Miracle-dz/UniFAR-FTUs huggingface dataset:
Visualization of Linear Operations in the Spherical Harmonics Domain

Linear operations on coefficients in the spherical harmonics (SH) transform domain that again yield SH-domain coefficients are an important toolset in many disciplines of research and engineering. They comprise rotations, spatially selective filters, and many other modifications for various applications, or describe the response of a MIMO system to an excitation. It is of particular importance to characterize these operations both qualitatively and quantitatively, and make them accessible for people to work with. In this paper, we identify different key properties of such operations and propose a method for their visualization. With our proposed method, we succeed to show many important aspects of an operation in a single plot and give rise to a comprehensive interpretation of the behavior of a system. In our evaluation, we show the potential of the proposed method on the basis of various practical examples from spatial audio signal processing, where SH-domain filtering is used to modify acoustic scenes given by higher-order Ambisonics signals.
Scalar–Tensor–Vector Modified Gravity in Light of the Planck 2018 Data

The recent data release by the Planck satellite collaboration presents a renewed challenge for modified theories of gravitation. Such theories must be capable of reproducing the observed angular power spectrum of the cosmic microwave background radiation. For modified theories of gravity, an added challenge lies in the fact that standard computational tools do not readily accommodate the features of a theory with a variable gravitational coupling coefficient. An alternative is to use less accurate but more easily modifiable semianalytical approximations to reproduce at least the qualitative features of the angular power spectrum. We extend a calculation that was used previously to demonstrate compatibility between the Scalar–Tensor–Vector–Gravity (STVG) theory, also known by the acronym MOG, and data from the Wilkinson Microwave Anisotropy Probe (WMAP) to show the consistency between the theory and the newly released Planck 2018 data. We find that within the limits of this approximation, the theory accurately reproduces the features of the angular power spectrum.
Unsupervised 3D Shape Completion through GAN Inversion

Most 3D shape completion approaches rely heavily on partial-complete shape pairs and learn in a fully super-vised manner. Despite their impressive performances on in-domain data, when generalizing to partial shapes in other forms or real-world partial scans, they often obtain unsatisfactory results due to domain gaps. In contrast to previous fully supervised approaches, in this paper we present ShapeInversion, which introduces Generative Adversarial Network (GAN) inversion to shape completion for the first time. ShapeInversion uses a GAN pre-trained on complete shapes by searching for a latent code that gives a complete shape that best reconstructs the given partial input. In this way, ShapeInversion no longer needs paired training data, and is capable of incorporating the rich prior captured in a well-trained generative model. On the ShapeNet bench-mark, the proposed ShapeInversion outperforms the SOTA unsupervised method, and is comparable with supervised methods that are learned using paired data. It also demonstrates remarkable generalization ability, giving robust results for real-world scans and partial inputs of various forms and incompleteness levels. Importantly, ShapeInversion naturally enables a series of additional abilities thanks to the involvement of a pre-trained GAN, such as producing multiple valid complete shapes for an ambiguous partial input, as well as shape manipulation and interpolation.
Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization

Distributionally robust supervised learning (DRSL) is emerging as a key paradigm for building reliable machine learning systems for real-world applications -- reflecting the need for classifiers and predictive models that are robust to the distribution shifts that arise from phenomena such as selection bias or nonstationarity. Existing algorithms for solving Wasserstein DRSL -- one of the most popular DRSL frameworks based around robustness to perturbations in the Wasserstein distance -- have serious limitations that limit their use in large-scale problems -- in particular they involve solving complex subproblems and they fail to make use of stochastic gradients. We revisit Wasserstein DRSL through the lens of min-max optimization and derive scalable and efficiently implementable stochastic extra-gradient algorithms which provably achieve faster convergence rates than existing approaches. We demonstrate their effectiveness on synthetic and real data when compared to existing DRSL approaches. Key to our results is the use of variance reduction and random reshuffling to accelerate stochastic min-max optimization, the analysis of which may be of independent interest.
Excitation Spectrum of Bose Gases beyond the Gross–Pitaevskii regime

We consider Bose gases of N particles in a box of volume one, interacting through a repulsive potential with scattering length of order N, for κ > 0. Such regimes interpolate between the Gross-Pitaevskii and thermodynamic limits. Assuming that κ is sufficiently small, we determine the ground state energy and the low-energy excitation spectrum, up to errors vanishing in the limit N → ∞.
Document Structure aware Relational Graph Convolutional Networks for Ontology Population

Ontologies comprising of concepts, their attributes, and relationships are used in many knowledge based AI systems. While there have been efforts towards populating domain specific ontologies, we examine the role of document structure in learning ontological relationships between concepts in any document corpus. Inspired by ideas from hypernym discovery and explainability, our method performs about 15 points more accurate than a stand-alone R-GCN model for this task.
Strain, Doping, and Electronic Transport of Large Area Monolayer MoS2 Exfoliated on Gold and Transferred to an Insulating Substrate

