to dominate the game. In this paper, we propose a symbolic testing framework that helps to answer the question of what kinds of expert-derived features could be learned by a neural network. Inspired by this testing framework, we introduce an efficient architecture named SHORING, which contains two components: \textit{event network} and \textit{sequence network}. The \textit{event} network learns arbitrarily yet efficiently high-order \textit{event-level} embeddings via a provable reparameterization trick, the \textit{sequence} network aggregates from sequence of \textit{event-level} embeddings. We argue that SHORING is capable of learning certain standard symbolic expressions which the standard multi-head self-attention network fails to learn, and conduct comprehensive experiments and ablation studies on four synthetic datasets and three real-world datasets. The results show that SHORING empirically outperforms the state-of-the-art methods.
Geometric vs Algebraic Nullity for Hyperpaths

We consider the question of how the eigenvarieties of a hypergraph relate to the algebraic multiplicities of their corresponding eigenvalues. Specifically, we (1) fully describe the irreducible components of the zero-eigenvariety of a loose $3$-hyperpath (its"nullvariety"), (2) use recent results of Bao-Fan-Wang-Zhu to compute the corresponding algebraic multiplicity of zero (its"nullity"), and then (3) for this special class of hypergraphs, verify a conjecture of Hu-Ye about the relationship between the geometric (multi-)dimension of the nullvariety and the nullity.
Slope and Generalization Properties of Neural Networks

Neural networks are very successful tools in for example advanced classification. From a statistical point of view, fitting a neural network may be seen as a kind of regression, where we seek a function from the input space to a space of classification probabilities that follows the “general” shape of the data, but avoids overfitting by avoiding memorization of individual data points. In statistics, this can be done by controlling the geometric complexity of the regression function. We propose to do something similar when fitting neural networks by controlling the slope of the network.After defining the slope and discussing some of its theoretical properties, we go on to show empirically in examples, using ReLU networks, that the distribution of the slope of a well-trained neural network classifier is generally independent of the width of the layers in a fully connected network, and that the mean of the distribution only has a weak dependence on the model architecture in general. We discuss possible applications of the slope concept, such as using it as a part of the loss function or stopping criterion during network training, or ranking data sets in terms of their complexity.
No-Reference Quality Assessment for Colored Point Cloud and Mesh Based on Natural Scene Statistics

—To improve the viewer’s quality of experience and optimize processing systems in computer graphics applications, the 3D quality assessment (3D-QA) has become an important task in the multimedia area. Point cloud and mesh are the two most widely used electronic representation formats of 3D models, the quality of which is quite sensitive to operations like simpliﬁcation and compression. Therefore, many studies concerning point cloud quality assessment (PCQA) and mesh quality assessment (MQA) have been carried out to measure the visual quality degradations caused by lossy operations. However, a large part of previous studies utilizes full-reference (FR) metrics, which means they may fail to predict the accurate quality level of 3D models when the reference 3D model is not available. Furthermore, limited numbers of 3D-QA metrics are carried out to take color features into consideration, which signiﬁcantly restricts the effectiveness and scope of application. In many quality assessment studies, natural scene statistics (NSS) have shown a good ability to quantify the distortion of natural scenes to statistical parameters. Therefore, we propose an NSS-based no-reference quality assessment metric for colored 3D models. In this paper, quality-aware features are extracted from the aspects of color and geometry directly from the 3D models. Then the statistic parameters are estimated using different distribution models to describe the characteristic of the 3D models. Our method is mainly validated on the colored point cloud quality assessment database (SJTU-PCQA) and the colored mesh quality assessment database (CMDM). The experimental results show that the proposed method outperforms all the state-of-art NR 3D-QA metrics and obtains an acceptable gap with the state-of-art FR 3D-QA metrics.
Scale Mixtures of Neural Network Gaussian Processes

Recent works have revealed that infinitely-wide feed-forward or recurrent neural networks of any architecture correspond to Gaussian processes referred to as Neural Network Gaussian Processes (NNGPs). While these works have extended the class of neural networks converging to Gaussian processes significantly, however, there has been little focus on broadening the class of stochastic processes that such neural networks converge to. In this work, inspired by the scale mixture of Gaussian random variables, we propose the scale mixture of NNGPs for which we introduce a prior distribution on the scale of the last-layer parameters. We show that simply introducing a scale prior on the last-layer parameters can turn infinitely-wide neural networks of any architecture into a richer class of stochastic processes. With certain scale priors, we obtain heavy-tailed stochastic processes, and in the case of inverse gamma priors, we recover Student's $t$ processes. We further analyze the distributions of the neural networks initialized with our prior setting and trained with gradient descents and obtain similar results as for NNGPs. We present a practical posterior-inference algorithm for the scale mixture of NNGPs and empirically demonstrate its usefulness on regression and classification tasks. In particular, we show that in both tasks, the heavy-tailed stochastic processes obtained from our framework are robust to out-of-distribution data.
Machine Learning for Malware Evolution Detection

