Miracle-dz/UniFAR-FTUs huggingface dataset:
A Large-scale Analysis of Hundreds of In-memory Key-value Cache Clusters at Twitter

Modern web services use in-memory caching extensively to increase throughput and reduce latency. There have been several workload analyses of production systems that have fueled research in improving the effectiveness of in-memory caching systems. However, the coverage is still sparse considering the wide spectrum of industrial cache use cases. In this work, we significantly further the understanding of real-world cache workloads by collecting production traces from 153 in-memory cache clusters at Twitter, sifting through over 80 TB of data, and sometimes interpreting the workloads in the context of the business logic behind them. We perform a comprehensive analysis to characterize cache workloads based on traffic pattern, time-to-live (TTL), popularity distribution, and size distribution. A fine-grained view of different workloads uncover the diversity of use cases: many are far more write-heavy or more skewed than previously shown and some display unique temporal patterns. We also observe that TTL is an important and sometimes defining parameter of cache working sets. Our simulations show that ideal replacement strategy in production caches can be surprising, for example, FIFO works the best for a large number of workloads.
Synthetic Data Set Generation for the Evaluation of Image Acquisition Strategies Applied to Deep Learning Based Industrial Component Inspection Systems

Automated visual inspection is an ongoing machine vision challenge for industry. Faced with increasingly demanding quality standards it is reasonable to address the transition from a manual inspection system to an automatic one using some advanced machine learning approaches such as deep learning models. However, the introduction of neural models in environments such as the manufacturing industry ﬁnd certain impairments or limitations. Indeed, due to the harsh conditions of manufacturing environments, there is usually the limitation of collecting a high quality database for training neural models. Also, the imbalance between non-defective and defective samples is very common issue in this type of scenarios. To alleviate these problems, this work proposes a pipeline to generate rendered images from CAD models of industrial components, to subsequently feed an anomaly detection model based on Deep Learning. Our approach can simulate the potential geometric and photometric transformations in which the parts could be presented to a real camera to faithfully reproduce the image acquisition behavior of an automatic inspection system. We evaluated the accuracy of several neural models trained with different synthetically generated data set simulating different transformations such as part temperature or part position and orientation with respect to a given camera. The results shows the feasibility of the proposed approach during the design and evaluation process of the image acquisition setup and to guarantee the success of the real future application.
Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa

Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evidence to inform long-term studies. To reduce the manual labelling effort by human experts and to scale to large, meaningful regions, we investigate in this study how domain adaptation techniques and deep learning can help to efficiently map land cover in Central Africa. We propose and evaluate a methodology that is based on unsupervised adaptation to reduce the cost of generating reference data for several cities and across different dates. We present the first application of domain adaptation based on fully convolutional networks for semantic segmentation of a dataset of historical panchromatic orthomosaics for land-cover generation for two focus cities Goma-Gisenyi and Bukavu. Our experimental evaluation shows that the domain adaptation methods can reach an overall accuracy between 60% and 70% for different regions. If we add a small amount of labelled data from the target domain, too, further performance gains can be achieved.
A Methodological Proposal for the Analysis of Lighting the House Building Façades

Natural lighting is a fundamental element in the habitability of dwellings. However, it is still difficult to calculate its effect on the façades of the buildings in detail, due to the morphological complexity of the property itself, as well as the environment that surrounds it. This study provides a methodological proposal that uses pre-existing open data to extrude buildings by using a GIS procedure. Based on three selected real estate properties with different characteristics in the city of Marbella (Spain), the hours of sunlight received by each building’s façade are calculated, taking into account the digital land model and the digital surface model of the area. The results confirm the usefulness of the method to measure and analyze differences in luminosity between buildings with similar urban characteristics and their surroundings, as well as to record the differences in luminosity between floors and the orientations of the same building at several heights. The methodological proposal opens a path for many applications related to energy efficiency, housing conditions, and property valuation.
A Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together

Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.
NVLSM: A Persistent Memory Key-Value Store Using Log-Structured Merge Tree with Accumulative Compaction

