Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimality Conditions for Special Semidefinite Bilevel Optimization Problems

In this paper, we consider an optimistic bilevel programming problem whose lower level is a semidefinite programming problem. Two main approaches, namely, the optimal value reformulation and the Karush--Kuhn--Tucker reformulation, are considered in order to transform the original problem into a single-level programming problem. Afterwards, the relationship between the original problem and its substitute is studied in each case and some necessary optimality conditions are derived as well. Therefore, among others, we exploit some calmness-type constraint qualifications studied in the general framework of finite-dimensional Hilbert spaces.
TREVERSE: TRial-and-Error Lightweight Secure ReVERSE Authentication With Simulatable PUFs

A physical unclonable function (PUF) generates hardware intrinsic volatile secrets by exploiting uncontrollable manufacturing randomness. Although PUFs provide the potential for lightweight and secure authentication for increasing numbers of low-end Internet of Things devices, practical and secure mechanisms remain elusive. We aim to explore simulatable PUFs (SimPUFs) that are physically unclonable but efficiently modeled mathematically through privileged one-time PUF access to address the above problem. Given a challenge, a securely stored SimPUF in possession of a trusted server computes the corresponding response and its bit-specific reliability. Consequently, naturally noisy PUF responses generated by a resource limited prover can be immediately processed by a one-way function (OWF) and transmitted to the server, because the resourceful server can exploit the SimPUF to perform a trial-and-error search over likely error patterns to recover the noisy response to authenticate the prover. Security of trial-and-error reverse (TREVERSE) authentication under the random oracle model is guaranteed by the hardness of inverting the OWF. We formally evaluate the TREVERSE authentication capability with two SimPUFs experimentally derived from popular silicon PUFs.
The Design and Evolution of Disney’s Hyperion Renderer

Walt Disney Animation Studios has transitioned to path-traced global illumination as part of a progression of brute-force physically based rendering in the name of artist efficiency. To achieve this without compromising our geometric or shading complexity, we built our Hyperion renderer based on a novel architecture that extracts traversal and shading coherence from large, sorted ray batches. In this article, we describe our architecture and discuss our design decisions. We also explain how we are able to provide artistic control in a physically based renderer, and we demonstrate through case studies how we have benefited from having a proprietary renderer that can evolve with production needs.
A Human Factors Approach to Exploring the Experience of Group Trip Planning from the Perspective of Intragroup Interaction

ABSTRACT Previous studies have investigated the experiences and characteristics of holiday decision-making among groups of travelers. This study adds to the knowledge of group trip holiday planning through exploring influential factors (including the individual and group characteristics of travelers), and linking those with their intragroup interactions when planning a group trip. A total of 261 usable questionnaires were collected across two university campuses in the UK and China. The survey employed a retrospective approach, asking participants to recall one of their past group trip planning experiences within the previous 3 months. This study found that intragroup interactions during a group trip planning process are influenced both by tourists’ individual factors, such as age, gender, and nationality, and by group characteristics, such as group size, common interest, group type, and group travel style. This study shows that common interest is the most influential factor in terms of its positive impact on group collaboration, feeling of connectedness, strength of preparation, and flexibility and spontaneity during group trip planning process. Further, in general, Chinese groups tend to spend less time on planning their trips before departure, but focus more on the details of the itinerary. Finally, the implications for technologies that are designed to facilitate the group trip planning process, with a view to enhancing the level of group enjoyment, are discussed based on the findings in this study.
Covering the Space of Tilts. Application to Affine Invariant Image Comparison

We propose a mathematical method to analyze the numerous algorithms performing Image Matching by Affine Simulation (IMAS). To become affine invariant they apply a discrete set of affine transforms to the images, previous to the comparison of all images by a Scale Invariant Image Matching (SIIM), like SIFT. Obviously this multiplication of images to be compared increases the image matching complexity. Three questions arise: a) what is the best set of affine transforms to apply to each image to gain full practical affine invariance? b) what is the lowest attainable complexity for the resulting method? c) how to choose the underlying SIIM method? We provide an explicit answer and a mathematical proof of quasi-optimality of the solution to the first question. As an answer to b) we find that the near-optimal complexity ratio between full affine matching and scale invariant matching is more than halved, compared to the current IMAS methods. This means that the number of key points necessary for affine matching can be halved, and that the matching complexity is divided by four for exactly the same performance. This also means that an affine invariant set of descriptors can be associated with any image. The price to pay for full affine invariance is that the cardinality of this set is 6.34 times larger than for a SIIM.
Core Lines in 3D Second‐Order Tensor Fields

