Miracle-dz/UniFAR-FTUs huggingface dataset:
An Erd\H{o}s--Fuchs Theorem for Ordered Representation Functions

Let $k\geq 2$ be a positive integer. We study concentration results for the ordered representation functions $r^{\leq}_k(A,n) = \# \big\{ (a_1 \leq \dots \leq a_k) \in A^k : a_1+\dots+a_k = n \big\}$ and $r^{ 0$ and $\star \in \{\leq, 0$. These results extend two theorems for the non-ordered representation function proved by Erdős and Fuchs in the case of $k=2$ (J. of the London Math. Society 1956).
Period Adaptation for Continuous Security Monitoring in Multicore Real-Time Systems

We propose HYDRA-C, a design-time evaluation framework for integrating monitoring mechanisms in multicore real-time systems (RTS). Our goal is to ensure that security (or other monitoring) mechanisms execute in a "continuous" manner – i.e., as often as possible, across cores. This is to ensure that any such mechanisms run with few interruptions, if any. HYDRA-C is intended to allow designers of RTS to integrate monitoring mechanisms without perturbing existing timing properties or execution orders. We demonstrate the framework using a proofof-concept implementation with intrusion detection mechanisms as security tasks. We develop and use both, (a) a custom intrusion detection system (IDS) as well as (b) Tripwire – an open source data integrity checking tool. We compare the performance of HYDRA-C with a state-of-the-art multicore RT security integration approach and find that our method does not impact the schedulability and, on average, can detect intrusions 19.05% faster without impacting the performance of RT tasks.
The Role of Private Health Plans in Medicare: Lessons from the Past, Looking into the Future

The National Academy of Social Insurance (NASI) convened a study panel in 2000 to consider the role of market-oriented reforms and private health plans in Medicare, as part of its broader project on the future of the Medicare program. The panel interpreted its charge broadly and set as its goal strengthening Medicare overall, including both the fee-for-service (FFS) system and private health plans. Members of the study panel were selected for their recognized expertise and knowledge of Medicare; they were also selected to represent different disciplines and diverse views on the role of private health plans in Medicare. The study panel met four times, convened several conference calls, and commissioned original research in pursuit of its mission. In its work, the panel strove to reach consensus on a wide range of challenging and complex issues. In most cases, the panel reached a common understanding and viewpoint, however, on some issues, there was a divergence of views among panel members, which is noted in the text.
Activation Status of Cord Blood γδ T Cells Reflects In Utero Exposure to Plasmodium falciparum Antigen

BACKGROUND: Placental Plasmodium falciparum infection modulates neonatal cell-mediated immune responses and is associated with increased susceptibility of infants to malaria. METHODS: By flow-cytometric analysis of maternal peripheral and cord blood samples collected at delivery, we measured and compared the activation status and proinflammatory cytokine activity of T cells from women segregated into groups according to malaria status. RESULTS: Stimulation with phorbol myristate acetate/ionomycin resulted in the highest percentages of tumor necrosis factor-alpha- and interferon-gamma-positive gamma delta T cells in peripheral blood samples and in corresponding cord blood samples from those treated for malaria during pregnancy. Cord blood samples from this group also contained significantly higher percentages of CD69(+) and CD25(+) gamma delta T cells and CD3(+) T cells, compared with samples from either the group with active placental P. falciparum infection at delivery or the group with no infection. Proinflammatory cytokine activity of cells from the group with placental P. falciparum infection at delivery was either similar to or lower than that of cells from the group with no infection. CONCLUSIONS: The findings imply that treatment of P. falciparum malaria during pregnancy leads to enhanced innate immune T cell activation and proinflammatory cytokine responses in both maternal and fetal compartments. Ongoing placental P. falciparum infection, conversely, is associated with an absence of such activity.
Tidal disruption of planetary bodies by white dwarfs – II. Debris disc structure and ejected interstellar asteroids

We make use of a new hybrid method to simulate the long-term, multiple-orbit disc formation through tidal disruptions of rocky bodies by white dwarfs, at high-resolution and realistic semimajor axis. We perform the largest yet suite of simulations for dwarf and terrestrial planets, spanning four orders of magnitude in mass, various pericentre distances, and semimajor axes between 3 and 150 au. This large phase space of tidal disruption conditions has not been accessible through the use of previous codes. We analyse the statistical and structural properties of the emerging debris discs, as well as the ejected unbound debris contributing to the population of interstellar asteroids. Unlike previous tidal disruption studies of small asteroids which form ring-like structures on the original orbit, we find that the tidal disruption of larger bodies usually forms dispersed structures of interlaced elliptic eccentric annuli on tighter orbits. We characterize the (typically power law) size distribution of the ejected interstellar bodies as well as their composition, rotation velocities, and ejection velocities. We find them to be sensitive to the depth (impact parameter) of the tidal disruption. Finally, we briefly discuss possible implications of our results in explaining the peculiar variability of Tabby’s star, the origin of the transit events of ZTF J0139+5245 and the formation of a planetary core around SDSS J1228+1040.
CELIAC DISEASE IN GONBAD-KAVOOS