Computer systems utilizing byte-addressable Non-Volatile Memory ( NVM ) as memory/storage can provide low-latency data persistence. The widely used key-value stores using Log-Structured Merge Tree ( LSM-Tree ) are still beneficial for NVM systems in aspects of the space and write efficiency. However, the significant write amplification introduced by the leveled compaction of LSM-Tree degrades the write performance of the key-value store and shortens the lifetime of the NVM devices. The existing studies propose new compaction methods to reduce write amplification. Unfortunately, they result in a relatively large read amplification. In this article, we propose NVLSM, a key-value store for NVM systems using LSM-Tree with new accumulative compaction. By fully utilizing the byte-addressability of NVM, accumulative compaction uses pointers to accumulate data into multiple floors in a logically sorted run to reduce the number of compactions required. We have also proposed a cascading searching scheme for reads among the multiple floors to reduce read amplification. Therefore, NVLSM reduces write amplification with small increases in read amplification. We compare NVLSM with key-value stores using LSM-Tree with two other compaction methods: leveled compaction and fragmented compaction. Our evaluations show that NVLSM reduces write amplification by up to 67% compared with LSM-Tree using leveled compaction without significantly increasing the read amplification. In write-intensive workloads, NVLSM reduces the average latency by 15.73%–41.2% compared to other key-value stores.
Axes Bundling and Brushing in Star Coordinates

Visual analysis of multidimensional data commonly involves dimensionality reduction to project the data samples into a lower-dimensional visual space. Star coordinates (SC) provide a means to explore the multidimensional data distribution by interactively changing the linear projection matrix. While SC have the advantages of being intuitive, allowing for relating the data samples to their original dimensions, having low computation costs, and scaling well with the number of data samples, they have the disadvantages of not scaling well to larger number of dimensions and being restricted to linear projections. We address these short-comings by introducing novel SC interactions. First, interactive bundling of axes is proposed to reduce the number of dimensions. While bundles are fully customizable, the bundling interactions are supported by visualizations of correlation matrices and hierarchical axes clustering dendrograms. Second, we enhance classical region brushing in SC projections with axes brushing, which allows for multidimensional cluster selection, even if two (separable) clusters are projected to the same area of the visible space. Axes brushing is supported by visualizing 1D histograms of data distributions along the SC axes. Our brushing interactions alleviate the restriction of SC to linear projections. The integration of histograms into SC also eases other interactions such as moving axes to change the projection matrix. A user study evaluates how analysis tasks for labeled and unlabeled multidimensional data can beneﬁt from our extensions.
Modeling Patterns in Map Use Contexts and Mobile Map Design Usability

Mobile map applications are increasingly used in various aspects of our lives, leading to an increase in different map use situations and, therefore, map use contexts. Several empirical usability studies have identified how map design is associated with and impacted by selected map use context attributes. This research seeks to expand on these studies and analyzes combinations of map use contexts to identify relevant contextual factors that influence mobile map design usability. In a study with 50 participants from Colombia, we assessed in an online survey the usability of 27 map design variations (consisting of three map-reading tasks, three base map styles, and three interactivity variants). We found that the overall map design is critical in supporting map-reading activities (e.g., identifying a location on a map was supported by a simplified base map, whereas selecting points on the map was supported by a more detailed base map). We then evaluated user patterns in the collected data with archetypal analysis. It was possible to create archetypal representations of the participants with a corresponding map design profile and establish a workflow for modeling patterns in usability and context data. We recommend that future research continues assessing archetypal analysis as it provides a means for context-based decision-making on map design adaptation and transferability.
Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs' Classifiers Incorporating Artificial Neural Network Using Landsat Data

A reliable land cover (LC) map is essential for planners, as missing proper land cover maps may deviate a project. This study is focusing on land cover classification and prediction using three well known classifiers and remote sensing data. Maximum Likelihood classifier (MLC), Spectral Angle Mapper (SAM), and Support Vector Machines (SVMs) algorithms are used as the representatives for parametric, non-parametric and subpixel capable methods for change detection and change prediction of Urmia City (Iran) and its suburbs. Landsat images of 2000, 2010, and 2020 have been used to provide land cover information. The results demonstrated 0.93–0.94 overall accuracies for MLC and SVMs’ algorithms, but it was around 0.79 for the SAM algorithm. The MLC performed slightly better than SVMs’ classifier. Cellular Automata Artificial neural network method was used to predict land cover changes. Overall accuracy of MLC was higher than others at about 0.94 accuracy, although, SVMs were slightly more accurate for large area segments. Land cover maps were predicted for 2030, which demonstrate the city’s expansion from 5500 ha in 2000 to more than 9000 ha in 2030.
Qualitative and Quantitative Chemometry as Stability-Indicating Methods for Determination of Dantrolene Sodium and Paracetamol

