Miracle-dz/UniFAR-FTUs huggingface dataset:
Playing Adaptively Against Stealthy Opponents: A Reinforcement Learning Strategy for the FlipIt Security Game

A rise in Advanced Persistant Threats (APTs) has introduced a need for robustness against long-running, stealthy attacks which circumvent existing cryptographic security guarantees. FlipIt is a security game that models the attacker-defender interactions in advanced scenarios such as APTs. Previous work analyzed extensively non-adaptive strategies in FlipIt, but adaptive strategies rise naturally in practical interactions as players receive feedback during the game. We model the FlipIt game as a Markov Decision Process and use reinforcement learning algorithms to design adaptive strategies. We prove theoretical results on the convergence of our new strategy against an opponent playing with a Periodic strategy. We confirm our analysis experimentally by extensive evaluation of the strategy against specific opponents. Our strategies converge to the optimal adaptive strategy for Periodic and Exponential opponents. Finally, we introduce a generalized Q-Learning strategy with composite states that outperforms a Greedy-based strategy for several distributions, including Periodic and Uniform, without prior knowledge of the opponent's strategy.
Adversarial Pixel-Level Generation of Semantic Images

Generative Adversarial Networks (GANs) have obtained extraordinary success in the generation of realistic images, a domain where a lower pixel-level accuracy is acceptable. We study the problem, not yet tackled in the literature, of generating semantic images starting from a prior distribution. Intuitively this problem can be approached using standard methods and architectures. However, a better-suited approach is needed to avoid generating blurry, hallucinated and thus unusable images since tasks like semantic segmentation require pixel-level exactness. In this work, we present a novel architecture for learning to generate pixel-level accurate semantic images, namely Semantic Generative Adversarial Networks (SemGANs). The experimental evaluation shows that our architecture outperforms standard ones from both a quantitative and a qualitative point of view in many semantic image generation tasks.
SeF: A Secure Fountain Architecture for Slashing Storage Costs in Blockchains

Full nodes, which synchronize the entire blockchain history and independently validate all the blocks, form the backbone of any blockchain network by playing a vital role in ensuring security properties. On the other hand, a user running a full node needs to pay a heavy price in terms of storage costs. E.g., the Bitcoin blockchain size has grown over 215GB, in spite of its low throughput. The ledger size for a high throughput blockchain Ripple has already reached 9TB, and it is growing at an astonishing rate of 12GB per day! In this paper, we propose an architecture based on 'fountain codes', a class of erasure codes, that enables any full node to 'encode' validated blocks into a small number of 'coded blocks', thereby reducing its storage costs by orders of magnitude. In particular, our proposed "Secure Fountain (SeF)" architecture can achieve a near-optimal trade-off between the storage savings per node and the 'bootstrap cost' in terms of the number of (honest) storage-constrained nodes a new node needs to contact to recover the blockchain. A key technical innovation in SeF codes is to make fountain codes secure against adversarial nodes that can provide maliciously formed coded blocks. Our idea is to use the header-chain as a 'side-information' to check whether a coded block is maliciously formed while it is getting decoded. Further, the 'rateless property' of fountain codes helps in achieving high decentralization and scalability. Our experiments demonstrate that SeF codes tuned to achieve 1000x storage savings enable full nodes to encode the 191GB Bitcoin blockchain into 195MB on average. A new node can recover the blockchain from an arbitrary set of storage-constrained nodes as long as the set contains ~1100 honest nodes on average. Note that for a 1000x storage savings, the fundamental bound on the number of honest nodes to contact is 1000: we need about 10% more in practice.
Mixed-Signal Charge-Domain Acceleration of Deep Neural Networks through Interleaved Bit-Partitioned Arithmetic

