Miracle-dz/UniFAR-FTUs huggingface dataset:
Leaky Frontends: Micro-Op Cache and Processor Frontend Vulnerabilities

This paper demonstrates a new class of security vulnerabilities due to the Micro-Op Caches, also called Decode Stream Buffer, and other components in the processor frontend. The vulnerabilities presented in this work exploit multiple paths in the processor frontend that the micro-ops can take: through the Micro-Instruction Translation Engine (MITE), through the Decode Stream Buffer (DSB), or through the Loop Stream Detector (LSD). Each path has its own unique timing and power signature, which leads to security vulnerabilities. The vulnerabilities can be used as side or covert channels for information leakage and can be exploited to create both timing and power attacks. As information leakage channels, the new vulnerabilities are orthogonal to the existing speculative execution attacks and can be used as covert transmission channels in a new variant of speculative attacks that is demonstrated in this work. The vulnerabilities further affect Intel SGX enclaves, and this work shows how information can be leaked from SGX enclaves through the sharing of the frontend paths. The transmission rates for new attacks based on the vulnerabilities presented can be as high as 1410 Kbps (1.41 Mbps) with an almost 0% error rate. Consequently, this work demonstrates that multiple paths in the processor frontend are a source of security vulnerabilities which have not been considered before and that focusing on just speculative execution attacks is not sufficient to secure today's processors.
Word Embedding Transformation for Robust Unsupervised Bilingual Lexicon Induction

Great progress has been made in unsupervised bilingual lexicon induction (UBLI) by aligning the source and target word embeddings independently trained on monolingual corpora. The common assumption of most UBLI models is that the embedding spaces of two languages are approximately isomorphic. Therefore the performance is bound by the degree of isomorphism, especially on etymologically and typologically distant languages. To address this problem, we propose a transformation-based method to increase the isomorphism. Embeddings of two languages are made to match with each other by rotating and scaling. The method does not require any form of supervision and can be applied to any language pair. On a benchmark data set of bilingual lexicon induction, our approach can achieve competitive or superior performance compared to state-of-the-art methods, with particularly strong results being found on distant languages.
A Structural Model of Business Card Exchange Networks

Social and professional networks affect labor market dynamics, knowledge diffusion and new business creation. To understand the determinants of how these networks are formed in the first place, we analyze a unique dataset of business card exchanges among a sample of over 240,000 users of the multi-platform contact management and professional social networking tool for individuals Eight. We develop a structural model of network formation with strategic interactions, and we estimate users’ payoffs that depend on the composition of business relationships, as well as indirect business interactions. We allow heterogeneity of users in both observable and unobservable characteristics to affect how relationships form and are maintained. The model’s stationary equilibrium delivers a likelihood that is a mixture of exponential random graph models that we can characterize in closed-form. We overcome several econometric and computational challenges in estimation, by exploiting a two-step estimation procedure, variational approximations and minorization-maximization methods. Our algorithm is scalable, highly parallelizable and makes efficient use of computer memory to allow estimation in massive networks. We show that users payoffs display homophily in several dimensions, e.g. location; furthermore, users unobservable characteristics also display homophily.
Connecting steady emission and very high energy flaring states in blazars: the case of Mrk 421

Various attempts have been made in the literature at describing the origin and the physical mechanisms behind flaring events in blazars with radiative emission models, but detailed properties of multiwavelength (MWL) light curves still remain difficult to reproduce. We have developed a versatile radiative code, based on a time-dependent treatment of particle acceleration, escape, and radiative cooling, allowing us to test different scenarios to connect the continuous low-state emission self-consistently with that during flaring states. We consider flares as weak perturbations of the quiescent state and apply this description to the 2010 February MWL flare of Mrk 421, the brightest very high energy (VHE) flare ever detected from this archetypal blazar, focusing on interpretations with a minimum number of free parameters. A general criterion is obtained, which disfavours a one-zone model connecting low and high state under our assumptions. A two-zone model combining physically connected acceleration and emission regions yields a satisfactory interpretation of the available time-dependent MWL light curves and spectra of Mrk 421, although certain details remain difficult to reproduce. The two-zone scenario finally proposed for the complex quiescent and flaring VHE emitting region involves both Fermi-I and Fermi-II acceleration mechanisms, respectively, at the origin of the quiescent and flaring emission.
Learning Bipedal Robot Locomotion from Human Movement

