Miracle-dz/UniFAR-FTUs huggingface dataset:
Parametrized Metrical Task Systems

We consider parametrized versions of metrical task systems and metrical service systems, two fundamental models of online computing, where the constrained parameter is the number of possible distinct requests $m$. Such parametrization occurs naturally in a wide range of applications. Striking examples are certain power management problems, which are modeled as metrical task systems with $m=2$. We characterize the competitive ratio in terms of the parameter $m$ for both deterministic and randomized algorithms on hierarchically separated trees. Our findings uncover a rich and unexpected picture that differs substantially from what is known or conjectured about the unparametrized versions of these problems. For metrical task systems, we show that deterministic algorithms do not exhibit any asymptotic gain beyond one-level trees (namely, uniform metric spaces), whereas randomized algorithms do not exhibit any asymptotic gain even for one-level trees. In contrast, the special case of metrical service systems (subset chasing) behaves very differently. Both deterministic and randomized algorithms exhibit gain, for $m$ sufficiently small compared to $n$, for any number of levels. Most significantly, they exhibit a large gain for uniform metric spaces and a smaller gain for two-level trees. Moreover, it turns out that in these cases (as well as in the case of metrical task systems for uniform metric spaces with $m$ being an absolute constant), deterministic algorithms are essentially as powerful as randomized algorithms. This is surprising and runs counter to the ubiquitous intuition/conjecture that, for most problems that can be modeled as metrical task systems, the randomized competitive ratio is polylogarithmic in the deterministic competitive ratio.
Competition and Coopetition among Social Media Content

Social media platforms are characterized by an immense volume of content that exists concurrently. In this study, we analyze competition and coopetition among social media content. Under a competitive dynamic, the diffusion of one piece of content deters the diffusion of another. Alternatively, a coopetition dynamic means that the spread of a social media post augments the diffusion of another. The purpose of our study is to investigate whether competition or coopetition emerges among social media content and identify determinants of the direction of the interaction. To that end, we formulated a generalized self-exciting point process model and evaluated the model using Twitter data. We generally find that a competitive relationship exists among content, but, interestingly, some content experienced a cooperative interplay. In particular, we observe an asymmetry between large and small content producers in that coopetition favors content published by large producers.
Enumeration of Stack-Sorting Preimages via a Decomposition Lemma

We give three applications of a recently-proven ``Decomposition Lemma," which allows one to count preimages of certain sets of permutations under West's stack-sorting map $s$. We first enumerate the permutation class $s^{-1}(\text{Av}(231,321))=\text{Av}(2341,3241,45231)$, finding a new example of an unbalanced Wilf equivalence. This result is equivalent to the enumeration of permutations sortable by ${\bf B}\circ s$, where ${\bf B}$ is the bubble sort map. We then prove that the sets $s^{-1}(\text{Av}(231,312))$, $s^{-1}(\text{Av}(132,231))=\text{Av}(2341,1342,\underline{32}41,\underline{31}42)$, and $s^{-1}(\text{Av}(132,312))=\text{Av}(1342,3142,3412,34\underline{21})$ are counted by the so-called ``Boolean-Catalan numbers," settling a conjecture of the current author and another conjecture of Hossain. This completes the enumerations of all sets of the form $s^{-1}(\text{Av}(\tau^{(1)},\ldots,\tau^{(r)}))$ for $\{\tau^{(1)},\ldots,\tau^{(r)}\}\subseteq S_3$ with the exception of the set $\{321\}$. We also find an explicit formula for $|s^{-1}(\text{Av}_{n,k}(231,312,321))|$, where $\text{Av}_{n,k}(231,312,321)$ is the set of permutations in $\text{Av}_n(231,312,321)$ with $k$ descents. This allows us to prove a conjectured identity involving Catalan numbers and order ideals in Young's lattice.
Reframing Threat Detection: Inside esINSIDER

We describe the motivation and design for esINSIDER, an automated tool that detects potential persistent and insider threats in a network. esINSIDER aggregates clues from log data, over extended time periods, and proposes a small number of cases for human experts to review. The proposed cases package together related information so the analyst can see a bigger picture of what is happening, and their evidence includes internal network activity resembling reconnaissance and data collection. The core ideas are to 1) detect fundamental campaign behaviors by following data movements over extended time periods, 2) link together behaviors associated with different meta-goals, and 3) use machine learning to understand what activities are expected and consistent for each individual network. We call this approach campaign analytics because it focuses on the threat actor's campaign goals and the intrinsic steps to achieve them. Linking different campaign behaviors (internal reconnaissance, collection, exfiltration) reduces false positives from business-as-usual activities and creates opportunities to detect threats before a large exfiltration occurs. Machine learning makes it practical to deploy this approach by reducing the amount of tuning needed.
Majorana bound states in magnetic skyrmions imposed onto a superconductor