Albeit low-power, mixed-signal circuitry suffers from significant overhead of Analog to Digital (A/D) conversion, limited range for information encoding, and susceptibility to noise. This paper aims to address these challenges by offering and leveraging the following mathematical insight regarding vector dot-product---the basic operator in Deep Neural Networks (DNNs). This operator can be reformulated as a wide regrouping of spatially parallel low-bitwidth calculations that are interleaved across the bit partitions of multiple elements of the vectors. As such, the computational building block of our accelerator becomes a wide bit-interleaved analog vector unit comprising a collection of low-bitwidth multiply-accumulate modules that operate in the analog domain and share a single A/D converter(ADC). This bit-partitioning results in a lower-resolution ADC while the wide regrouping alleviates the need for A/D conversion per operation, amortizing its cost across multiple bit-partitions of the vector elements. Moreover, the low-bitwidth modules require smaller encoding range and also provide larger margins for noise mitigation. We also utilize the switched-capacitor design for our bit-level reformulation of DNN operations. The proposed switched-capacitor circuitry performs the regrouped multiplications in the charge domain and accumulates the results of the group in its capacitors over multiple cycles. The capacitive accumulation combined with wide bit-partitioned regrouping reduces the rate of A/D conversions, further improving the overall efficiency of the design. With such mathematical reformulation and its switched-capacitor implementation, we define one possible 3D-stacked microarchitecture, dubbed BiHiwe, that leverages clustering and hierarchical design to best utilize power-efficiency of the mixed-signal domain and 3D stacking. We also build models for noise, computational non-idealities, and variations. For ten DNN benchmarks, BiHiwe delivers 5.5x speedup over a leading purely-digital 3D-stacked accelerator Tetris, with a mere of less than 0.5% accuracy loss achieved by careful treatment of noise, computation error, and various forms of variation. Compared to RTX~2080~TI with tensor cores and Titan Xp GPUs, all with 8-bit execution, BiHiwe offers 35.4x and 70.1x higher Performance-per-Watt, respectively. Relative to the mixed-signal RedEye, ISAAC, and PipeLayer, BiHiwe offers 5.5x, 3.6x, and 9.6x improvement in Performance-per-Watt respectively. The results suggest that BiHiwe is an effective initial step in a road that combines mathematics, circuits, and architecture.
SCALAR: an AMR code to simulate axion-like dark matter models

We present a new code, SCALAR, based on the high-resolution hydrodynamics and N-body code RAMSES, to solve the Schrödinger equation on adaptive refined meshes. The code is intended to be used to simulate axion or fuzzy dark matter models where the evolution of the dark matter component is determined by a coupled Schrödinger-Poisson equation, but it can also be used as a stand-alone solver for both linear and non-linear Schrödinger equations with any given external potential. This paper describes the numerical implementation of our solver and presents tests to demonstrate how accurately it operates.
Formulating causal questions and principled statistical answers

Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline (“point exposure”) and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score‐based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a “simulation learner,” that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on www.ofcaus. org, where SAS and Stata code for analysis is also provided.
Modeling Food Popularity Dependencies using Social Media data

The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like popularity of spatio-temporal popularity of various cuisines can be analysed. Tracking the popularity of food types and retail locations across space and time can also be useful for business owners and restaurant investors. In this work, we present an approach using off-the shelf machine learning techniques to identify trends and popularity of cuisine types in an area using geo-tagged data from social media, Google images and Yelp. After adjusting for time, we use the Kernel Density Estimation to get hot spots across the location and model the dependencies among food cuisines popularity using Bayesian Networks. We consider the Manhattan borough of New York City as the location for our analyses but the approach can be used for any area with social media data and information about retail businesses.
One Embedding To Do Them All

Online shopping caters to the needs of millions of users daily. Search, recommendations, personalization have become essential building blocks for serving customer needs. Efficacy of such systems is dependent on a thorough understanding of products and their representation. Multiple information sources and data types provide a complete picture of the product on the platform. While each of these tasks shares some common characteristics, typically product embeddings are trained and used in isolation. In this paper, we propose a framework to combine multiple data sources and learn unified embeddings for products on our e-commerce platform. Our product embeddings are built from three types of data sources - catalog text data, a user's clickstream session data and product images. We use various techniques like denoising auto-encoders for text, Bayesian personalized ranking (BPR) for clickstream data, Siamese neural network architecture for image data and combined ensemble over the above methods for unified embeddings. Further, we compare and analyze the performance of these embeddings across three unrelated real-world e-commerce tasks specifically checking product attribute coverage, finding similar products and predicting returns. We show that unified product embeddings perform uniformly well across all these tasks.
Filter Early, Match Late: Improving Network-Based Visual Place Recognition

CNNs have excelled at performing place recognition over time, particularly when the neural network is optimized for localization in the current environmental conditions. In this paper we investigate the concept of feature map filtering, where, rather than using all the activations within a convolutional tensor, only the most useful activations are used. Since specific feature maps encode different visual features, the objective is to remove feature maps that detract from the ability to recognize a location across appearance changes. Our key innovation is to filter the feature maps in an early convolutional layer, but then continue to run the network and extract a feature vector using a later, more viewpoint invariant layer in the same network. Our approach improves the condition and viewpoint invariance of a pre-trained network, using as little as a single training image pair from the deployment environment. An exhaustive experimental analysis is performed to determine the full scope of causality between early layer filtering and late layer extraction. For validity, we use three datasets: Oxford RobotCar, Nordland, and Gardens Point, achieving overall superior performance to NetVLAD. The work provides a number of new avenues for exploring CNN optimizations, without requiring any re-training of the network weights.
Implications from Late-time X-Ray Detections of Optically Selected Tidal Disruption Events: State Changes, Unification, and Detection Rates