Teaching an anthropomorphic robot from human example offers the opportunity to impart humanlike qualities on its movement. In this work we present a reinforcement learning based method for teaching a real world bipedal robot to perform movements directly from human motion capture data. Our method seamlessly transitions from training in a simulation environment to executing on a physical robot without requiring any real world training iterations or offline steps. To overcome the disparity in joint configurations between the robot and the motion capture actor, our method incorporates motion re-targeting into the training process. Domain randomization techniques are used to compensate for the differences between the simulated and physical systems. We demonstrate our method on an internally developed humanoid robot with movements ranging from a dynamic walk cycle to complex balancing and waving. Our controller preserves the style imparted by the motion capture data and exhibits graceful failure modes resulting in safe operation for the robot. This work was performed for research purposes only.
A risk analysis framework for real-time control systems

We present a Monte Carlo simulation framework for analysing the risk involved in deploying real-time control systems in safety-critical applications, as well as an algorithm design technique allowing one (in certain situations) to robustify a control algorithm. Both approaches are very general and agnostic to the initial control algorithm. We present examples showing that these techniques can be used to analyse the reliability of implementations of non-linear model predictive control algorithms.
A multiresolution Discrete Element Method for triangulated objects with implicit timestepping

Simulations of many rigid bodies colliding with each other sometimes yield particularly interesting results if the colliding objects differ significantly in size and are non-spherical. The most expensive part within such a simulation code is the collision detection. We propose a family of novel multiscale collision detection algorithms that can be applied to triangulated objects within explicit and implicit time stepping methods. They are well-suited to handle objects that cannot be represented by analytical shapes or assemblies of analytical objects. Inspired by multigrid methods and adaptive mesh refinement, we determine collision points iteratively over a resolution hierarchy, and combine a functional minimisation plus penalty parameters with the actual comparision-based geometric distance calculation. Coarse surrogate geometry representations identify"no collision"scenarios early on and otherwise yield an educated guess which triangle subsets of the next finer level potentially yield collisions. They prune the search tree, and furthermore feed conservative contact force estimates into the iterative solve behind an implicit time stepping. Implicit time stepping and non-analytical shapes often yield prohibitive high compute cost for rigid body simulations. Our approach reduces these cost algorithmically by one to two orders of magnitude. It also exhibits high vectorisation efficiency due to its iterative nature.
Permutation Invariance and Uncertainty in Multitemporal Image Super-Resolution

Recent advances have shown how deep neural networks can be extremely effective at super-resolving remote-sensing imagery, starting from a multitemporal collection of low-resolution (LR) images. However, existing models have neglected the issue of temporal permutation, whereby the temporal ordering of the input images does not carry any relevant information for the super-resolution (SR) task and causes such models to be inefficient with the, often scarce, ground-truth data that available for training. Thus, models ought not to learn feature extractors that rely on temporal ordering. In this article, we show how building a model that is fully invariant to temporal permutation significantly improves performance and data efficiency. Moreover, we study how to quantify the uncertainty of the super-resolved image so that the final user is informed on the local quality of the product. We show how uncertainty correlates with temporal variation in the series, and how quantifying it further improves model performance. Experiments on the Proba-V challenge dataset show significant improvements over the state of the art without the need for self-ensembling, as well as improved data efficiency, reaching the performance of the challenge winner with just 25% of the training data.
Performance Analysis of a Foreground Segmentation Neural Network Model

In recent years the interest in segmentation has been growing, being used in a wide range of applications such as fraud detection, anomaly detection in public health and intrusion detection. We present an ablation study of FgSegNet_v2, analysing its three stages: (i) Encoder, (ii) Feature Pooling Module and (iii) Decoder. The result of this study is a proposal of a variation of the aforementioned method that surpasses state of the art results. Three datasets are used for testing: CDNet2014, SBI2015 and CityScapes. In CDNet2014 we got an overall improvement compared to the state of the art, mainly in the LowFrameRate subset. The presented approach is promising as it produces comparable results with the state of the art (SBI2015 and Cityscapes datasets) in very different conditions, such as different lighting conditions.
Quotient Space-Based Keyword Retrieval in Sponsored Search

Synonymous keyword retrieval has become an important problem for sponsored search ever since major search engines relax the exact match product's matching requirement to a synonymous level. Since the synonymous relations between queries and keywords are quite scarce, the traditional information retrieval framework is inefficient in this scenario. In this paper, we propose a novel quotient space-based retrieval framework to address this problem. Considering the synonymy among keywords as a mathematical equivalence relation, we can compress the synonymous keywords into one representative, and the corresponding quotient space would greatly reduce the size of the keyword repository. Then an embedding-based retrieval is directly conducted between queries and the keyword representatives. To mitigate the semantic gap of the quotient space-based retrieval, a single semantic siamese model is utilized to detect both the keyword--keyword and query-keyword synonymous relations. The experiments show that with our quotient space-based retrieval method, the synonymous keyword retrieving performance can be greatly improved in terms of memory cost and recall efficiency. This method has been successfully implemented in Baidu's online sponsored search system and has yielded a significant improvement in revenue.
Modeling multi-scale data via a network of networks

