Miracle-dz/UniFAR-FTUs huggingface dataset:
When Automatic Filtering Comes to the Rescue: Pre-Computing Company Competitor Pairs in Owler

Competitor data constitutes information significantly valuable for many business applications. Meltwater provides users with access to a large Company Information System (CIS), Owler, which contains competitor pairs and other useful information about companies. Meltwater has been seeking a practical solution to discover more competitor pairs in Owler. The first attempt, a fully-manual workflow (called MW_Manual) for finding more competitor pairs in Owler consisted of two manual steps: a filtering step that excludes obvious non-competitor company pairs, and a further inspection process that inspects each left company pair after the filtering step. MW_Manual was cost prohibitive because the results of the filtering step contained too many non-competitor pairs. Inspecting such non-competitor pairs caused an overhead to the overall workload. To reduce the manual workload, especially the required human effort in the manual inspection process, Meltwater has transformed MW_Manual into a semi-automatic workflow (called MW_CPFilter) by replacing the manual filtering with an automatic yet more precise process that adopts a system called CPFilter. This paper presents CPFilter, a system used in the filtering process of MW_CPFilter. CPFilter automatically pre-computes likely competitor pairs from existing competitor pairs in Owler. CPFilter combines (i) the generation of new competitor candidate pairs by inference from existing competitors and other company-specific knowledge, with (ii) the validation of each candidate competitor pair of two companies by checking whether or not empirical evidence that indicates the competitor relationships of these two companies can be found. CPFilter has three key advantages compared with the manual filtering process and previous works: (i) it resulted in a high workload reduction rate of 0.81, (ii) it is domain-independent so that it can be applied to different sectors in Owler, and (iii) its results are explainable so that humans can easily understand its results.
Exploiting Structure in Regular Expression Queries

Regular expression, or regex, is widely used to extract critical information from a large corpus of formatted text by finding patterns of interest. In tasks like log processing, the speed of regex matching is crucial. Data scientists and developers regularly use regex libraries that implement optimized regular expression matching using modern automata theory. However, computing state transitions in the underlying regex evaluation engine can be inefficient when a regex query contains a multitude of string literals. This inefficiency is further exasperated when analyzing large data volumes. This paper presents BLARE, Blazingly Fast Regular Expression, a regular expression matching framework that is inspired by the mechanisms that are used in database engines, which use a declarative framework to explore multiple equivalent execution plans, all of which produce the correct final result. Similarly, BLARE decomposes a regex into multiple regex and string components and then creates evaluation strategies in which the components can be evaluated in an order that is not strictly a left-to-right translation of the input regex query. Rather than using a cost-based optimization approach, BLARE uses an adaptive runtime strategy based on a multi-armed bandit approach to find an efficient execution plan. BLARE is also modular and can be built on top of any existing regex library. We implemented BLARE on four commonly used regex libraries, RE2, PCRE2, Boost Regex, and ICU Regex, and evaluated it using two production workloads and one open-source workload. BLARE was 1.6× to 3.7× faster than RE2 and 3.4× to 7.9× faster than Boost Regex. PCRE2 did not finish on one of the workloads, but on the remaining two workloads, BLARE improved the performance of PCRE2 by 3.1× to over 100×. For the open-source dataset, BLARE provided a speed up of 61.7× for ICU Regex. BLARE code is publicly available at https://github.com/mush-zhang/Blare.
Mitigating Filter Bubbles Under a Competitive Diffusion Model

While social networks greatly facilitate information dissemination, they are well known to have contributed to the phenomena of filter bubbles and echo chambers. This in turn can lead to societal polarization and erosion of trust in public institutions. Mitigating filter bubbles is an urgent open problem. Recently, approaches based on the influence maximization paradigm have been proposed in our community for mitigating filter bubbles by balancing exposure to opposing viewpoints. However, existing works ignore the inherent competition between the adoption of opposing viewpoints by users. In this paper, we propose a realistic model for the filter bubble problem, which unlike previous work, captures thecompetition between opposing opinions propagating in a network as well as thecomplementary nature of the reward forexposing users to both those opinions. We formulate an optimization problem for mitigating filter bubbles under our model. We establish several evidences of the intrinsic difficulty in developing constant approximation to the problem and develop a heuristic and two instance-dependent approximation algorithms. Our experiments over 4 real datasets show that our heuristic far outperforms two state-of-the-art baselines as well as other algorithms in both efficiency and mitigating filter bubbles. We also empirically demonstrate that our best heuristic performs close to the optimal objective, which is obtained by utilizing the theoretical bounds of our approximation algorithms.
Going with the Flow: Real-Time Max-Flow on Asynchronous Dynamic Graphs