Gold-assisted mechanical exfoliation currently represents a promising method to separate ultralarge (centimeter scale) transition metal dichalcogenide (TMD) monolayers (1L) with excellent electronic and optical properties from the parent van der Waals (vdW) crystals. The strong interaction between Au and chalcogen atoms is key to achieving this nearly perfect 1L exfoliation yield. On the other hand, it may significantly affect the doping and strain of 1L TMDs in contact with Au. In this paper, we systematically investigated the morphology, strain, doping, and electrical properties of large area 1L MoS2 exfoliated on ultraflat Au films (0.16–0.21 nm roughness) and finally transferred to an insulating Al2O3 substrate. Raman mapping and correlative analysis of the E′ and A1′ peak positions revealed a moderate tensile strain (ε ≈ 0.2%) and p-type doping (n ≈ −0.25 × 1013 cm–2) of 1L MoS2 in contact with Au. Nanoscale resolution current mapping and current–voltage (I–V) measurements by conductive atomic force microscopy (C-AFM) showed direct tunneling across the 1L MoS2 on Au, with a broad distribution of tunneling barrier values (ΦB from 0.7 to 1.7 eV) consistent with p-type doping of MoS2. After the final transfer of 1L MoS2 on Al2O3/Si, the strain was converted to compressive strain (ε ≈ −0.25%). Furthermore, an n-type doping (n ≈ 0.5 × 1013 cm–2) was deduced by Raman mapping and confirmed by electrical measurements of an Al2O3/Si back-gated 1L MoS2 transistor. These results provide a deeper understanding of the Au-assisted exfoliation mechanism and can contribute to its widespread application for the realization of novel devices and artificial vdW heterostructures.
Communicating With Extremely Large-Scale Array/Surface: Unified Modeling and Performance Analysis

Wireless communications with extremely large-scale array (XL-array) correspond to systems whose antenna sizes are so large that conventional modeling assumptions, such as uniform plane wave (UPW) impingement, are no longer valid. This paper studies the mathematical modeling and performance analysis of XL-array communications. By deviating from the conventional modeling approach that treats the array elements as sizeless points, we explicitly model their physical area/aperture, which enables a unified modeling for the classical discrete antenna arrays and the emerging active continuous surfaces. As such, a generic array/surface model that accurately takes into account the variations of signal phase, amplitude and projected aperture across array elements is proposed. Based on the proposed model, a closed-form expression of the resulting signal-to-noise ratio (SNR) with the optimal single-user maximum ratio combining/transmission (MRC/MRT) beamforming is derived. The expression reveals that instead of scaling linearly with the antenna number $M$ as in conventional UPW modeling, the SNR with the more generic model increases with $M$ with diminishing return, which is governed by the collective properties of the array, such as the array occupation ratio and the physical sizes of the array along each dimension, while irrespective of the properties of the individual array element. In addition, we have derived an alternative insightful expression for the optimal SNR in terms of the vertical and horizontal angular spans, which are fully determined by the geometric angles formed by the array/surface and user location. Furthermore, we also show that our derived results include the far-field UPW modeling as a special case. One important finding during the study of far-field approximation is the necessity to introduce a new distance criterion to complement the classical Rayleigh distance, termed uniform-power distance (UPD), which concerns the signal amplitude/power variations across array elements, instead of phase variations as for Rayleigh distance. Extensive numerical results are provided to demonstrate the necessity of proper modeling for XL-array communications by comparing the proposed model with various benchmark models.
Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity

Machine learning models' goal is to make correct predictions for specific tasks by learning important properties and patterns from data. By doing so, there is a chance that the model learns properties that are unrelated to its primary task. Property Inference Attacks exploit this and aim to infer from a given model (\ie the target model) properties about the training dataset seemingly unrelated to the model's primary goal. If the training data is sensitive, such an attack could lead to privacy leakage. This paper investigates the influence of the target model's complexity on the accuracy of this type of attack, focusing on convolutional neural network classifiers. We perform attacks on models that are trained on facial images to predict whether someone's mouth is open. Our attacks' goal is to infer whether the training dataset is balanced gender-wise. Our findings reveal that the risk of a privacy breach is present independently of the target model's complexity: for all studied architectures, the attack's accuracy is clearly over the baseline. We discuss the implication of the property inference on personal data in the light of Data Protection Regulations and Guidelines.
Morph Call: Probing Morphosyntactic Content of Multilingual Transformers

