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Shein Discount Coupon : Dial Customer Service for more details
<p>3x Author | Viral Writer | Technician| Solve Your shopping site related problems</p> <p>However, <strong><a href="https://www.setupyouraccounts.com/shein-customer-service/">Shein Customer Service</a></strong> works with pristine business models. Regardless of this, the plank maintain dynamic methodology to the fulfillment and comfort of the guests. It essentially signifies each guest never adapt on terms that are unsuitable. Occupied and time cognizant individuals feel profoundly happy to know our mentally supportive system never stop seven amid the dull hours. Because of nonstop accessibility, they can contact assign any-time of time. This accommodation makes them concentrate more on significant industry objectives and appreciate ideal dimension of solace.<br> </p>
Data set published in the IEEE TCAD article "Custom Multi-Cache Architectures for Heap-Manipulating Programs"
<p>This data set contains the results presented in the paper "Custom Multi-Cache Architectures for Heap-Manipulating Programs", published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) in 2016.</p> <p>The data set consists of two parts, a Microsoft Excel file ('FPGA_implementation_results.xlsx') and a Matlab script ('plot_cache_performance.m', in combination with measurement results in an ascii file).</p> <p>The Excel file contains<br /> - the FPGA resource utilisation,<br /> - execution time measurements,<br /> - hit rate measurement of the multi-cache system,<br /> - and power measurements</p> <p>of different FPGA designs with different on-chip cache configurations. The resource utilisation is split into FPGA slices, LUTs, FlipFlops, DSP slices and block RAMs. Results in this file can be found in Table I-IV in the paper. Please refer to the paper for more information or email f.winterstein12@imperial.ac.uk.</p> <p>The Matlab script loads a data file ('cache_performance_N16384_L1') containing the hit rate measurements for different cache sizes of two direct-mapped cache with 64bit line width. The script produces a 3D 'skyscraper' plot, i.e. a grid of coloured bars. Each bar corresponds to the hit rate measured at the particular cache size configuration. The plot is saved in the file 'surf.pdf'. The script was used to produce Figure 4 of the paper. Please refer to the paper for more information or email f.winterstein12@imperial.ac.uk.</p> <p>In addition to this description, we include an author copy of the paper. Note that this is not the official version of the paper. Please cite the original IEEE TCAD article if you use the data.</p>
Custom script for feature extraction from Genbank files
<p><span>Microbes are thought to be distributed and circulated around the world, but the connection between marine and terrestrial microbiomes is largely unknown. We use <em>Plantibacter</em>, a representative plant-associated genus, as our research model to show the global distribution and adaptation of plant-related bacteria in plant-free environments, especially in the remote Southern Ocean and the deep Atlantic Ocean. The marine isolates and their plant-associated relatives shared over 98% whole-genome average nucleotide identity (ANI), indicating recent divergence and ongoing speciation from plant-related niches to marine environments. Comparative genomics revealed that the marine strains acquired new genes via horizontal gene transfer from non-<em>Plantibacter</em> species and refined existing genes through positive selection to improve adaptation to new habitats. Meanwhile, marine strains retained the ability to interact with plants, such as modifying root system architecture and promoting germination. <em>Plantibacter </em>species were further found to be widely distributed in marine environments, revealing an unrecognized phenomenon that plant-associated microbiomes have colonized the ocean, which could serve as a reservoir for plant growth-promoting microbes. This study demonstrates the presence of an active reservoir of terrestrial plant growth-promoting bacteria in remote marine systems and advances our understanding of the microbial connections between plant-associated and plant-free environments at the genome level.</span></p>
Custom codes in the potato NLRome paper
<p>All custom scripts used in this study are available at GitHub (<a href="https://github.com/HongboDoll/PotatoPanNLRome">https://github.com/HongboDoll/PotatoNLRome</a>)</p>
ExpertSegmentation: Sample datasets for segmentation of microscopy with domain-informed targets via custom loss
<p>Sample datasets for submission to NeurIPS 2024 titled ExpertSegmentation: Segmentation for microscopy with domain-informed targets via custom loss. Hand-labels were generated using Ilastik. Files include:</p> <ol> <li><strong>NMC_3D</strong>: 3D, 2-phase, microCT image of a Lithium-ion battery LiNiMnCoO2 electrode</li> <li><strong>NMC_3D_Labels: </strong>Hand labels for NMC_3D volume.</li> <li><strong>NMC_2D</strong>: 2D, 4-phase, SEM image of a Lithium-ion battery LiNiMnCoO2 electrode cross-section</li> <li><strong>NMC_2D_Labels: </strong>Hand labels for NMC_2D image.</li> <li><strong>Graphite_3D</strong>: 3D, 3-phase, microCT image of a Laser-ablated lithium-ion battery graphite electrode</li> <li><strong>Graphite_3D_Labels: </strong>Hand labels for Graphite_3D volume.</li> <li><strong>PEMFC_3D</strong>: 3D, 5-phase, microCT image of a Polymer Electrolyte Membrane Fuel Cell (PEMFC)</li> <li><strong>PEMFC_3D_Labels: </strong>Hand labels for PEMFC_3D volume.</li> </ol> <p>See references for dataset sources.</p>
