Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Approximate Search for Sets of Vectors

We consider a similarity measure between two sets $A$ and $B$ of vectors, that balances the average and maximum cosine distance between pairs of vectors, one from set $A$ and one from set $B$. As a motivation for this measure, we present lineage tracking in a database. To practically realize this measure, we need an approximate search algorithm that given a set of vectors $A$ and sets of vectors $B_1,. ,B_n$, the algorithm quickly locates the set $B_i$ that maximizes the similarity measure. For the case where all sets are singleton sets, essentially each is a single vector, there are known efficient approximate search algorithms, e.g., approximated versions of tree search algorithms, locality-sensitive hashing (LSH), vector quantization (VQ) and proximity graph algorithms. In this work, we present approximate search algorithms for the general case. The underlying idea in these algorithms is encoding a set of vectors via a"long"single vector. The proposed approximate approach achieves significant performance gains over an optimized, exact search on vector sets.
Concentric toroids as a wideband and efficient substrate for surface-enhanced Raman spectroscopy applications

ABSTRACT Designing a structure having high local field enhancement, wideband resonance, and large hot spot area is the key element to obtain a large enhancement factor for surface-enhanced Raman spectroscopy applications. Here, the concentric toroid structures in dimer configuration is proposed, which shows a large local field intensity in a wide spectral range and the region that leads to a high-surface-enhanced Raman scattering signal intensity. Calculations show that the average surface-enhanced Raman scattering enhancement is up to 60 times more compared to the conventional dimer toroid structures with similar size.
On the fast approximation of point clouds using Chebyshev polynomials

Abstract Suppose a large and dense point cloud of an object with complex geometry is available that can be approximated by a smooth univariate function. In general, for such point clouds the “best” approximation using the method of least squares is usually hard or sometimes even impossible to compute. In most cases, however, a “near-best” approximation is just as good as the “best”, but usually much easier and faster to calculate. Therefore, a fast approach for the approximation of point clouds using Chebyshev polynomials is described, which is based on an interpolation in the Chebyshev points of the second kind. This allows to calculate the unknown coefficients of the polynomial by means of the Fast Fourier transform (FFT), which can be extremely efficient, especially for high-order polynomials. Thus, the focus of the presented approach is not on sparse point clouds or point clouds which can be approximated by functions with few parameters, but rather on large dense point clouds for whose approximation perhaps even millions of unknown coefficients have to be determined.
Synthesis of polyethylene/exfoliated MoS2 nanocomposites by in situ exfoliation polymerization using Ziegler–Natta catalyst intercalated MoS2

A new synthetic route for polyethylene (PE)/exfoliated MoS2 (EMoS2) nanocomposites using novel Ziegler–Natta catalyst intercalated MoS2 was designed. The catalyst was synthesized by the intercalation of MoS2 with a Grignard reagent, followed by anchoring of TiCl4 into the MoS2 galleries. The intercalated MoS2 was exfoliated to form PE/EMoS2 nanocomposites during in situ ethylene polymerization. The resultant PE/EMoS2 nanocomposites had a layered morphology, and they were well dispersed in the PE matrix. In addition, the thermal stability and mechanical properties of PE were significantly enhanced with the introduction of EMoS2. Thus, this work provides a facile approach to the production of PE/EMoS2 nanocomposites.
Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e. genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and were always highest when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.
Towards decoding selective attention through cochlear implant electrodes as sensors in subjects with contralateral acoustic hearing

