Miracle-dz/UniFAR-FTUs huggingface dataset:
Selecting efficient and reliable preservation strategies: modeling long-term information integrity using large-scale hierarchical discrete event simulation

This article addresses the problem of formulating efficient and reliable operational preservation policies that ensure bit-level information integrity over long periods, and in the presence of a diverse range of real-world technical, legal, organizational, and economic threats. We develop a systematic, quantitative prediction framework that combines formal modelling, discrete-event-based simulation, hierarchical modelling, and then use empirically calibrated sensitivity analysis to identify effective strategies. Specifically, the framework formally defines an objective function for preservation that maps a set of preservation policies and a risk profile to a set of preservation costs, and an expected collection loss distribution. In this framework, a curator’s objective is to select optimal policies that minimize expected loss subject to budget constraints. To estimate preservation loss under different policy conditions optimal policies, we develop a statistical hierarchical risk model that includes four sources of risk: the storage hardware; the physical environment; the curating institution; and the global environment. We then employ a general discrete event-based simulation framework to evaluate the expected loss and the cost of employing varying preservation strategies under specific parameterization of risks. Source code is available at:https://github.com/MIT-Informatics/PreservationSimulation The framework offers flexibility for the modeling of a wide range of preservation policies and threats. Since this framework is open source and easily deployed in a cloud computing environment, it can be used to produce analysis based on independent estimates of scenario-specific costs, reliability, and risk. We present results summarizing hundreds of thousands of simulations using this framework. This exploratory analysis points to a number of robust and broadly applicable preservation strategies, provides novel insights into specific preservation tactics, and provides evidence that challenges received wisdom. An earlier version of this paper was published previously in IJDC 15(1) 2020
Benders' decomposition for freeway network design under endogenous autonomous vehicles demand

Autonomous vehicles (AVs) have the potential to provide cost-effective mobility options along with overall system-level benefits in terms of congestion and vehicular emissions. Additional resource allocation at the network level, such as AV-exclusive lanes, can further foster the usage of AVs rendering this mode of travel more attractive than legacy vehicles (LV). In this study, we propose an integrated mixed-integer programming framework for optimal AV-exclusive lane design on a freeway network which accounts for commuters' demand split among AVs and LVs via a logit model. We incorporate the link transmission model (LTM) as the underlying traffic flow model. The LTM is modified to integrate two vehicle classes namely, LVs and AVs with a lane-based approach. The presence of binary variables to represent lane design and the logit model for endogenous demand estimation results in a nonconvex mixed-integer nonlinear program (MINLP) formulation. We propose a Benders' decomposition approach to tackle this challenging optimization problem. Our approach iteratively explores possible lane designs in the Benders' master problem and, at each iteration, solves a sequence of system-optimum dynamic traffic assignment (SODTA) problems which is shown to converge to fixed-points representative of logit-compatible demand splits. Further, we prove that the proposed solution method converges to a local optima of the nonconvex problem and identify under which conditions this local optima is a global solution. The proposed approach is implemented on two hypothetical freeway networks with single and multiple origins and destinations. Our numerical results reveal that the optimal lane design of freeway network is non-trivial and can inform on the value of accounting for endogenous demand in the proposed freeway network design problem.
Gamma-Rays From Large-Scale Outflows In Starburst Galaxies

The combined effects of supernova explosions and stellar winds produce a hot bubble in the central regions of starburst galaxies. As the bubble expands, it can outbreak into the galactic halo driving a superwind that transports hot gas and fields to the intergalactic space. We present estimates of cosmic ray generation and gamma-ray emission in both this large-scale wind and the bow shocks created around the embedded clouds.
Community detectability and structural balance dynamics in signed networks

We investigate signed networks with community structure with respect to their spectra and their evolution under a dynamical model of structural balance, a prominent theory of signed social networks. The spectrum of the adjacency matrix generated by a stochastic block model with two equal-size communities shows detectability transitions in which the community structure becomes manifest when its signal eigenvalue appears outside the main spectral band. The spectrum also exhibits "sociality" transitions involving the homogeneous structure representing the average tie value. We derive expressions for the eigenvalues associated with the community and homogeneous structure as well as the transition boundaries, all in good agreement with numerical results. Using the stochastically generated networks as initial conditions for a simple model of structural balance dynamics yields three outcome regimes: two hostile factions that correspond with the initial communities, two hostile factions uncorrelated with those communities, and a single harmonious faction of all nodes. The detectability transition predicts the boundary between the assortative and mixed two-faction states and the sociality transition predicts that between the mixed and harmonious states. Our results may yield insight into the dynamics of cooperation and conflict among actors with distinct social identities.
Gaia's revolution in stellar variability

