Meta (Israel)

Abstract. Mobile multicam photogrammetry systems offer a number of advantages over other 3d imaging modalities, including rapid acquisition speed, low cost sensors, and the availability of mature open-source software systems for photogrammetric alignment and reconstruction.. Through a number of case studies we compare the Looq, a home-grown GPS enabled multi-cam mobile mapping system, to colocated models from terrestrial photogrammetry, aerial photogrammetry, two terrestrial LiDAR systems, and two SLAM LiDAR systems. We evaluate these systems across varied urban and natural environments, in GPS enabled and denied environments, tight interior spaces, and open landscapes in Europe and North America. The comparative performance of these systems is described qualitatively and quantitatively.

Endometriosis (EMS) is an oestrogen-dependent condition characterised by ectopic endometrial-like tissue growth with a chronic and inflammatory nature leading to severe symptoms and reduced quality of life. Emerging evidence implicates gut microbiome dysbiosis in EMS pathogenesis, driving chronic inflammation, immune dysfunction, and altered bacterial taxa within patient gut microbiome. This review examines the intricate relationship between gut dysbiosis and EMS, with a focus on immunomodulatory mechanisms and the downstream consequences of the bacterial contamination theory. It evaluates recent findings regarding microbial imbalances and microbial diversity, pinpointing gaps in current research that mandate further understanding. For example, while microbial markers like Lactobacillus depletion and elevated Escherichia coli have been observed in patients, their diagnostic potential remains poorly defined. Additionally, it addresses the broader implications of EMS, including its physical, mental and healthcare burdens. Simultaneously, critiquing current drawbacks in diagnostic and therapeutic strategies such as their invasiveness and limited efficacy. The review further evaluates novel microbiome-based strategies namely Lactobacillus-based probiotics and faecal microbiota transplantation (FMT), assessing their potential in modulating immune responses and alleviating EMS symptoms while considering associated challenges. Lastly, it highlights the emerging role of metabolomics in identifying non-invasive and diagnostic biomarkers like short-chain fatty acids (SCFAs), implicated in the interplay between microbial metabolites and immune signalling pathways in EMS.

Traditional methods for trial selection do not support exhaustive characterization of perceptual thresholds in high-dimensional settings; they require exponentially more data with increasing stimulus dimensionality. Here, we study the efficacy of adaptive sampling for trial selection combined with a Wishart Process model (WPM) for dense characterization of thresholds. Specifically, we simulated color-discrimination performance using the CIELAB color space, which is intended to be perceptually uniform and which coarsely approximates human color sensitivity. We restricted attention to a plane in color space. We generated CIELAB-based 'ground-truth' responses for a forced-choice task in which an observer identifies the odd-colored stimulus (comparison) from two other stimuli that are identical to each other (reference). The reference (R & B calibrated monitor channel intensities) and comparison stimuli (R+ΔR & B+ΔB) were chosen using AEPsych, a non-parametric method that adaptively selects informative trials (Owens et al., 2021). The monitor G intensity was held fixed. We fit the generated responses with a semi-parametric WPM which expresses smoothness of performance over the stimulus space, on the assumption of multivariate-Gaussian performance-limiting internal noise. This approach accurately recovered the ‘ground-truth’ thresholds with 2,800 trials, ~10x fewer trials than required by conventional methods (~30,000 assuming 25 reference stimuli x 8 comparison directions/reference x 150 trials/direction). Evaluation using the Bures-Wasserstein distance showed close agreement between ‘ground-truth’ threshold ellipses and model predictions (mean = 0.009, SD = 0.003; compare to a baseline set by distance between the ‘ground-truth’ ellipses and their inscribed circles: mean = 0.040, SD = 0.010). Crucially, our approach densely characterizes discrimination, interpolating to predict thresholds for any comparison direction around any reference. The approach generalizes to higher stimulus dimensions (full color space) and to any modality where smoothly varying multivariate-Gaussian noise limits performance, for example auditory localization and motor reaching.

