Miracle-dz/UniFAR-FTUs huggingface dataset:
Declarative cartography under fine-grained access control

Visualization of spatial data is of increasing importance in science and society, but opens up justified concerns about data privacy and security. A classic methodology for cartography through generalization is data selection; however, data selection can be challenging under security constraints for two main reasons. First, individual records are kept in the visualization, so a data security approach such as access control needs to be put in place to avoid leakage of information about protected records to unauthorized parties. Second, it can be computationally hard to pick out records from a large spatial dataset so as to create an aesthetically pleasing visualization respecting user constraints and optimization goals. The latter expense can get compounded by the need to additionally respect access control restrictions. This paper presents a way to integrate label-based access control into an existing technique for declarative cartography termed global selection. Through a set of theorems and new algorithms, we demonstrate that we can reuse derivation and resolution of record conflicts when computing global selections across access roles in a security hierarchy. In experiments with realistic datasets, the runtime of the best among these new methods achieves an improvement of up to 2x-5x compared with repeatedly computing the global selection in medium-to-large security hierarchies.
Algorithmic Meta-Theorems for Monotone Submodular Maximization

We consider a monotone submodular maximization problem whose constraint is described by a logic formula on a graph. Formally, we prove the following three `algorithmic metatheorems. ' (1) If the constraint is specified by a monadic second-order logic on a graph of bounded treewidth, the problem is solved in $n^{O(1)}$ time with an approximation factor of $O(\log n)$. (2) If the constraint is specified by a first-order logic on a graph of low degree, the problem is solved in $O(n^{1 + \epsilon})$ time for any $\epsilon > 0$ with an approximation factor of $2$. (3) If the constraint is specified by a first-order logic on a graph of bounded expansion, the problem is solved in $n^{O(\log k)}$ time with an approximation factor of $O(\log k)$, where $k$ is the number of variables and $O(\cdot)$ suppresses only constants independent of $k$.
Multiagent Reinforcement Learning Methods to Resolve Demand Capacity Balance Problems

In this article, we explore the computation of joint policies for autonomous agents to resolve congestions problems in the air traffic management (ATM) domain. Agents, representing flights, have limited information about others' payoffs and preferences, and need to coordinate to achieve their tasks while adhering to operational constraints. We formalize the problem as a multiagent Markov decision process (MDP) towards deciding flight delays to resolve demand and capacity balance (DCB) problems in ATM. To this end, we present multiagent reinforcement learning methods that allow agents to interact and form own policies in coordination with others. Experimental study on real-world cases, confirms the effectiveness of our approach in resolving the demand-capacity balance problem.
An Exploration of the Factor Structure of Executive Functioning in Children

There has been considerable debate and interest in the factor structure of executive functioning (EF). For children and young people, there is evidence of a progression from a single factor to a more differentiated structure, although the precise nature of these factors differs between investigations. The purpose of the current study was to look at this issue again with another sample, and try to understand possible reasons for previous differences between investigations. In addition, we examined the relationship between less central EF tasks, such as fluency and planning, to the more common tasks of updating/executive working memory (EWM), inhibition, and switching/shifting. A final aim was to carry out analyses which are relevant to the debate about whether EF is influenced by language ability, or language ability is influenced by EF. We reasoned that if language ability affects EF, a factor analysis of verbal and non-verbal EF tasks might result in the identification of a factor which predominantly contains verbal tasks and a factor that predominately contains non-verbal tasks. Our investigation involved 128 typically developing participants (mean age 10:4) who were given EF assessments that included verbal and non-verbal versions of each task: EWM; switching; inhibition; fluency; and planning. Exploratory factor analyses on EWM, switching, and inhibition produced a structure consisting of inhibition in one factor and the remaining tasks in another. It was decided to exclude verbal planning from the next analyses of all the ten tasks because of statistical considerations. Analysis of the remaining nine EF tasks produced two factors, one factor containing the two inhibition tasks, and another factor that contained all the other tasks (switching, EWM, fluency, and non-verbal planning). There was little evidence that the verbal or non-verbal elements in these tasks affected the factor structure. Both these issues are considered in the discussion, where there is a general evaluation of findings about the factor structure of EF.
Landmark-Based Updating of the Head Direction System by Retrosplenial Cortex: A Computational Model