Malware evolves over time and antivirus must adapt to such evolution. Hence, it is critical to detect those points in time where malware has evolved so that appropriate countermeasures can be undertaken. In this research, we perform a variety of experiments on a significant number of malware families to determine when malware evolution is likely to have occurred. All of the evolution detection techniques that we consider are based on machine learning and can be fully automated -- in particular, no reverse engineering or other labor-intensive manual analysis is required. Specifically, we consider analysis based on hidden Markov models (HMM) and the word embedding techniques HMM2Vec and Word2Vec.
Towards Better Adversarial Synthesis of Human Images from Text

This paper proposes an approach that generates multiple 3D human meshes from text. The human shapes are represented by 3D meshes based on the SMPL model. The model's performance is evaluated on the COCO dataset, which contains challenging human shapes and intricate interactions between individuals. The model is able to capture the dynamics of the scene and the interactions between individuals based on text. We further show how using such a shape as input to image synthesis frameworks helps to constrain the network to synthesize humans with realistic human shapes.
Impact of excitation energy on hot carrier properties in InGaAs MQW structure

Hot carrier solar cells aim to overcome the theoretical limit of single-junction photovoltaic devices by suppressing the thermalization of hot carriers and extracting them through energy selective contacts. Designing efficient hot carrier absorbers requires further investigation on hot carrier properties in materials. Although the thermalization of hot carriers is responsible for a large portion of energy loss in solar cells, it is still one of the least understood phenomena in semiconductors. Here, the impact of excitation energy on the properties of photo-generated hot carriers in an InGaAs multi-quantum well (MQW) structure at various lattice temperatures and excitation powers is studied. Photoluminescence (PL) emission of the sample is detected by a hyperspectral luminescence imager, which creates spectrally and spatially resolved PL maps. The thermodynamic properties of hot carriers, such as temperature and quasi-Fermi level splitting, are carefully determined via applying full PL spectrum fitting, which solves the Fermi-Dirac integral and considers the band-filling effect in the nanostructured material. In addition, the impact of thermalized power density and carrier scattering with longitudinal optical phonons on the spectral linewidth broadening under two excitation energies is studied.
Evaluating the Cybersecurity Risk of Real-world, Machine Learning Production Systems

Although cyberattacks on machine learning (ML) production systems can be harmful, today, security practitioners are ill-equipped, lacking methodologies and tactical tools that would allow them to analyze the security risks of their ML-based systems. In this article, we perform a comprehensive threat analysis of ML production systems. In this analysis, we follow the ontology presented by NIST for evaluating enterprise network security risk and apply it to ML-based production systems. Specifically, we (1) enumerate the assets of a typical ML production system, (2) describe the threat model (i.e., potential adversaries, their capabilities, and their main goal), (3) identify the various threats to ML systems, and (4) review a large number of attacks, demonstrated in previous studies, which can realize these threats. To quantify the risk posed by adversarial machine learning (AML) threat, we introduce a novel scoring system that assigns a severity score to different AML attacks. The proposed scoring system utilizes the analytic hierarchy process (AHP) for ranking—with the assistance of security experts—various attributes of the attacks. Finally, we developed an extension to the MulVAL attack graph generation and analysis framework to incorporate cyberattacks on ML production systems. Using this extension, security practitioners can apply attack graph analysis methods in environments that include ML components thus providing security practitioners with a methodological and practical tool for both evaluating the impact and quantifying the risk of a cyberattack targeting ML production systems.
A Fuzzy Scheduling Strategy for Deadline-Based Workﬂow Applications in Uncertain Edge-Cloud Environments

—Workﬂow scheduling is critical to performing many practical workﬂow applications. Scheduling based on edge-cloud computing can help addressing the high complexity of workﬂow applications, while decreasing the data transmission delay. However, due to the nature of heterogeneous resources in edge-cloud environments and the complicated data dependencies between the tasks in such a workﬂow, signiﬁcant challenges for workﬂow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. Existing studies are mainly done subject to rigorous conditions without ﬂuctuations, ignoring the fact that workﬂow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of workﬂow applications mainly caused by task computation and data transmission, while satisfying the workﬂow deadline in uncertain edge-cloud environments. The Triangular Fuzzy Numbers (TFNs) are adopted to represent task processing time and data transferring time. A cost-driven fuzzy scheduling strategy based on an Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm is proposed, which is employed the operators of Genetic Algorithm (GA). This strategy introduces the randomly two-point crossover operator, neighborhood mutation operator, and adaptive multipoint mutation operator of GA to effectively avoid converging on local optima. The experimental results show that our strategy can effectively reduce the workﬂow execution cost in uncertain edge-cloud environments, compared with other benchmark solutions.
Sonodynamic effect in A375 melanoma cells with chlorin e6 induced by 20 kHz ultrasound

