Miracle-dz/UniFAR-FTUs huggingface dataset:
Caching with Time Windows and Delays

We consider two generalizations of the classical weighted paging problem that incorporate the notion of delayed service of page requests. The first is the (weighted) Paging with Time Windows (PageTW) problem, which is like the classical weighted paging problem except that each page request only needs to be served before a given deadline. This problem arises in many practical applications of online caching, such as the "deadline" I/O scheduler in the Linux kernel and video-on-demand streaming. The second, and more general, problem is the (weighted) Paging with Delay (PageD) problem, where the delay in serving a page request results in a penalty being assessed to the objective. This problem generalizes the caching problem to allow delayed service, a line of work that has recently gained traction in online algorithms (e.g., Emek et al. STOC '16, Azar et al. STOC '17, Azar and Touitou FOCS '19). We give $O(\log k\log n)$-competitive algorithms for both the PageTW and PageD problems on $n$ pages with a cache of size $k$. This significantly improves on the previous best bounds of $O(k)$ for both problems (Azar et al. STOC '17). We also consider the offline PageTW and PageD problems, for which we give $O(1)$ approximation algorithms and prove APX-hardness. These are the first results for the offline problems; even NP-hardness was not known before our work. At the heart of our algorithms is a novel "hitting-set" LP relaxation of the PageTW problem that overcomes the $\Omega(k)$ integrality gap of the natural LP for the problem. To the best of our knowledge, this is the first example of an LP-based algorithm for an online algorithm with delays/deadlines.
Understanding Digital Culture

This is more than just another book on Internet studies. Tracing the pervasive influence of 'digital culture' throughout contemporary life, this text integrates socio-economic understandings of the 'information society' with the cultural studies approach to production, use, and consumption of digital media and multimedia. Refreshingly readable and packed with examples from profiling databases and mashups to cybersex and the truth about social networking, Understanding Digital Culture: crosses disciplines to give a balanced account of the social, economic and cultural dimensions of the information society illuminates the increasing importance of mobile, wireless and converged media technologies in everyday life unpacks how the information society is transforming and challenging traditional notions of crime, resistance, war and protest, community, intimacy and belonging charts the changing cultural forms associated with new media and its consumption, including music, gaming, microblogging and online identity illustrates the above through a series of contemporary, in-depth case studies of digital culture. This is the perfect text for students looking for a full account of the information society, virtual cultures, sociology of the Internet and new media.
Investigating phase transitions from local crystallographic analysis based on machine learning of atomic environments

Traditionally, phase transitions are explored using a combination of macroscopic functional characterization and scattering techniques, providing insight into average properties and symmetries of the lattice but local atomic level mechanisms during phase transitions generally remain unknown. Here we explore the mechanisms of a phase transition between the trigonal prismatic and distorted octahedral phases of layered chalogenides in the MoS2 ReS2 system from the observations of local degrees of freedom, namely atomic positions by Scanning Transmission Electron Microscopy (STEM). We employ local crystallographic analysis based on machine learning of atomic environments to build a picture of the transition from the atomic level up and determine local and global variables controlling the local symmetry breaking. In particular, we argue that the dependence of the average symmetry breaking distortion amplitude on global and local concentration can be used to separate local chemical and global electronic effects on transition. This approach allows exploring atomic mechanisms beyond the traditional macroscopic descriptions, utilizing the imaging of compositional fluctuations in solids to explore phase transitions over a range of realized and observed local stoichiometries and atomic configurations.
Research on Spatiotemporal Land Deformation (2012-2018) over Xi'an, China, with Multi-Sensor SAR Datasets

The ancient city of Xi’an, China, has been suffering severe land subsidence and ground fissure hazards since the 1960s, mainly due to the over-withdrawal of groundwater and large-scale urban construction. This has threatened and will continue to threaten the stability of urban infrastructure, such as the construction and operation of high buildings and subway lines. It is necessary to map the spatiotemporal variations of land subsidence over Xi’an, and to analyze their causes and the correlation with underground water level changes and ground fissure deformation. Time series of land subsidence were observed with the interferometric synthetic aperture radar (InSAR) technique, using multi-sensor SAR datasets from 2012 to 2018. Four land subsidence rate maps over Xi’an city were retrieved from TerraSAR-X, ALOS/PALSAR2, and Sentinel-1 data, each with different tracks. The InSAR derived results were then cross-validated with three independent SAR data stacks, and calibrated with GPS and leveling observations. Next, the spatiotemporal evolutions of three main regional land subsidence zones were quantitatively analyzed in detail, and the surface deformation of the Xi’an subway network was spatially analyzed. Third, the correlations between land subsidence and ground water withdrawal, ground fissure deformation, landforms, and faults were intensively analyzed. Finally, a flat lying sill model with distributed contractions was implemented to model the InSAR deformation over one typical subsidence zone, which further suggested that the ground deformation was mainly caused by groundwater withdrawal. This systematic research can provide sound evidence to serve decision-making for land subsidence mitigation in Xi’an, and may also guide land subsidence research in other cities.
Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives

