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In recent years, there has been a revolution in data-driven policing. With that has come scrutiny on how bias in historical data affects algorithmic decision making. In this exploratory work, we introduce a debiasing technique for place-based algorithmic patrol management systems. We show that the technique efficiently eliminates racially biased features while retaining high accuracy in the models. Finally, we provide a lengthy list of potential future research in the realm of fairness and data-driven policing which this work uncovered., Comment: 20 pages (91 Appendix pages), 6 figures (20 supplementary figures), 14 supplementary tables

The principal goal of data science is to derive meaningful information from data. To do this, data scientists develop a space of analytic possibilities and from it reach their information goals by using their knowledge of the domain, the available data, the operations that can be performed on those data, the algorithms/models that are fed the data, and how all of these facets interweave. In this work, we take the first steps towards automating a key aspect of the data science pipeline: data analysis. We present an extensible taxonomy of data analytic operations that scopes across domains and data, as well as a method for codifying domain-specific knowledge that links this analytics taxonomy to actual data. We validate the functionality of our analytics taxonomy by implementing a system that leverages it, alongside domain labelings for 8 distinct domains, to automatically generate a space of answerable questions and associated analytic plans. In this way, we produce information spaces over data that enable complex analyses and search over this data and pave the way for fully automated data analysis.

Wireless communications in the terahertz band (0.1-10 THz) is a promising and key wireless technology enabling ultra-high data rate communication over multi-gigahertz-wide bandwidths, thus fulfilling the demand for denser networks. The complex propagation environment at such high frequencies introduces several challenges, such as high spreading and molecular absorption losses. As such, intelligent reflecting surfaces have been proposed as a promising solution to enable communication in the presence of blockage or to aid a resource-limited quasi-omnidirectional transmitter direct its radiated power. In this paper, we present a metallic reflectarray design achieving controlled non-specular reflection at true terahertz frequencies (i.e., 1-1.05 THz). We conduct extensive experiments to further characterize and validate its working principle using terahertz time-domain spectroscopy and demonstrate its effectiveness with information-carrying signals using a continuous-wave terahertz testbed. Our results show that the reflectarray can help facilitate robust communication links over non-specular paths and improve the reliability of terahertz communications, thereby unleashing the true potential of the terahertz band., Comment: Accepted and to be published in ACM mmNets 2023. Copyright {\copyright} 2023 by the Association for Computing Machinery, Inc. (ACM). Permission to make digital or hard copies of portions of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage

It can be challenging to build effective open question answering (open QA) systems for languages other than English, mainly due to a lack of labeled data for training. We present a data efficient method to bootstrap such a system for languages other than English. Our approach requires only limited QA resources in the given language, along with machine-translated data, and at least a bilingual language model. To evaluate our approach, we build such a system for the Icelandic language and evaluate performance over trivia style datasets. The corpora used for training are English in origin but machine translated into Icelandic. We train a bilingual Icelandic/English language model to embed English context and Icelandic questions following methodology introduced with DensePhrases (Lee et al., 2021). The resulting system is an open domain cross-lingual QA system between Icelandic and English. Finally, the system is adapted for Icelandic only open QA, demonstrating how it is possible to efficiently create an open QA system with limited access to curated datasets in the language of interest.

In this paper, we present the first Entity Linking corpus for Icelandic. We describe our approach of using a multilingual entity linking model (mGENRE) in combination with Wikipedia API Search (WAPIS) to label our data and compare it to an approach using WAPIS only. We find that our combined method reaches 53.9% coverage on our corpus, compared to 30.9% using only WAPIS. We analyze our results and explain the value of using a multilingual system when working with Icelandic. Additionally, we analyze the data that remain unlabeled, identify patterns and discuss why they may be more difficult to annotate., Comment: 9 pages, 5 figures, submitted to Dataset Creation for Lower-Resourced Languages, an LREC 2022 Workshop, 9am-1pm June 24th, 2022

We train several language models for Icelandic, including IceBERT, that achieve state-of-the-art performance in a variety of downstream tasks, including part-of-speech tagging, named entity recognition, grammatical error detection and constituency parsing. To train the models we introduce a new corpus of Icelandic text, the Icelandic Common Crawl Corpus (IC3), a collection of high quality texts found online by targeting the Icelandic top-level-domain (TLD). Several other public data sources are also collected for a total of 16GB of Icelandic text. To enhance the evaluation of model performance and to raise the bar in baselines for Icelandic, we translate and adapt the WinoGrande dataset for co-reference resolution. Through these efforts we demonstrate that a properly cleaned crawled corpus is sufficient to achieve state-of-the-art results in NLP applications for low to medium resource languages, by comparison with models trained on a curated corpus. We further show that initializing models using existing multilingual models can lead to state-of-the-art results for some downstream tasks.

A politically informed citizenry is imperative for a welldeveloped democracy. While the US government has pursued policies for open data, these efforts have been insufficient in achieving an open government because only people with technical and domain knowledge can access information in the data. In this work, we conduct user interviews to identify wants and needs among stakeholders. We further use this information to sketch out the foundational requirements for a functional political information technical system., Comment: Presented at AAAI FSS-21: Artificial Intelligence in Government and Public Sector, Washington, DC, USA

Terahertz time domain spectroscopy (THz TDS) is used to measure the melting kinetics of fructose molecular crystals. Combining single crystal anisotropy measurements with density functional calculations we assign the phonon frequencies and interrogate how specific phonons behave with melting. While nearly all the low frequency phonons continuously red shift with heating and melting, the lowest energy phonon polarized along the c-axis blue shifts at the melting temperature, suggesting an initial structural change immediately before melting. We find that the kinetics follow a 3D growth model with large activation energies consistent with previous differential scanning calorimetry (DSC) measurements. The large activation energies indicate multiple H-bonds must break collectively for the transition. The results suggest the generality of the kinetics for molecular crystals and that THz TDS with picosecond resolution could be used to measure ultra-fast kinetics.