We consider a superconducting film exchange-coupled to a close-by chiral magnetic layer and study how magnetic skyrmions can induce the formation of Majorana bound states (MBS) in the superconductor. Inspired by a proposal by Yang et al. [Phys. Rev. B 93, 224505 (2016)], which suggested MBS in skyrmions of even winding number, we explore whether such skyrmions could result from a merger of ordinary skyrmions. We conclude that the formation of higher-winding skyrmions is not realistic in chiral magnets. Subsequently, we present a possibility to obtain MBS from realistic skyrmions of winding number one, if a skyrmion-vortex pair is formed instead of a bare skyrmion. Specifically, we show that MBS are supported in a pair of a circular skyrmion and a vortex which both have a winding number of one. We back up our analytical prediction with results from numerical diagonalization and obtain the spatial profile of the MBS. In light of recent experimental progress on the manipulation of skyrmions, such systems are promising candidates to achieve direct spatial control of MBS.
Brokerage from within: A conceptual framework

Situated between various social worlds, brokers are highly mobile figures, in a physical and an ideational sense; they channel scarce information and resources, translate different languages and jargons, and mediate and facilitate between individuals and/or organisations, the local and the global, in a wide range of settings. Taking an in-depth ethnographic look at the actual work of brokers and their particular life stories, contributions to this special issue examine brokers’ successes and failures, their vulnerabilities and limitations, (changing) interests and motivations within the cultural contexts that these brokers are part of. By adopting a comparative perspective in a thematic and a geographic sense, this special issue discusses the role of brokerage in diverse settings such as the transnational world of trade and development, peacebuilding and activism, refugee care and health care, government services and colonialism. In preparing the ground for our individual contributions, this introductory article identifies gaps in the existing brokerage literature and develops the conceptual framework for the special issue.
Spatial Beam Tracking and Data Detection for an FSO Link to a UAV in the Presence of Hovering Fluctuations

Recent advances in small-scale unmanned aerial vehicles (UAVs) have opened up new horizons for establishing UAV-based free-space optical (FSO) links. However, FSO technology requires precise beam alignment while random fluctuations of hovering UAVs can induce beam misalignment and angle-of-arrival (AoA) fluctuations. For an FSO link to a UAV, we consider a quadrant detector array for optical beam tracking and study the effect of random hovering fluctuations of the UAV on the performance of the tracking method, and based on the degree of instabilities for the UAV, the optimum size of the detectors for minimizing the tracking error is found. Furthermore, for optimal detection of On - Off keying symbols, the receiver requires instantaneous channel fading coefficients. We propose a blind method to estimate the channel coefficients, i.e., without using any pilot symbols, to increase link bandwidth efficiency. To evaluate the performance of the considered system, closed-form expressions of tracking error and bit-error rate are derived. Moreover, Monte-Carlo simulation is carried out to corroborate the accuracy of the derived analytical expressions.
Social Media, Open Platforms, and Democracy: Transparency Enabler, Slayer of Democracy, Both?

Government agencies have increased their use of social media as a means to connect to and engage with their citizenry, disseminate and promote policies, to inform the public, and in general promote more transparency in government. Recently, however, social media platforms are being used by some inside government to criticize those who do not agree with their policies; circumvent expected administrative, legislative, and judicial processes; and create a policy making process that resides outside constitutional and deliberative channels. Further, as discovered during the 2016 U.S. presidential elections, foreign governments have used social media platforms to interfere with sovereign nation elections through concerted efforts to falsify facts, create false stories (“fake news”), and sow discord among electorates. In the U.S. context, social media wields significant influence on voters, democratic institutions, and the deliberative democratic process. Using the U.S. context, this article presents a preliminary exploration of the emerging perils that social media represents to democracies, from administrative (management and operations of government) and democratic (governance) perspectives.
I Understand What You Are Saying: Leveraging Deep Learning Techniques for Aspect Based Sentiment Analysis