Objectives. Focusing attention on one speaker in a situation with multiple background speakers or noise is referred to as auditory selective attention. Decoding selective attention is an interesting line of research with respect to future brain-guided hearing aids or cochlear implants (CIs) that are designed to adaptively adjust sound processing through cortical feedback loops. This study investigates the feasibility of using the electrodes and backward telemetry of a CI to record electroencephalography (EEG). Approach. The study population included six normal-hearing (NH) listeners and five CI users with contralateral acoustic hearing. Cortical auditory evoked potentials (CAEP) and selective attention were recorded using a state-of-the-art high-density scalp EEG and, in the case of CI users, also using two CI electrodes as sensors in combination with the backward telemetry system of these devices, denoted as implant-based EEG (iEEG). Main results. In the selective attention paradigm with multi-channel scalp EEG the mean decoding accuracy across subjects was 94.8% and 94.6% for NH listeners and CI users, respectively. With single-channel scalp EEG the accuracy dropped but was above chance level in 8–9 out of 11 subjects, depending on the electrode montage. With the single-channel iEEG, the selective attention decoding accuracy could only be analyzed in two out of five CI users due to a loss of data in the other three subjects. In these two CI users, the selective attention decoding accuracy was above chance level. Significance. This study shows that single-channel EEG is suitable for auditory selective attention decoding, even though it reduces the decoding quality compared to a multi-channel approach. CI-based iEEG can be used for the purpose of recording CAEPs and decoding selective attention. However, the study also points out the need for further technical development for the CI backward telemetry regarding long-term recordings and the optimal sensor positions.
Dissociating the Neural Correlates of Planning and Executing Tasks with Nested Task Sets

Task processing (e.g., the preparation and execution of responses) and task representation (e.g., the activation and maintenance of stimulus-response and context information) are two facets of cognitive control supported by lateral frontal cortex (LFC). However, the mechanistic overlap (or distinction) between these two facets is unknown. We explored this by combining a complex task mapping with a pre-cueing procedure. Participants made match/non-match judgments on pairs of stimuli during fMRI recording. Pre-cues on each trial gave variable amounts of information to the participant in anticipation of the stimulus. Our results demonstrated that regions throughout LFC were more active at the stimulus (when responses could be executed) than at the cue (when they could only be prepared), indicating that they supported execution of the task agnostic to the specific task representation. A subset of regions in left caudal LFC showed increased activity with more cue information at the cue and the reverse at the stimulus, suggesting their involvement in reducing uncertainty within the task representation. These results suggest that one component of task processing is preparing and executing the task according to the relevant representation, confined to left caudal LFC, while non-representational functions that occur primarily during execution are supported by different regions throughout the rest of LFC. We further conducted an exploratory investigation of connectivity between the two groups of regions in this study and their potential relationship to the fronto-parietal and cingulo-opercular networks. Regions with both patterns of activity appear to be part of the fronto-parietal network.
The genome sequence of Sorbus pohuashanensis provides insights into population evolution and leaf sunburn response

Sorbus pohuashanensis is a potential horticulture and medicinal plant, but its genomic and genetic background remains unknown. Here, we de novo sequenced and assembled the S. pohuashanensis (Hance) Hedl. reference genome using PacBio long reads. Based on the new reference genome, we resequenced a core collection of 22 Sorbus spp. samples, which were divided into two groups (G1 and G2) based on phylogenetic and PCA analysis. These phylogenetic clusters were highly in accordance with the classification based on leaf shape. Natural hybridization between the G1 and G2 groups was evidenced by a sample (R21) with a highly heterozygous genotype. Nucleotide diversity (π) analysis showed that G1 has a higher diversity than G2, and that G2 originated from G1. During the evolution process, the gene families involved in photosynthesis pathways expanded and gene families involved in energy consumption contracted. Comparative genome analysis showed that S. pohuashanensis has a high level of chromosomal synteny with Malus domestica and Pyrus communis. RNA-seq data suggested that flavonol biosynthesis and heat-shock protein (HSP)-heat-shock factor (HSF) pathways play important roles in protection against sunburn. This research provides new insight into the evolution of Sorbus spp. genomes. In addition, the genomic resources and the identified genetic variations, especially those genes related to stress resistance, will help future efforts to introduce and breed Sorbus spp.
Spatial, Phenological, and Inter-Annual Variations of Gross Primary Productivity in the Arctic from 2001 to 2019

