Miracle-dz/UniFAR-FTUs huggingface dataset:
SROM: Simple Real-time Odometry and Mapping using LiDAR data for Autonomous Vehicles

In this paper, we present SROM, a novel realtime Simultaneous Localization and Mapping (SLAM) system for autonomous vehicles. The keynote of the paper showcases SROM's ability to maintain localization at low sampling rates or at high linear or angular velocities where most popular LiDAR based localization approaches get degraded fast. We also demonstrate SROM to be computationally efficient and capable of handling high-speed maneuvers. It also achieves low drifts without the need for any other sensors like IMU and/or GPS. Our method has a two-layer structure wherein first, an approximate estimate of the rotation angle and translation parameters are calculated using a Phase Only Correlation (POC) method. Next, we use this estimate as an initialization for a point-to-plane ICP algorithm to obtain fine matching and registration. Another key feature of the proposed algorithm is the removal of dynamic objects before matching the scans. This improves the performance of our system as the dynamic objects can corrupt the matching scheme and derail localization. Our SLAM system can build reliable maps at the same time generating high-quality odometry. We exhaustively evaluated the proposed method in many challenging highways/country/urban sequences from the KITTI dataset and the results demonstrate better accuracy in comparisons to other state-of-the-art methods with reduced computational expense aiding in real-time realizations. We have also integrated our SROM system with our in-house autonomous vehicle and compared it with the state-of-the-art methods like LOAM and LeGO-LOAM.
Lower Bounds for Semi-adaptive Data Structures via Corruption

In a dynamic data structure problem we wish to maintain an encoding of some data in memory, in such a way that we may efficiently carry out a sequence of queries and updates to the data. A long-standing open problem in this area is to prove an unconditional polynomial lower bound of a trade-off between the update time and the query time of an adaptive dynamic data structure computing some explicit function. Ko and Weinstein provided such lower bound for a restricted class of {\em semi-adaptive\/} data structures, which compute the Disjointness function. There, the data are subsets $x_1,\dots,x_k$ and $y$ of $\{1,\dots,n\}$, the updates can modify $y$ (by inserting and removing elements), and the queries are an index $i \in \{1,\dots,k\}$ (query $i$ should answer whether $x_i$ and $y$ are disjoint, i.e., it should compute the Disjointness function applied to $(x_i, y)$). The semi-adaptiveness places a restriction in how the data structure can be accessed in order to answer a query. We generalize the lower bound of Ko and Weinstein to work not just for the Disjointness, but for any function having high complexity under the smooth corruption bound.
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting

Unsupervised domain adaptation (UDA) for nuclei instance segmentation is important for digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift across datasets. In this work, we propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images, by learning from fluorescence microscopy images. More specifically, we first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation. Thirdly, in order to avoid the influence of the source-biased features, we propose a task re-weighting mechanism to dynamically add trade-off weights for the task-specific loss functions. Experimental results on three datasets indicate that our proposed method outperforms state-of-the-art UDA methods significantly, and demonstrates a similar performance as fully supervised methods.
Rare decays of the top quark mediated by Z′ gauge bosons and flavor violation

The rare top quark decays mediated by a new neutral massive gauge boson that is predicted in models with extended gauge symmetries are studied. We focus on the processes $t\to cV, uV$ induced at the one loop level, where $V =\gamma, g$, by considering different extended models. It is found that, within a broad range of mass of the new neutral gauge boson, the models predict branching ratios for the decays in study that are competitive with respect to the corresponding branching ratios in the standard model. In order to establish bound on our branching ratios, we consider the recent experimental bounds as $m_{Z^\prime}\geq$ 3.8-4.5 TeV, depending on the model, which also impose restrictions on our calculation. Even in this case, the resulting branching ratios are of the same order of magnitude as that predicted by the standard model. It should be noted that for the case of two models studied here, since no experimental bound exists to compare with, the results could be important, as they are, in the best of cases, two orders of magnitude larger than the predicted by the standard model.
Stem lesions on Betula pendula seedlings in Finnish forest nurseries and the pathogenicity of Phytophthora cactorum