Background: Northeast Iran has one of the highest rates of esophageal cancer in the world which is mainly squamous cell carcinoma (E SCC). Celiac disease (CD) has been identified as a risk factor for ESCC. The aim of this study is to determine the prevalence of CD in Gonbad at northeast Iran and probable relation between celiac and ESCC. Materials and Methods: fourteen hundred inhabitants of north eastern Iran were randomly selected. The subjects underwent blood sampling for determination of IgA antibodies against tissue transglutaminase (anti-TTG). Subjects with positive anti-TTG underwent an interview, upper endoscopy and duodenal biopsy. The duodenal biopsies were classified according to Marsh criteria. Results: A total of 1209 subjects (female: 699) with mean age of 50Â±11.7 years were studied. Twelve subjects (female: 9) had a positive anti-TTG (1%). Four patients did not accept endoscopy. Eight cases underwent endoscopy and duodenal biopsy. Four, two and two subjects had Marsh III, II and I respectively. Flatulence and diarrhea (the most symptoms) were in five and four subjects and characteristic skin manifestation was reported in three subjects. One subject was already diagnosed as CD. Conclusions: although prevalence of ESCC in northeast Iran is significantly higher than central Iran, the prevalence of gluten sensitive enteropathy is the same (1%). It dose not appear that CD has any impact on the prevalence of ESCC in Iran.
GhostNet: More Features From Cheap Operations

Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https://github.com/huawei-noah/ghostnet.
AdapNet: Adaptability Decomposing Encoder–Decoder Network for Weakly Supervised Action Recognition and Localization

The point process is a solid framework to model sequential data, such as videos, by exploring the underlying relevance. As a challenging problem for high-level video understanding, weakly supervised action recognition and localization in untrimmed videos have attracted intensive research attention. Knowledge transfer by leveraging the publicly available trimmed videos as external guidance is a promising attempt to make up for the coarse-grained video-level annotation and improve the generalization performance. However, unconstrained knowledge transfer may bring about irrelevant noise and jeopardize the learning model. This article proposes a novel adaptability decomposing encoder–decoder network to transfer reliable knowledge between the trimmed and untrimmed videos for action recognition and localization by bidirectional point process modeling, given only video-level annotations. By decomposing the original features into the domain-adaptable and domain-specific ones based on their adaptability, trimmed–untrimmed knowledge transfer can be safely confined within a more coherent subspace. An encoder–decoder-based structure is carefully designed and jointly optimized to facilitate effective action classification and temporal localization. Extensive experiments are conducted on two benchmark data sets (i.e., THUMOS14 and ActivityNet1.3), and the experimental results clearly corroborate the efficacy of our method.
Generalized B-Opers

Opers were introduced by Beilinson-Drinfeld [arXiv:math.AG/0501398]. In [J. Math. Pures Appl. 82 (2003), 1-42] a higher rank analog was considered, where the successive quotients of the oper filtration are allowed to have higher rank. We dedicate this paper to introducing and studying generalized $B$-opers (where "$B$" stands for "bilinear"), obtained by endowing the underlying vector bundle with a bilinear form which is compatible with both the filtration and the connection. In particular, we study the structure of these $B$-opers, by considering the relationship of these structures with jet bundles and with geometric structures on a Riemann surface.
Generalizing Complex Hypotheses on Product Distributions: Auctions, Prophet Inequalities, and Pandora's Problem

This paper explores a theory of generalization for learning problems on product distributions, complementing the existing learning theories in the sense that it does not rely on any complexity measures of the hypothesis classes. The main contributions are two general sample complexity bounds: (1) $\tilde{O} \big( \frac{nk}{\epsilon^2} \big)$ samples are sufficient and necessary for learning an $\epsilon$-optimal hypothesis in any problem on an $n$-dimensional product distribution, whose marginals have finite supports of sizes at most $k$; (2) $\tilde{O} \big( \frac{n}{\epsilon^2} \big)$ samples are sufficient and necessary for any problem on $n$-dimensional product distributions if it satisfies a notion of strong monotonicity from the algorithmic game theory literature. As applications of these theories, we match the optimal sample complexity for single-parameter revenue maximization (Guo et al., STOC 2019), improve the state-of-the-art for multi-parameter revenue maximization (Gonczarowski and Weinberg, FOCS 2018) and prophet inequality (Correa et al., EC 2019), and provide the first and tight sample complexity bound for Pandora's problem.
Liquid crystals on deformable surfaces