Vortices are important features in vector fields that show a swirling behavior around a common core. The concept of a vortex core line describes the center of this swirling behavior. In this work, we examine the extension of this concept to 3D second‐order tensor fields. Here, a behavior similar to vortices in vector fields can be observed for trajectories of the eigenvectors. Vortex core lines in vector fields were defined by Sujudi and Haimes to be the locations where stream lines are parallel to an eigenvector of the Jacobian. We show that a similar criterion applied to the eigenvector trajectories of a tensor field yields structurally stable lines that we call tensor core lines. We provide a formal definition of these structures and examine their mathematical properties. We also present a numerical algorithm for extracting tensor core lines in piecewise linear tensor fields. We find all intersections of tensor core lines with the faces of a dataset using a simple and robust root finding algorithm. Applying this algorithm to tensor fields obtained from structural mechanics simulations shows that it is able to effectively detect and visualize regions of rotational or hyperbolic behavior of eigenvector trajectories.
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.
Pulmonary metastatic colonisation and granulomas in NOX2‐deficient mice

Metastasis is the leading cause of death in cancer patients, and successful colonisation of a secondary organ by circulating tumour cells (CTCs) is the rate‐limiting step of this process. We used tail‐vein injection of B16‐F10 melanoma cells into mice to mimic the presence of CTCs and to allow for the assessment of host (microenvironmental) factors that regulate pulmonary metastatic colonisation. We found that mice deficient for the individual subunits of the NADPH oxidase of myeloid cells, NOX2 (encoded by Cyba, Cybb, Ncf1, Ncf2, and Ncf4), all showed decreased pulmonary metastatic colonisation. To understand the role of NOX2 in controlling tumour cell survival in the pulmonary microenvironment, we focused on Cyba‐deficient (Cybatm1a) mice, which showed the most significant decrease in metastatic colonisation. Interestingly, histological assessment of pulmonary metastatic colonisation was not possible in Cybatm1a mice, owing to the presence of large granulomas composed of galectin‐3 (Mac‐2)‐positive macrophages and eosinophilic deposits; granulomas of variable penetrance and severity were also found in Cybatm1a mice that were not injected with melanoma cells, and these contributed to their decreased survival. The decreased pulmonary metastatic colonisation of Cybatm1a mice was not due to any overt defects in vascular permeability, and bone marrow chimaeras confirmed a role for the haematological system in the reduced metastatic colonisation phenotype. Examination of the lymphocyte populations, which are known key regulators of metastatic colonisation, revealed an enhanced proportion of activated T and natural killer cells in the lungs of Cybatm1a mice, relative to controls. The reduced metastatic colonisation, presence of granulomas and altered immune cell populations observed in Cybatm1a lungs were mirrored in Ncf2‐deficient (Ncf2tm1a) mice. Thus, we show that NOX2 deficiency results in both granulomas and the accumulation of antitumoural immune cells in the lungs that probably mediate the decreased pulmonary metastatic colonisation. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
Differentiation of Adsorptive and Viscous Effects of Dietary Fibres on Bile Acid Release by Means of In Vitro Digestion and Dialysis

To explain the cholesterol-reducing effects of dietary fibres, one of the major mechanisms proposed is the reduced reabsorption of bile acids in the ileum. The interaction of dietary fibres with bile acids is associated with their viscous or adsorptive effects. Since these fibre characteristics are difficult to investigate in vivo, suitable in vitro methodologies can contribute to understanding the mechanistic principles. We compared the commonly used centrifugal approach with a modified dialysis method using dietary fibre-rich materials from different sources (i.e., barley, citrus, lupin, and potato). Digestion was simulated in vitro with oral, gastric, and small intestinal digestion environments. The chyme was dialysed and released bile acids were analysed by high-performance liquid chromatography. The centrifugation method showed adsorptive effects only for cholestyramine (reference material) and a high-fibre barley product (1.4 µmol taurocholic acid/100 mg dry matter). Alternatively, the dialysis approach showed higher values of bile acid adsorption (2.3 µmol taurocholic acid/100 mg dry matter) for the high-fibre barley product. This indicated an underestimated adsorption when using the centrifugation method. The results also confirmed that the dialysis method can be used to understand the influence of viscosity on bile acid release. This may be due to entrapment of bile acids in the viscous chyme matrix. Further studies on fibre structure and mechanisms responsible for viscous effects are required to understand the formation of entangled networks responsible for the entrapment of the bile acids.
Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators

We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some probability at the end of each batch. Our approach forces the single generator not to constrain its output to satisfy a single discriminator, but, instead, to satisfy a dynamic ensemble of discriminators. We show that this leads to a more generalized generator, promoting variety in the generated samples and avoiding the common mode collapse problem commonly experienced with generative adversarial networks (GANs). We further provide evidence that the proposed framework, named Dropout-GAN, promotes sample diversity both within and across epochs, eliminating mode collapse and stabilizing training.
Document Similarity for Texts of Varying Lengths via Hidden Topics

Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information. In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics. We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines. We also highlight the benefits of the incorporation of domain knowledge to text matching.
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?

One of the most widely used training methods for large-scale machine learning problems is distributed asynchronous stochastic gradient descent (DASGD). However, a key issue in its implementation is that of delays: when a "worker" node asynchronously contributes a gradient update to the "master", the global model parameter may have changed, rendering this information stale. In massively parallel computing grids, these delays can quickly add up if a node is saturated, so the convergence of DASGD is uncertain under these conditions. Nevertheless, by using a judiciously chosen quasilinear step-size sequence, we show that it is possible to amortize these delays and achieve global convergence with probability 1, even under polynomially growing delays, reaffirming in this way the successful application of DASGD to large-scale optimization problems.
Variational Network Inference: Strong and Stable with Concrete Support

Traditional methods for the discovery of latent network structures are limited in two ways: they either assume that all the signal comes from the network (i.e. there is no source of signal outside the network) or they place constraints on the network parameters to ensure model or algorithmic stability. We address these limitations by proposing a model that incorporates a Gaussian process prior on a network-independent component and formally proving that we get algorithmic stability for free, while providing a novel perspective on model stability as well as robustness results and precise intervals for key inference parameters. We show that, on three applications, our approach outperforms previous methods consistently.
Use of 18F-FDG-PET/CT for Retroperitoneal/Intra-Abdominal Soft Tissue Sarcomas

Rationale To assess the diagnostic value of 18F-FDG-PET/CT for different retroperitoneal soft tissue sarcomas (STS) and other similar tumors. To analyze the predictive value of 18F-FDG-PET/CT for histological grade and main prognostic factors. Methods 195 patients with 44 different diseases have been included. Relationship between SUVmax, Clinical, pathological, and prognostic information has been analyzed. Results Malignant tumors do not show higher SUVmax than benign ones (P=0.443). We divided all 44 different diseases into two groups; SUVmax of group 1 is significantly higher than group 2 (P ≤ 0.001). The ROC curve suggests 4.35 is the cutoff value to distinguish groups 1 and 2 (sensitivity = 0.789; specificity = 0.736). SUVmax correlates with Ki-67 index, mitotic count, vascular resection, histological grade, and recurrent STS without considering pathological diagnosis (P=0.001, P=0.012, P=0.002, P ≤ 0.001, and P=0.037, resp.). Conclusion 18F-FDG-PET/CT cannot simply distinguish malignant and benign tumors in retroperitoneal/intra-abdominal cavity; however, the SUVmax of malignant tumors, inflammatory pseudotumor, and PPGL group is higher than the SUVmax of benign tumors, lymph node metastasis, hematoma, and low malignant STS group. Guidance of “SUVmax location” may be helpful for biopsy and pathology dissection.
Effects of age on the identification of emotions in facial expressions: a meta-analysis

