Miracle-dz/UniFAR-FTUs huggingface dataset:
Don't Pick the Cherry: An Evaluation Methodology for Android Malware Detection Methods

In evaluating detection methods, the malware research community relies on scan results obtained from online platforms such as VirusTotal. Nevertheless, given the lack of standards on how to interpret the obtained data to label apps, researchers hinge on their intuitions and adopt different labeling schemes. The dynamicity of VirusTotal's results along with adoption of different labeling schemes significantly affect the accuracies achieved by any given detection method even on the same dataset, which gives subjective views on the method's performance and hinders the comparison of different malware detection techniques. In this paper, we demonstrate the effect of varying (1) time, (2) labeling schemes, and (3) attack scenarios on the performance of an ensemble of Android repackaged malware detection methods, called dejavu, using over 30,000 real-world Android apps. Our results vividly show the impact of varying the aforementioned 3 dimensions on dejavu's performance. With such results, we encourage the adoption of a standard methodology that takes into account those 3 dimensions in evaluating newly-devised methods to detect Android (repackaged) malware.
The asymptotic evolution of the stellar merger V1309 Sco: a Blue Straggler in the making?

Stellar mergers are estimated to be common events in the Galaxy. The best studied stellar merger case to date is V1309 Sco (= Nova Scorpii 2008) which was originally misclassified as a Nova event. Later identified as the merger of the components of a cool overcontact binary system with 1.52 Msun and 0.16 Msun, V1309 Sco showed an initial period of P = 1.4 days before the merger. Post-outburst evolution demonstrated that V1309 Sco was unlike the typical Classical Novae and Symbiotic Recurrent Novae with significant dust production around it, and indicated that the system may become a post-AGB (or pre-PN) soon. Here we present a study of V1309 Sco about ten years after the outburst, based on near-IR variability and colour data from the ESO surveys VISTA Variables in the Via Lactea (VVV) and VVV eXtended (VVVX). We find that reasonable equilibrium in this stellar merger is being reached and that the star has settled into a nearly constant magnitude. A dramatic change in its near-IR colours from (J-Ks) = 1.40 in 2010 to (J-Ks) = 0.42 in 2015 and a possible low amplitude periodic signal with P = 0.49 days in the post-outburst data are consistent with a "blue straggler" star, predicted to be formed from a stellar merger.
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving

We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving. Our efforts are put on extracting the underlying 3D information in a 2D image and determining the accurate 3D bounding box of object without point cloud or stereo data. Leveraging the off-the-shelf 2D object detector, we propose an artful approach to efficiently obtain a coarse cuboid for each predicted 2D box. The coarse cuboid has enough accuracy to guide us to determine the 3D box of the object by refinement. In contrast to previous state-of-the-art methods that only use the features extracted from the 2D bounding box for box refinement, we explore the 3D structure information of the object by employing the visual features of visible surfaces. The new features from surfaces are utilized to eliminate the problem of representation ambiguity brought by only using 2D bounding box. Moreover, we investigate different methods of 3D box refinement and discover that a classification formulation with quality aware loss have much better performance than regression. Evaluated on KITTI benchmark, our approach outperforms current state-of-the-art methods for single RGB image based 3D object detection.
The case for two-dimensional galaxy–galaxy lensing

We revisit the performance and biases of the two-dimensional approach to galaxy–galaxy lensing. This method exploits the information for the actual positions and ellipticities of source galaxies, rather than using only the ensemble properties of statistically equivalent samples. We compare the performance of this method with the traditionally used one-dimensional tangential shear signal on a set of mock data that resemble the current state-of-the-art weak lensing surveys. We find that under idealised circumstances the confidence regions of joint constraints for the amplitude and scale parameters of the NFW model in the two-dimensional analysis can be more than three times tighter than the one-dimensional results. Moreover, this improvement depends on the lens number density and it is larger for higher densities. We compare the method against the results from the hydrodynamical EAGLE simulation in order to test for possible biases that might arise due to lens galaxies being missed, and find that the method is able to return unbiased estimates of halo masses when compared to the true properties of the EAGLE galaxies. Because of its advantage in high galaxy density areas, the method is especially suitable for studying the properties of satellite galaxies in clusters of galaxies.
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications. Our approach is to learn local controllers which require only local information and local communications at test time by imitating the policy of centralized controllers using global information at training time. By extending aggregation graph neural networks to time varying signals and time varying network support, we learn a single common local controller which exploits information from distant teammates using only local communication interchanges. We apply this approach to a decentralized linear quadratic regulator problem and observe how faster communication rates and smaller network degree increase the value of multi-hop information. Separate experiments learning a decentralized flocking controller demonstrate performance on communication graphs that change as the robots move.
Lecture notes on quantitative unique
 continuation for solutions of second order elliptic
 equations