Processing graphs that evolve over time has seen renewed attention. Processing solutions on dynamic graphs (often dubbed "graph streaming" solutions) aim to maintain the state for a graph query as the graph evolves over time, and to timely offer a solution (approximate, or precise) when requested by the user. In this space, and in the context of shared-nothing platforms, solutions have been proposed only for relatively simple problems (e.g., BFS, SSSP, PageRank), and some are limited to incremental-only evolutions traces. Support for more complex problems remains rather unexplored. To close this gap, we present a solution for the maximum flow problem that supports both add and delete events. We build this solution on top of an event-based abstraction. Integral to this abstraction is that events tied to both graph topology changes and algorithmic maintenance are processed asynchronously, concurrently, and autonomously (i.e., without shared state). We show that our implementation provides favourable time-to-solution and scales well by evaluating it on a real-world dynamic graph with 80 million edges. We compare its performance with snapshot-based solutions both internally (with our own implementation of a shared-nothing static algorithm) and externally (with Galois, a popular shared-memory framework for static graphs).
An Experimental Investigation Of The Relationship Between Decision Makers, Decision Aids and Decision Making Effort

AbstractThis paper reports the results of two laboratory experiments designed to assess the impact of decision aid use on effort expenditures by decision makers. The traditional assumption in the decision support systems (DSS) literature is that if decision makers are provided with expanded processing capabilities they will use them to analyze problems in more depth and as a result make better decisions. Empirical studies investigating the relationship between DSS and decision quality have not borne this out. The explanation for such outcomes could be found in behavioral decision making theories. The literature on behavioral decision making indicates that the conservation of effort may be more important than increased decision quality in some cases. If this is so, then the use of a decision aid may result in effort savings but not improved decision performance. The two experiments reported here compare the information processing effort expended by users of a decision aid designed to support preferential c.
From Embodied Doing to Computational Thinking in Kindergarten: A Punctuated Motor Control Model

We propose a motor control-based characterization of how computational thinking (CT) can emerge from embodied performance. The account is based on children's activity during a tangible coding task, and a mechanism proposed by cognitive and motor neuroscience studies. For the task, the child used navigational symbols (forward, backward, rotate right, rotate left) to program the movements of a tangible robot on a 2-D grid. We propose that the development of CT through this task can be understood in terms of “tool incorporation into the body schema. ” To illustrate the proposed mechanism, we use video data from one of three teaching sessions, where a group of four kindergartners learned to code using Cubetto (a tactile screen-free grid-based robotic toy). We argue that learning the task (i.e. being able to control Cubetto to perform goal-oriented movements) is challenging because the CT task requires learners to bridge three distinct discontinuities (spatial, temporal, and representational), to achieve control over the robot. We hypothesize that learners and facilitators are likely to engage in moves (both epistemic and pedagogical) that help bridge these gaps, and thus support the incorporation of the robot and its controller into the body schema. We characterize two such moves and explicate how they might support the incorporation process. The study is part of a larger program of design-based research aimed at helping young children develop CT.
GIO: Generating Efficient Matrix and Frame Readers for Custom Data Formats by Example

Data Scientists deal with a wide variety of file data formats and data representations. Probably the most difficult to handle are custom data formats that liberally define their own particular flat or nested structure with multiple custom delimiters, multi-line records, or undocumented semantics of attribute sequences, co-appearances, and repetitions. As a prerequisite for exploratory ML model training, data scientists need to map these data representations into regular frames or matrices. Unfortunately, existing tools and frameworks provide only limited support for aiding this process, which causes redundant manual efforts and unnecessary data quality issues. In this paper, we initiate work on automatic matrix and frame reader generation by example. A user provides a sample of raw text data and its mapped matrix or frame representation. Our GIO framework then first identifies the mapping rules from raw to structured data, and subsequently generates source code of an efficient, multi-threaded reader for reading full raw datasets of this format. In order to facilitate manual improvements, both the mapping rules, and generated reader can be modified as needed. Our experiments show that GIO is able to correctly identify the mapping rules for basic text formats like CSV, LibSVM, MatrixMarket; custom text formats from publishing, automotive, and health care; as well as various nested formats such as JSON and XML. Additionally, the automatically generated readers yield competitive performance compared to hand-coded readers and tuned libraries like RapidJSON.
Enhancing the vocabulary learning skills of autistic children using augmented reality: a participatory design perspective