Summary Phytophthora cactorum was isolated from necrotic stem lesions on 20–80% of diseased Betula pendula seedlings sampled from three nurseries in Finland. Fusarium avenaceum and Godronia sp. were also found in all nurseries. Inoculation of P. cactorum on B. pendula seedlings caused necrotic lesions and the spread of these lesions around the stem was detrimental. Neither the timing of N application nor two different PK-levels applied to the seedlings the previous year significantly affected the size of lesions caused by P. cactorum.
Examining supernova events in Type 1 active galactic nuclei

A statistical study of intermediate Palomar Transient Factory supernovae (SNe) in Type 1 active galactic nuclei (AGNs) has shown a major deficit of supernovae around Type 1 AGN host galaxies, with respect to Type 2 AGN hosts. The aim of this work is to test whether there is any preference for Type 1 AGNs to host SN of a specific kind. Through the analysis of SN occurrence and their type (thermonuclear versus core-collapse), we can directly link the type of stars producing the SN events, thus this is an indirect way to study host galaxies in Type 1 AGNs. We examine the detection fractions of SNe, the host galaxies and compare the sample properties to typical host galaxies in the Open Supernova Catalog (OSC). The majority of the host galaxies in the AGN sample are late type, similar to typical galaxies hosting SN within the OSC. The findings are supportive of a deficiency of SNe near Type 1 AGNs, although we cannot with certainty assess the overall detection fractions of SNe in Type 1 AGNs relative to other SN host galaxies. We can state that Type 1 AGN has equal detection fractions of thermonuclear versus core-collapse SNe. However, we note the possibility of a higher detection rate of core-collapse supernovae in Type 1 AGN with insecure AGN classifications.
Purification and characterization of a post-proline dipeptidyl aminopeptidase from Streptococcus cremoris AM2

Summary The present study describes the purification of a post-proline dipeptidyl aminopeptidase from the cytoplasm of Streptococcus cremoris AM2. On the basis of its elution from a calibrated Sephadex G200 column, the enzyme had a molecular weight of 117000 and exhibited a broad pH optimum activity between 6·0 and 9·0. The activity was most comprehensively inhibited by phenylmethylsulphonylfluoride and more modestly inhibited by 1,10-phenanthroline and 8-hydroxyquinoline but not by EDTA. A range of peptides containing either proline or alanine as the penultimate amino acid residue could act as substrates. The presence of proline on the carboxy side of the scissile bond prevented hydrolysis. However the enzyme could release Pro-Pro from Pro-Pro-Gly-Phe-Ser-Pro. The significance of this substrate specificity is considered in the context of removal of either single proline residues or prolylproline sequences from oligopeptides during cheese ripening.
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild

Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive machine type communication. Given that its deployment is rapidly progressing worldwide, measurement campaigns and performance analyses are needed to better understand the system and move toward its enhancement. With this aim, this article presents a large-scale measurement campaign and empirical analysis of NB-IoT on operational networks, and discloses valuable insights in terms of deployment strategies and radio coverage performance. The reported results also serve as examples showing the potential usage of the collected dataset, which we make open source along with a lightweight data visualization platform.
An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control

In the 34 developed and 156 developing countries, there are ~132 million disabled people who need a wheelchair, constituting 1.86% of the world population. Moreover, there are millions of people suffering from diseases related to motor disabilities, which cause inability to produce controlled movement in any of the limbs or even head. This paper proposes a system to aid people with motor disabilities by restoring their ability to move effectively and effortlessly without having to rely on others utilizing an eye-controlled electric wheelchair. The system input is images of the user’s eye that are processed to estimate the gaze direction and the wheelchair was moved accordingly. To accomplish such a feat, four user-specific methods were developed, implemented, and tested; all of which were based on a benchmark database created by the authors. The first three techniques were automatic, employ correlation, and were variants of template matching, whereas the last one uses convolutional neural networks (CNNs). Different metrics to quantitatively evaluate the performance of each algorithm in terms of accuracy and latency were computed and overall comparison is presented. CNN exhibited the best performance (i.e., 99.3% classification accuracy), and thus it was the model of choice for the gaze estimator, which commands the wheelchair motion. The system was evaluated carefully on eight subjects achieving 99% accuracy in changing illumination conditions outdoor and indoor. This required modifying a motorized wheelchair to adapt it to the predictions output by the gaze estimation algorithm. The wheelchair control can bypass any decision made by the gaze estimator and immediately halt its motion with the help of an array of proximity sensors, if the measured distance goes below a well-defined safety margin. This work not only empowers any immobile wheelchair user, but also provides low-cost tools for the organization assisting wheelchair users.
Aggregated Private Information Retrieval: A First Practical Implementation to Support Large-Scale Disease Analytics

