Miracle-dz/UniFAR-FTUs huggingface dataset:
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions

Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. There are several variants of the similarity search problem, and one of the most relevant is the r-near neighbor (r-NN) problem: given a radius r>0 and a set of points S, construct a data structure that, for any given query point q, returns a point p within distance at most r from q. In this paper, we study the r-NN problem in the light of fairness. We consider fairness in the sense of equal opportunity: all points that are within distance r from the query should have the same probability to be returned. In the low-dimensional case, this problem was first studied by Hu, Qiao, and Tao (PODS 2014). Locality sensitive hashing (LSH), the theoretically strongest approach to similarity search in high dimensions, does not provide such a fairness guarantee. To address this, we propose efficient data structures for r-NN where all points in S that are near q have the same probability to be selected and returned by the query. Specifically, we first propose a black-box approach that, given any LSH scheme, constructs a data structure for uniformly sampling points in the neighborhood of a query. Then, we develop a data structure for fair similarity search under inner product that requires nearly-linear space and exploits locality sensitive filters. The paper concludes with an experimental evaluation that highlights (un)fairness in a recommendation setting on real-world datasets and discusses the inherent unfairness introduced by solving other variants of the problem.
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums

We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing. Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer. We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline. For Subtask B, all systems were below a majority class baseline, but several systems were very close to it. The leaderboard and the data from the competition can be found at http://competitions.codalab. org/competitions/20022.
The mid-infrared and CO gas properties of an extreme star-forming FeLoBAL quasar

We present a detailed study of a high-redshift iron low-ionization broad absorption line (FeLoBAL) quasar (SDSS1214 at $z = 1.046$), including new interferometric $^{12}$CO $J$=2-1 observations, optical through far-infrared photometry, and mid-infrared spectroscopy. The CO line is well-fit by a single Gaussian centered 40 kms$^{-1}$ away from the systemic velocity and implies a total molecular gas mass of $M_\textrm{gas} = 7.3 \times 10^{10} \textrm{M}_\odot$. The infrared SED requires three components: an active galactic nucleus (AGN) torus, an AGN polar dust component, and a starburst. The starburst dominates the infrared emission with a luminosity of log($L_\textrm{SB}[\textrm{L}_\odot]) = 12.91^{+0.02}_{-0.02}$, implying a star formation rate of about 2000 $\textrm{M}_{\odot}$yr$^{-1}$, the highest known among FeLoBAL quasars. The AGN torus and polar dust components are less luminous, at log($L_\textrm{AGN}[\textrm{L}_\odot]) = 12.36^{+0.14}_{-0.15}$ and log($L_\textrm{dust}[\textrm{L}_\odot]) = 11.75^{+0.26}_{-0.46}$, respectively. If all of the molecular gas is used to fuel the ongoing star formation, then the lower limit on the subsequent duration of the starburst is 40 Myr. We do not find conclusive evidence that the AGN is affecting the CO gas reservoir. The properties of SDSS1214 are consistent with it representing the endpoint of an obscured starburst transitioning through a LoBAL phase to that of a classical quasar.
Renewal Time Points for Hawkes Processes

In the last decade Hawkes processes have received much attention as models for functional connectivity in neural spiking networks and other dynamical systems with a cascade behavior. In this paper we establish a renewal approach for analyzing this process. We consider the ordinary nonlinear Hawkes process as well as the more recently described age dependent Hawkes process. We construct renewal-times and establish moment results for these. This gives rise to study the Hawkes process as a Markov chain. As an application, we prove asymptotic results such as a functional CLT and a time-average CLT.
A proposal for using design science in small-scale postgraduate research projects in information technology

Design science is a research paradigm where the development and evaluation of an artefact is a key contribution. Design science is used in many domains and this paper draws on those domains to formulate a generic structure for design science research suitable for small-scale postgraduate information technology research projects. The paper includes guidelines for writing proposals and a generic research report structure. The paper presents ethical issues to consider in design science research and contributes guidelines for assessment.
Piano Crossing – Walking on a Keyboard Authors

Piano Crossing is an interactive art installation which turns a pedestrian crossing marked with white stripes into a piano keyboard so that pedestrians can generate music by walking over it. Matching tones are created when a pedestrian steps on a particular stripe or key. A digital camera is directed at the crossing from above. A special computer vision application was developed, which maps the stripes of the pedestrian crossing to piano keys and detects by means of an image over which key the center of gravity of each pedestrian is placed at any given moment. Black stripes represent the black piano keys. The application consists of two parts: (1) initialization, where the model of the abstract piano keyboard is mapped to the image of the pedestrian crossing, and (2) the detection of pedestrians at the crossing, so that musical tones can be generated according to their locations. The art installation Piano crossing was presented to the public for the first time during the 51st Jazz Festival in Ljubljana in July 2010.
GRAM: Scalable Generative Models for Graphs with Graph Attention Mechanism

