Miracle-dz/UniFAR-FTUs huggingface dataset:
First-order Gradual Information Flow Types with Gradual Guarantees

Gradual type systems seamlessly integrate statically-typed programs with dynamically-typed programs. The runtime for gradual type systems can be viewed as a monitor which refines and enforces constraints to ensure type-preservation. Gradual typing has been applied to information flow types, where information flow monitors are derived from gradual information flow types. However, existing work gives up the dynamic gradual guarantee -- the property that loosening the policies of a program should not cause more runtime errors -- in favor of noninterference -- the key security property for information flow control systems. In this paper, we re-examine the connection between gradual information flow types and information flow monitors, and identify the root cause for the tension between satisfying gradual guarantees and noninterference. We develop a runtime semantics for a simple imperative language with gradual information flow types that provides both noninterference and the dynamic gradual guarantee. We leverage a proof technique developed for FlowML, which reduces noninterference proofs to preservation proofs, to prove the key security property.
Implicit Cooperation: Emotion Detection for Validation and Adaptation of Automated Vehicles' Driving Behavior

Human emotion detection in automated vehicles helps to improve comfort and safety. Research in the automotive domain focuses a lot on sensing drivers' drowsiness and aggression. We present a new form of implicit driver-vehicle cooperation, where emotion detection is integrated into an automated vehicle's decision-making process. Constant evaluation of the driver's reaction to vehicle behavior allows us to revise decisions and helps to increase the safety of future automated vehicles.
Timing of young radio pulsars – II. Braking indices and their interpretation

In Paper I of this series, we detected a significant value of the braking index ($n$) for 19 young, high-$\dot{E}$ radio pulsars using $\sim$ 10 years of timing observations from the 64-m Parkes radio telescope. Here we investigate this result in more detail using a Bayesian pulsar timing framework to model timing noise and to perform selection to distinguish between models containing exponential glitch recovery and braking index signatures. We show that consistent values of $n$ are maintained with the addition of substantial archival data, even in the presence of glitches. We provide strong arguments that our measurements are unlikely due to exponential recovery signals from unseen glitches even though glitches play a key role in the evolution of a pulsar's spin frequency. We conclude that, at least over decadal time scales, the value of $n$ can be significantly larger than the canonical 3 and discuss the implications for the evolution of pulsars.
Multi-Task Learning for Influence Estimation and Maximization

We address the problem of influence maximization when the social network is accompanied by diffusion cascades. In the literature, such information is used to compute influence probabilities, which is utilized by stochastic diffusion models in influence maximization. Motivated by the recent criticism on diffusion models and the galloping advancements in influence learning, we propose IMINFECTOR (Influence Maximization with INFluencer vECTORs), a method that uses representations learned from diffusion cascades to perform model-independent influence maximization. The first part of our methodology is a multi-task neural network that learns embeddings of nodes that initiate cascades (influencer vectors) and embeddings of nodes that participate in them (susceptible vectors). The norm of an influencer vector captures a node’s aptitude to initiate lengthy cascades and is used to reduce the number of candidate seeds. The combination of influencer and susceptible vectors form the diffusion probabilities between nodes. These are used to reformulate the computation of the influence spread and propose a greedy solution to influence maximization that retains the theoretical guarantees. We apply our method in three sizable datasets and evaluate it using cascades from future time steps. IMINFECTOR’s scalability and accuracy outperform various competitive algorithms and metrics from the diverse landscape of influence maximization.
On the Exact Feasibility of Convex Scenario Programs With Discarded Constraints

We revisit the so-called sampling and discarding approach used to quantify the probability of constraint violation of a solution to convex scenario programs when some of the original samples are allowed to be discarded. Motivated by two scenario programs that possess analytic solutions and the fact that the existing bound for scenario programs with discarded constraints is not tight, we analyze a removal scheme that consists of a cascade of optimization problems, where, at each step, we remove a superset of the active constraints. By relying on results from compression learning theory, we show that such a removal scheme leads to less conservative bounds for the probability of constraint violation than the existing ones. We also show that the proposed bound is tight by characterizing a class of optimization problems which achieves the given upper bound. The performance improvement of the proposed methodology is illustrated by an example that involves a resource sharing linear program.
InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining

Multi-modal pretraining for learning high-level multi-modal representation is a further step towards deep learning and artificial intelligence. In this work, we propose a novel model, namely InterBERT (BERT for Interaction), which owns strong capability of modeling interaction between the information flows of different modalities. The single-stream interaction module is capable of effectively processing information of multiple modalilties, and the two-stream module on top preserves the independence of each modality to avoid performance downgrade in single-modal tasks. We pretrain the model with three pretraining tasks, including masked segment modeling (MSM), masked region modeling (MRM) and image-text matching (ITM); and finetune the model on a series of vision-and-language downstream tasks. Experimental results demonstrate that InterBERT outperforms a series of strong baselines, including the most recent multi-modal pretraining methods, and the analysis shows that MSM and MRM are effective for pretraining and our method can achieve performances comparable to BERT in single-modal tasks. Besides, we propose a large-scale dataset for multi-modal pretraining in Chinese, and we develop the Chinese InterBERT which is the first Chinese multi-modal pretrained model. We pretrain the Chinese InterBERT on our proposed dataset of 3.1M image-text pairs from the mobile Taobao, the largest Chinese e-commerce platform. We finetune the model for text-based image retrieval, and recently we deployed the model online for topic-based recommendation.
liOS: Lifting iOS Apps for Fun and Profit

Although iOS is the second most popular mobile operating system and is often considered the more secure one, approaches to automatically analyze iOS applications are scarce and generic app analysis frameworks do not exist. This is on the one hand due to the closed ecosystem putting obstacles in the way of reverse engineers and on the other hand due to the complexity of reverse engineering and analyzing app binaries. Reliably lifting accurate call graphs, control flows, and data dependence graphs from binary code, as well as reconstructing object-oriented high-level concepts is a non-trivial task and the choice of the lifted target representation determines the analysis capabilities. None of the various existing intermediate representations is a perfect fit for all types of analysis, while the detection of vulnerabilities requires techniques ranging from simple pattern matching to complex inter-procedural data flow analyses. We address this gap by introducing liOS, a binary lifting and analysis framework for iOS applications that extracts lifted information from several frontends and unifies them in a “supergraph” representation that tolerates missing parts and is further extended and interlinked by liOS “passes”. A static analysis of the binary is then realized in the form of graph traversal queries, which can be considered as an advancement of classic program query languages. We illustrate this approach by means of a typical JavaScript/Objective-C bridge, which can lead to remote code execution in iOS applications.
Bordifications of hyperplane arrangements and their curve complexes

The complement of an arrangement of hyperplanes in Cn has a natural bordification to a manifold with corners formed by removing (or “blowing up”) tubular neighborhoods of the hyperplanes and certain of their intersections. When the arrangement is the complexification of a real simplicial arrangement, the bordification closely resembles Harvey's bordification of moduli space. We prove that the faces of the universal cover of the bordification are parameterized by the simplices of a simplicial complex C , the vertices of which are the irreducible “parabolic subgroups” of the fundamental group of the arrangement complement. So, the complex C plays a similar role for an arrangement complement as the curve complex does for moduli space. Also, in analogy with curve complexes and with spherical buildings, we prove that C has the homotopy type of a wedge of spheres. Our results apply in particular to spherical Artin groups, where the associated arrangement is a reflection arrangement of a finite Coxeter group.
Data Science in Economics

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The novel data science methods and applications are investigated in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide range of economics research, from stock market, marketing, E-commerce, to corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on the advancement of hybrid models. On the other hand, based on the accuracy metric it is also reported that the hybrid models outperform other learning algorithms. It is further expected that the trends would go toward the advancements of sophisticated hybrid deep learning models.
Exotic ribbon disks and symplectic surfaces.

We construct pairs of surfaces in symplectic 4-manifolds that are topologically isotopic yet are not equivalent under any ambient diffeomorphism, including the first such examples of holomorphic disks in the 4-ball, symplectic 2-spheres, and symplectic surfaces of nonzero self-intersection, as well as Lagrangian 2-spheres that are homotopic but not topologically equivalent. The underlying construction provides infinitely many pairs of exotic ribbon disks in the 4-ball, and we provide an entirely topological exposition of such disks.
Orchestrating NLP Services for the Legal Domain

Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based on a portfolio of Natural Language Processing and Content Curation services as well as a Multilingual Legal Knowledge Graph that contains semantic information and meaningful references to legal documents. We also describe different use cases with which we experiment and develop prototypical solutions.
Maximum Likelihood Estimation for Mixed Fractional Vasicek Processes