Crime reporting is a prevalent form of journalism with the power to shape public perceptions and social policies. How does the language of these reports act on readers? We seek to address this question with the SuspectGuilt Corpus of annotated crime stories from English-language newspapers in the U.S. For SuspectGuilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings. SuspectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments. We use SuspectGuilt to train and assess predictive models which validate the usefulness of the corpus, and show that these models benefit from genre pretraining and joint supervision from the text-level ratings and span-level annotations. Such models might be used as tools for understanding the societal effects of crime reporting.
The Bethe-Salpeter Equation Formalism: From Physics to Chemistry.

The Bethe-Salpeter equation (BSE) formalism is steadily asserting itself as a new efficient and accurate tool in the ensemble of computational methods available to chemists in order to predict optical excitations in molecular systems. In particular, the combination of the so-called approximation, giving access to reliable ionization energies and electron affinities, and the BSE formalism, able to model UV/Vis spectra, has shown to provide accurate singlet excitation energies with a typical error of 0.1-0.3 eV. With a similar computational cost as time-dependent density-functional theory (TD-DFT), BSE is able to provide an accuracy on par with the most accurate global and range-separated hybrid functionals without the unsettling choice of the exchange-correlation functional, resolving further known issues (e.g., charge-transfer excitations). In this Perspective article, we provide a historical overview of BSE, with a particular focus on its condensed-matter roots. We also propose a critical review of its strengths and weaknesses in different chemical situations.
High-Fidelity Generative Image Compression

We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In contrast to previous work, i) we obtain visually pleasing reconstructions that are perceptually similar to the input, ii) we operate in a broad range of bitrates, and iii) our approach can be applied to high-resolution images. We bridge the gap between rate-distortion-perception theory and practice by evaluating our approach both quantitatively with various perceptual metrics and a user study. The study shows that our method is preferred to previous approaches even if they use more than 2x the bitrate.
Neural Optimal Control for Representation Learning

The intriguing connections recently established between neural networks and dynamical systems have invited deep learning researchers to tap into the well-explored principles of differential calculus. Notably, the adjoint sensitivity method used in neural ordinary differential equations (Neural ODEs) has cast the training of neural networks as a control problem in which neural modules operate as continuous-time homeomorphic transformations of features. Typically, these methods optimize a single set of parameters governing the dynamical system for the whole data set, forcing the network to learn complex transformations that are functionally limited and computationally heavy. Instead, we propose learning a data-conditioned distribution of \emph{optimal controls} over the network dynamics, emulating a form of input-dependent fast neural plasticity. We describe a general method for training such models as well as convergence proofs assuming mild hypotheses about the ODEs and show empirically that this method leads to simpler dynamics and reduces the computational cost of Neural ODEs. We evaluate this approach for unsupervised image representation learning; our new "functional" auto-encoding model with ODEs, AutoencODE, achieves state-of-the-art image reconstruction quality on CIFAR-10, and exhibits substantial improvements in unsupervised classification over existing auto-encoding models.
Linear Last-iterate Convergence for Matrix Games and Stochastic Games

Optimistic Gradient Descent Ascent (OGDA) algorithm for saddle-point optimization has received growing attention due to its favorable last-iterate convergence. However, its behavior for simple two-player matrix games is still not fully understood -- previous analysis lacks explicit convergence rates, only applies to exponentially small learning rate, or requires additional conditions such as uniqueness of the optimal solution. In this work, we significantly expand the understanding of OGDA, introducing a set of sufficient conditions under which OGDA exhibits concrete last-iterate convergence rates with a constant learning rate. Specifically, we show that matrix games satisfy these conditions and OGDA converges exponentially fast without any additional assumptions. More generally, our conditions hold for smooth bilinear functions and strongly-convex-strongly-concave functions over a constrained set. We provide experimental results to further support our theory. To further demonstrate the significance of our results for matrix games, we greatly generalize the ideas to finite-horizon stochastic/Markov games and provide the first algorithm that simultaneously ensures 1) linear last-iterate convergence when playing against itself and 2) low regret when playing against an arbitrary slowly-changing opponent.
Reliability andValidity of a Two-QuestionVersion of theWorld Health Organization ’ sAlcohol , Smoking and Substance Involvement Screening Test : TheASSIST-FC