Quantifying the spatial, seasonal (phenological), and inter-annual variations of gross primary productivity (GPP) in the Arctic is critical for comprehending the terrestrial carbon cycle and its feedback to climate warming in this region. Here, we evaluated the accuracy of the MOD17A2H GPP product using the FLUXNET 2015 dataset in the Arctic, then explored the spatial patterns, seasonal variations, and interannual trends of GPP, and investigated the dependence of the spatiotemporal variations in GPP on land cover types, latitude, and elevation from 2001 to 2019. The results showed that MOD17A2H was consistent with in situ measurements (R = 0.8, RMSE = 1.26 g C m−2 d−1). The functional phenology was also captured by the MOD17A2H product (R = 0.62, RMSE = 9 days) in the Arctic. The spatial variation of the seasonal magnitude of GPP and its interannual trends is partly related to land cover types, peaking in forests and lowest in grasslands. The interannual trend of GPP decreased as the latitude and elevation increased, except for the latitude between 62°~66° N and elevation below 700 m. Our study not only revealed the variation of GPP in the Arctic but also helped to understand the carbon cycle over this region.
Supporting the Annotation Experience Through CorEx and Word Mover's Distance

Online communities can be used to promote destructive behaviours, as in pro-Eating Disorder (ED) communities. Research needs annotated data to study these phenomena. Even though many platforms have already moderated this type of content, Twitter has not, and it can still be used for research purposes. In this paper, we unveiled emojis, words, and uncommon linguistic patterns within the ED Twitter community by using the Correlation Explanation (CorEx) algorithm on unstructured and non-annotated data to retrieve the topics. Then we annotated the dataset following these topics. We analysed then the use of CorEx and Word Mover’s Distance to retrieve automatically similar new sentences and augment the annotated dataset.
Critical Points Extraction from Building Façades by Analyzing Gradient Structure Tensor

This paper proposes a building façade contouring method from LiDAR (Light Detection and Ranging) scans and photogrammetric point clouds. To this end, we calculate the confidence property at multiple scales for an individual point cloud to measure the point cloud’s quality. The confidence property is utilized in the definition of the gradient for each point. We encode the individual point gradient structure tensor, whose eigenvalues reflect the gradient variations in the local neighborhood areas. The critical point clouds representing the building façade and rooftop (if, of course, such rooftops exist) contours are then extracted by jointly analyzing dual-thresholds of the gradient and gradient structure tensor. Based on the requirements of compact representation, the initial obtained critical points are finally downsampled, thereby achieving a tradeoff between the accurate geometry and abstract representation at a reasonable level. Various experiments using representative buildings in Semantic3D benchmark and other ubiquitous point clouds from ALS DublinCity and Dutch AHN3 datasets, MLS TerraMobilita/iQmulus 3D urban analysis benchmark, UAV-based photogrammetric dataset, and GeoSLAM ZEB-HORIZON scans have shown that the proposed method generates building contours that are accurate, lightweight, and robust to ubiquitous point clouds. Two comparison experiments also prove the superiority of the proposed method in terms of topological correctness, geometric accuracy, and representation compactness.
Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities.
3D Instance Segmentation and Object Detection Framework Based on the Fusion of Lidar Remote Sensing and Optical Image Sensing

Since single sensor and high-density point cloud data processing have certain direct processing limitations in urban traffic scenarios, this paper proposes a 3D instance segmentation and object detection framework for urban transportation scenes based on the fusion of Lidar remote sensing technology and optical image sensing technology. Firstly, multi-source and multi-mode data pre-fusion and alignment of Lidar and camera sensor data are effectively carried out, and then a unique and innovative network of stereo regional proposal selective search-driven DAGNN is constructed. Finally, using the multi-dimensional information interaction, three-dimensional point clouds with multi-features and unique concave-convex geometric characteristics are instance over-segmented and clustered by the hypervoxel storage in the remarkable octree and growing voxels. Finally, the positioning and semantic information of significant 3D object detection in this paper are visualized by multi-dimensional mapping of the boundary box. The experimental results validate the effectiveness of the proposed framework with excellent feedback for small objects, object stacking, and object occlusion. It can be a remediable or alternative plan to a single sensor and provide an essential theoretical and application basis for remote sensing, autonomous driving, environment modeling, autonomous navigation, and path planning under the V2X intelligent network space– ground integration in the future.
A Non-Canonical Convergence of Carbohydrate and Glutamine Metabolism is Required After Metabolic Rewiring in a Solid Environment