In this paper, we consider the problem of estimating the drift parameters in the mixed fractional Vasicek model, which is an extended model of the traditional Vasicek model. Using the fundamental martingale and the Laplace transform, both the strong consistency and the asymptotic normality of the maximum likelihood estimators are studied for all H∈(0,1), H≠1/2. On the other hand, we present that the MLE can be simulated when the Hurst parameter H>1/2.
Learning medical triage from clinicians using Deep Q-Learning

Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately. While reliable decision-tree methods exist to triage patients based on their presentation, those trees implicitly require human inference and are not immediately applicable in a fully automated setting. On the other hand, learning triage policies directly from experts may correct for some of the limitations of hard-coded decision-trees. In this work, we present a Deep Reinforcement Learning approach (a variant of DeepQ-Learning) to triage patients using curated clinical vignettes. The dataset, consisting of 1374 clinical vignettes, was created by medical doctors to represent real-life cases. Each vignette is associated with an average of 3.8 expert triage decisions given by medical doctors relying solely on medical history. We show that this approach is on a par with human performance, yielding safe triage decisions in 94% of cases, and matching expert decisions in 85% of cases. The trained agent learns when to stop asking questions, acquires optimized decision policies requiring less evidence than supervised approaches, and adapts to the novelty of a situation by asking for more information. Overall, we demonstrate that a Deep Reinforcement Learning approach can learn effective medical triage policies directly from expert decisions, without requiring expert knowledge engineering. This approach is scalable and can be deployed in healthcare settings or geographical regions with distinct triage specifications, or where trained experts are scarce, to improve decision making in the early stage of care.
Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE)

Rationale: Computer aided detection (CAD) algorithms for Pulmonary Embolism (PE) algorithms have been shown to increase radiologists' sensitivity with a small increase in specificity. However, CAD for PE has not been adopted into clinical practice, likely because of the high number of false positives current CAD software produces. Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics. Methods: 91 Computed tomography pulmonary angiography scans were annotated by at least one radiologist by segmenting all pulmonary emboli visible on the study. 20 annotated CTPAs were open to the public in the form of a medical image analysis challenge. 20 more were kept for evaluation purposes. 51 were made available post-challenge. 8 submissions, 6 of them novel, were evaluated on the 20 evaluation CTPAs. Performance was measured as per embolus sensitivity vs. false positives per scan curve. Results: The best algorithms achieved a per-embolus sensitivity of 75% at 2 false positives per scan (fps) or of 70% at 1 fps, outperforming the state of the art. Deep learning approaches outperformed traditional machine learning ones, and their performance improved with the number of training cases. Significance: Through this work and challenge we have improved the state-of-the art of computer aided detection algorithms for pulmonary embolism. An open database and an evaluation benchmark for such algorithms have been generated, easing the development of further improvements. Implications on clinical practice will need further research.
Secure Platooning of Autonomous Vehicles Under Attacked GPS Data

In this paper, we study how to secure the platooning of autonomous vehicles when an unknown vehicle is under attack and bounded system uncertainties exist. For the attacked vehicle, its position and speed measurements from GPS can be manipulated arbitrarily by a malicious attacker. First, to find out which vehicle is under attack, two detectors are proposed by using the relative measurements (by camera or radar) and the local innovation obtained through measurements from neighboring vehicles. Then, based on the results of the detectors, we design a local state observer for each vehicle by applying a saturation method to the measurement innovation. Moreover, based on the neighbor state estimates provided by the observer, a distributed controller is proposed to achieve the consensus in vehicle speed and keep fixed desired distance between two neighboring vehicles. The estimation error by the observer and the platooning error by the controller are shown to be asymptotically upper bounded under certain conditions. The effectiveness of the proposed methods is also evaluated in numerical simulations.
A Cooperative Learning Framework for Resource Management in MEC: An ADMM Perspective

We consider the problem of intelligent and efficient resource management framework in mobile edge computing (MEC), which can reduce delay and energy consumption, featuring distributed optimization and efficient congestion avoidance mechanism. In this paper, we present a Cooperative Learning framework for resource management in MEC from an Alternating Direction Method of Multipliers (ADMM) perspective, called CL-ADMM framework. First, in order to caching task efficiently in a group, a novel task popularity estimating scheme is proposed, which is based on semi-Markov process model, then a greedy task cooperative caching mechanism has been established, which can effectively reduce delay and energy consumption. Secondly, for addressing group congestion, a dynamic task migration scheme based on cooperative improved Q-learning is proposed, which can effectively reduce delay and alleviate congestion. Thirdly, for minimizing delay and energy consumption for resources allocation in a group, we formulate it as an optimization problem with a large number of variables, and then exploit a novel ADMM based scheme to address this problem, which can reduce the complexity of problem with a new set of auxiliary variables, guaranteeing its convergences. Numerical results demonstrate the effectiveness of the CL-ADMM and it can effectively reduce delay and energy consumption for MEC. Index Terms – mobile edge computing; resource management; ADMM; cooperative learning
Robust Ferromagnetism in Highly Strained 
SrCoO3
 Thin Films