Maintaining a sense of direction is fundamental to navigation, and is achieved in the brain by a network of head direction (HD) cells, which update their signal using stable environmental landmarks. How landmarks are detected and their stability determined is still unknown. Recently we reported a new class of cells (Jacob et al., 2017), the bidirectional cells, in a brain region called retrosplenial cortex (RSC) which relays environmental sensory information to the HD system. A subset of these cells, between-compartment (BC) cells, are directionally tuned (like ordinary HD cells) but follow environmental cues in preference to the global HD signal, resulting in opposing (i.e., bidirectional) tuning curves in opposed environments. Another subset, within-compartment (WC) cells, unexpectedly expressed bidirectional tuning curves in each one of the opposed compartments. Both BC and WC cells lost directional tuning in an open field, unlike HD cells. Two questions arise from this discovery: (i) how do these cells acquire their unusual response properties, and (ii) what are they for? We propose that bidirectional cells reflect a two-way interaction between local direction, as indicated by the visual environment, and global direction as signaled by the HD system. We suggest that BC cells receive strong inputs from visual cues, while WC cells additionally receive modifiable inputs from HD cells which, due to Hebbian coactivation of visual inputs plus two opposing sets of HD inputs, acquire the ability to fire in both directions. A neural network model instantiating this hypothesis is presented, which indeed forms both BC and WC bidirectional cells with properties similar to those seen experimentally. We then demonstrate how tuning specificity degrades when WC/BC cells are exposed to multiple directionalities, replicating the observed loss of WC and BC directional tuning in the open field. We suggest that the function of these neurons is to assess the stability of environmental landmarks, thereby determining their utility as reference points by which to set the HD sense of direction. This role could extend to the ability of the HD system to prefer distal over proximal landmarks, and to correct for parallax errors.
MIMO Over-the-Air Computation for High-Mobility Multimodal Sensing

In future Internet-of-Things networks, sensors or even access points can be mounted on ground/aerial vehicles for smart-city surveillance or environment monitoring. For such high-mobility sensing, it is impractical to collect data from a large population of sensors using any traditional orthogonal multi-access scheme due to the excessive latency. To tackle the challenge, a technique called over-the-air computation (AirComp) was recently developed to enable a data-fusion center to receive a desired function of sensing data from concurrent sensor transmissions, by exploiting the superposition property of a multi-access channel. This paper aims at further developing multiple-input-multiple output (MIMO) AirComp for enabling high-mobility multimodal sensing. Specifically, we design MIMO-AirComp equalization and channel feedback techniques for spatially multiplexing multifunction computation. Given the objective of minimizing the computation error, a close-to-optimal equalizer is derived in closed-form using differential geometry. The solution can be computed as the weighted centroid of points on a Grassmann manifold, where each point represents the subspace spanned by the channel matrix of a sensor. As a by-product, the problem of MIMO-AirComp equalization is proved to have the same form as the classic problem of multicast beamforming, establishing the AirComp-multicasting duality. Its significance lies in making the said Grassmannian-centroid solution transferable to the latter problem which otherwise is solved using the computation-intensive semidefinite relaxation method. Last, building on the AirComp architecture, an efficient channel-feedback technique is designed for direct acquisition of the equalizer at the access point from simultaneous feedback by all sensors. This overcomes the difficulty of provisioning orthogonal feedback channels for many sensors.
Semantic Web based software platform for curriculum harmonization

This paper presents a software platform for comparing informatics teacher education curricula. The ontological model of the chosen informatics teachers' curriculum from the Republic of Serbia and the reference informatics teachers' curriculum model were created. The semi-automatic software platform is based on the standard techniques and methods of ontology matching. The created ontological models of the teacher education curricula are compared using the developed software. Analysis of the results of comparison includes consideration of classes' matching, obtained system evaluation (by the expert team) and a harmonization of the Revised Bloom's taxonomy categories. Also, the obtained results are compared with the results obtained for other combinations of input ontological models (secondary informatics and informatics teacher education curricula models). The analysis of the results revealed the need to improve the content and structure of the observed model curricula as well as the limits of the developed system and possibilities of improving the software platform.
Intent-aware Item-based Collaborative Filtering for Personalised Diversification