MOTIVATION Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related: entities represented by nodes in a higher-level (higher-scale) network can themselves be modeled as networks at a lower level. We argue that systems involving such entities should be integrated with a "network of networks" (NoN) representation. Then, we ask whether entity label prediction using multi-level NoN data via our proposed approaches is more accurate than using each of single-level node and graph data alone, i.e., than traditional node label prediction on the higher-level network and graph label prediction on the lower-level networks. To obtain data, we develop the first synthetic NoN generator and construct a real biological NoN. We evaluate accuracy of considered approaches when predicting artificial labels from the synthetic NoNs and proteins' functions from the biological NoN. RESULTS For the synthetic NoNs, our NoN approaches outperform or are as good as node- and network-level ones depending on the NoN properties. For the biological NoN, our NoN approaches outperform the single-level approaches for just under half of the protein functions, and for 30% of the functions, only our NoN approaches make meaningful predictions, while node- and network-level ones achieve random accuracy. So, NoN-based data integration is important. AVAILABILITY The software and data are available at https://nd.edu/~cone/NoNs. SUPPLEMENTARY INFORMATION Attached to the submission.
Learning to Detect Fortified Areas

High resolution data models like grid terrain models made from LiDAR data are a prerequisite for modern day Geographic Information Systems applications. Besides providing the foundation for the very accurate digital terrain models, LiDAR data is also extensively used to classify which parts of the considered surface comprise relevant elements like water, buildings and vegetation. In this paper we consider the problem of classifying which areas of a given surface are fortified by for instance, roads, sidewalks, parking spaces, paved driveways and terraces. We consider using LiDAR data and orthophotos, combined and alone, to show how well the modern machine learning algorithms Gradient Boosted Trees and Convolutional Neural Networks are able to detect fortified areas on large real world data. The LiDAR data features, in particular the intensity feature that measures the signal strength of the return, that we consider in this project are heavily dependent on the actual LiDAR sensor that made the measurement. This is highly problematic, in particular for the generalisation capability of pattern matching algorithms, as this means that data features for test data may be very different from the data the model is trained on. We propose an algorithmic solution to this problem by designing a neural net embedding architecture that transforms data from all the different sensor systems into a new common representation that works as well as if the training data and test data originated from the same sensor. The final algorithm result has an accuracy above 96 percent, and an AUC score above 0.99.
Promising Properties of a Sub-5-nm Monolayer 
MoSi2N4
 Transistor

Two-dimensional (2D) semiconductors have attracted tremendous interests as natural passivation and atomically thin channels that could facilitate continued transistor scaling. However, air-stable 2D semiconductors with high performance were quite elusive. Recently, extremely air-stable MoSi2N4 monolayer had been successfully fabricated [Hong et al., Science 369, 670 (2020)]. To further reveal its potential applications in the sub-5 nm MOSFETs, there is an urgent need to develop integrated circuits. Here we report first-principles quantum transport simulations on the performance limits of nand p-type sub-5 nm monolayer (ML) MoSi2N4 metal-oxide-semiconductor FETs (MOSFETs). We find that the on-state current in the MoSi2N4 MOSFETs can be effectively manipulated by the length of gate and underlap (UL), as well as the doping concentration. Very strikingly, we also find that the n-type devices the optimized on-state current can reach up to 1390 and 1025 μA/μm for the high performance (HP) and low power (LP) applications, respectively, both of which satisfy the International Technology Roadmap for Semiconductors (ITRS) requirements. Whereas, the optimized on-state current can meet the LP application (348 μA/μm) for the p-type devices. Finally, we find that the MoSi2N4 MOSFETs have ultra-low subthreshold swing and power delay product, which have potential to realize the high speed and low power consumption devices. Our results show that MoSi2N4 is an ideal 2D channel material for future competitive ultrascaled devices.
Deception detection in text and its relation to the cultural dimension of individualism/collectivism