Data and custom codes from "Rapid evolution in salmon life-history induced by direct and indirect effects of fishing"
<p>Data and custom codes from Czorlich, Y., Aykanat, T., Erkinaro, J., Orell, P. & Primmer, C.R. (2021) <em>Rapid evolution in salmon life-history induced by direct and indirect effects of fishing. </em>Science.</p> <p><strong>Codes:</strong></p> <p>The R file "Fishing_effort_parallel.R" was used to estimate fishing effort/intensity (beta in equation 8) given the length distribution, the gear-specific catchability and harvest rate</p> <p>"Fishing_selection_estimate.R" was used to estimate fishery-induced selection at <em>vgll3.</em></p> <p><strong>Datasets:</strong></p> <p>Genetic_phenotypic_data.xlsx: Genetic and phenotypic data about salmon from the Teno mainstem population</p> <p>sonar_data.xlsx: Number of salmon per length class entering the river in 2018 and 2019. The length classes of salmon caught in those years by one of the fishing methods are also included</p> <p>annual_catch_data.xlsx: Total mass (kg) of salmon caught by each fishing method between 1975 to 2014.</p> <p>Environmental_data.xlsx: Data about Barents Sea temperature, biomass of key species, fishing data</p> <p>individual_weight_salmon_catches.xlsx: Individual weight of salmon caught with different fishing gears in the last decades</p> <p><strong>Data sources:</strong></p> <p>- Genetic data (Tenojoki population, random sampling): From Czorlich et al. 2018, https://datadryad.org/stash/dataset/doi:10.5061/dryad.7hm4708</p> <p>- Data about krill biomass (1980 – 2013) were taken from (<em>1</em>, <em>2</em>).</p> <p>- Capelin biomass estimated from acoustic survey and the landed capelin catches were derived from (<em>3</em>) for 1973 – 2013.</p> <p>- Herring biomass data were retrieved from (<em>4</em>) for the 1973-1998 period. Herring biomass was calculated from the number of 1-2 year old herring and the mean weight per age as reported in (<em>3</em>) for 1988 – 2013.</p> <p>- The annual biomass of cod (a predator of forage fish) was derived from VPA analyses ((<em>5</em>), table 3.24). Landed cod biomass was also taken from (<em>5</em>).</p> <p>- An index for mesozooplankton (a forage fish food source) corresponding to the sum of <em>Calanus</em> biomass indices from different parts of the Barents Sea was used (<em>6</em>).</p> <p>- The annual sea temperature in the Kola section of the Barents Sea measured in the upper 200 meters was from <a href="http://www.pinro.vniro.ru/">pinro.vniro.ru</a></p> <p>- The total number of nets used to catch salmon in the Finnmark coastal region was calculated for each year using data from (7)</p> <p>- Other data were generated for this study, please check the Material and Methods. </p> <p><em>References:</em></p> <p>1. E. Eriksen, P. Dalpadado, Long-term changes in Krill biomass and distribution in the Barents Sea: Are the changes mainly related to capelin stock size and temperature conditions? <em>Polar Biology</em>. <strong>34</strong>, 1399–1409 (2011).</p> <p>2. ICES, “Report of the Working Group on the Integrated Assessments of the Barents Sea. ICES CM 2017/SSGIEA:04. 186 pp.” (2017).</p> <p>3. ICES, “Report of the Arctic Fisheries Working Group (AFWG). ICES CM 2015/ACOM:05. 639 pp.” (2015).</p> <p>4. R. Toresen, O. J. Østvedt, Variation in abundance of Norwegian spring-spawning herring (Clupea harengus, Clupeidae) throughout the 20th century and the influence of climatic fluctuations. <em>Fish and Fisheries</em>. <strong>85</strong>, 385–391 (2000).</p> <p>5. ICES, “Report of the Arctic Fisheries Working Group (AFWG). ICES CM 2016/ACOM:06. 621 pp.” (2016).</p> <p>6. L. C. Stige et al., Spatiotemporal statistical analyses reveal predator-driven zooplankton fluctuations in the Barents Sea. <em>Progress in Oceanography</em>. <strong>120</strong>, 243–253 (2014).</p> <p>7. E. Niemelä, T. Kalske, E. Hassinen, “Numbers of fishing gears used in Kolarctic salmon project area, numbers of allowed sites for salmon fishing and numbers of salmon fishermen in Finnmark; development until the year 2013” (2013).</p>
Types of Custom Boxes For Bath Bombs