Despite widespread use of online reviews in consumer purchase decision making, the potential value of online reviews in facilitating digital collaboration among product/service providers, consumers, and online retailers remains under explored. One of the significant barriers to realizing the above potential lies in the difficulty of understanding online reviews due to their sheer volume and free-text form. To promote digital collaborations, we investigate aspect based sentiment dynamics of online reviews by proposing a semi-supervised, deep learning facilitated analytical pipeline. This method leverages deep learning techniques for text representation and classification. Additionally, building on previous studies that address aspect extraction and sentiment identification in isolation, we address both aspects and sentiments analyses simultaneously. Further, this study presents a novel perspective to understanding the dynamics of aspect based sentiments by analyzing aspect based sentiment in time series. The findings of this study have significant implications with regards to digital collaborations among consumers, product/service providers and other stakeholders of online reviews.
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization

Backtracking line-search is an old yet powerful strategy for finding a better step sizes to be used in proximal gradient algorithms. The main principle is to locally find a simple convex upper bound of the objective function, which in turn controls the step size that is used. In case of inertial proximal gradient algorithms, the situation becomes much more difficult and usually leads to very restrictive rules on the extrapolation parameter. In this paper, we show that the extrapolation parameter can be controlled by locally finding also a simple concave lower bound of the objective function. This gives rise to a double convex-concave backtracking procedure which allows for an adaptive choice of both the step size and extrapolation parameters. We apply this procedure to the class of inertial Bregman proximal gradient methods, and prove that any sequence generated by these algorithms converges globally to a critical point of the function at hand. Numerical experiments on a number of challenging non-convex problems in image processing and machine learning were conducted and show the power of combining inertial step and double backtracking strategy in achieving improved performances.
Quality-Aware Broadcasting Strategies for Position Estimation in VANETs

The dissemination of vehicle position data all over the network is a fundamental task in Vehicular Ad Hoc Network (VANET) operations, as applications often need to know the position of other vehicles over a large area. In such cases, inter-vehicular communications should be exploited to satisfy application requirements, although congestion control mechanisms are required to minimize the packet collision probability. In this work, we face the issue of achieving accurate vehicle position estimation and prediction in a VANET scenario. State of the art solutions to the problem try to broadcast the positioning information periodically, so that vehicles can ensure that the information their neighbors have about them is never older than the inter-transmission period. However, the rate of decay of the information is not deterministic in complex urban scenarios: the movements and maneuvers of vehicles can often be erratic and unpredictable, making old positioning information inaccurate or downright misleading. To address this problem, we propose to use the Quality of Information (QoI) as the decision factor for broadcasting. We implement a threshold-based strategy to distribute position information whenever the positioning error passes a reference value, thereby shifting the objective of the network to limiting the actual positioning error and guaranteeing quality across the VANET. The threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, as well as improving the overall positioning accuracy by more than 20% in realistic urban scenarios.
PoMo: Generating Entity-Specific Post-Modifiers in Context

We introduce entity post-modifier generation as an instance of a collaborative writing task. Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity. For example, for the sentence, “Barack Obama, _______, supported the #MeToo movement. ”, the phrase “a father of two girls” is a contextually relevant post-modifier. To this end, we build PoMo, a post-modifier dataset created automatically from news articles reflecting a journalistic need for incorporating entity information that is relevant to a particular news event. PoMo consists of more than 231K sentences with post-modifiers and associated facts extracted from Wikidata for around 57K unique entities. We use crowdsourcing to show that modeling contextual relevance is necessary for accurate post-modifier generation. We adapt a number of existing generation approaches as baselines for this dataset. Our results show there is large room for improvement in terms of both identifying relevant facts to include (knowing which claims are relevant gives a >20% improvement in BLEU score), and generating appropriate post-modifier text for the context (providing relevant claims is not sufficient for accurate generation). We conduct an error analysis that suggests promising directions for future research.
Streamlined Dense Video Captioning

Dense video captioning is an extremely challenging task since accurate and coherent description of events in a video requires holistic understanding of video contents as well as contextual reasoning of individual events. Most existing approaches handle this problem by first detecting event proposals from a video and then captioning on a subset of the proposals. As a result, the generated sentences are prone to be redundant or inconsistent since they fail to consider temporal dependency between events. To tackle this challenge, we propose a novel dense video captioning framework, which models temporal dependency across events in a video explicitly and leverages visual and linguistic context from prior events for coherent storytelling. This objective is achieved by 1) integrating an event sequence generation network to select a sequence of event proposals adaptively, and 2) feeding the sequence of event proposals to our sequential video captioning network, which is trained by reinforcement learning with two-level rewards---at both event and episode levels---for better context modeling. The proposed technique achieves outstanding performances on ActivityNet Captions dataset in most metrics.
AMASS: Archive of Motion Capture As Surface Shapes