1 Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, UT 84112, USA 2 Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA 3 Blais Proteomics Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA 4 Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA 5 Children’s Medical Center Research Institute, University of Texas (UT) Southwestern Medical Center, Dallas, TX 75390, USA 6 Howard Hughes Medical Institute, UT Southwestern Medical Center, Dallas, TX 75390, USA 7 Howard Hughes Medical Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA 8 Present address: Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA * Correspondence: rutter@biochem.utah. edu (J. Rutter)
A promiscuity locus confers Lotus burttii nodulation with rhizobia from five different genera

Legumes acquire access to atmospheric nitrogen through nitrogen fixation by rhizobia in root nodules. Rhizobia are soil dwelling organisms and there is a tremendous diversity of rhizobial species in different habitats. From the legume perspective, host range is a compromise between the ability to colonize new habitats, where the preferred symbiotic partner may be absent, and guarding against infection by suboptimal nitrogen fixers. Here, we investigate natural variation in rhizobial host range across Lotus species. We find that Lotus burttii is considerably more promiscuous than Lotus japonicus, represented by the Gifu accession, in its interactions with rhizobia. This promiscuity allows Lotus burttii to form nodules with Mesorhizobium, Rhizobium, Sinorhizobium, Bradyrhizobium, and Allorhizobium species that represent five distinct genera. Using recombinant inbred lines, we have mapped the Gifu/burttii promiscuity QTL to the same genetic locus regardless of rhizobial genus, suggesting a general genetic mechanism for host-range expansion. The Gifu/burttii QTL now provides an opportunity for genetic and mechanistic understanding of promiscuous legume-rhizobia interactions.
A Review and Cluster Analysis of German Polarity Resources for Sentiment Analysis

The domain of German polarity dictionaries is heterogeneous with many small dictionaries created for different purposes and using different methods. This paper aims to map out the landscape of freely available German polarity dictionaries by clustering them to uncover similarities and shared features. We find that, although most dictionaries seem to agree in their assessment of a word’s sentiment, subsets of them form groups of interrelated dictionaries. These dependencies are in most cases an immediate reflex of how these dictionaries were designed and compiled. As a consequence, we argue that sentiment evaluation should be based on multiple and diverse sentiment resources in order to avoid error propagation and amplification of potential biases
PySeq2500: An open source toolkit for repurposing HiSeq 2500 sequencing systems as versatile fluidics and imaging platforms

Fluorescence microscopy is a key method in the life sciences. State of the art -omics methods combine fluorescence microscopy with complex protocols to visualize tens to thousands of features in each of millions of pixels across samples. These -omics methods require precise control of temperature, reagent application, and image acquisition parameters during iterative chemistry and imaging cycles conducted over the course of days or weeks. Automated execution of such methods enables robust and reproducible data generation. However, few commercial solutions exist for temperature controlled, fluidics coupled fluorescence imaging, and implementation of bespoke instrumentation requires specialized engineering expertise. Here we present PySeq2500, an open source Python code base and flow cell design that converts the Illumina HiSeq 2500 instrument into an open platform for programmable applications. Customizable PySeq2500 protocols enable experimental designs involving simultaneous 4-channel image acquisition, temperature control, reagent exchange, stable positioning, and sample integrity over extended experiments. To demonstrate accessible automation of complex, multi-day workflows, we use the PySeq2500 system for unattended execution of iterative indirect immunofluorescence imaging (4i). Our automated 4i method uses off-the-shelf antibodies over multiple cycles of staining, imaging, and antibody elution to build highly multiplexed maps of cell types and pathological features in mouse and postmortem human spinal cord sections. As demonstrated here, PySeq2500 enables non-specialists to develop and implement state of the art fluidics coupled imaging methods in a widely available benchtop system.