Epitaxial strain provides important pathways to control the magnetic and electronic states in transition metal oxides. However, the large strain is usually accompanied by a strong reduction of the oxygen vacancy formation energy, which hinders the direct manipulation of their intrinsic properties. Here using a post-deposition ozone annealing method, we obtained a series of oxygen stoichiometric SrCoO3 thin films with the tensile strain up to 3.0%. We observed a robust ferromagnetic ground state in all strained thin films, while interestingly the tensile strain triggers a distinct metal to insulator transition along with the increase of the tensile strain. The persistent ferromagnetic state across the electrical transition therefore suggests that the magnetic state is directly correlated with the localized electrons, rather than the itinerant ones, which then calls for further investigation of the intrinsic mechanism of this magnetic compound beyond the double-exchange mechanism.
Auto-Correlation of Returns in Major Cryptocurrency Markets

This paper is the first of a series of short articles that explore the efficiency of major cryptocurrency markets. A number of statistical tests and properties of statistical distributions will be used to assess if cryptocurrency markets are efficient, and how their efficiency changes over time. In this paper, we analyze autocorrelation of returns in major cryptocurrency markets using the following methods: Pearson's autocorrelation coefficient of different orders, Ljung-Box test, and first-order Pearson's autocorrelation coefficient in a rolling window. All experiments are conducted on the BTC/USD, ETH/USD, ETH/BTC markets on Bitfinex exchange, and the XBT/USD market on Bitmex exchange, each on 5-minute, 1-hour, 1-day, and 1-week time frames. The results are represented visually on charts. Statistically significant autocorrelation is persistently present on the 5m and 1H time frames on all markets. The tests disagree on the 1D and 1W time frames. The results of this article are fully reproducible. Used datasets, source code, and a runnable Jupyter Notebook are available on GitHub.
Squeezed Deep 6DoF Object Detection using Knowledge Distillation

The detection of objects considering a 6DoF pose is a common requirement to build virtual and augmented reality applications. It is usually a complex task which requires real-time processing and high precision results for adequate user experience. Recently, different deep learning techniques have been proposed to detect objects in 6DoF in RGB images. However, they rely on high complexity networks, requiring a computational power that prevents them from working on mobile devices. In this paper, we propose an approach to reduce the complexity of 6DoF detection networks while maintaining accuracy. We used Knowledge Distillation to teach portables Convolutional Neural Networks (CNN) to learn from a real-time 6DoF detection CNN. The proposed method allows real-time applications using only RGB images while decreasing the hardware requirements. We used the LINEMOD dataset to evaluate the proposed method, and the experimental results show that the proposed method reduces the memory requirement by almost 99% in comparison to the original architecture with the cost of reducing half the accuracy in one of the metrics. Code is available at https://github.com/heitorcfelix/singleshot6Dpose.
A Hybrid High-Order method for creeping flows of non-Newtonian fluids

In this paper, we design and analyze a Hybrid High-Order discretization method for the steady motion of non-Newtonian, incompressible fluids in the Stokes approximation of small velocities. The proposed method has several appealing features including the support of general meshes and high-order, unconditional inf-sup stability, and orders of convergence that match those obtained for scalar Leray–Lions problems. A complete well-posedness and convergence analysis of the method is carried out under new, general assumptions on the strain rate-shear stress law, which encompass several common examples such as the power-law and Carreau–Yasuda models. Numerical examples complete the exposition.
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability

We consider the general (stochastic) contextual bandit problem under the realizability assumption, that is, the expected reward, as a function of contexts and actions, belongs to a general function class [Formula: see text]. We design a fast and simple algorithm that achieves the statistically optimal regret with only [Formula: see text] calls to an offline regression oracle across all T rounds. The number of oracle calls can be further reduced to [Formula: see text] if T is known in advance. Our results provide the first universal and optimal reduction from contextual bandits to offline regression, solving an important open problem in the contextual bandit literature. A direct consequence of our results is that any advances in offline regression immediately translate to contextual bandits, statistically and computationally. This leads to faster algorithms and improved regret guarantees for broader classes of contextual bandit problems.