The outstanding performance of transformer-based language models on a great variety of NLP and NLU tasks has stimulated interest in exploration of their inner workings. Recent research has been primarily focused on higher-level and complex linguistic phenomena such as syntax, semantics, world knowledge and common-sense. The majority of the studies is anglocentric, and little remains known regarding other languages, specifically their morphosyntactic properties. To this end, our work presents Morph Call, a suite of 46 probing tasks for four Indo-European languages of different morphology: Russian, French, English and German. We propose a new type of probing tasks based on detection of guided sentence perturbations. We use a combination of neuron-, layer- and representation-level introspection techniques to analyze the morphosyntactic content of four multilingual transformers, including their understudied distilled versions. Besides, we examine how fine-tuning on POS-tagging task affects the probing performance.
Generating isospectral but not isomorphic quantum graphs

Quantum graphs are defined by having a Laplacian defined on the edges of a metric graph with boundary conditions on each vertex such that the resulting operator, $\mathbf{L}$, is self-adjoint. We use Neumann boundary conditions although we do a slight excursion into graphs with Dirichlet and $\delta$-type boundary condititons towards the end of the paper. The spectrum of $\mathbf{L}$ does not determine the graph uniquely, that is, there exist non-isomorphic graphs with the same spectra. There are few known examples of pairs of non-isomorphic but isospectral quantum graphs. In this paper we start to correctify this situation by finding hundreds of isospectral sets, using computer algebra. We have found all sets of isospectral but non-isomorphic equilateral connected quantum graphs with at most nine vertices. This includes thirteen isospectral triplets and one isospectral set of four. One of the isospectral triplets involves a loop where we could prove isospectrality. We also present several different combinatorial methods to generate arbitrarily large sets of isospectral graphs, including infinite graphs in different dimensions. As part of this we have found a method to determine if two vertices have the same Titchmarsh-Weyl $M$-function. We give combinatorial methods to generate sets of graphs with arbitrarily large number of vertices with the same $M$-function. We also find several sets of graphs that are isospectral under both Neumann and Dirichlet boundary conditions as well as under more general, $\delta$-type and $\delta'_s$-type, boundary conditions. We discuss the possibilities that our program is incorrect, present our tests and open source it for inspection at http://github.com/meapistol/Spectra-of-graphs
Gyrogroups associated with groups

Abstract In this paper, we have studied the properties of the associated gyrogroup of a given group G of nilpotency class 3. We have proved that if 3 does not divide the order of the group G, then the nilpotency class of the associated gyrogroup is same as that of the group G. We have also studied the problem of abelian inner mapping group in this context.
Refined Young Inequality and Its Application to Divergences

We give bounds on the difference between the weighted arithmetic mean and the weighted geometric mean. These imply refined Young inequalities and the reverses of the Young inequality. We also studied some properties on the difference between the weighted arithmetic mean and the weighted geometric mean. Applying the newly obtained inequalities, we show some results on the Tsallis divergence, the Rényi divergence, the Jeffreys–Tsallis divergence and the Jensen–Shannon–Tsallis divergence.
The knot quandle of the twist-spun trefoil is a central extension of a Schl\"{a}fli quandle

A quandle is an algebraic system which excels at describing limited symmetries of a space. We introduce the concept of Schl\"{a}fli quandles which are defined relating to chosen rotational symmetries of regular tessellations. On the other hand, quandles have a good chemistry with knot theory. Associated with a knot we have its knot quandle. We show that the knot quandle of the $m$-twist-spun trefoil is a central extension of the Schl\"{a}fli quandle related to the regular tessellation $\{ 3, m \}$ in the sense of the Schl\"{a}fli symbol if $m \geq 3$.
Effects of long-term fertilization history and current N and S fertilizer applications on nitrous oxide production from S-deficient soils in a laboratory incubation 1

Abstract: Nitrous oxide (N2O) production in four soils with unique fertilization management histories — collected from long-term fertility treatments receiving no fertilizer, NPKS, PKS, and NPK in a 5 yr cereal–forage rotation — in response to three sources of added N [100 kg N ha-1 urea, NH4Cl, Ca(NO3)2] with and without co-addition of elemental S (20 kg S ha-1) plus a 0-N and 0-S control was investigated in a 7 wk laboratory incubation in a loamtextured soil at 40% water-filled pore space. In all soils, cumulative N2O emissions and apparent, cumulative, net nitrification were significantly higher following addition of urea compared with other N fertilizers with and without co-addition of elemental S. Lower N2O emissions were observed in soils without a history of long-term N fertilization following addition of urea compared with soils that had historically received urea. Because the preincubation soil total N levels were similar in soils with a history of urea application (NPKS) and without urea (PKS), the results of this investigation suggest that the higher N2O production in the NPKS soil may be the result of a priming effect and (or) changes in microbial community composition induced by long-term urea applications rather than differences in the long-term soil N balance.
Twisted magnetic topological insulators