Stellar variability studies are now reaching a completely new level thanks to ESA's Gaia mission, which enables us to locate many variable stars in the Hertzsprung-Russell diagram and determine the various instability strips/bands. Furthermore, this mission also allows us to detect, characterise and classify many millions of new variable stars thanks to its very unique nearly simultaneous multi-epoch survey with different instruments (photometer, spectro-photometer, radial velocity spectrometer). An overview of what can be found in literature in terms of mostly data products by the Gaia consortium is given. This concerns the various catalogues of variable stars derived from the Gaia time series and also the location and motion of variable stars in the observational Hertzsprung-Russell diagram. In addition, we provide a list of a few thousands of variable white dwarf candidates derived from the DR2 published data, among them probably many hundreds of new pulsating white dwarfs. On a very different topic, we also show how Gaia allows us to reveal the 3D structures of and around the Milky Way thanks to the RR Lyrae stars.
On the thermodynamic derivation of Nernst relation

What is the maximum voltage of a cell with a given electrochemical reaction? The answer to this question has been given more than a century ago by Walther Nernst and bears his name. Unfortunately, the assumptions behind the answer have been forgotten by many authors, which leads to wrong forms of the Nernst relation. Such mistakes can be overcome by applying a correct thermodynamic derivation independently of the form in which the reaction is written. The correct form of Nernst relation is important for instance in modelling of vanadium redox flow batteries or zinc-air batteries. In particular, the presence of corrosion can impact the OCV in the case of zinc-air batteries.
Capsule Attention for Multimodal EEG and EOG Spatiotemporal Representation Learning with Application to Driver Vigilance Estimation

Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers, thus help with avoiding distracted or impaired driving. In this paper, we propose a novel multimodal architecture for in-vehicle vigilance estimation from Electroencephalogram and Electrooculogram. However, most current works in the area lack an effective framework for learning the part-whole relationships within the data and learning useful spatiotemporal representations. To tackle this problem and other issues associated with multimodal biological signal analysis, we propose an architecture composed of a capsule attention mechanism following a deep Long Short-Term Memory (LSTM) network. Our model learns both temporal and hierarchical/spatial dependencies in the data through the LSTM and capsule feature representation layers. To better explore the discriminative ability of the learned representations, we study the effect of the proposed capsule attention mechanism including the number of dynamic routing iterations as well as other parameters. Experiments show the robustness of our method by outperforming other solutions and baseline techniques, setting a new state-of-the-art.
Bodies in an interacting active fluid: far-field influence of a single body and interaction between two bodies

Because active particles break time-reversal symmetry, an active fluid can sustain currents even without an external drive. We show that when a passive body is placed in a fluid of pairwise interacting active particles, it generates long-range currents, corresponding to density and pressure gradients. By using a multipole expansion and a far-field constitutive relation, we show that the leading-order behavior of all three corresponds to a source dipole. Then, when two bodies or more are placed in the active fluid, generic long-range interactions between the bodies occur. We find these to be qualitatively different from other fluid mediated interactions, such as hydrodynamic or thermal Casimir. The interactions can be predicted by measuring a few single-body properties in separate experiments. Moreover, they are anisotropic and do not satisfy an action-reaction principle. These results extend previous results on non-interacting active particles. Our framework may point to a path towards self-assembly.
SilentDelivery: Practical Timed-Delivery of Private Information Using Smart Contracts

This article proposes SilentDelivery, a secure, scalable, and cost-efficient protocol for implementing timed information delivery service in a decentralized blockchain network. SilentDelivery employs a novel combination of threshold secret sharing and decentralized smart contracts. The protocol maintains shares of the decryption key of the private information of an information sender using a group of mailman recruited in a blockchain network before the specified future time-frame and restores the information to the information recipient at the required time-frame. To tackle the key challenges that limit the security and scalability of the protocol, SilentDelivery incorporates two novel countermeasure strategies. The first strategy, namely silent recruitment, enables a mailman to get recruited by a sender silently without the knowledge of any third party. The second strategy, namely dual-mode execution, makes the protocol run in a lightweight mode by default, where the cost of running smart contracts is significantly reduced. We rigorously analyze the security of SilentDelivery and implement the protocol over the Ethereum official test network. The results demonstrate that SilentDelivery is more secure and scalable compared to the state of the art and reduces the cost of running smart contracts by 85 percent.
Analyzing mass media influence using natural language processing and time series analysis