Large datasets are the cornerstone of recent advances in computer vision using deep learning. In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion. While there are many different datasets available, they each use a different parameterization of the body, making it difficult to integrate them into a single meta dataset. To address this, we introduce AMASS, a large and varied database of human motion that unifies 15 different optical marker-based mocap datasets by representing them within a common framework and parameterization. We achieve this using a new method, MoSh++, that converts mocap data into realistic 3D human meshes represented by a rigged body model. Here we use SMPL [Loper et al., 2015], which is widely used and provides a standard skeletal representation as well as a fully rigged surface mesh. The method works for arbitrary marker sets, while recovering soft-tissue dynamics and realistic hand motion. We evaluate MoSh++ and tune its hyperparameters using a new dataset of 4D body scans that are jointly recorded with markerbased mocap. The consistent representation of AMASS makes it readily useful for animation, visualization, and generating training data for deep learning. Our dataset is significantly richer than previous human motion collections, having more than 40 hours of motion data, spanning over 300 subjects, more than 11000 motions, and will be publicly available to the research community.
Efficient Attack Countermeasure Selection Accounting for Recovery and Action Costs

The losses arising from a system being hit by cyber attacks can be staggeringly high, but defending against such attacks can also be costly. This work proposes an attack countermeasure selection approach based on cost impact analysis that takes into account the impacts of actions by both the attacker and the defender. We consider a networked system providing services whose functionality depends on other components in the network. We model the costs and losses to service availability from compromises and defensive actions to the components, and show that while containment of the attack can be an effective defense, it may be more cost-efficient to allow parts of the attack to continue further whilst focusing on recovering services to a functional state. Based on this insight, we build a countermeasure selection method that chooses the most cost-effective action based on its impact on expected losses and costs over a given time horizon. Our method is evaluated using simulations in synthetic graphs representing network dependencies and vulnerabilities, and performs well in comparison to alternatives.
Comparison of Control Techniques for Series Resonant Converter

There are many different derivatives of variable and fixed frequency switching control techniques used in control of load resonant converters. In this study; among these techniques frequency modulation (FM), phase shift modulation (PSM) and pulse density modulation (PDM) are applied separately to series resonant converter (SRC). The techniques are examined and compared in many respects. An experimental setup is built, which consists of a 400W converter, a control circuit and a resistive load to verify theoretical studies. The converter is controlled by FM, PSM and PDM separately for 120 kHz basic operating frequency and different output currents. Experimental results are compared in terms of efficiency, output voltage ripple, soft switching, switch voltage, ease of application and hardware. The comparison results are presented. Thus, effect of each technique on the operating parameters of the converter is revealed. Moreover, it is also observed that FM has better performance than the other two techniques in many aspects.
Inclination effects on the X-ray emission of Galactic black-hole binaries

Context. Galactic black-hole X-ray binaries (BHBs) emit a compact, optically thick, mildly relativistic radio jet when they are in hard and hard-intermediate states. In these states, BHBs exhibit a correlation between the time lag of hard with respect to softer photons and the photon index of the power law component that characterizes the X-ray spectral continuum above ∼10 keV. The correlation, however, shows large scatter. In recent years, several works have brought to light the importance of taking into account the inclination of the systems to understand the X-ray and radio phenomenology of BHBs. Aims. Our objective is to investigate the role that the inclination plays on the correlation between the time lag and photon index. Methods. We obtained RXTE energy spectra and light curves of a sample of BHBs with different inclination angles. We computed the photon index and the time lag between hard and soft photons and performed a correlation and linear regression analysis of the two variables. We also computed energy spectra and light curves of BHBs using the Monte Carlo technique that reproduces the process of Comptonization in the jet. We account for the inclination effects by recording the photons that escape from the jet at different angles. From the simulated light curves and spectra we obtained model-dependent photon index and time lags, which we compared with those obtained from the real data. Results. We find that the correlation between the time lag and photon index is tight in low-inclination systems and becomes weaker in high-inclination systems. The amplitude of the lags is also larger at low- and intermediate-inclination angles than at high inclination. We also find that the photon index and time lag, obtained from the simulated spectra and light curves, also follow different relationships for different inclination angle ranges. Our jet model reproduces the observations remarkably well. The same set of models that reproduces the correlation for the low-inclination systems, also accounts for the correlation for intermediate- and high-inclination systems fairly well. Conclusions. The large dispersion observed in the time lag – photon index correlation in BHBs can naturally be explained as an inclination effect. Comptonization in the jet explains the steeper dependence of the lags on the photon index in low- and intermediate-inclination systems than in high-inclination systems.