Graphs are ubiquitous real-world data structures, and generative models that approximate distributions over graphs and derive new samples from them have significant importance. Among the known challenges in graph generation tasks, scalability handling of large graphs and datasets is one of the most important for practical applications. Recently, an increasing number of graph generative models have been proposed and have demonstrated impressive results. However, scalability is still an unresolved problem due to the complex generation process or difficulty in training parallelization. In this paper, we first define scalability from three different perspectives: number of nodes, data, and node/edge labels. Then, we propose GRAM, a generative model for graphs that is scalable in all three contexts, especially in training. We aim to achieve scalability by employing a novel graph attention mechanism, formulating the likelihood of graphs in a simple and general manner. Also, we apply two techniques to reduce computational complexity. Furthermore, we construct a unified and non-domain-specific evaluation metric in node/edge-labeled graph generation tasks by combining a graph kernel and Maximum Mean Discrepancy. Our experiments on synthetic and real-world graphs demonstrated the scalability of our models and their superior performance compared with baseline methods.
Adapting Neural Networks for the Estimation of Treatment Effects

This paper addresses the use of neural networks for the estimation of treatment effects from observational data. Generally, estimation proceeds in two stages. First, we fit models for the expected outcome and the probability of treatment (propensity score) for each unit. Second, we plug these fitted models into a downstream estimator of the effect. Neural networks are a natural choice for the models in the first step. The question we address is: how can we adapt the design and training of the neural networks used in the first step in order to improve the quality of the final estimate of the treatment effect? We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects. The first is a new architecture, the Dragonnet, that exploits the sufficiency of the propensity score for estimation adjustment. The second is a regularization procedure, targeted regularization, that induces a bias towards models that have non-parametrically optimal asymptotic properties `out-of-the-box`. Studies on benchmark datasets for causal inference show these adaptations outperform existing methods. Code is available at this http URL.
Risks from Learned Optimization in Advanced Machine Learning Systems

We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer - a situation we refer to as mesa-optimization, a neologism we introduce in this paper. We believe that the possibility of mesa-optimization raises two important questions for the safety and transparency of advanced machine learning systems. First, under what circumstances will learned models be optimizers, including when they should not be? Second, when a learned model is an optimizer, what will its objective be - how will it differ from the loss function it was trained under - and how can it be aligned? In this paper, we provide an in-depth analysis of these two primary questions and provide an overview of topics for future research.
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis

Despite the widespread usage of machine learning throughout organizations, there are some key principles that are commonly missed. In particular: 1) There are at least four main families for supervised learning: logical modeling methods, linear combination methods, case-based reasoning methods, and iterative summarization methods. 2) For many application domains, almost all machine learning methods perform similarly (with some caveats). Deep learning methods, which are the leading technique for computer vision problems, do not maintain an edge over other methods for most problems (and there are reasons why). 3) Neural networks are hard to train and weird stuff often happens when you try to train them. 4) If you don't use an interpretable model, you can make bad mistakes. 5) Explanations can be misleading and you can't trust them. 6) You can pretty much always find an accurate-yet-interpretable model, even for deep neural networks. 7) Special properties such as decision making or robustness must be built in, they don't happen on their own. 8) Causal inference is different than prediction (correlation is not causation). 9) There is a method to the madness of deep neural architectures, but not always. 10) It is a myth that artificial intelligence can do anything.
Balanced Ranking with Diversity Constraints

Many set selection and ranking algorithms have recently been enhanced with diversity constraints that aim to explicitly increase representation of historically disadvantaged populations, or to improve the over-all representativeness of the selected set. An unintended consequence of these constraints, however, is reduced in-group fairness: the selected candidates from a given group may not be the best ones, and this unfairness may not be well-balanced across groups. In this paper we study this phenomenon using datasets that comprise multiple sensitive attributes. We then introduce additional constraints, aimed at balancing the in-group fairness across groups, and formalize the induced optimization problems as integer linear programs. Using these programs, we conduct an experimental evaluation with real datasets, and quantify the feasible trade-offs between balance and overall performance in the presence of diversity constraints.
Gene expression pathways induced by axotomy and decentralization of rat superior cervical ganglion neurons