Background Emotion identification is a fundamental component of social cognition. Although it is well established that a general cognitive decline occurs with advancing age, the effects of age on emotion identification is still unclear. A meta-analysis by Ruffman and colleagues (2008) explored this issue, but much research has been published since then, reporting inconsistent findings. Methods To examine age differences in the identification of facial expressions of emotion, we conducted a meta-analysis of 24 empirical studies (N = 1,033 older adults, N = 1,135 younger adults) published after 2008. Additionally, a meta-regression analysis was conducted to identify potential moderators. Results Results show that older adults less accurately identify facial expressions of anger, sadness, fear, surprise, and happiness compared to younger adults, strengthening the results obtained by Ruffman et al. (2008). However, meta-regression analyses indicate that effect sizes are moderated by sample characteristics and stimulus features. Importantly, the estimated effect size for the identification of fear and disgust increased for larger differences in the number of years of formal education between the two groups. Discussion We discuss several factors that might explain the age-related differences in emotion identification and suggest how brain changes may account for the observed pattern. Furthermore, moderator effects are interpreted and discussed.
The Re-Expanded Cloud: Distributed Uplink Offloading for Mobile Edge Computing

Mobile edge computing (MEC) is envisioned as re- expanded cloud compared to fog computing. As making mobile services computing sank to the edge of network further, performance improvement can be got on time aspect. Therefore, MEC is seemed as a potential technology for delay-sensitive applications. Based on that, a reasonable computation offloading strategy would reduce system consumption for MEC further and release its computational capability. In view of resource shortage, especially bandwidth competition problem among Small Cells and edge computational requirements of HetNet, we focus on distributed uplink offloading for MEC in macro-micro coordination scene. It mainly contains two steps. First, based on Lyapunov to solve offloading decision-making problem for users in each Small Cell. Second, complete offloading update order- making of Small Cells in Macro Cell with proposed deviation update decision algorithm (DUDA). Our strategy makes up for the lack of system stability and uplink analysis in existing research. Numerical results demonstrate the effectiveness of our strategy.
Artefact-free high density localization microscopy analysis

High density analysis methods for localization microscopy increase acquisition speed but produce artefacts. We demonstrate that these artefacts can be eliminated by combining a Haar wavelet kernel (HAWK) analysis with standard single frame fitting. We tested the performance on synthetic, fixed cell and live cell data, demonstrating that HAWK pre-processing gives reconstructions which reflect the structure of the sample, enabling high speed, artefact-free super-resolution in live cells. Localization microscopy methods such as photoactivatable localization microscopy (PALM) 1 and stochastic optical reconstruction microscopy (STORM) 2 rely on the localization of small numbers of well-separated fluorophores in each frame of the raw dataset. Consequently data acquisition is slow, and generally limited to examination of fixed cells. Severe artefacts including false structure, collapse of multiple features into one, and artificial sharpening are possible if the activation density of fluorophores is too high 3,4,5 . However, assessing the activation density is extremely challenging, particularly since the density of fluorophores often varies considerably across the sample. Currently the only assessment methods for the raw data are subjective visual checks or time consuming post-acquisition reclassification 5 . Resolution assessment methods including Fourier Ring Correlation (FRC) 6 will report artificial sharpening as enhanced resolution 5,6 , and are therefore unsuitable to assess data which may contain mis-localizations. A
Predicting the vulnerability of biometric systems to attacks based on morphed biometric information

Morphing techniques can be used to create artificial biometric samples or templates, which resemble the biometric information of two or more individuals in signal and feature domain. If morphed biometric samples or templates are infiltrated to a biometric recognition system, the subjects contributing to the morphed sample can be both successfully verified against a single enrolled template. Hence, the unique link between individuals and their biometric reference data is not warranted. This leads to serious security gaps in biometric applications, in particular, the issuance and verification process of electronic travel documents. Recently, different biometric systems have been attacked using morphed biometric samples. However, so far a systematic approach to predict the vulnerability of the system to such attacks has not been proposed. In this work, the authors present a framework to evaluate the vulnerability of biometric systems to attacks using morphed biometric information. Based on a biometric system's mated/non-mated score distribution and its decision threshold, a theoretical vulnerability assessment is proposed. In an experimental evaluation, the vulnerability of a face and an iris recognition system is quantified based on the presented framework. Obtained results are verified against real attacks based on morphed face images and morphed iris-based templates.