Liquid crystals with molecules constrained to the tangent bundle of a curved surface show interesting phenomena resulting from the tight coupling of the elastic and bulk-free energies of the liquid crystal with geometric properties of the surface. We derive a thermodynamically consistent Landau-de Gennes-Helfrich model which considers the simultaneous relaxation of the Q-tensor field and the surface. The resulting system of tensor-valued surface partial differential equation and geometric evolution laws is numerically solved to tackle the rich dynamics of this system and to compute the resulting equilibrium shape. The results strongly depend on the intrinsic and extrinsic curvature contributions and lead to unexpected asymmetric shapes.
Mass models of gas-rich void dwarf galaxies

We construct mass models of eight gas rich dwarf galaxies that lie in the Lynx–Cancer void. From NFW fits to the dark matter halo profile, we find that the concentration parameters of haloes of void dwarf galaxies are similar to those of dwarf galaxies in normal density regions. We also measure the slope of the central dark matter density profiles, obtained by converting the rotation curves derived using 3D (fat) and 2D (ROTCUR) tilted ring fitting routines, into mass densities. We find that the average slope (α = −1.39 ± 0.19), obtained from 3D fitting is consistent with that expected from an NFW profile. On the other hand, the average slope measured using the 2D approach is closer to what would be expected for an isothermal profile. This suggests that systematic effects in velocity field analysis have a significant effect on the slope of the central dark matter density profiles. Given the modest number of galaxies we use for our analysis, it is important to check these results using a larger sample.
A nonrealization theorem in the context of Descartes' rule of signs

For a real degree $d$ polynomial $P$ with all nonvanishing coefficients, with $c$ sign changes and $p$ sign preservations in the sequence of its coefficients ($c+p=d$), Descartes' rule of signs says that $P$ has $pos\leq c$ positive and $neg\leq p$ negative roots, where $pos\equiv c($ mod $2)$ and $neg\equiv p($ mod $2)$. For $1\leq d\leq 3$, for every possible choice of the sequence of signs of coefficients of $P$ (called sign pattern) and for every pair $(pos, neg)$ satisfying these conditions there exists a polynomial $P$ with exactly $pos$ positive and $neg$ negative roots (all of them simple); that is, all these cases are realizable. This is not true for $d\geq 4$, yet for $4\leq d\leq 8$ (for these degrees the exhaustive answer to the question of realizability is known) in all nonrealizable cases either $pos=0$ or $neg=0$. It was conjectured that this is the case for any $d\geq 4$. For $d=9$, we show a counterexample to this conjecture: for the sign pattern $(+,-,-,-,-,+,+,+,+,-)$ and the pair $(1,6)$ there exists no polynomial with $1$ positive, $6$ negative simple roots and a complex conjugate pair and, up to equivalence, this is the only case for $d=9$.
Data Augmentation Using Adversarial Training for Construction-Equipment Classification

Deep learning-based construction-site image analysis has recently made great progress with regard to accuracy and speed, but it requires a large amount of data. Acquiring sufficient amount of labeled construction-image data is a prerequisite for deep learning-based construction-image recognition and requires considerable time and effort. In this paper, we propose a "data augmentation" scheme based on generative adversarial networks (GANs) for construction-equipment classification. The proposed method combines a GAN and additional "adversarial training" to stably perform "data augmentation" for construction equipment. The "data augmentation" was verified via binary classification experiments involving excavator images, and the average accuracy improvement was 4.094%. In the experiment, three image sizes (32-32-3, 64-64-3, and 128-128-3) and 120, 240, and 480 training samples were used to demonstrate the robustness of the proposed method. These results demonstrated that the proposed method can effectively and reliably generate construction-equipment images and train deep learning-based classifiers for construction equipment.
Characterizing the structure of halo merger trees using a single parameter: the tree entropy