In this research, combined effect of chlorin e6 and noninvasive 20 kHz, low-powered ultrasound on melanoma cells is proposed for the first time. The proposed combination incorporates the application of safe low-frequency ultrasound that is used for transdermal drug delivery. We demonstrate that impact of the 20 kHz ultrasound on chlorin e6 leads to strong sonodynamic effect in melanoma cells. We also show that chlorin e6 keeps its monomeric form in the cells and does not aggregate after exposure to ultrasound. We propose a two-step model where sonoluminescence serves as an internal light source for the chlorin e6 photoactivation.
Security implications of digitalization: The dangers of data colonialism and the way towards sustainable and sovereign management of environmental data

Digitalization opens up new opportunities in the collection, analysis, and presentation of data which can contribute to the achievement of the 2030 Agenda and its Sustainable Development Goals (SDGs). In particular, the access to and control of environmental and geospatial data is fundamental to identify and understand global issues and trends. Also immediate crises such as the COVID-19 pandemic demonstrate the importance of accurate health data such as infection statistics and the relevance of digital tools like video conferencing platforms. However, today much of the data is collected and processed by private actors. Thus, governments and researchers depend on data platforms and proprietary systems of big tech companies such as Google or Microsoft. The market capitalization of the seven largest US and Chinese big tech companies has grown to 8.7tn USD in recent years, about twice the size of Germany's gross domestic product (GDP). Therefore, their market power is enormous, allowing them to dictate many rules of the digital space and even interfere with legislations. Based on a literature review and nine expert interviews this study presents a framework that identifies the risks and consequences along the workflow of collecting, processing, storing, using of data. It also includes solutions that governmental and multilateral actors can strive for to alleviate the risks. Fundamental to this framework is the novel concept of "data colonialism" which describes today's trend of private companies appropriating the digital sphere. Historically, colonial nations used to grab indigenous land and exploit the cheap labor of slave workers. In a similar way, today's big tech corporations use cheap data of their users to produce valuable services and thus create enormous market power.
Unified Identification and Tuning Approach Using Deep Neural Networks For Visual Servoing Applications

—Vision based control of Unmanned Aerial Vehicles (UAVs) has been adopted by a wide range of applications due to the availability of low-cost on-board sensors and computers. Tuning such systems to work properly requires extensive domain speciﬁc experience which limits the growth of emerging applications. Moreover, obtaining performance limits of UAV based visual servoing with the current state-of-the-art is not possible due to the complexity of the models used. In this paper, we present a systematic approach for real-time identiﬁcation and tuning of visual servoing systems based on a novel robustiﬁed version of the recent deep neural networks with the modiﬁed relay feedback test (DNN-MRFT) approach. The proposed robust DNN-MRFT algorithm can be used with a multitude of vision sensors and estimation algorithms despite the high levels of sensor’s noise. Sensitivity of MRFT to perturbations is investigated and its effect on identiﬁcation and tuning performance is analyzed. DNN-MRFT was able to detect performance changes due to the use of slower vision sensors, or due to the integration of accelerometer measurements. Experimental identiﬁcation results were closely matching simulation results, which can be used to explain system behaviour and anticipate the closed loop performance limits given a certain hardware and software setup. Finally, we demonstrate the capability of the DNN-MRFT tuned visual servoing systems to reject external disturbances. Some advantages of the suggested robust identiﬁcation approach compared to existing visual ser-voing design approaches are presented.
The persistent cup-length invariant

We define a persistent cohomology invariant called persistent cup-length which is able to extract non trivial information about the evolution of the cohomology ring structure across a filtration. We also devise algorithms for the computation of this invariant and we furthermore show that the persistent cup-length is 2-Lipschitz continuous with respect to the homotopy interleaving and Gromov-Hausdorff distances.
Robust and efficient multilevel‐ILU preconditioning of hybrid Newton–GMRES for incompressible Navier–Stokes equations

We introduce a robust and efficient preconditioner for a hybrid Newton–GMRES method for solving the nonlinear systems arising from incompressible Navier–Stokes equations. When the Reynolds number is relatively high, these systems often involve millions of degrees of freedom (DOFs), and the nonlinear systems are difficult to converge, partially due to the strong asymmetry of the system and the saddle‐point structure. In this work, we propose to alleviate these issues by leveraging a multilevel ILU preconditioner called HILUCSI, which is particularly effective for saddle‐point problems and can enable robust and rapid convergence of the inner iterations in Newton–GMRES. We further use Picard iterations with the Oseen systems to hot‐start Newton–GMRES to achieve global convergence, also preconditioned using HILUCSI. To further improve efficiency and robustness, we use the Oseen operators as physics‐based sparsifiers when building preconditioners for Newton iterations and introduce adaptive refactorization and iterative refinement in HILUCSI. We refer to the resulting preconditioned hybrid Newton–GMRES as HILUNG. We demonstrate the effectiveness of HILUNG by solving the standard 2D driven‐cavity problem with Re 5000 and a 3D flow‐over‐cylinder problem with low viscosity. We compare HILUNG with some state‐of‐the‐art customized preconditioners for INS, including two variants of augmented Lagrangian preconditioners and two physics‐based preconditioners, as well as some general‐purpose approximate‐factorization techniques. Our comparison shows that HILUNG is much more robust for solving high‐Re problems and it is also more efficient in both memory and runtime for moderate‐Re problems.