Many autistic children1 face challenges with vocabulary learning. Augmented Reality (AR) has the potential to improve their learning process by leveraging their visuo-perceptual strengths. However, there is a gap in the literature on how AR solutions should be designed, and what guidelines should be considered. Extant solutions supporting autistic children in vocabulary learning using AR have been created without taking into consideration insights from children, their parents, or experts. This paper explores the potential of AR in enhancing vocabulary learning in autistic children through a participatory design approach. Involving experts with a background in psychology, pedagogy, speech therapy, and special education, as well as autistic children and their parents, resulted in a better understanding of the underlying mechanisms used to teach vocabulary and the interaction modalities offered by AR to engage the children. This work will facilitate the design of an AR technology to support vocabulary learning in autistic children.
Query-Guided Resolution in Uncertain Databases

We present a novel framework for uncertain data management. We start with a database whose tuple correctness is uncertain and an oracle that can resolve the uncertainty, i.e., decide if a tuple is correct or not. Such an oracle may correspond, e.g., to a data expert or to a crowdsourcing platform. We wish to use the oracle to clean the database with the goal of ensuring the correct answer for specific mission-critical queries. To avoid the prohibitive cost of cleaning the entire database and to minimize the expected number of calls to the oracle, we must carefully select tuples whose resolution would suffice to resolve the uncertainty in query results. In other words, we need a query-guided process for the resolution of uncertain data. We develop an end-to-end solution to this problem, based on the derivation of query answers and on correctness probabilities for the uncertain data. At a high level, we first track Boolean provenance to identify which input tuples contribute to the derivation of each output tuple, and in what ways. We then design an active learning solution for iteratively choosing tuples to resolve, based on the provenance structure and on an evolving estimation of tuple correctness probabilities. We conduct an extensive experimental study to validate our framework in different use cases.
Revisiting the experimental dielectric function datasets of gold in accordance with the Brendel-Bormann model

As a result of adopting various sample preparation techniques and optical measurement strategies, there are different experimental datasets available for presenting the complex dielectric function of gold. Therefore, evaluating them and selecting the most reliable ones seem essential for the corresponding optical-based designs and applications. The Brendel-Bormann model, one of the most accurate theoretical models for rendering the complex dielectric function of materials, is considered an appropriate criterion for this purpose. Despite the model's computational complexity, MS Excel has been employed as a fast and accessible tool for calculating the model. According to the results, ‘Palik' and ‘Babar' exhibit the most accurate datasets representing the real part of the dielectric function of gold in the short- (<500 nm) and long-wavelength (>500 nm) ranges, respectively. While the proposition reverses for the imaginary part of the dielectric function. This validity owes to using gold samples with the lowest structural and surface imperfections.
Salivary corticosterone reflects plasmatic levels in a wild seabird

Wild animals have been increasingly exposed to a wide range of stressors, mainly due to the intensification of human activities and habitat modifications. Consequently, new tools in order to assess the physiological and health status of wild animals have been developed. In particular, glucocorticoids have received a special attention. Primarily metabolic hormones, they are also used to evaluate the stress level of organisms. While historically measured in blood samples, new less-invasive methods have been recently developed to measure glucocorticoids in matrices such as faeces, hairs/feathers, or saliva. To date, measurements in saliva are still in their infancy despite the numerous advantages of the matrix: non-invasive, reflects the biologically active portion of glucocorticoids, allows to measure both baseline and stress-induced levels. In addition, most studies using saliva have been performed on domestic and captive animals, and recent development in wild animals have focused on mammals. Here, we show, for the first time that saliva could also be reliably used in free-ranging birds, as glucocorticoid levels in saliva strongly correlated with plasma levels. This promising result opens new avenues for a non-invasive sampling method to assess health status of wild birds in conservation biology and ecology.
Turning Ecology Against Pesticide Resistance: Exploiting Competition in Pest Populations Through Pesticide Use

Modern agriculture is dependent on the efficacy of pesticides in controlling pests. However, such efficacy is threatened by the evolution of resistance. Although pesticides are essentially novel chemical compounds, target pests can manifest resistance soon after these compounds are introduced. Inspired by recent advances in medical research, we propose an ecologically inspired paradigm in pest management through pesticide use that capitalizes on competitive interactions between pesticide-sensitive and pesticide-resistant individuals. This principle relies on a reactive use of pesticides, constantly tracking pest response, promoting the containment of the pest population below economic injury levels, and buffering resistant individuals’ proliferation. Using a mathematical model, we show that containing pests’ abundance at critical levels, instead of seeking immediate eradication, diminishes management failure by reducing resistant individuals’ growth rates, and consequently the time until resistant-pest dominance. Therefore, management strategies that maximize the abundance of pesticide-sensitive individuals, or explore the potential competitive ability of these populations, can hamper resistance proliferation. Synthesis and applications The insights provided by our simulations reconcile key challenges in environmental management, emphasizing the importance of managing threats to the food production systems using a proactive ecological and evolutionary-informed approach. They also highlight several opportunities for new experimental and theoretical research into the ecological facet of managing pesticide resistance in crops.
Listeria monocytogenes infection in pregnant macaques alters the maternal gut microbiome