Methods and Result: Three stability indicating chemometric methods are applied for the simultaneous determination of Dantrolene sodium (DNT) and Paracetamol (PAR). Partial Least Squares (PLS), Concentration Residuals Augmented Classical Least Squares (CRACLS) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) were selected for that purpose. DNT and PAR were determined in the linearity range of (2 – 10 g mL) and (12 – 28 g mL), respectively, in the presence of their degradation products. The presented methods were compared for their qualitative and quantitative analyses and validated according to the ICH guidelines. Furthermore, statistical comparison between the results obtained by the proposed methods and the reported chromatographic method showed no significant differences.
All‐Inorganic CsPbI3 Perovskite Phase‐Stabilized by Poly(ethylene oxide) for Red‐Light‐Emitting Diodes

Despite the excellent photoelectronic properties of the all‐inorganic cesium lead iodide (CsPbI3) perovskite, which does not contain volatile and hygroscopic organic components, only a few CsPbI3 devices are developed mainly owing to the frequent formation of an undesirable yellow δ‐phase at room temperature. Herein, it is demonstrated that a small quantity of poly(ethylene oxide) (PEO) added to the precursor solution effectively inhibits the formation of the yellow δ‐phase during film preparation, and promotes the development of a black α‐phase at a low crystallization temperature. A systematic study reveals that a thin, dense, pinhole‐free CsPbI3 film is produced in the α‐phase and is stabilized with PEO that effectively reduces the grain size during crystallization. A thin α‐phase CsPbI3 film with excellent photoluminescence is successfully employed in a light‐emitting diode with an inverted configuration of glass substrate/indium tin oxide/zinc oxide/poly(ethyleneimine)/α‐CsPbI3/poly(4‐butylphenyl‐diphenyl‐amine)/WO3/Al, yielding the characteristic red emission of the perovskite film at 695 nm with brightness, external quantum efficiency, and emission band width of ≈101 cd m−2, 1.12%, and 32 nm, respectively.
Synthesis of VO2 Nanomaterials Using Green Solvents for Thermochromic Window Coatings

Current demand for energy-saving coatings has led to the study of thermochromic smart windows using VO2 materials with optical switching properties. However, scale-up of these nanomaterials and their transition properties are poorly understood. Here, we demonstrate a simple one-step hydrothermal method to synthesize W doped VO2(M)/GO with various nanostructures such as nanotubes and graphene wrapped nanobelts, which enable tuning the phase transition temperature (Tc) to as low as 45 C in heating and 40C in cooling. Various experimental parameters including reaction temperature, reaction time, and W doping concentration are examined. Compared with reaction temperature, reaction time was found to play a key role in the synthesis process. The result shows reaction at 6.5 days, 300C with 3 at. % W doping gave a pure M phase and the lowest Tc of 42C while Tc could be tuned with different doping contents.
Arctic rain on snow events: bridging observations to understand environmental and livelihood impacts

When rain falls on an existing cover of snow, followed by low temperatures, or falls as freezing rain, it can leave a hard crust. These Arctic rain on snow (ROS) events can profoundly influence the environment and in turn, human livelihoods. Impacts can be immediate (e.g. on human travel, herding, or harvesting) or evolve or accumulate, leading to massive starvation-induced die-offs of reindeer, caribou, and musk oxen, for example. We provide here a review and synthesis of Arctic ROS events and their impacts, addressing human-environment relationships, meteorological conditions associated with ROS events, and challenges in their detection. From our assessment of the state of the science, we conclude that while (a) systematic detection of ROS events, their intensity, and trends across the Arctic region can be approached by combining data from satellite remote sensing, atmospheric reanalyses, and meteorological station records; (b) obtaining knowledge and information most germane to impacts, such as the thickness of ice layers, how ice layers form within a snowpack, and antecedent conditions that can amplify impacts, necessitates collaboration and knowledge co-production with community members and indigenous knowledge-holders.
Tailoring hot carrier cooling and recombination dynamics of mixed halide perovskite by incorporating Au@CZTS core–shell nanocrystal