<p><strong>There are many types of bath bomb boxes available, which are given below:</strong></p> <p><strong>Boxes By Style</strong></p> <p><em><strong>1. Boxes With Transparent Window</strong></em></p> <p>Some <a href="https://www.thecosmeticboxes.com/product/bath-bomb-boxes/"><strong>Boxes For Bath Bombs</strong></a> feature a transparent window, allowing customers to check the product before buying it. </p> <p><em><strong>2. Boxes With Colored Ribbons</strong></em></p> <p>Others have colored ribbons to enhance the presentation of Bath Bombs Boxes Wholesale. <br> Regardless of the type of box you choose, it must meet your brand guidelines. These tips will help you create the most appealing packaging for your bath bombs. </p> <p><strong>Examples Of Bath Bomb Boxes</strong></p> <ul> <li><em><strong>Custom Printed Boxes For Bath Bombs</strong></em></li> </ul> <p><a href="https://www.thecosmeticboxes.com/product/bath-bomb-boxes/"><strong>Custom Bath Bomb Boxes</strong></a> for bath bombs are an excellent way to show off your products. They're durable and attractive, so customers will want to keep them around. You can add window panes and even your company's logo or product-related information to these boxes. </p> <p><strong>Show Your Brand UP</strong></p> <p>A unique box can give your brand a distinct advantage over your competition. While a typical bath bomb box can be purchased anywhere, a unique and beautiful box can make your bath bombs stand out among the crowd.</p> <p>Whether your product is a luxury item or an essential item, a beautiful box will help your brand shine. Mailer bath bomb boxes are an environmentally-friendly choice, and they're versatile and easy to tailor. </p> <p><a href="https://www.thecosmeticboxes.com/product/bath-bomb-boxes/"><strong>Eco Friendly Bath Bomb Packaging</strong></a> is ideal for products that don't need external packaging and need to stay dry for transport. They're also lightweight and cheap to ship compared to other types of packaging. Order free samples and try out different designs to make your bath bombs more visually appealing.</p>
Custom language model checkpoints used in "Testing the limits of natural language models for predicting human language judgments"
<p>Checkpoint files for an RNN, LSTM, BILSTM and n-gram models used the paper "Testing the limits of natural language models for predicting human language judgments"</p>
See no evil in the voice-to-voice customer service context
<p>A sample of more than 28,000 front-line employee (FLE) - customer interactions, extrapolating from foundational framing, we pit conventional service approaches against one another to propose a dual-process model, situating customer frustration/satisfaction as mediators of the indirect relationships between resolution/relational tactics and call duration – a key customer service efficiency outcome.</p>
Genome of the isolates: Enhanced cultured diversity of the mouse gut microbiota enables custom-made synthetic communities
<p>The draft genome of the isolates in Mouse Intestinal Bacteria Collection (miBC)</p> <p> </p> <p>Microbiome research is hampered by the fact that many bacteria are still unknown and by the lack of publicly available isolates. Fundamental and clinical research is in need of comprehensive and well-curated repositories of cultured bacteria from the intestine of mammalian hosts. Due to host-specific features of the gut microbiota, it is sound to establish collections of isolates from single host species. Hence, this project established a collection of bacterial strains isolated from the intestine of mice.</p> <p>The original version of the collection published in 2016 (Lagkouvardos, et. al. 2016.<em> Nat. Microbiol.</em>). was doubled by the addition of 112 strains, representing a total of 141 species across 6 phyla and 35 families for the entire collection. As we aimed to create a well-curated resource, all bacterial species within miBC have been taxonomically described and are publicly available.</p> <p> </p>
Data and code for article "Nature reserve customized method of photo and video camera traps materials processing using two-stage neural network approach"
<p><strong>DESCRIPTION</strong> 📓</p> <p>"data" folder directory contains the datasets for classification and detection. </p> <ol> <li>The detection dataset has <strong>YOLOv5 format</strong> and contains three classes <strong>[tigers, leopards, empty]</strong>. The class empty is about <strong>10%</strong> of the total data. The leopard and tiger classes contain <strong>3500</strong> images each. The entire amount of data for the detection task is <strong>7600</strong> images.</li> <li>The classification dataset contains two classes <strong>[tigers, leopards]</strong>. Images for classification are cropped images from the detection task using bounding boxes. Each class has <strong>3500</strong> images</li> </ol> <p> </p> <p>The "weights" folder contains pretrained models for classification and detection tasks. </p> <ul> <li>The detector weights were pre-trained on <strong>231k</strong> images from camera traps located throughout Russia.</li> <li>The classifier weights were pre-trained on <strong>416k</strong> images that were cropped with <strong>bounding boxes</strong> from photographs for the detection task. Some of the images for the classification task were taken from the <strong>Internet</strong>. The classifiers were trained for <strong>29 classes</strong>.</li> <li>You can also find folder <strong>tigers_vs_leopards</strong> in both the detection and classification directory, where there are weights that have been trained on a part of the camera trap images available at the link below.</li> </ul> <p><em>Classification weights</em></p> <ol> <li>EfficientNetv2-M</li> <li><strong>ResNeSt-101e</strong> (🚀 RECOMMENDED)</li> <li>ResNet-101d</li> <li>ReXnet-100</li> <li>SeResNet-152d</li> </ol> <p><em>Detection weights</em></p> <ol> <li>YOLOR-W6-1280</li> <li>YOLOX-X-640</li> <li>YOLOv5-X-640</li> <li>YOLOv5-X-1280</li> <li>YOLOv5-M6-1280</li> <li><strong>YOLOv5-L6-1280</strong> (🚀 RECOMMENDED)</li> </ol> <p>Read README.md file for more details</p>
