On the Seesaw Scale in Supersymmetric SO(10) Models

The seesaw mechanism, which is responsible for the description of neutrino masses and mixing, requires a scale lower than the unification scale. We propose a new model with spinor superfields playing important roles to generate this seesaw scale, with special attention paid on the Goldstone mode of the $U(1)_{B-L}$ symmetry breaking.Comment: 15 page

Research on self-cross transformer model of point cloud change detecter

With the vigorous development of the urban construction industry, engineering deformation or changes often occur during the construction process. To combat this phenomenon, it is necessary to detect changes in order to detect construction loopholes in time, ensure the integrity of the project and reduce labor costs. Or the inconvenience and injuriousness of the road. In the study of change detection in 3D point clouds, researchers have published various research methods on 3D point clouds. Directly based on but mostly based ontraditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are to convert 3D point clouds into DSM, which loses a lot of original information. Although deep learning is used in remote sensing methods, in terms of change detection of 3D point clouds, it is more converted into two-dimensional patches, and neural networks are rarely applied directly. We prefer that the network is given at the level of pixels or points. Variety. Therefore, in this article, our network builds a network for 3D point cloud change detection, and proposes a new module Cross transformer suitable for change detection. Simultaneously simulate tunneling data for change detection, and do test experiments with our network

The tomato SlIAA15 is involved in trichome formation and axillary shoot development

The Aux/IAA genes encode a large family of short-lived proteins known to regulate auxin signalling in plants. Functional characterization of SlIAA15, a member of the tomato (Solanum lycopersicum) Aux/IAA family, shows that the encoded protein acts as a strong repressor of auxin-dependent transcription. The physiological significance of SlIAA15 was addressed by a reverse genetics approach, revealing that SlIAA15 plays multiple roles in plant developmental processes. The SlIAA15 down-regulated lines display lower trichome number, reduced apical dominance with associated modified pattern of axillary shoot development, increased lateral root formation and decreased fruit set. Moreover, the leaves of SlIAA15-inhibited plants are dark green and thick, with larger pavement cells, longer palisade cells and larger intercellular space of spongy mesophyll cells. The SlIAA15-suppressed plants exhibit a strong reduction in type I, V and VI trichome formation, suggesting that auxin-dependent transcriptional regulation is required for trichome initiation. Concomitant with reduced trichome formation, the expression of some R2R3 MYB genes, putatively involved in the control of trichome differentiation, is altered. These phenotypes uncover novel and specialized roles for Aux/IAAs in plant developmental processes, clearly indicating that members of the Aux/IAA gene family in tomato perform both overlapping and specific functions

The complete chloroplast genome of Piper sarmentosum Roxburgh, 1820 (Piperaceae)

Piper sarmentosum Roxb. (Piperaceae) is a traditional medicinal herb native to Southeast Asia. The complete genome of P. sarmentosum was sequenced and characterized in this study with the aim of providing genomic resources for the evolution and molecular breeding of P. sarmentosum. It has a typical quadripartite structure, with a large single-copy (LSC) region of 88,979 bp, a small single-copy (SSC) region of 18,274 bp, and two copies of 27,068 bp inverted-repeat regions (IRa and IRb). A total of 130 genes were annotated, comprising 85 protein-coding genes (PCGs), 8 ribosomal RNA (rRNA) genes, and 37 transfer RNA (tRNA) genes. The phylogenetic tree showed that P. sarmentosum in the current study is closely related to Piper longum

AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data

Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring, and summarizing regular patterns, as well as detecting, classifying, and interpreting abnormal cases. Based on the time-varying and multivariate features of air quality data, a dimension reduction method Composite Least Square Projection (CLSP) is proposed, which allows appreciating and interpreting the data patterns in the context of attributes. On the basis of the observed regular patterns, multiple abnormal cases are further detected, including the multivariate anomalies by the proposed Noise Hierarchical Clustering (NHC) method, abruptly changing timestamps by Time diversity (TD) indicator, and cities with unique patterns by the Geographical Surprise (GS) measure. Moreover, we combine TD and GS to group anomalies based on their underlying spatiotemporal correlations. AirInsight includes multiple coordinated views and rich interactive functions to provide contextual information from different aspects and facilitate a comprehensive understanding. In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city. Experiments show that CLSP improves the accuracy of Least Square Projection (LSP) and that NHC has the ability to separate noises. Meanwhile, several case studies and task-based user evaluation demonstrate that our system is effective and practical for exploring and interpreting multivariate spatiotemporal patterns and anomalies in air quality data

Adaptive feature guidance: Modelling visual search with graphical layouts

| openaire: EC/H2020/637991/EU//COMPUTEDWe present a computational model of visual search on graphical layouts. It assumes that the visual system is maximising expected utility when choosing where to fixate next. Three utility estimates are available for each visual search target: one by unguided perception only, and two, where perception is guided by long-term memory (location or visual feature). The system is adaptive, starting to rely more upon long-term memory when its estimates improve with experience. However, it needs to relapse back to perception-guided search if the layout changes. The model provides a tool for practitioners to evaluate how easy it is to find an item for a novice or an expert, and what happens if a layout is changed. The model suggests, for example, that (1) layouts that are visually homogeneous are harder to learn and more vulnerable to changes, (2) elements that are visually salient are easier to search and more robust to changes, and (3) moving a non-salient element far away from original location is particularly damaging. The model provided a good match with human data in a study with realistic graphical layouts.Peer reviewe

Ability-based optimization of touchscreen interactions

| openaire: EC/H2020/637991/EU//COMPUTEDAbility-based optimization is a computational approach for improving interface designs for users with sensorimotor and cognitive impairments. Designs are created by an optimizer, evaluated against task-specific cognitive models, and adapted to individual abilities. The approach does not necessitate extensive data collection and could be applied both automatically and manually by users, designers, or caretakers. As a first step, the authors present optimized touchscreen layouts for users with tremor and dyslexia that potentially improve text-entry speed and reduce error.Peer reviewe

Modelling Learning of New Keyboard Layouts

Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.Peer reviewe