We present Chandra X-ray observations of four optically selected tidal disruption events (TDEs) obtained 4–9 yr after discovery. Three sources were detected with luminosities between 9 × 1040 and 3 × 1042 erg s−1. The spectrum of PTF09axc is consistent with a power law of index 2.5 ± 0.1, whereas the spectrum of PTF09ge is very soft. The power-law spectrum of PTF09axc and prior literature findings provide evidence that TDEs transition from an early-time soft state to a late-time hard state many years after disruption. We propose that the time to peak luminosity for optical and X-ray emission may differ substantially in TDEs, with X-rays being produced or becoming observable later. This delay helps explain the differences in observed properties such as Lopt/LX of optically and X-ray-selected TDEs. We update TDE rate predictions for the eROSITA instrument: it ranges from 3 to 990 yr−1, depending sensitively on the distribution of black hole spins and the time delay between disruption and peak X-ray brightness. We further predict an asymmetry in the number of retrograde and prograde disks in samples of optically and X-ray-selected TDEs. The details of the observational biases can contribute to observed differences between optically and X-ray-selected TDEs (with optically selected TDEs being fainter in X-rays for retrograde TDE disks).
Feature Ranking of Active Region Source Properties in Solar Flare Forecasting and the Uncompromised Stochasticity of Flare Occurrence

Solar flares originate from magnetically active regions (ARs) but not all solar ARs give rise to a flare. Therefore, the challenge of solar flare prediction benefits from an intelligent computational analysis of physics-based properties extracted from AR observables, most commonly line-of-sight or vector magnetograms of the active region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive AR properties, mainly inferred by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (SDO/HMI) in the course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine-learning methods that allow feature ranking as a function of predictive capability, we show that (i) an objective training and testing process is paramount for the performance of every supervised machine-learning method; (ii) most properties include overlapping information and are therefore highly redundant for flare prediction; (iii) solar flare prediction is still—and will likely remain—a predominantly probabilistic challenge.
Determination of Optimal Foreign Exchange Reserves in Nigeria

Disclaimer This Working Paper should not be reported as representing the views of the CBN.The views expressed herein are those of the author(s) and are not necessarily those of the Central Bank of Nigeria and its Management. This paper examines the optimal level of international reserves for Nigeria by minimizing the central bank’s cost function. The study imposed the cumulative quarterly output loss associated with the global financial crisis and Nigerian Banking sector crisis between 2008Q1 and 2009Q4 (i.e. 52.80 per cent) and 2009Q1 – 2010Q4 (i.e. 32.22 per cent) as the maximum and minimum output losses for the entire study period. The study found that while actual reserves had been above the optimal reserves level between 2008Q1 – 2010Q4, the average core reserves available to the economy was however, insufficient to absorb the adverse economic impact of financial crises if they occur in future. JEL – E58 F31 F34
Processing Large Quantities of Qualitative Data in Organizational Surveys: Challenges, Solutions, and Applications

Qualitative comments provide rich information useful for organizational action planning. Before releasing comments, it is often necessary to scrub them of identifiable information and profanity to maintain the integrity of the survey and protect respondents from repercussions or false accusations. However, processing large quantities of comments (e.g., >50,000) is prohibitively time consuming. We present the method used to develop an automation to filter identifiable information and profanity from comments which greatly expedited the review process and allowed useful applications of comments in the field. We observed high sensitivity, specificity, and accuracy rates comparable to common medical screens with diagnostic utility.
Imaging the upper mantle transition zone with a generalized Radon transform of SS precursors