In these lectures we present some useful techniques to study quantitative properties of solutions of elliptic PDEs. Our aim is to outline a proof of a recent result on propagation of smallness. The ideas are also useful in the study of the zero sets of eigenfunctions of Laplace-Beltrami operator and we discuss the connection. Some basic facts about second order elliptic PDEs in divergent form are collected in the Appendix at the end of the notes.
Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters

In this article we explore how the different semantics of spectrograms' time and frequency axes can be exploited for musical tempo and key estimation using Convolutional Neural Networks (CNN). By addressing both tasks with the same network architectures ranging from shallow, domain-specific approaches to deep variants with directional filters, we show that axis-aligned architectures perform similarly well as common VGG-style networks developed for computer vision, while being less vulnerable to confounding factors and requiring fewer model parameters.
WGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN

We present a deep neural network based singing voice synthesizer, inspired by the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm. We use vocoder parameters for acoustic modelling, to separate the influence of pitch and timbre. This facilitates the modelling of the large variability of pitch in the singing voice. Our network takes a block of consecutive frame-wise linguistic and fundamental frequency features, along with global singer identity as input and outputs vocoder features, corresponding to the block of features. This block-wise approach, along with the training methodology allows us to model temporal dependencies within the features of the input block. For inference, sequential blocks are concatenated using an overlap-add procedure. We show that the performance of our model is competitive with regards to the state-of-the-art and the original sample using objective metrics and a subjective listening test. We also present examples of the synthesis on a supplementary website and the source code via GitHub.
On Fundamental Bounds of Failure Identifiability by Boolean Network Tomography

Boolean network tomography is a powerful tool to infer the state (working/failed) of individual nodes from path-level measurements obtained by egde-nodes. We consider the problem of optimizing the capability of identifying network failures through the design of monitoring schemes. Finding an optimal solution is NP-hard and a large body of work has been devoted to heuristic approaches providing lower bounds. Unlike previous works, we provide upper bounds on the maximum number of identifiable nodes, given the number of monitoring paths and different constraints on the network topology, the routing scheme, and the maximum path length. These upper bounds represent a fundamental limit on identifiability of failures via Boolean network tomography. Our analysis provides insights on how to design topologies and related monitoring schemes to achieve the maximum identifiability under various network settings. Through analysis and experiments we demonstrate the tightness of the bounds and efficacy of the design insights for engineered as well as real networks.
CEO Awards and Financial Misconduct

We propose that CEOs are more likely to engage in financial misconduct after the media names them as being among the best business leaders. We theorize this occurs because winning such an award is a meaningful event that increases the CEO’s self-worth but also increases the CEO’s sense of psychological entitlement, including the freedom to break rules. We test our ideas by examining scenarios where award-winning CEOs feel especially entitled and therefore are most likely to commit misconduct. Using a sample of award-winning CEOs from Chinese publicly listed firms, we find that award-winning CEOs are more likely to commit financial misconduct in the post-award period than in the pre-award period. In addition, the effect of winning a CEO award on financial misconduct is stronger when CEOs are underpaid or from industries in which awards are rare and therefore more special. We also validate aspects of our theory that are difficult to observe. First, we use bivariate probit models with partial observability to confirm that our results hold when accounting for unobserved misconduct. Second, we use survey data that capture the psychological entitlement of a subsample of CEOs to confirm the mediating effect of psychological entitlement on the relationship between winning an award and committing financial misconduct.
Flow structures govern particle collisions in turbulence

The role of the spatial structure of a turbulent flow in enhancing particle collision rates in suspensions is an open question. We show and quantify, as a function of particle inertia, the correlation between the multiscale structures of turbulence and particle collisions: Straining zones contribute predominantly to rapid head-on collisions compared to vortical regions. We also discover the importance of vortex-strain worm-rolls, which goes beyond ideas of preferential concentration and may explain the rapid growth of aggregates in natural processes, such as the initiation of rain in warm clouds.
A Scalable and Optimal Graph-Search Method for Secure State Estimation