DATA: Chatbots in Airport Customer Service – Exploring Use Cases and Technology Acceptance
<p>This dataset (n=191) investigates use cases and technology acceptance of chatbots in airport customer service.</p>
Figure 1. CNN architecture (adopted from Krizhevsky et al. '12)-Measuring Customer Behavior with Deep Convolutional Neural Networks
<p>The architecture of a CNN can be described as following. A small pixel region goes to input neurons and then connects to a first convolution hidden layer (Figure1). There we can see a set of learnable filters, which are activated during the presentation some particular type of feature in pixel region in the input. On this phase, CNN does shift invariance, which is carried by feature map. Subsampling layer goes next. There we have two processes: local averaging and sampling. As a result, we get declining resolution of feature map. To correspond this task CNN needs supervised learning. Before starting the experiment, we gave a set of labeled videos with different emotional experience. The system analyses images and finds similar features. Then the system creates a map, where it arranges videos in accordance with similar features. Thereby, images with similar emotions form certain class. To test the system, we add other videos and correct the system when it refers them improperly. The proposed model consists of four convolutional layers, followed by max-pooling layers, and three fully-connected layers with a final classificatory presented with MLP (with six basic outputs, corresponding to basic emotions for emotion classification and two outputs for motion classification for typical and non-typical behavior). The input data was presented as infrared camera output.</p>
Mechanical drawings and calibration code for a custom built staircase to patient biomechanics
<p>This repository accompanies a preprint (doi: , and hopefully one day a peer-reviewed manuscript) that describes the steps we took to design, fabricated, calibrate and validate a costume built staircase that we then integrated into a clinical gait analysis test session. This repository has 4 sub-directories that each provide specific information neccesary to recreate our staircase or build your own.</p> <p>1. matlab.zip - this archived folder has 3 subfolders. i) calibrate_validate, ii) transfer_mocap_2_osim, and iii) batch_osim. This archived folder also has a readme. please read it. I cannot provide any warranty on this software but am happy to help reasonable requests. My contact info is in the readme. Also - check out my github < github.com/joshrbaxter > for updated code</p> <p>2. stairs_drawings.zip - this archived folder has the bill of materials, mechanical drawings, and a cute 3D PDF.</p> <p>3. trc_forces.zip - this folder has the motion capture data exported from Motion Analysis's Cortex software (version 7). </p> <p>4. osim.zip - this folder contains all of the files required to run the opensim model along with all of the output files from inverse dynamics and inverse kinematics. these files were all generated from matlab routines provided in the matlab.zip folder.</p> <p> </p>
MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY
<p><span>This study investigates the effectiveness of various machine learning models in predicting product demand based on customer satisfaction data. Four models—Linear Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM)—were evaluated using performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score. The results indicate that Gradient Boosting achieved the highest accuracy, with an MAE of 2.56, MSE of 12.75, RMSE of 3.57, and R² score of 0.82, effectively capturing the complex, non-linear relationships inherent in customer satisfaction factors. Random Forest also demonstrated strong performance, while Linear Regression and SVM showed limitations in handling intricate datasets. These findings underscore the importance of utilizing advanced machine learning techniques for accurate demand forecasting, highlighting the critical role of customer satisfaction data in enhancing predictive capabilities. The insights gained from this research can guide organizations in optimizing inventory management and improving customer satisfaction in a rapidly evolving market.</span></p>
SENTIMENT ANALYSIS OF CUSTOMER FEEDBACK IN THE BANKING SECTOR: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS
<p><span>This study investigates the application of sentiment analysis to customer feedback in the banking sector, utilizing natural language processing (NLP) techniques and machine learning models to classify customer sentiments into positive, neutral, and negative categories. Feedback was sourced from online platforms, including bank websites, social media, and third-party review sites. Data preprocessing steps, such as tokenization, stemming, and feature extraction using TF-IDF, were employed to prepare the text for analysis. Various machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Naïve Bayes, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that LSTM outperformed all models with a 91% accuracy, followed closely by SVM at 89%. These findings demonstrate the potential of advanced machine learning techniques in accurately classifying sentiments and provide valuable insights into customer satisfaction and areas for improvement within the banking sector. Future work aims to further optimize models for better classification of neutral feedback and explore more advanced deep learning models, such as BERT.</span></p>
TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING
<p>In recent years, the fintech industry has experienced rapid growth, driven by technological advancements and evolving consumer expectations. Fintech companies offer innovative financial services, such as digital banking, investment platforms, and payment solutions, catering to the needs of a tech-savvy customer base. However, as competition intensifies, customer retention has emerged as a critical challenge for these companies. According to a study by Ransom (2021), acquiring a new customer can cost five times more than retaining an existing one, making it imperative for fintech organizations to focus on strategies that enhance customer loyalty. The financial technology (fintech) sector has experienced unprecedented growth in recent years, fundamentally transforming how individuals and businesses access and manage financial services. Characterized by the integration of technology with financial services, fintech encompasses a wide array of offerings, including digital banking, peer-to-peer lending, robo-advisory services, and payment processing. As of 2023, the global fintech market was valued at approximately $309 billion and is projected to reach around $1.5 trillion by 2030, according to a report by Fortune Business Insights. This remarkable growth is largely attributed to advancements in digital technology, increasing smartphone penetration, and a growing consumer preference for online financial solutions. Moreover, the COVID-19 pandemic accelerated the adoption of digital financial services, as consumers sought contactless transactions and remote banking options.</p>
Dataset Literature Review Online Business AND Customers
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "Online Business" dan "Customers"</p>
Data set from Fischertechnik Smart Factory Model at University of St.Gallen (Custom Python Configuration)
<p>This is about 60 mins worth of data collected from Fischertechnik Industry 9.0V smart factory model available at the University of St.Gallen.</p> <p>In this data set, we used a custom Python-based software stack to control the smart factory via a business process system (Camunda Platform) that calls the functionality of the smart factory via web services implemented in Python flask. MQTT is used to collect the data.</p> <p>Each entry in the file (low-level_log_20230206-140808.txt) corresponds to one message (as JSON object) received on a specific topic via MQTT. Each line contains all the readings of all the sensors, actuators and additional data from <strong>one </strong>CPS component (i.e., production station) at <strong>one </strong>point in time.</p> <p>The data set contains the following files</p> <ul> <li>low-level_log_20230206-140808.txt: low-level IoT data from all the sensors and actuators <ul> <li>*.bpmn: executable BPMN 2.0 models of three different processes that have been executed several times via the Camunda Platform BPM system to control the smart factory</li> </ul> </li> <li>camunda_process-instance.json: event log generated by the BPM system regarding the process instance execution</li> <li>camunda_activity-instance.json: event log generated by the BPM system regarding the activity instance execution</li> </ul> <p>Check the following publications to learn more about our research using the model factory:</p> <p>Malburg, L., Seiger, R., Bergmann, R., & Weber, B. (2020). Using physical factory simulation models for business process management research. In <em>Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13–18, 2020, Revised Selected Papers 18</em> (pp. 95-107). Springer International Publishing.</p> <p>Seiger, R., Zerbato, F., Burattin, A., García-Bañuelos, L., & Weber, B. (2020, October). Towards iot-driven process event log generation for conformance checking in smart factories. In <em>2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)</em> (pp. 20-26). IEEE.</p> <p>Seiger, R., Malburg, L., Weber, B., & Bergmann, R. (2022). Integrating process management and event processing in smart factories: A systems architecture and use cases. <em>Journal of Manufacturing Systems</em>, <em>63</em>, 575-592.</p>
Custom made python script using network assignment and scoring to estimate the impact of biological processes.
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.