Motivated by the discovery of quantum anomalous Hall effect in Cr-doped Bi2Se3, we study the generic states for magnetic topological insulators and explore the physical properties for both magnetism and itinerant electrons. First-principles calculations are exploited to investigate the magnetic interactions between magnetic Co atoms adsorbed on the Bi2Se3 (111) surface. Due to the absence of inversion symmetry on the surface, there are Dzyaloshinskii-Moriya like twisted spin interactions between the local moments of Co ions. These non-ferromagnetic interactions twist the collinear spin configuration of the ferromagnet and generate various magnetic orders beyond a simple ferromagnet. Among them, the spin spiral state generates alternating counterpropagating modes across each period of spin states, and the skyrmion lattice even supports a chiral mode around the core of each skyrmion. The skyrmion lattice opens a gap at the surface Dirac point, resulting in anomalous Hall effect. These results would inspire further experimental investigation of magnetic topological insulators.
Performance portable back-projection algorithms on CPUs: agnostic data locality and vectorization optimizations

Computed Tomography (CT) is a key 3D imaging technology that fundamentally relies on the compute-intense back-projection operation to generate 3D volumes. GPUs are typically used for back-projection in production CT devices. However, with the rise of power-constrained micro-CT devices, and also the emergence of CPUs comparable in performance to GPUs, back-projection for CPUs could become favorable. Unlike GPUs, extracting parallelism for back-projection algorithms on CPUs is complex given that parallelism and locality are not explicitly defined and controlled by the programmer, as is the case when using CUDA for instance. We propose a collection of novel back-projection algorithms that reduce the arithmetic computation, robustly enable vectorization, enforce a regular memory access pattern, and maximize the data locality. We also implement the novel algorithms as efficient back-projection kernels that are performance portable over a wide range of CPUs. Performance evaluation using a variety of CPUs from different vendors and generations demonstrates that our back-projection implementation achieves on average 5.2 times speedup over the multi-threaded implementation of the most widely used, and optimized, open library. With a state‐of‐the‐art CPU, we reach performance that rivals top-performing GPUs.
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means

Industrial Information Technology infrastructures are often vulnerable to cyberattacks. To ensure security to the computer systems in an industrial environment, it is required to build effective intrusion detection systems to monitor the cyber-physical systems (e.g., computer networks) in the industry for malicious activities. This article aims to build such intrusion detection systems to protect the computer networks from cyberattacks. More specifically, we propose a novel unsupervised machine learning approach that combines the K-Means algorithm with the Isolation Forest for anomaly detection in industrial big data scenarios. Since our objective is to build the intrusion detection system for the big data scenario in the industrial domain, we utilize the Apache Spark framework to implement our proposed model that was trained in large network traffic data (about 123 million instances of network traffic) stored in Elasticsearch. Moreover, we evaluate our proposed model on the live streaming data and find that our proposed system can be used for real-time anomaly detection in the industrial setup. In addition, we address different challenges that we face while training our model on large datasets and explicitly describe how these issues were resolved. Based on our empirical evaluation in different use cases for anomaly detection in real-world network traffic data, we observe that our proposed system is effective to detect anomalies in big data scenarios. Finally, we evaluate our proposed model on several academic datasets to compare with other models and find that it provides comparable performance with other state-of-the-art approaches.
A digital score of tumour‐associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma

The infiltration of T‐lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival. In this study, our aim was to explore the prognostic significance of tumour‐associated stroma infiltrating lymphocytes (TASILs) in head and neck squamous cell carcinoma (HNSCC) through an AI‐based automated method. A deep learning‐based automated method was employed to segment tumour, tumour‐associated stroma, and lymphocytes in digitally scanned whole slide images of HNSCC tissue slides. The spatial patterns of lymphocytes and tumour‐associated stroma were digitally quantified to compute the tumour‐associated stroma infiltrating lymphocytes score (TASIL‐score). Finally, the prognostic significance of the TASIL‐score for disease‐specific and disease‐free survival was investigated using the Cox proportional hazard analysis. Three different cohorts of haematoxylin and eosin (H&E)‐stained tissue slides of HNSCC cases (n = 537 in total) were studied, including publicly available TCGA head and neck cancer cases. The TASIL‐score carries prognostic significance (p = 0.002) for disease‐specific survival of HNSCC patients. The TASIL‐score also shows a better separation between low‐ and high‐risk patients compared with the manual tumour‐infiltrating lymphocytes (TILs) scoring by pathologists for both disease‐specific and disease‐free survival. A positive correlation of TASIL‐score with molecular estimates of CD8+ T cells was also found, which is in line with existing findings. To the best of our knowledge, this is the first study to automate the quantification of TASILs from routine H&E slides of head and neck cancer. Our TASIL‐score‐based findings are aligned with the clinical knowledge, with the added advantages of objectivity, reproducibility, and strong prognostic value. Although we validated our method on three different cohorts (n = 537 cases in total), a comprehensive evaluation on large multicentric cohorts is required before the proposed digital score can be adopted in clinical practice. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.