A key question of collective social behavior is related to the influence of mass media on public opinion. Different approaches have been developed to address quantitatively this issue, ranging from field experiments to mathematical models. In this work we propose a combination of tools involving natural language processing and time series analysis. We compare selected features of mass media news articles with measurable manifestation of public opinion. We apply our analysis to news articles belonging to the 2016 US presidential campaign. We compare variations in polls (as a proxy of public opinion) with changes in the connotation of the news (sentiment) or in the agenda (topics) of a selected group of media outlets. Our results suggest that the sentiment content by itself is not enough to understand the differences in polls, but the combination of topics coverage and sentiment content provides an useful insight of the context in which public opinion varies. The methodology employed in this work is far general and can be easily extended to other topics of interest.
To Follow or not to Follow: Selective Imitation Learning from Observations

Learning from demonstrations is a useful way to transfer a skill from one agent to another. While most imitation learning methods aim to mimic an expert skill by following the demonstration step-by-step, imitating every step in the demonstration often becomes infeasible when the learner and its environment are different from the demonstration. In this paper, we propose a method that can imitate a demonstration composed solely of observations, which may not be reproducible with the current agent. Our method, dubbed selective imitation learning from observations (SILO), selects reachable states in the demonstration and learns how to reach the selected states. Our experiments on both simulated and real robot environments show that our method reliably performs a new task by following a demonstration. Videos and code are available at this https URL .
Continuous Speech Recognition using EEG and Video

In this paper we investigate whether electroencephalography (EEG) features can be used to improve the performance of continuous visual speech recognition systems. We implemented a connectionist temporal classification (CTC) based end-to-end automatic speech recognition (ASR) model for performing recognition. Our results demonstrate that EEG features are helpful in enhancing the performance of continuous visual speech recognition systems.
Feature Fusion Use Unsupervised Prior Knowledge to Let Small Object Represent

Fusing low level and high level features is a widely used strategy to provide details that might be missing during convolution and pooling. Different from previous works, we propose a new fusion mechanism called FillIn which takes advantage of prior knowledge described with superpixel segmentation. According to the prior knowledge, the FillIn chooses small region on low level feature map to fill into high level feature map. By using the proposed fusion mechanism, the low level features have equal channels for some tiny region as high level features, which makes the low level features have relatively independent power to decide final semantic label. We demonstrate the effectiveness of our model on PASCAL VOC 2012, it achieves competitive test result based on DeepLabv3+ backbone and visualizations of predictions prove our fusion can let small objects represent and low level features have potential for segmenting small objects.
Visualizing and Analyzing Entity Activity on the Bitcoin Network

We present BitConduite, a visual analytics tool for explorative analysis of financial activity within the Bitcoin network. Bitcoin is the largest cryptocurrency worldwide and a phenomenon that challenges the underpinnings of traditional financial systems - its users can send money pseudo-anonymously while circumventing traditional banking systems. Yet, despite the fact that all financial transactions in Bitcoin are available in an openly accessible online ledger - the blockchain - not much is known about how different types of actors in the network (we call them entities) actually use Bitcoin. BitConduite offers an entity-centered view on transactions, making the data accessible to non-technical experts through a guided workflow for classification of entities according to several activity metrics. Other novelties are the possibility to cluster entities by similarity and exploration of transaction data at different scales, from large groups of entities down to a single entity and the associated transactions. Two use cases illustrate the workflow of the system and its analytic power. We report on feedback regarding the approach and the the software tool gathered during a workshop with domain experts, and we discuss the potential of the approach based on our findings.
On the continuous loading of a U-magneto-optical trap on an atom-chip in an ultra high vacuum

Here, we report our studies on the continuous loading of a U-magneto-optical trap (U-MOT) on an atom-chip with a rubidium-(Rb)-vapor-generated background using a dispenser source in an ultra high vacuum environment. Using the U-MOT loading curves, the partial pressure due to the Rb vapor and the pressure due to the background gas have been estimated near the MOT cloud position. The estimated pressure due to the Rb vapor increased from ∼ Torr to Torr as the Rb-dispenser current was increased from 2.8 to 3.4 A. The increase in dispenser current also resulted in a decrease in loading time as well as the lifetime of the MOT cloud. This study will be useful for magnetic trapping experiments where accurate information of the pressure in a chamber is important for the lifetime of the magnetic trap.
Sampled limited memory methods for massive linear inverse problems