Linking the properties of galaxies to the assembly history of their dark matter haloes is a central aim of galaxy evolution theory. This paper introduces a dimensionless parameter s ∈ [0, 1], the ‘tree entropy’, to parametrize the geometry of a halo’s entire mass assembly hierarchy, building on a generalization of Shannon’s information entropy. By construction, the minimum entropy (s = 0) corresponds to smoothly assembled haloes without any mergers. In contrast, the highest entropy (s = 1) represents haloes grown purely by equal-mass binary mergers. Using simulated merger trees extracted from the cosmological N-body simulation SURFS, we compute the natural distribution of s, a skewed bell curve peaking near s = 0.4. This distribution exhibits weak dependences on halo mass M and redshift z, which can be reduced to a single dependence on the relative peak height δc/σ(M, z) in the matter perturbation field. By exploring the correlations between s and global galaxy properties generated by the SHARK semi-analytic model, we find that s contains a significant amount of information on the morphology of galaxies – in fact more information than the spin, concentration, and assembly time of the halo. Therefore, the tree entropy provides an information-rich link between galaxies and their dark matter haloes.
AGN types and unification model

Abstract The motivation of the “unified model” is to explain the main properties of the large zoo of active galactic nuclei with a single physical object. The discovery of broad permitted lines in the polarized spectrum of type 2 Seyfert galaxies in the mid 80’s led to the idea of an obscuring torus, whose orientation with respect to our line of sight was the reason of the different optical spectra. However, after many years of observations with different techniques, including IR and mm interferometry, the resulting properties of the observed dust structures differ from the torus model that would be needed to explain the type 1 vs type 2 dichotomy. Moreover, in the last years, multi-frequency monitoring of active galactic nuclei has shown an increasing number of transitions from one type to the other one, which cannot be explained in terms of the simple orientation of the dusty structure surrounding the active galactic nucleus (AGN). The interrelations between the AGN and the host galaxy, as also shown in the Magorrian relation, suggest that the evolution of the host galaxy may also have an important role in the observed manifestation of the nuclei. As an example, the observed delay between the maximum star formation activity and the onset of the AGN activity, and the higher occurrence of type 2 nuclei in star forming galaxies, have suggested the possible evolutionary path from, e.g., H II → AGN2 → AGN1. In the next years the models of unification need to also consider this observational framework and not only simple orientation effects.
CYCLIC BRANCHED COVERINGS OF BRIESKORN SPHERES BOUNDING ACYCLIC 4-MANIFOLDS

Abstract We show that standard cyclic actions on Brieskorn homology 3-spheres with non-empty fixed set do not extend smoothly to any contractible smooth 4-manifold it may bound. The quotient of any such extension would be an acyclic 4-manifold with boundary a related Brieskorn homology sphere. We briefly discuss well-known invariants of homology spheres that obstruct acyclic bounding 4-manifolds and then use a method based on equivariant Yang–Mills moduli spaces to rule out extensions of the actions.
Fully Unsupervised Probabilistic Noise2Void

Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing. A new category of DL methods such as Noise2Void or Noise2Self can be used fully unsupervised, requiring nothing but the noisy data. However, this comes at the price of reduced reconstruction quality. The recently proposed Probabilistic Noise2Void (PN2V) improves results, but requires an additional noise model for which calibration data needs to be acquired. Here, we present improvements to PN2V that (i) replace histogram based noise models by parametric noise models, and (ii) show how suitable noise models can be created even in the absence of calibration data. This is a major step since it actually renders PN2V fully unsupervised. We demonstrate that all proposed improvements are not only academic but indeed relevant.
DeviceWatch: Identifying Compromised Mobile Devices through Network Traffic Analysis and Graph Inference

In this paper, we propose to identify compromised mobile devices from a network administrator's point of view. Intuitively, inadvertent users (and thus their devices) who download apps through untrustworthy markets are often allured to install malicious apps through in-app advertisement or phishing. We thus hypothesize that devices sharing a similar set of apps will have a similar probability of being compromised, resulting in the association between a device being compromised and apps in the device. Our goal is to leverage such associations to identify unknown compromised devices (i.e., devices possibly having yet currently not having known malicious apps) using the guilt-by-association principle. Admittedly, such associations could be quite weak as it is often hard, if not impossible, for an app to automatically download and install other apps without explicit initiation from a user. We describe how we can magnify such weak associations between devices and apps by carefully choosing parameters when applying graph-based inferences. We empirically show the effectiveness of our approach with a comprehensive study on the mobile network traffic provided by a major mobile service provider. Concretely, we achieve nearly 98\% accuracy in terms of AUC (area under the ROC curve). Given the relatively weak nature of association, we further conduct in-depth analysis of the different behavior of a graph-inference approach, by comparing it to active DNS data. Moreover, we validate our results by showing that detected compromised devices indeed present undesirable behavior in terms of their privacy leakage and network infrastructure accessed.
CSPNet: A New Backbone that can Enhance Learning Capability of CNN

Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on ResNet, ResNeXt, and DenseNet.