To identify genes potentially involved in remodelling synaptic connections, we induced the temporary detachment of pre‐ and post‐synaptic elements by axotomy or denervation of rat superior cervical ganglion neurons. cDNA microarray analysis followed by stringent selection criteria allowed the identification of a panel of genes whose expression was modulated by axotomy at various time points after injury. Among these genes, 11 were validated by real‐time reverse transcriptase‐polymerase chain reaction on independently prepared samples after superior cervical ganglion neuron axotomy (1, 3 and 6 days) and compared with the effect of decentralization (8 h, 1 and 3 days). These genes code for extracellular matrix/space [apolipoprotein D (apoD), decorin, collagen alpha1 type I, collagen alpha1 type III] and intermediate filament (vimentin) proteins, for modulators of neurite outgrowth (thrombin receptor, plasminogen activator inhibitor‐1, bone morphogenetic protein 4, annexin II and S‐100‐related protein, clone 42C) and for a nerve cell transcription factor (brain finger protein). Eight of these 11 genes showed significant and persistent modulations after both types of injury. Finally, protein levels of apoD were shown to increase in superior cervical ganglion after axotomy. Our results identify hitherto unrecorded genes responsive to axotomy and decentralization of superior cervical ganglion neurons, and probably involved in synapse formation, remodelling and elimination.
Correcting Swath-Dependent Bias of MODIS FRP Observations With Quantile Mapping

Active fire observations with satellite instruments exhibit a well-documented increase of the detection threshold with increasing pixel footprint size, i.e., distance from the sub-satellite point. This results in a viewing angle-dependent, negative bias in gridded representations of the observed Fire Radiative Power (FRP), which in turn is frequently being used for climate monitoring of biomass burning and for pyrogenic emission inventories. We present a method based on quantile mapping to alleviate this bias and apply it to the gridded-FRP from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments. The gridded-FRP observations are corrected with a correction function that depends on the satellite viewing angle and the magnitude of FRP in each grid cell. Assuming the fire observations at nadir to be the best representation of the truth, we derive a correction function by mapping cumulative distribution function (CDF) of off-nadir gridded-FRP to the CDF of near-nadir gridded-FRP. The method can be directly applied to correct the negative bias in gridded-FRP observations at a grid resolution of 1 ∘ or more. The performance of the correction methodology is confirmed through comparisons with co-located Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations: After bias correction, the gridded-FRP observations from both satellites agree better than before, particularly over savanna, tropical forests, and extra-tropical forests. Experiments with the Global Fire Assimilation System (GFAS) show that the impacts of the bias-corrected MODIS/Aqua gridded-FRP observations and VIIRS/Suomi-NPP gridded-FRP observations on regional FRP analyses are comparable, which confirms the potential for improving fire emission inventories and climate monitoring based on FRP.
GAN Driven Semi-distant Supervision for Relation Extraction

Distant supervision has been widely used in relation extraction tasks without hand-labeled datasets recently. However, the automatically constructed datasets comprise numbers of wrongly labeled negative instances due to the incompleteness of knowledge bases, which is neglected by current distant supervised methods resulting in seriously misleading in both training and testing processes. To address this issue, we propose a novel semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. In our approach, we construct accurate instances by both knowledge base and entity descriptions determined to avoid wrong negative labeling and further utilize unlabeled instances sufficiently using generative adversarial network (GAN) framework. Experimental results on real-world datasets show that our approach can achieve significant improvements in distant supervised relation extraction over strong baselines.
Distilling Neural Representations of Data Structure Manipulation using fMRI and fNIRS

Data structures permeate many aspects of software engineering, but their associated human cognitive processes are not thoroughly understood. We leverage medical imaging and insights from the psychological notion of spatial ability to decode the neural representations of several fundamental data structures and their manipulations. In a human study involving 76 participants, we examine list, array, tree, and mental rotation tasks using both functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI). We find a nuanced relationship: data structure and spatial operations use the same focal regions of the brain but to different degrees. They are related but distinct neural tasks. In addition, more difficult computer science problems induce higher cognitive load than do problems of pure spatial reasoning. Finally, while fNIRS is less expensive and more permissive, there are some computing-relevant brain regions that only fMRI can reach.
Evaluation of user capacity and channel model effect in HiperLAN/2 system