Abstract The bacterium Listeria monocytogenes (Lm) is associated with adverse pregnancy outcomes. Infection occurs through consumption of contaminated food that is disseminated to the maternal–fetal interface. The influence on the gastrointestinal microbiome during Lm infection remains unexplored in pregnancy. The objective of this study was to determine the impact of listeriosis on the gut microbiota of pregnant macaques. Methods: A non-human primate model of listeriosis in pregnancy has been previously described. Both pregnant and non-pregnant cynomolgus macaques were inoculated with Lm and bacteremia and fecal shedding were monitored for 14 days. Non-pregnant animal tissues were collected at necropsy to determine bacterial burden, and fecal samples from both pregnant and non-pregnant animals were evaluated by 16S rRNA next-generation sequencing. Results: Unlike pregnant macaques, non-pregnant macaques did not exhibit bacteremia, fecal shedding, or tissue colonization by Lm. Dispersion of Lm during pregnancy was associated with a significant decrease in alpha diversity of the host gut microbiome, compared to non-pregnant counterparts. The combined effects of pregnancy and listeriosis were associated with a significant loss in microbial richness, although there were increases in some genera and decreases in others. Conclusions: Although pregnancy alone is not associated with gut microbiome disruption, we observed dysbiosis with listeriosis during pregnancy. The macaque model may provide an understanding of the roles that pregnancy and the gut microbiota play in the ability of Lm to establish intestinal infection and disseminate throughout the host, thereby contributing to adverse pregnancy outcomes and risk to the developing fetus. Summary sentence Intestinal microbial composition in macaques is influenced by interaction between the pregnant state and exposure to Listeria monocytogenes, associated in particular with significant changes to Akkermansia, Eubacterium ruminantium, Methanobrevibacter, Prevotella, and Treponema. Graphical Abstract
An experimental platform for stochastic analyses of single serotonergic fibers in the mouse brain

The self-organization of the serotonergic matrix, a massive axon meshwork in all vertebrate brains, is driven by the structural and dynamical properties of its constitutive elements. Each of these elements, a single serotonergic axon (fiber), has a unique trajectory and can be supported by a soma that executes one of many available transcriptional programs. It necessitates the development of specialized methods for single-fiber analyses, both at the experimental and theoretical levels. We developed an integrated system that facilitates experimental isolation of single serotonergic fibers in brain tissue, including regions with high fiber densities, and demonstrated the potential of their quantitative analyses based on stochastic modeling. Single fibers were visualized using two transgenic mouse models, one of which is the first implementation of the Brainbow toolbox in this system. The trajectories of serotonergic fibers were automatically traced in the three spatial dimensions with a novel algorithm, and their properties were captured with a single parameter associated with the directional von Mises-Fisher probability distribution. The system represents an end-to-end workflow that can be imported into various studies, including those investigating serotonergic dysfunction in brain disorders. It also supports new research directions inspired by single-fiber analyses in the serotonergic matrix, including supercomputing simulations and modeling in physics.
Male and female syringeal muscles exhibit superfast shortening velocities in Zebra finches

Vocalisations play a key role in the communication behaviour of many vertebrates. Vocal production requires extremely precise motor control, which is executed by superfast vocal muscles that can operate at cycle frequencies over 100 Hz and up to 250 Hz. The mechanical performance of these muscles has been quantified with isometric performance and the workloop technique, but due to methodological limitations we lack a key muscle property characterising muscle performance, the force-velocity (FV) relationship. Here we establish a method that allows quantification of the FV relationship in extremely fast muscles, and test if the maximal shortening velocity of zebra finch syringeal muscles is different between males and females. We show that syringeal muscles exhibit extremely high maximal shortening velocities of 46 L0 s-1, far exceeding most other vocal and skeletal muscles, and that isometric properties positively correlate with maximal shortening velocities. While male and female muscles differ in isometric speed measures, maximal shortening velocity surprisingly is not sex-dependent. We also show that cyclical methods to measure force-length properties used in classical laryngeal studies give the same result as conventional stepwise methodologies, suggesting either approach is appropriate. Next to force, instantaneous power also trades for speed, further highlighting these muscles are tuned to operate at high frequencies. We argue that the high thermal dependence of superfast vocal muscle performance may impact vocal behaviour. Summary statement Zebra finch syringeal muscle exhibits superfast shortening of 46 L0 s-1. Shortening is not sex-specific but correlates with isometric performance – faster twitches and tetani are associated with faster shortening.