In many modern imaging applications the desire to reconstruct high resolution images, coupled with the abundance of data from acquisition using ultra-fast detectors, have led to new challenges in image reconstruction. A main challenge is that the resulting linear inverse problems are massive. The size of the forward model matrix exceeds the storage capabilities of computer memory, or the observational dataset is enormous and not available all at once. Row-action methods that iterate over samples of rows can be used to approximate the solution while avoiding memory and data availability constraints. However, their overall convergence can be slow. In this paper, we introduce a sampled limited memory row-action method for linear least squares problems, where an approximation of the global curvature of the underlying least squares problem is used to speed up the initial convergence and to improve the accuracy of iterates. We show that this limited memory method is a generalization of the damped block Kaczmarz method, and we prove linear convergence of the expectation of the iterates and of the error norm up to a convergence horizon. Numerical experiments demonstrate the benefits of these sampled limited memory row-action methods for massive 2D and 3D inverse problems in tomography applications.
The Network-based Candidate Forwarding Set Optimization Approach for Opportunistic Routing in Wireless Sensor Network

In wireless sensor networks (WSNs), the opportunistic routing has better performances on packet delivery probability than the deterministic routing. For reducing the transmission delay and duplicate transmission in opportunistic routing, the candidate forwarding set optimization technology is proposed. This technology is crucial to opportunistic routing. The prior arts have limitations on improving the routing performances. Therefore, in this paper, we propose the concept of the network-based approach to address the disadvantages of prior arts. In the network-based approach, the nodes in the candidate forwarding set are divided into different fully connected relay networks. For the forwarding nodes in CFS, more than one relay network can be constructed and only one relay network can be chosen as the final relay network. So, first, we propose the fully connected relay network judgment algorithm to judge whether the chosen network is fully connected or not. Next, the properties of these networks are investigated. In this algorithm, the relay network selection takes the packet delivery probability between the sender and the relay networks, the transmission delay, and the forwarding priorities of nodes in relay networks into account to choose the most effective relay network. The nodes in this relay network will be chosen as final forwarding nodes. By these, the transmission delay and duplicate transmission are reduced while the efficiency of opportunistic routing is kept. Based on these innovations, the proposed algorithm can improve network performances greater than that of ExOR and SOAR.
Electromagnetic 0.1–100 kHz noise does not disrupt orientation in a night-migrating songbird implying a spin coherence lifetime of less than 10 µs

According to the currently prevailing theory, the magnetic compass sense in night-migrating birds relies on a light-dependent radical-pair-based mechanism. It has been shown that radio waves at megahertz frequencies disrupt magnetic orientation in migratory birds, providing evidence for a quantum-mechanical origin of the magnetic compass. Still, many crucial properties, e.g. the lifetime of the proposed magnetically sensitive radical pair, remain unknown. The current study aims to estimate the spin coherence time of the radical pair, based on the behavioural responses of migratory birds to broadband electromagnetic fields covering the frequency band 0.1–100 kHz. A finding that the birds were unable to use their magnetic compass under these conditions would imply surprisingly long-lived (greater than 10 µs) spin coherence. However, we observed no effect of 0.1–100 kHz radiofrequency (RF) fields on the orientation of night-migratory Eurasian blackcaps (Sylvia atricapilla). This suggests that the lifetime of the spin coherence involved in magnetoreception is shorter than the period of the highest frequency RF fields used in this experiment (i.e. approx. 10 µs). This result, in combination with an earlier study showing that 20–450 kHz electromagnetic fields disrupt magnetic compass orientation, suggests that the spin coherence lifetime of the magnetically sensitive radical pair is in the range 2–10 µs.
A minimal computational model for three-dimensional cell migration

During migration, eukaryotic cells can continuously change their three-dimensional morphology, resulting in a highly dynamic and complex process. Further complicating this process is the observation that the same cell type can rapidly switch between different modes of migration. Modelling this complexity necessitates models that are able to track deforming membranes and that can capture the intracellular dynamics responsible for changes in migration modes. Here we develop an efficient three-dimensional computational model for cell migration, which couples cell mechanics to a simple intracellular activator–inhibitor signalling system. We compare the computational results to quantitative experiments using the social amoeba Dictyostelium discoideum. The model can reproduce the observed migration modes generated by varying either mechanical or biochemical model parameters and suggests a coupling between the substrate and the biomechanics of the cell.