Abstract Automatic deception detection is a crucial task that has many applications both in direct physical and in computer-mediated human communication. Our focus is on automatic deception detection in text across cultures. In this context, we view culture through the prism of the individualism/collectivism dimension, and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to cultural norms in respect to the individualism/collectivism divide. In addition, we investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features from several levels of linguistic analysis, namely phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over eleven data sets from five languages (English, Dutch, Russian, Spanish, and Romanian), from six countries (United States of America, Belgium, India, Russia, Mexico, and Romania), and we applied two classification methods, namely logistic regression and fine-tuned BERT models. The results showed that the undertaken task is fairly complex and demanding. Furthermore, there are indications that some linguistic cues of deception have cultural origins and are consistent in the context of diverse domains and data set settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in unified manners and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.
DNA barcoding of Austrian snow scorpionflies (Mecoptera, Boreidae) reveals potential cryptic diversity in Boreus westwoodi

Background Snow scorpionflies (genus Boreus) belong to a family of Mecoptera, Boreidae, that has been vastly neglected by entomological researchers due to their shift in seasonality to the winter months. Their activity during this time is regarded as a strategy for predator avoidance and regular sightings on snow fields suggest that this also facilitates dispersal. However, many aspects about snow scorpionflies, especially systematics, taxonomy, distribution of species, phylogenetics and phylogeography have remained fairly unexplored until today. In this study, we fill some of these gaps by generating a reference DNA barcode database for Austrian snow scorpionflies in the frame of the Austrian Barcode of Life initiative and by characterising morphological diversity in the study region. Methods Initial species assignment of all 67 specimens was based on male morphological characters previously reported to differ between Boreus species and, for females, the shape of the ovipositor. DNA barcoding of the mitochondrial cytochrome c oxidase subunit 1 (COI) gene was carried out for all 67 samples and served as a basis for BIN assignment, genetic distance calculations, as well as alternative species delimitation analyses (ABGD, GMYC, bGMYC, bPTP) and a statistical parsimony network to infer phylogenetic relationships among individual samples/sampling sites. Results Morphological investigations suggested the presence of both Boreus hyemalis and Boreus westwoodi in Austria. DNA barcoding also separated the two species, but resulted in several divergent clades, the paraphyly of B. westwoodi in Austria, and high levels of phylogeographic structure on a small geographic scale. Even though the different molecular species delimitation methods disagreed on the exact number of species, they unequivocally suggested the presence of more than the traditionally recognized two Boreus species in Austria, thus indicating potential cryptic species within the genus Boreus in general and especially in B. westwoodi.
Regulation and Consumer Interest in an Antioxidant-Enriched Ham Associated with Reduced Colorectal Cancer Risks

An economic experiment was conducted in France in 2020 to evaluate consumer attitudes toward two ham products associated with different colorectal cancer risks. We focused specifically on comparing a conventional ham and a new hypothetical antioxidant-enriched ham with a reduced risk of provoking colorectal cancer. Study participants were given descriptions of the two hams before carrying out successive rounds of willingness-to-pay (WTP) assessments. The results show that WTP was higher for the antioxidant-enriched ham than for the conventional ham. WTP estimates were also impacted by providing additional information about the reduction in colorectal cancer risk associated with the antioxidant-enriched ham. Based on the participants’ WTP, we came up with ex ante estimates for the social impacts of introducing the antioxidant-enriched ham onto the market, and we suggest that it would be socially optimal to promote the product. Competition arising from pre-existing product labelling and marketing assertions could greatly limit the market potential of antioxidant-enriched ham, which suggests that alternative approaches may be necessary, such as regulations mandating antioxidant enrichment. These results also concern all countries with high levels of meat consumption.
Control of Macrophage Inflammation by P2Y Purinergic Receptors

Macrophages comprise a phenotypically and functionally diverse group of hematopoietic cells. Versatile macrophage subsets engage to ensure maintenance of tissue integrity. To perform tissue stress surveillance, macrophages express many different stress-sensing receptors, including purinergic P2X and P2Y receptors that respond to extracellular nucleotides and their sugar derivatives. Activation of G protein-coupled P2Y receptors can be both pro- and anti-inflammatory. Current examples include the observation that P2Y14 receptor promotes STAT1-mediated inflammation in pro-inflammatory M1 macrophages as well as the demonstration that P2Y11 receptor suppresses the secretion of tumor necrosis factor (TNF)-α and concomitantly promotes the release of soluble TNF receptors from anti-inflammatory M2 macrophages. Here, we review macrophage regulation by P2Y purinergic receptors, both in physiological and disease-associated inflammation. Therapeutic targeting of anti-inflammatory P2Y receptor signaling is desirable to attenuate excessive inflammation in infectious diseases such as COVID-19. Conversely, anti-inflammatory P2Y receptor signaling must be suppressed during cancer therapy to preserve its efficacy.