HiperLAN/2 is an upcoming standard, which is being specified by the ETSI/BRAN project. High Performance Radio Local Area Network type 2 (HiperLAN/2) is one of the wireless broadband access networks, shall provide high-speed communications of up to 54 Mbps between mobile terminals and various broadband infrastructure networks. This paper presents an overview of the H/2 standard together with simulated physical layer performance results for each of the transmission mode define in the standard for channel A, C and E as these channels are the most common environments where H/2 will be deployed. Furthermore, the effect on system's throughput as the number of users and number of connections per user increases are also investigated. At the end, some comparison are made between HiperLAN/2 and IEEE 802.11(a) standards as they are two competing systems.
Strong link between BWT and XBW via Aho-Corasick automaton and applications to Run-Length Encoding

The boom of genomic sequencing makes compression of set of sequences inescapable. This underlies the need for multi-string indexing data structures that helps compressing the data. The most prominent example of such data structures is the Burrows-Wheeler Transform (BWT), a reversible permutation of a text that improves its compressibility. A similar data structure, the eXtended Burrows-Wheeler Transform (XBW), is able to index a tree labelled with alphabet symbols. A link between a multi-string BWT and the Aho-Corasick automaton has already been found and led to a way to build a XBW from a multi-string BWT. We exhibit a stronger link between a multi-string BWT and a XBW by using the order of the concatenation in the multi-string. This bijective link has several applications: first, it allows to build one data structure from the other; second, it enables one to compute an ordering of the input strings that optimises a Run-Length measure (i.e., the compressibility) of the BWT or of the XBW.
Monoclonal antibodies to polymorphic epitopes on Ia antigens: three independent groups of determinants expressed on DRw6 homozygous cells

Murine monoclonal antibodies directed against polymorphic epitopes on HLA‐linked antigens were readily obtained following a single immunization with DRw6‐homozygous cells. Five antibodies directed against class II antigens were isolated. Two precipitated DC‐like molecules and 3 DR‐like molecules from the immunizing lymphoblastoid cell line. Analysis of association with HLA‐DR antigens in a panel of 60 individuals revealed 2 antibodies directed against the DC1 specificity (S1, R1) and one against the MT2 specificity (R3). Antibodies S2 and S3 showed significant association with DRw6 (χ2 = 30.27) and DRw6 and w8 (χ2 = 18.86), respectively. An analysis of 29 haplotypes indicated that epitopes defined by these monoclonal antibodies appear to identify polymorphic determinants on three independent gene products which can be encoded by a single HLA haplotype.
AC3R: Automatically Reconstructing Car Crashes from Police Reports

Autonomous driving carries the promise to drastically reduce car accidents, but recently reported fatal crashes involving self-driving cars suggest that the self-driving car software should be tested more thoroughly. For addressing this need, we introduce AC3R (Automatic Crash Constructor from Crash Report) which elaborates police reports to automatically recreate car crashes in a simulated environment that can be used for testing self-driving car software in critical situations. AC3R enables developers to quickly generate relevant test cases from the massive historical dataset of recorded car crashes. We demonstrate how AC3R can generate simulations of different car crashes and report the findings of a large user study which concluded that AC3R simulations are accurate. A video illustrating AC3R in action is available at: https://youtu.be/V708fDG_ux8
Consolidating a Model for Describing Situated Software Practices

Many prescriptive approaches to developing software intensive systems have been advocated but each is based on assumptions about context. It has been found that practitioners do not follow prescribed methodologies, but rather select and adapt specific practices according to local needs. As researchers, we would like to be in a position to support such tailoring. However, at the present time we simply do not have sufficient evidence relating practice and context for this to be possible. We have long understood that a deeper understanding of situated software practices is crucial for progress in this area, and have been exploring this problem from a number of perspectives. In this position paper, we draw together the various aspects of our work into a holistic model and discuss the ways in which the model might be applied to support the long term goal of evidence-based decision support for practitioners. The contribution specific to this paper is a discussion on model evaluation, including a proof-of-concept demonstration of model utility. We map Kernel elements from the Essence system to our model and discuss gaps and limitations exposed in the Kernel. Finally, we overview our plans for further refining and evaluating the model.
The Drosophila virilis dopa decarboxylase gene is developmentally regulated when integrated into Drosophila melanogaster.

The dopa decarboxylase gene (Ddc) has been isolated from Drosophila virilis and introduced into the germ‐line of Drosophila melanogaster by P‐element mediated transformation. The integrated gene is induced at the correct stages during development with apparently normal tissue specificity, indicating that cis‐acting elements required for regulation are functionally conserved between the two species. A comparison of the DNA sequences from the 5′ flanking regions reveals a cluster of small (8‐16 bp) conserved sequence elements within 150 bp upstream of the RNA startpoint, a region required for normal expression of the D. melanogaster Ddc gene.