Diversity has been identified as one of the key dimensions of recommendation utility that should be considered besides the overall accuracy of the system. A common diversification approach is to rerank results produced by a baseline recommendation engine according to a diversification criterion. The intent-aware framework is one of the frameworks that has been proposed for recommendations diversification. It assumes existence of a set of aspects associated with items, which also represent user intentions, and the framework promotes diversity across the aspects to address user expectations more accurately. In this paper we consider item-based collaborative filtering and suggest that the traditional view of item similarity is lacking a user perspective. We argue that user preferences towards different aspects should be reflected in recommendations produced by the system. We incorporate the intent-aware framework into the item-based recommendation algorithm by injecting personalised intent-aware covariance into the item similarity measure, and explore the impact of such change on the performance of the algorithm. Our experiments show that the proposed method improves both accuracy and diversity of recommendations, offering better accuracy/diversity tradeoff than existing solutions.
Understanding Differences among Executions with Variational Traces

One of the main challenges of debugging is to understand why the program fails for certain inputs but succeeds for others. This becomes especially difficult if the fault is caused by an interaction of multiple inputs. To debug such interaction faults, it is necessary to understand the individual effect of the input, how these inputs interact and how these interactions cause the fault. The differences between two execution traces can explain why one input behaves differently than the other. We propose to compare execution traces of all input options to derive explanations of the behavior of all options and interactions among them. To make the relevant information stand out, we represent them as variational traces that concisely represents control-flow and data-flow differences among multiple concrete traces. While variational traces can be obtained from brute-force execution of all relevant inputs, we use variational execution to scale the generation of variational traces to the exponential space of possible inputs. We further provide an Eclipse plugin Varviz that enables users to use variational traces for debugging and navigation. In a user study, we show that users of variational traces are more than twice as fast to finish debugging tasks than users of the standard Eclipse debugger. We further show that variational traces can be scaled to programs with many options.
Collaborative interactive evolution in minecraft

Interactive Evolution (ie) has shown promise in domains where objective-driven approaches are problematic. Collaborating by branching from the evolutionary stepping stones discovered by others (cie) has allowed users to evolve a large variety of artefacts. In this paper we explore the potential of collaboratively evolving behaviours in Minecraft through an online cie system. Results suggest that it is indeed possible to solve a maze navigation task in Minecraft collaboratively, which is difficult to achieve individually. We hope that the released framework will allow others to build more complex domains, solving a series of more and more challenging tasks through many collaborating humans.
Messenger RNA Sequencing and Pathway Analysis Provide Novel Insights Into the Susceptibility to Salmonella enteritidis Infection in Chickens

Salmonella enteritidis (SE) is a foodborne pathogen that negatively affects both animal and human health. Controlling poultry SE infection will have great practical significance for human public health, as poultry are considered to be important sources and carriers of the disease. In this study, the splenic transcriptomes of challenged-susceptible (S), challenged-resistant (R) and non-challenged (C) chicks (3-days old, specific-pathogen-free White Leghorn) were characterized in order to identify the immune-related gene markers and pathways. A total of 934 significant differentially expressed genes (DEGs) were identified in comparisons among the C, R and S birds. First reported here, the DEGs involved in the Forkhead box O (FoxO) signaling pathway, especially FoxO3, were identified as potential markers for host resistance to SE infection. The challenged-susceptible birds exhibited strong activation of the FoxO signaling pathway, which may be a major defect causing immune cell apoptosis as part of SE-induced pathology; these S birds also showed weak activation of mitogen-activated protein kinase (MAPK)-related genes, contrasting with strong splenic activation in the R birds. Interestingly, suppression of several pathways in the immune response against Salmonella, including cytokine-cytokine receptor interaction and Jak-STAT, was only found in S birds and there was evidence of cross-talk among these pathways, perhaps contributing to susceptibility to Salmonella infection. These findings will help facilitate understanding resistance and susceptibility to SE infection in the earliest phases of the host immune response through Salmonella-induced pathways, provide new approaches to develop strategies for SE prevention and treatment, and may enhance innate resistance by genetic selection in animals.
Personalized Tourist Package Recommendation using Graph Based Approach

Location Based Social Networks (LBSN) benefit the users by allowing them to share their locations and life moments with their friends. The users can also review the locations they have visited. Classical recommender systems provide users a ranked list of single items. This is not suitable for applications like trip planning,where the recommendations should contain multiple items in an appropriate sequence. The problem of generating such recommendations is challenging due to various critical aspects, which includes user interest, budget constraints and high sparsity in the available data used to solve the problem. In this paper, we propose a graph based approach to recommend a set of personalized travel packages. Each recommended package comprises of a sequence of multiple Point of Interests (POIs). Given the current location and spatio-temporal constraints, our goal is to recommend a package which satisfies the constraints. This approach utilizes the data collected from LBSNs to learn user preferences and also models the location popularity.
Improved Approximations for Free-Order Prophets and Second-Price Auctions