We demonstrate the feasibility of using inverse scattering for high-resolution imaging of discontinuities in the upper mantle beneath oceanic regions (far from sources and receivers) using broadband wavefield observations consisting of SS and its precursors. The generalized Radon transform (GRT) that we developed for this purpose detects (in the broadband data) signals due to scattering from elasticity contrasts in Earth’s mantle. Synthetic tests with realistic source–receiver distributions demonstrate that the GRT is able to detect and image deep mantle interfaces, even in the presence of noise, depth phases (‘ghosts’), phase conversions, and multiples generated by reverberation within the transition zone. As a proof of concept, we apply the GRT to ∼50,000 broadband seismograms to delineate interfaces in the depth range from 300 to 1000 km beneath the northwest Pacific. We account for smooth 3D mantle heterogeneity using first-order perturbation theory and independently obtained global tomography models. The preliminary results reveal laterally continuous (but undulating) scatter zones near 410 and 660 km depth and a weaker, broader, and more complex structure near 520 km depth. The images also suggest the presence of multiple, laterally intermittent interfaces near 350 km and between 800 and 1000 km depth, that is, above and below the transition zone sensu stricto. Filtering of the data (we consider four pass-bands: 20–50 s, 10–50 s, 5–50 s, and 2–50 s) reveals a prominent frequency dependence of the magnitude, width, and complexity of the interfaces, in particular of the scatter zone near 520 km depth; such dependencies may put important constraints on the mineralogy and phase chemistry of the transition
Cooperation challenges to public safety organizations on the use of unmanned aircraft systems ( UAS )

This study deals with the importance of networking for co-operating authorities and their duties. As an example, we will dissect unmanned aircraft system (UAS) utilization for improving and speeding up a situational awareness and a real-time picture. Networking is emphasized between cooperation with national authorities, because the players are under the supervision of different ministries. Inter-ministerial co-operation is already in a good shape, but given the relevant persons in the mutual interaction may be scarce. In this point of view, the challenge for UASs use include ministries fragmented budgets, a lack of common practices of the new system of exploitation and the lack of cultural activities. It has revealed a need for networking between the authorities in cooperation of implementation of UAS. Different levels of networking means to cooperation between organizations: performing a similar task teams to cooperate, or individual experts formed a collaborative network. UAS cooperation with the authorities will act in all of the above (sectored, regional, level) mention areas. The importance of cooperation between authorities has discovered an important subject to be developed. The Finnish Government’s Security and Defense Policy states that the close cooperation between the authorities achieves synergies between overlap functions by cutting and support functions to enable efficient use of. Situational awareness and government collaboration will be developed both nationally and internationally. Efficient use of resources in society is a sensible, economical and appropriate. Therefore, in UAS development activities, must participate many part-sides (Police, Fire and Rescue Services, Border Guard, Customs, etc.). Strategy work requires a new perspective and you must be able to see large complexes. Different entities interact with each other and strategic decisions require courage. Successful organizations create a successful strategy, implement it and they are able to renew their strategies with the latest requirements. Keywords—Public safety, UAS, UAV, Unmanned aircraft system, Unmanned air vehicles
Fast and efficient removal of silver ( I ) from aqueous solutions using aloe vera shell ash supported Ni 0 . 5 Zn 0 . 5 Fe 2 O 4 magnetic nanoparticles

Silver (I) was removed from aqueous environment by aloe vera shell ash supported Ni0.5Zn0.5Fe2O4 magnetic nanoparticles. The adsorbent was characterized by several methods including X-ray diffraction (XRD), scanning electron microscopy (SEM), BET isotherm, vibrating sample magnetometer (VSM) and Fourier transform infrared spectroscopy (FT-IR). To determine the absorption of silver (I) by this adsorbent, different pH values (2−7), adsorbent dose (0.01−0.5 g), concentrations of Ag (50, 100, 200, 300, 500, 700 and 1000 mg/L) and exposure time (5−100 min) were experimented. The highest removal efficiency of Ag was achieved under optimum condition (30 min and pH=5). The optimum adsorbent dose was 0.20 g (in 50 mL of 100 mg/L Ag solution), which achieved a removal efficiency of 98.3%. The maximum monolayer adsorption capacity based on the Langmuir isotherm is 243.90 mg/g. Characterization results revealed that specific surface area and porous volume were 814.23 m/g and 0.726 cm/g, respectively. The experimental data were fitted well with the Langmuir and Freundlich isotherm models. Synthesized adsorbent has desired surface area and adsorptive capacity for silver (I) adsorption in aquatic environment.
Corporate Bond Yields in the Transmission Mechanism of Monetary Policy

Financial markets are a major channel through which monetary policy influences the economy. The policy transmission mechanism in the United States is analysed within a cointegrated vector errorcorrection model framework, with emphasis on the role played by the corporate bond market. The corporate bond yield is found to significantly react to monetary policy shocks, both directly and indirectly. It is also a significant transmitter of these shocks to the general economy via its effect on inflation and output. Impulse response analysis shows corporate bonds reacting faster and deeper to a contractionary monetary shock than Treasuries, making the corporate-Treasury bond yield spread a potential target in implementing monetary policy.