649 ABSTRACT. Objective: The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), developed for the World Health Organization (WHO), screens for risks associated with the use of tobacco, alcohol, and seven categories of drugs. Although the ASSIST has acceptable psychometric properties, it is relatively long for a screening test. This study was designed to identify a subset of questions from the full ASSIST instrument having comparable psychometric properties for the classification of low-, moderate-, and high-risk substance use. Method: The study used three data sets from prior studies using the WHO ASSIST. Samples 1 and 3 were obtained from WHO multisite studies conducted in seven countries. Sample 2 included patient data from a U.S.-based screening and brief intervention program that incorporated the ASSIST into its clinical protocol. Samples 1 and 2 were used to conduct psychometric analyses for combinations of ASSIST items. Sample 3 was used to estimate sensitivity, specificity, and positive and negative predictive value for a two-item ASSIST. Results: Based on correlation statistics, reliability metrics, and validation analyses, a new, two-item version is proposed. The ASSIST-FC contains one question about the frequency (F) of current use and a second question about current or past concern (C) expressed by others. The ASSIST-FC demonstrates no substantial loss in reliability, validity, and predictive ability when statistically compared with the full-length ASSIST. Conclusions: The ASSIST-FC has advantages for clinical applications in settings where a brief, efficient, reliable screening test is needed to identify patients with hazardous and harmful substance use who would benefit from a brief intervention. It can also be used to identify patients who are manifesting symptoms of substance dependence that would require further diagnostic evaluation. (J. Stud. Alcohol Drugs, 79, 649–657, 2018)
Hydrodynamic attractor of a hybrid viscous fluid in Bjorken flow

The nonequilibrium evolution in a boost-invariant Bjorken flow of a hybrid viscous fluid model containing two interacting components with different viscosities, such that they represent strongly and weakly self-coupled sectors, is shown to be characterized by a hydrodynamic attractor which has an early-time behavior that is reminiscent of the so-called bottom-up thermalization scenario in heavy-ion collisions. The hydrodynamization times for the two sectors can differ strongly, with details depending on the curve realized on the two-dimensional attractor surface, which might account for different scenarios for small and large systems in nuclear collisions. The total system behaves like a single viscous fluid with a dynamically determined effective shear viscosity.
LOFAR observations of X-ray cavity systems

We present LOFAR observations at 120–168 MHz of 42 systems with possible X-ray cavities in their hot atmosphere, of which 17 are groups or ellipticals, 19 are nearby clusters (z < 0.3), and six are higher redshift clusters (z > 0.3). The X-ray cavities, formed by the radio lobes of the central active galactic nucleus (AGN), are evidence of radio-mode AGN feedback. In the groups and ellipticals sample, half of the systems have X-ray cavities for which no associated lobe emission was detected. Conversely, we report the discovery of large radio lobes in NGC 6338, extending far beyond the emission reported previously in the literature. In the case of the nearby clusters, our observations show that there is little low-frequency radio emission that extends beyond the cavities (e.g. MS 0735.6+7421, A2052). For the first time, we report secure cavity-radio associations in 2A 0335+096, ZwCl 2701, and ZwCl 8276 that strengthens their interpretation as AGN-created cavities. However, in some known cavity systems (e.g. A1795 and ZwCl 3146) we report the lack of detectable low-frequency radio emission associated with the cavities. Our sample of higher redshifts systems is small, and unfortunately the present LOFAR observations are not able to resolve the lobes in many of them. Nevertheless, our sample represents one of the best available for investigating the connection between radio and jet power in radio-mode AGN feedback.
Associating Facebook Measurable Activities with Personality Traits: A Fuzzy Sets Approach