Natural language often struggles to accurately associate positional and attribute information with multiple instances, which limits current text-based visual generation models to simpler compositions featuring only a few dominant instances. To address this limitation, this work enhances diffusion models by introducing regional instance control, where each instance is governed by a bounding box paired with a free-form caption. Previous methods in this area typically rely on implicit position encoding or explicit attention masks to separate regions of interest (ROIs), resulting in either inaccurate coordinate injection or large computational overhead. Inspired by ROI-Align in object detection, we introduce a complementary operation called ROI-Unpool. Together, ROI-Align and ROI- Unpool enable explicit, efficient, and accurate ROI manipulation on high-resolution feature maps for visual generation. Building on ROI-Unpool, we propose ROICtrl, an adapter for pretrained diffusion models that enables precise regional instance control. ROICtrl is compatible with community-finetuned diffusion models, as well as with existing spatial-based add-ons (e.g., ControlNet, T2I- Adapter) and embedding-based add-ons (e.g., IP-Adapter, ED-LoRA), extending their applications to multi-instance generation. Experiments show that ROICtrl achieves superior performance in regional instance control while significantly reducing computational costs.

This research aims to identify the manifestations, causes and patterns of bullying against women in the virtual community, and for this the descriptive approach was used, and the electronic questionnaire was used as a main tool in the collection of information, applying it to a sample of women users of the social media website Facebook, With a number of 87 female respondents. and The following results were achieved: Women in the Virtual Society suffer from bullying by their friends on Facebook, and this appears through many behaviors such as stalking, invasion of personal information, opposition, deception and fraud, cursing...; In the light of these results, the study suggests broadcasting programs in the media of all kinds that would sensitize Algerian women on the dangers of cyber bullying and cybercrime.

The success of deep neural networks (DNNs) is heavily dependent on computational resources. While DNNs are often employed on cloud servers, there is a growing need to operate DNNs on edge devices. Edge devices are typically limited in their computational resources, yet, often multiple edge devices are deployed in the same environment and can reliably communicate with each other. In this work we propose to facilitate the application of DNNs on the edge by allowing multiple users to collaborate during inference to improve their accuracy. Our mechanism, coined edge ensembles, is based on having diverse predictors at each device, which form an ensemble of models during inference. To mitigate the communication overhead, the users share quantized features, and we propose a method for aggregating multiple decisions into a single inference rule. We analyze the latency induced by edge ensembles, showing that its performance improvement comes at the cost of a minor additional delay under common assumptions on the communication network. Our experiments demonstrate that collaborative inference via edge ensembles equipped with compact DNNs substantially improves the accuracy over having each user infer locally, and can outperform using a single centralized DNN larger than all the networks in the ensemble together.

Purpose - The purpose of the research presented in the article is to identify the characteristics, the extent of the phenomenon of granfluencers and the factors affecting its development in the world of social media, including Facebook, Instagram, YouTube, and TikTok channels. Research method - The descriptive part of the article uses an analysis of the literature and statistical sources for characterizing the behavior of seniors in social media. The second part is based on the qualitative research method, which includes the case study method. The article uses an analysis of cases of the so¬ called granfluencers in the social media space. The use of this method made it possible to explain the context of the described phenomenon and to capture the experiences and interactions of granfluencers in a natural marketing context. Results - We can expect the number of active social media users of mature age to increase, especially since demographic trends are inexorable – societies are clearly aging. This provides an opportunity to break the image of seniors who shy away from new technologies, limit themselves to TV and radio, and spend time at home in un creative ways. Originality / value / implications / recommendations - We should expect the development of the phenomenon of granfluencer marketing in popular social media. Granfluencers break all behavioral stereotypes and beauty canons associated with young age and perfect appearance. Older users of the Internet and social media are now looking for authenticity, freshness, a new approach in terms of image creation by their leaders. We should expect an increase in the interest of Polish researchers in this issue and in depth research on the phenomenon of granfluencering in Poland. It will undoubtedly become more important in the context of the observed socio¬ demographic changes, including the redefinition of the lifestyle of the older generation.