We study the fundamental problem of selling a single indivisible item to one of $n$ buyers with independent and potentially nonidentical value distributions. We focus on two simple and widely used selling mechanisms: the second price auction with \emph{eager} personalized reserve prices and the sequential posted price mechanism. Using a new approach, we improve the best-known performance guarantees for these mechanisms. We show that for every value of the number of buyers $n$, the eager second price (ESP) auction and sequential posted price mechanisms respectively earn at least $0.6620$ and $0.6543$ fractions of the optimal revenue. We also provide improved performance guarantees for these mechanisms when the number of buyers is small, which is the more relevant regime for many applications of interest. This in particular implies an improved bound of $0.6543$ for free-order prophet inequalities. Motivated by our improved revenue bounds, we further study the problem of optimizing reserve prices in the ESP auctions when the sorted order of personalized reserve prices among bidders is exogenous. We show that this problem can be solved polynomially. In addition, by analyzing a real auction dataset from Google's advertising exchange, we demonstrate the effectiveness of order-based pricing.
A geometric evolutionary search for melody composition

In this paper, we present a genetic algorithm using geometric crossover to preserve musically important properties. The length of the chromosome is variable when we use a musical note as a gene. A geometric crossover based on the edit distance was used to combine chromosomes of variable length and succeeded in creating a better child by using a model in which melody pattern is learned. The model can evaluate the score of melody in a specified chord. As a result, we could successfully create a melody preserving parent's good patterns.
CellProfiler 3.0: Next-generation image processing for biology

CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.
COMPASS: compact array storage with value index

Efficient array storage is the backbone of scientific data processing. With an explosion of data, rapidly answering queries on array data is becoming increasingly important. Although most of the array storages today support subsetting of an array based on dimensions efficiently, they fall back to full scan while executing value-based filter operations. This has lead to an interest in approximate query processing, but such methods can have substantial inaccuracies. This paper presents COMPASS, an array storage system with integrated value index support. Our approach efficiently encodes arrays as bin-based indices and corresponding residuals describing elements in each bin. Our query processing method uses bin-based indices, with residuals decompressed as needed, to ensure that accuracy is not sacrificed. Our evaluation shows that compared with current array storage systems such as SciDB, our method achieves a smaller storage footprint, but most importantly, can perform filter operations an order of magnitude faster on low selectivity queries. Meanwhile, COMPASS maintains comparable performance on high-selectivity queries or dimension-based subsetting operations.
Towards bundling minimal trees in polygonal maps

Minimal trees in polygonal maps aim at minimizing the connectivity in a network while avoiding obstacle collision. Being closely related to the Steiner Tree Problem, yet with a different scope, minimal trees aim at connecting origin-destination pairs, given in a bipartite network, to allow the joint transport of information, goods, resources and people. In this paper, we propose a method to tackle the bundling problem of minimal trees in modular bipartite networks by using a two-layer optimization based on Differential Evolution with a convex representation of coordinates. Our computational experiments in polygonal domains considering both convex and non-convex geometry show the feasibility and the efficiency of the proposed approach.
Construction of a Geobacter Strain With Exceptional Growth on Cathodes

Insoluble extracellular electron donors are important sources of energy for anaerobic respiration in biogeochemical cycling and in diverse practical applications. The previous lack of a genetically tractable model microorganism that could be grown to high densities under anaerobic conditions in pure culture with an insoluble extracellular electron donor has stymied efforts to better understand this form of respiration. We report here on the design of a strain of Geobacter sulfurreducens, designated strain ACL, which grows as thick (ca. 35 μm) confluent biofilms on graphite cathodes poised at -500 mV (versus Ag/AgCl) with fumarate as the electron acceptor. Sustained maximum current consumption rates were >0.8 A/m2, which is >10-fold higher than the current consumption of the wild-type strain. The improved function on the cathode was achieved by introducing genes for an ATP-dependent citrate lyase, completing the complement of enzymes needed for a reverse TCA cycle for the synthesis of biosynthetic precursors from carbon dioxide. Strain ACL provides an important model organism for elucidating the mechanisms for effective anaerobic growth with an insoluble extracellular electron donor and may offer unique possibilities as a chassis for the introduction of synthetic metabolic pathways for the production of commodities with electrons derived from electrodes.