With the outbreak of the coronavirus, governments rely more and more on location data shared by European mobile network operators to monitor the advancements of the disease. In order to comply with often strict privacy requirements, this location data, however, has to be anonymized, limiting its usefulness for making statements about a filtered part of the population, like already infected people. In this research, we aim to assist with the disease tracking efforts by designing a protocol to detect coronavirus hotspots from mobile data while still maintaining compliance with privacy expectations. We use various state-of-the-art privacy-preserving cryptographic primitives to design a protocol that can best be described as aggregated private information retrieval (APIR). Our protocol is based on homomorphic encryption, with additional measures to protect against malicious requests from clients. We have implemented our APIR protocol in the SEAL library and tested it for parameters suitable to create a coronavirus hotspot map for entire nationstates. This demonstrates that it is feasible to apply our APIR protocol to support nationwide disease analysis while still preserve the privacy of infected people.
An obscured AGN population hidden in the VIPERS galaxies: identification through spectral energy distribution decomposition

The detection of X-ray emission constitutes a reliable and efficient tool for the selection of Active Galactic Nuclei (AGNs), although it may be biased against the most heavily absorbed AGNs. Simple mid-IR broad-band selection criteria identify a large number of luminous and absorbed AGNs, yet again host contamination could lead to non-uniform and incomplete samples. Spectral Energy Distribution (SED) decomposition is able to decouple the emission from the AGN versus that from star-forming regions, revealing weaker AGN components. We aim to identify the obscured AGN population in the VIPERS survey in the CFHTLS W1 field through SED modelling. We construct SEDs for 6,860 sources and identify 160 AGNs at a high confidence level using a Bayesian approach. Using optical spectroscopy, we confirm the nature of ~85% of the AGNs. Our AGN sample is highly complete (~92%) compared to mid-IR colour selected AGNs, including a significant number of galaxy-dominated systems with lower luminosities. In addition to the lack of X-ray emission (80%), the SED fitting results suggest that the majority of the sources are obscured. We use a number of diagnostic criteria in the optical, infrared and X-ray regime to verify these results. Interestingly, only 35% of the most luminous mid-IR selected AGNs have X-ray counterparts suggesting strong absorption. Our work emphasizes the importance of using SED decomposition techniques to select a population of type II AGNs, which may remain undetected by either X-ray or IR colour surveys.
A successful community‐level strategy for conservation prioritization

Summary 1. Regions of the world with highest biodiversity and greatest conservation needs are often simultaneously data poor. An effective surrogate strategy would be invaluable for conservation prioritization in such regions. Large-scale environmental data are readily available, but the effectiveness of environmental surrogate strategies for conservation planning has not been confirmed. In this study, we compare a range of such strategies. 2. Environmental surrogacy is based on the idea that by covering a wide range of different environmental conditions, one also achieves high species representation. The effectiveness of this strategy may be enhanced by using community-level modelling techniques, in conjunction with best-available biological (species distribution) data, to calibrate the relationship between environ mental gradients and community richness and composition. We develop a novel approach, called maximization of complementary richness, which accounts for gradients in species richness and non-constant turnover rates of community composition in environmental space. 3. We show that our novel technique can achieve notably higher species representation than what is achieved using past approaches. Simple strategies, such as direct use of environmental data only or environmental clustering, achieved species representation only slightly better than random selection of sites. P-median selection from ordinations of community composition had intermediate performance. Performance of our new maximization of complementary richness technique was closer to the representation levels of optimal reserve networks than to random selection of sites. 4. Synthesis and applications. We found that there are three critical components that are relevant for success: a good model for community turnover (compositional dissimilarity), a good model for species richness, and a selection procedure that appropriately utilizes both turnover and richness information. By taking these into account, one can achieve reasonable levels of species representation. We conclude that using surrogates based on community-level modelling is a highly promising strategy for cost-effective conservation prioritization.
Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization

We describe Artemis (Annotation methodology for Rich, Tractable, Extractive, Multi-domain, Indicative Summarization), a novel hierarchical annotation process that produces indicative summaries for documents from multiple domains. Current summarization evaluation datasets are single-domain and focused on a few domains for which naturally occurring summaries can be easily found, such as news and scientific articles. These are not sufficient for training and evaluation of summarization models for use in document management and information retrieval systems, which need to deal with documents from multiple domains. Compared to other annotation methods such as Relative Utility and Pyramid, Artemis is more tractable because judges don’t need to look at all the sentences in a document when making an importance judgment for one of the sentences, while providing similarly rich sentence importance annotations. We describe the annotation process in detail and compare it with other similar evaluation systems. We also present analysis and experimental results over a sample set of 532 annotated documents.
Formation of Orion fingers

"Orion fingers" are a system of dozens of bowshocks, with the wings of shocks pointing to a common system of origin, which is centered on a dynamically disintegrating system of several massive stars. The shock heads propagate with velocities of up to 300-400 km/s, but the formation and physical properties of the "bullets" leading the shocks are not known. Here we summarize two possible scenarios for the formation of the "bullets" and the resulting bowshocks ("fingers"). In the first scenario, bullets are self-gravitating, Jupiter-mass objects which were formed rapidly and then ejected during the strong dynamical interactions of massive stars and their disks. This scenario naturally explains the similar timescales for the outflow of bullets and for the dynamical interaction of the massive stars, but has some difficulty explaining the observed high velocities of the bullets. In the second scenario, bullets are formed via hydrodynamic instabilities in a massive, infrared-driven wind, naturally explaining the high velocities and the morphology of outflow, but the bullets are not required to be self-gravitating. The processes that created the Orion fingers are likely not unique to this particular star-forming region and may result in free-floating, high-velocity, core-less planets.
Predicting atmospheric optical properties for radiative transfer computations using neural networks

The radiative transfer equations are well known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parametrization (RRTMGP). To minimize computa- tional costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimized BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within 0.5 W m−2 compared to RRTMGP. Our neural network-based gas optics parametrization is up to four times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations while retaining high accuracy. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
Soft Gazetteers for Low-Resource Named Entity Recognition

Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of “soft gazetteers” that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score.
Sample Complexity of Uniform Convergence for Multicalibration

There is a growing interest in societal concerns in machine learning systems, especially in fairness. Multicalibration gives a comprehensive methodology to address group fairness. In this work, we address the multicalibration error and decouple it from the prediction error. The importance of decoupling the fairness metric (multicalibration) and the accuracy (prediction error) is due to the inherent trade-off between the two, and the societal decision regarding the "right tradeoff" (as imposed many times by regulators). Our work gives sample complexity bounds for uniform convergence guarantees of multicalibration error, which implies that regardless of the accuracy, we can guarantee that the empirical and (true) multicalibration errors are close. We emphasize that our results: (1) are more general than previous bounds, as they apply to both agnostic and realizable settings, and do not rely on a specific type of algorithm (such as deferentially private), (2) improve over previous multicalibration sample complexity bounds and (3) implies uniform convergence guarantees for the classical calibration error.
Cluster-based dual evolution for multivariate systems

This paper proposes a cluster-based method to analyse multivariate systems that change over time. At each point in time, we partition data points into an appropriate number of clusters and thereby track both the total number of clusters and the individual constituents' cluster memberships changing over time. We can also compare and contrast several multivariate systems and determine patterns and anomalies between different data. We apply this method to study the evolution of COVID-19 cases and deaths over time. We detect significant similarities in the evolution of cases and deaths up to a suitable offset in time. We compute this offset and use it to identify anomalous countries in the progression of cases to deaths.
An Unsupervised Semantic Sentence Ranking Scheme for Text Documents

This paper presents Semantic SentenceRank (SSR), an unsupervised scheme for automatically ranking sentences in a single document according to their relative importance. In particular, SSR extracts essential words and phrases from a text document, and uses semantic measures to construct, respectively, a semantic phrase graph over phrases and words, and a semantic sentence graph over sentences. It applies two variants of article-structure-biased PageRank to score phrases and words on the first graph and sentences on the second graph. It then combines these scores to generate the final score for each sentence. Finally, SSR solves a multi-objective optimization problem for ranking sentences based on their final scores and topic diversity through semantic subtopic clustering. An implementation of SSR that runs in quadratic time is presented, and it outperforms, on the SummBank benchmarks, each individual judge’s ranking and compares favorably with the combined ranking of all judges.