Transport equations for electron thermal energy in the high $\beta_e$ intracluster medium (ICM) are developed that include scattering from both classical collisions and self-generated whistler waves. The calculation employs an expansion of the kinetic electron equation along the ambient magnetic field in the limit of strong scattering and assumes whistler waves with low phase speeds $V_w\sim{v}_{te}/\beta_e\ll{v}_{te}$ dominate the turbulent spectrum, with $v_{te}$ the electron thermal speed and $\beta_e\gg1$ the ratio of electron thermal to magnetic pressure. We find: (1) temperature-gradient-driven whistlers dominate classical scattering when $L_c>L/\beta_e$, with $L_c$ the classical electron mean-free-path and $L$ the electron temperature scale length, and (2) in the whistler dominated regime the electron thermal flux is controlled by both advection at $V_w$ and a comparable diffusive term. The findings suggest whistlers limit electron heat flux over large regions of the ICM, including locations unstable to isobaric condensation. Consequences include: (1) the Field length decreases, extending the domain of thermal instability to smaller length-scales, (2) the heat flux temperature dependence changes from $T_e^{7/2}/L$ to $V_wnT_e\sim{T}_e^{1/2}$, (3) the magneto-thermal and heat-flux driven buoyancy instabilities are impaired or completely inhibited, and (4) sound waves in the ICM propagate greater distances, as inferred from observations. This description of thermal transport can be used in macroscale ICM models., Comment: Accepted for publication in The Astrophysical Journal

The rapid increase in the availability of accurate 3D scanning devices has moved facial recognition and analysis into the 3D domain. 3D facial landmarks are often used as a simple measure of anatomy and it is crucial to have accurate algorithms for automatic landmark placement. The current state-of-the-art approaches have yet to gain from the dramatic increase in performance reported in human pose tracking and 2D facial landmark placement due to the use of deep convolutional neural networks (CNN). Development of deep learning approaches for 3D meshes has given rise to the new subfield called geometric deep learning, where one topic is the adaptation of meshes for the use of deep CNNs. In this work, we demonstrate how methods derived from geometric deep learning, namely multi-view CNNs, can be combined with recent advances in human pose tracking. The method finds 2D landmark estimates and propagates this information to 3D space, where a consensus method determines the accurate 3D face landmark position. We utilise the method on a standard 3D face dataset and show that it outperforms current methods by a large margin. Further, we demonstrate how models trained on 3D range scans can be used to accurately place anatomical landmarks in magnetic resonance images., Comment: This is a pre-print of an article published in proceedings of the asian conference on computer vision 2018 (LNCS 11361). The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-20887-5_44

Data from seven data storage tags recovered from Atlantic salmon marked as smolts were analyzed for depth movements and patterns of deep diving during the marine migration. The salmon mostly stayed at the surface and showed diurnal activity especially from autumn until spring. During the first months at sea the salmon stayed at shallower depths (< 100 m). The salmon took short deep dives (> 100 m), that were rare or absent during the first summer at sea but increased in frequency and duration especially in late winter. The maximum depth of the dives varied from 419 to 1187 m. Most of dives were short, (< 5 hours) but could last up to 33 hours. The duration of dives increased in late winter until spring and the overall depth and maximum depth per dive increased exponentially over time. The initiation of the dives was more common in evenings and at night, suggesting nocturnal diving. We hypothesized that deep diving is related to feeding of salmon as mesopelagic fish can be important food for salmon during winter., Comment: 12 pages, 4 figures

We study unbiased $(1+1)$ evolutionary algorithms on linear functions with an unknown number $n$ of bits with non-zero weight. Static algorithms achieve an optimal runtime of $O(n (\ln n)^{2+\epsilon})$, however, it remained unclear whether more dynamic parameter policies could yield better runtime guarantees. We consider two setups: one where the mutation rate follows a fixed schedule, and one where it may be adapted depending on the history of the run. For the first setup, we give a schedule that achieves a runtime of $(1\pm o(1))\beta n \ln n$, where $\beta \approx 3.552$, which is an asymptotic improvement over the runtime of the static setup. Moreover, we show that no schedule admits a better runtime guarantee and that the optimal schedule is essentially unique. For the second setup, we show that the runtime can be further improved to $(1\pm o(1)) e n \ln n$, which matches the performance of algorithms that know $n$ in advance. Finally, we study the related model of initial segment uncertainty with static position-dependent mutation rates, and derive asymptotically optimal lower bounds. This answers a question by Doerr, Doerr, and K\"otzing.

I describe a method for computer algebra that helps with laborious calculations typically encountered in theoretical microhydrodynamics. The program mimics how humans calculate by matching patterns and making replacements according to the rules of algebra and calculus. This note gives an overview and walks through an example, while the accompanying code repository contains the implementation details, a tutorial, and more examples. The code repository is attached as supplementary material to this note, and maintained at https://github.com/jeinarsson/matte