Organic–inorganic halide perovskite has emerged as the front-runner of absorber materials for highly efficient solar cell in recent years. The incorporation of metallic (Au, Ag) nanoparticles (NPs) within the perovskite contributes to the effective tuning of their optoelectronic properties via enhancing the channels of solar energy transfer and promoting carrier transport. Placing a dielectric shell over the metal NP further enhances the carrier mobility and reduces the carrier recombination in the semiconductor material. Here, we have extensively investigated the effect of the Au@CZTS core–shell nanocrystal (NC) on hot carrier (HC) cooling dynamics and excited carrier recombination dynamics in bulk MAPbI3−X Cl X perovskite using femtosecond transient absorption spectroscopy with a temporal and spectral resolution of 120 fs and 0.8 nm respectively. The HC cooling dynamics indicates the formation of longitudinal optical (LO) phonons within the first 0.6 ps and a delayed conversion of LO phonons to longitudinal acoustic (LA) phonons from 8 ps to 15.9 ps due to the incorporation of the Au@CZTS core–shell NC in bulk perovskite. Further, the investigation of carrier recombination dynamics shows that at a fixed pump fluence of 3.19 μJ cm −2 the rate constants decrease nearly 1 order of magnitude for (a) Auger recombination (from 1.2 × 10−32 cm6 s−1 to 1.7 × 10−34 cm6 s−1), (b) band-to-band recombination (from 8 × 10−14 cm3 s−1 to 8 × 10−15 cm3 s−1) and (c) trap state recombination (from 5.5 × 108 μs−1 to 5 × 107 μs−1) after the modification of bulk perovskite by Au@CZTS core–shell NC. Delayed conversion of LO phonons to LA phonons confirms the presence of an enhanced ‘hot phonon bottleneck’ effect in modified bulk perovskite. Lowering of the recombination rate constants provides an opportunity for developing high-performance perovskite-based photovoltaics.
Development of a system for operational monitoring of the soil agrochemical indicators

Today digitalization is being introduced into all spheres of human activity, including rural areas. One of the tasks of agricultural organizations is to control the state of soils and monitor crops at all stages of the production cycle. For this, chemical or instrumental methods are used, which have low mobility and high labor intensity of taking soil samples, analyzing them and processing the results. The paper presents a description of the system, which includes three modules. The first module is peripheral equipment, combined into a sensor unit that measures soil indicators and collects data from the measuring elements. The second module represents the communication channels that include the infrastructure of the Internet of Things, giving the possibility to the sensor nodes to be organized into a sensor network providing data collection and wireless transmission. The third module is an information system that provides reception, recording and analysis of data transmitted from sensors. According to these approaches, control over the application of mineral and organic fertilizers, pesticides and herbicides is increased. They are applied only in areas where it is required and in precisely calculated doses, excluding oversaturation with harmful substances and a decrease in anthropogenic impact on the environment.
Higher order stroboscopic averaged functions: a general relationship
 with Melnikov functions

In the research literature, one can find distinct notions for higher order averaged functions of regularly perturbed non-autonomous T-periodic differential equations of the kind x′=ε F(t,x,ε ). By one hand, the classical (stroboscopic) averaging method provides asymptotic estimates for its solutions in terms of some uniquely defined functions gi's, called averaged functions, which are obtained through near-identity stroboscopic transformations and by solving homological equations. On the other hand, a Melnikov procedure is employed to obtain bifurcation functions fi's which controls in some sense the existence of isolated T-periodic solutions of the differential equation above. In the research literature, the bifurcation functions fi's are sometimes likewise called averaged functions, nevertheless, they also receive the name of Poincaré–Pontryagin–Melnikov functions or just Melnikov functions. While it is known that f1=Tg1, a general relationship between gi and fi is not known so far for i≥ 2. Here, such a general relationship between these two distinct notions of averaged functions is provided, which allows the computation of the stroboscopic averaged functions of any order avoiding the necessity of dealing with near-identity transformations and homological equations. In addition, an Appendix is provided with implemented Mathematica algorithms for computing both higher order averaging functions.
On the Latent Holes of VAEs for Text Generation

In this paper, we provide the first focused study on the discontinuities (aka. holes) in the latent space of Variational Auto-Encoders (VAEs), a phenomenon which has been shown to have a detrimental effect on model capacity. When investigating latent holes, existing works are exclusively centred around the encoder network and they merely explore the existence of holes. We tackle these limitations by proposing a highly efficient Tree-based Decoder-Centric (TDC) algorithm for latent hole identification, with a focal point on the text domain. In contrast to past studies, our approach pays attention to the decoder network, as a decoder has a direct impact on the model's output quality. Furthermore, we provide, for the first time, in-depth empirical analysis of the latent hole phenomenon, investigating several important aspects such as how the holes impact VAE algorithms' performance on text generation, and how the holes are distributed in the latent space.