The growing complexity of modern Cyber-Physical Systems (CPS) and the frequent communication between their components make them vulnerable to malicious attacks. As a result, secure state estimation is a critical requirement for the control of these systems. Many existing secure state estimation methods suffer from combinatorial complexity which grows with the number of states and sensors in the system. This complexity can be mitigated using optimization-based methods that relax the original state estimation problem, although at the cost of optimality as these methods often identify attack-free sensors as attacked. In this paper, we propose a new scalable, in terms of the number of states and sensors it can handle, and optimal graph-search algorithm to correctly identify malicious attacks in CPS modeled as linear time-invariant systems and to securely estimate their state. The graph consists of layers, each one containing two nodes capturing a truth assignment of any given sensor, and directed edges connecting adjacent layers only. Then, our algorithm searches the layers of this graph incrementally, favoring directions at higher layers with more attack-free assignments, while actively managing a repository of nodes to be expanded at later iterations. The proposed search bias and the ability to revisit nodes in the repository and self-correct, allow our graph-search algorithm to reach the optimal assignment faster and tackle larger problems. We show that our algorithm is complete and optimal provided that process and measurement noises do not dominate the attack signal. Moreover, we provide numerical simulations that demonstrate the ability of our algorithm to correctly identify attacked sensors and securely reconstruct the state. Our simulations show that our method outperforms existing algorithms both in terms of optimality and execution time.
Urbanization reduces gene flow but not genetic diversity of stream salamander populations in the New York City metropolitan area

Natural landscape heterogeneity and barriers resulting from urbanization can reduce genetic connectivity between populations. The evolutionary, demographic, and ecological effects of reduced connectivity may lead to population isolation and ultimately extinction. Alteration to the terrestrial and aquatic environment caused by urban influence can affect gene flow, specifically for stream salamanders who depend on both landscapes for survival and reproduction. To examine how urbanization affects a relatively common stream salamander species, we compared genetic connectivity of Eurycea bislineata (northern two‐lined salamander) populations within and between streams in an urban, suburban, and rural habitat around the New York City (NYC) metropolitan area. We report reduced genetic connectivity between streams within the urban landscape found to correspond with potential barriers to gene flow, that is, areas with more dense urbanization (roadways, industrial buildings, and residential housing). The suburban populations also exhibited areas of reduced connectivity correlated with areas of greater human land use and greater connectivity within a preserve protected from development. Connectivity was relatively high among neighboring rural streams, but a major roadway corresponded with genetic breaks even though the habitat contained more connected green space overall. Despite greater human disturbance across the landscape, urban and suburban salamander populations maintained comparable levels of genetic diversity to their rural counterparts. Yet small effective population size in the urban habitats yielded a high probability of loss of heterozygosity due to genetic drift in the future. In conclusion, urbanization impacted connectivity among stream salamander populations where its continual influence may eventually hinder population persistence for this native species in urban habitats.
Black hole–neutron star mergers from triples

Mergers of black hole (BH) and neutron star (NS) binaries are expected to be observed by gravitational wave observatories in the coming years. Until now, LIGO has only set an upper limit on this merger rate. BH-NS binaries are expected to merge in isolation, as their formation is suppressed in star clusters by the mass segregation and the strong heating by BHs in the cluster core. Another viable scenario to BH-NS mergers is in triple systems. In this paper, we carry out a systematic statistical study of the dynamical evolution of triples comprised of an inner BH-NS binary by means of high-precision $N$-body simulations, including Post-Newtonian (PN) terms up to 2.5PN order. We start from the main sequence massive stars and model the supernovae (SN) events that lead to the formation of BHs and NSs. We adopt different prescriptions for the natal velocity kicks imparted during the SN processes and illustrate that large kicks lead to more compact and massive triples that merge on shorter timescales. We also show that BH-NS merging in triples have a significant eccentricity in the LIGO band, typically much larger than BH-NS merging in isolated binaries. Finally, we estimate a rate of $\Gamma_\mathrm{BH-NS}\approx 1.0\times 10^{-3}-3.5\times 10^{-2} \ \mathrm{Gpc}^{-3}\ \mathrm{yr}^{-1}$, for non-zero velocity kicks, and $\Gamma_\mathrm{BH-NS}=19 \ \mathrm{Gpc}^{-3}\ \mathrm{yr}^{-1}$, for no natal kicks. Our rate estimate overlaps with the expected BH-NS rate in isolated binaries and within the LIGO upper limit.
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation

The problem of explaining the behavior of deep neural networks has recently gained a lot of attention. While several attribution methods have been proposed, most come without strong theoretical foundations, which raises questions about their reliability. On the other hand, the literature on cooperative game theory suggests Shapley values as a unique way of assigning relevance scores such that certain desirable properties are satisfied. Unfortunately, the exact evaluation of Shapley values is prohibitively expensive, exponential in the number of input features. In this work, by leveraging recent results on uncertainty propagation, we propose a novel, polynomial-time approximation of Shapley values in deep neural networks. We show that our method produces significantly better approximations of Shapley values than existing state-of-the-art attribution methods.
Processing of BiFeO3 thin films to control their dielectric response

Abstract BiFeO3 (BFO) thin films were studied to control their oxygen-related processing parameters in order to obtain specific electrical characteristics in terms of conductivity and dielectric relaxation. BFO thin films prepared with Fe and Bi excesses and post-annealed in oxygen atmosphere showed higher electrical conductivity and lower conduction activation energies than single phase ones. Distinct parameters indicated different conduction mechanisms in the films, associated with the first ionization of oxygen vacancies in BFO films with Fe and Bi excesses and the second one in single phase films. Higher conductive films show lower relaxation times compared to single phase ones.
Folding of copper proteins: role of the metal?

Abstract Copper is a redox–active transition metal ion required for the function of many essential human proteins. For biosynthesis of proteins coordinating copper, the metal may bind before, during or after folding of the polypeptide. If the metal binds to unfolded or partially folded structures of the protein, such coordination may modulate the folding reaction. The molecular understanding of how copper is incorporated into proteins requires descriptions of chemical, thermodynamic, kinetic and structural parameters involved in the formation of protein–metal complexes. Because free copper ions are toxic, living systems have elaborate copper-transport systems that include particular proteins that facilitate efficient and specific delivery of copper ions to target proteins. Therefore, these pathways become an integral part of copper protein folding in vivo. This review summarizes biophysical-molecular in vitro work assessing the role of copper in folding and stability of copper-binding proteins as well as protein–protein copper exchange reactions between human copper transport proteins. We also describe some recent findings about the participation of copper ions and copper proteins in protein misfolding and aggregation reactions in vitro.
What’s known about implementing co-located paediatric integrated care: a scoping review

Abstract Several studies have demonstrated clinical benefits of integrated care for a range of child and adolescent mental health outcomes. However, there is a significant gap between the evidence for efficacy of integrated care interventions vs their implementation in practice. While several studies have examined large-scale implementation of co-located integrated care for adults, much less is known for children. The goal of this scoping review was to understand how co-located mental health interventions targeting children and adolescents have been implemented and sustained. The literature was systematically searched for interventions targeting child and adolescent mental health that involved a mental health specialist co-located in a primary care setting. Studies reporting on the following implementation outcomes were included: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability. This search identified 34 unique studies, including randomized controlled trials, observational studies, and survey/mixed method approaches. Components facilitating implementation of on-site integrated behavioural healthcare included interprofessional communication and collaboration at all stages of implementation; clear protocols to facilitate intervention delivery; and co-employment of integrated care providers by specialty clinics. Some studies found differences in service use by demographic factors, and others reported funding challenges affecting sustainability, warranting further study.
Circulation of the European northwest shelf: a Lagrangian perspective

Abstract. The dynamics of the European northwest shelf (ENWS), the surrounding deep ocean, and the continental slope between them are analysed in a framework of numerical simulations using Lagrangian methods. Several sensitivity experiments are carried out in which (1) the tides are switched off, (2) the wind forcing is low-pass filtered, and (3) the wind forcing is switched off. To measure accumulation of neutrally buoyant particles, a quantity named the “normalised cumulative particle density (NCPD)” is introduced. Yearly averages of monthly results in the deep ocean show no permanent particle accumulation areas at the surface. On the shelf, elongated accumulation patterns persist in yearly averages, often occurring along the thermohaline fronts. In contrast, monthly accumulation patterns are highly variable in both regimes. Tides substantially affect the particle dynamics on the shelf and thus the positions of fronts. The contribution of wind variability to particle accumulation in specific regions is comparable to that of tides. The role of vertical velocities in the dynamics of Lagrangian particles is quantified for both the eddy-dominated deep ocean and for the shallow shelf. In the latter area, winds normal to coasts result in upwelling and downwelling, illustrating the importance of vertical dynamics in shelf seas. Clear patterns characterising the accumulation of Lagrangian particles are associated with the vertical circulations.