In this study we identify potential associations between people’s personality (utilizing the popular Big Five personality model) and measurable Facebook activities such as number of likes received, number of posts, number of comments on posts. Extant literature suggests that personality can be manifested through different features of the Facebook profiles but under an implicit assumption that those users may belong in a single psychographic group. However, it has been shown that people may share characteristics, common acts and behaviors of more than one psychographic group. In this study we aim to address limitations of previous studies, by adopting a fuzzy set approach which is capable to handle users’ membership in multiple psychographic groups. Furthermore, fsQCA offers equifinality, which means that research can end up to the same outcome, beginning from different initial combinations of data. The work presented here provides empirical evidence concerning the association between Facebook activities and users' personalities in a novel way indicating the significance of this relationship and providing alternative combinations that lead to the same output. Furthermore, it paves the ground towards predicting social platforms' measurements, other than Facebook, relying on users' personalities, using the same technique but on different fields of study and social media platforms.
MATCEL-PROP, A SIMULATION FOR AN OPTIMAL POWERTRAIN USED FOR ROAD VEHICLES

By customizing a mathematical model, previously developed by the authors, has done a simulation to define a completely stabilized operating modes of propulsion systems and their dynamic, economic and pollution parameters. This provides, even the design phase, definition of additional constructive and functional criteria against the current, to ensure stable and economic operation, in an area as extensive. Keywords—road vehicles, powertrain, simulation, optimal.
Gender and national differences in emotional intelligence and empathy: Comparison of Croatian and Portuguese samples

The study focused on the relationship of emotional and cognitive components of empathy with emotional intelligence abilities (labelling, understanding, and regulating emotions). Since both emotional intelligence and empathy have important role in a person’s socio-emotional health and well-being, we need to better understand the relations between these two related constructs. Additionally, having samples from two different countries enabled us comparison on the national level. Two samples of adults participated in the study, namely from Croatia (N = 138) and Portugal (N = 138). We found significant moderate correlations between empathy and emotional intelligence measures in both samples. Four dimensions from the Interpersonal Responsivity Index (IRI; empathic concerns, fantasy, personal distress and perspective-taking) had significant moderate correlations with each other, with similar patterns of correlations in both samples. The strongest correlations were found for the TEQ (affective component of empathy) and perspective-taking (cognitive component of empathy) and different emotional intelligence components. Participants in the Portuguese sample had higher scores on the emotional component of empathy (empathic care and the TEQ), while cognitive component (perspective-taking) was more expressed in the Croatian sample. Participants in the Portuguese sample also had higher scores on all three components of emotional intelligence than participants in the Croatian sample. Regardless of the nationality, women had higher scores on the measures of empathic care, fantasy, perspective-taking and the TEQ, while men scored higher on the measure of personal distress. Regarding emotional intelligence scales, women had higher results on the Perceive and Understand
Thruster concept for transverse acceleration by the beating electrostatic waves ponderomotive force

A thruster concept is presented for transversely accelerating a magnetized plasma by means of the ponderomotive force that arises when a plasma mode is absorbed while propagating across a uniform magnetic eld. This concept oers a number of advantages for electric propulsion including electrodeless operation and variable specic impulse. A simple slab geometry is analyzed and a general expression for thrust is presented for this concept for arbitrary plasma modes. The normalized exhaust velocity as a function of wave frequency and input power is then derived for the specic case where the ponderomotive force is provided by the absorption of two, beating electrostatic waves. It is shown that this absorption process is particularly suited for the thruster concept and yields a specic impulse that can be tuned by changing the frequency of the exciting waves. A design is presented for an annular thruster that would implement this concept with beating electrostatic waves, and it is shown that this concept can produce signicant exhaust velocities that are competitive with the Hall thruster for comparable power levels.
Learning a functional control for high-frequency finance

We use a deep neural network to generate controllers for optimal trading on high-frequency data. For the first time, a neural network learns the mapping between the preferences of the trader, i.e. risk aversion parameters, and the optimal controls. An important challenge in learning this mapping is that, in intra-day trading, trader's actions influence price dynamics in closed loop via the market impact. The exploration–exploitation tradeoff generated by the efficient execution is addressed by tuning the trader's preferences to ensure long enough trajectories are produced during the learning phase. The issue of scarcity of financial data is solved by transfer learning: the neural network is first trained on trajectories generated thanks to a Monte-Carlo scheme, leading to a good initialization before training on historical trajectories. Moreover, to answer to genuine requests of financial regulators on the explainability of machine learning generated controls, we project the obtained ‘blackbox controls’ on the space usually spanned by the closed-form solution of the stylized optimal trading problem, leading to a transparent structure. For more realistic loss functions that have no closed-form solution, we show that the average distance between the generated controls and their explainable version remains small. This opens the door to the acceptance of ML-generated controls by financial regulators.