At CEATEC, I experienced the innovative Fujitsu interactive basketball space enabled by Fujitsu’s Imaginative and Prescriptive AI x Sports technology that utilizes no markers or wearable sensors.
Following my observation period, I received insights from Shota Goto, the coach of Pink Wave’s crew, informed by Human Movement Analytics expertise, aimed at refining my second shot performance.
In a unique collaboration, a Noh teacher takes to the Fujitsu stage, as the innovative Kozuchi AI platform utilises Human Movement Analytics to display his every move in meticulous detail, accurately visualising skeleton positions with unparalleled precision.
Filed in . Discover the fascinating intricacies of machine learning algorithms, including decision trees, random forests, support vector machines, k-nearest neighbors, and neural networks.
Corporations are scrambling to find financial efficiencies in the wake of zero-interest charges, but one area remains a significant drain on their bottom line: Observability often becomes the second-largest cloud spend for companies, eclipsed only by cloud provisioning costs themselves, as organizations struggle to amass and make sense of vast amounts of data and techniques in their digital estates. The cryptocurrency market’s volatility has sparked intense debate, with some going so far as to claim that its underlying dynamics are underscored by stories such as Coinbase’s reported $40 million loss due to a single Bitcoin price fluctuation.
What makes observability truly invaluable is its role in facilitating proactive troubleshooting. As complex cloud infrastructures and microservices persist, ensuring seamless operations becomes increasingly crucial; accordingly, IT teams require robust observability insights to proactively mitigate security vulnerabilities and minimize downtime risks.
A new startup aims to revolutionize the fee management landscape by offering an innovative solution that simplifies the process of searching for and paying healthcare service providers.
Dash0, pronounced “Sprint-zero”, is a competitor to Datadog that eschews the conventional focus on significantly lowering observability costs in its sales pitch. Despite founder Mirko Novakovic’s departure from the company, he still advocates that corporations should allocate a significant portion of their cloud computing budget – roughly 10% to 20% – towards financial services. To foster trust, they must improve transparency, firstly through transparent pricing and secondly by increasing the observability of their processes.
Dash0 might leverage OpenTelemetry’s open supply framework to achieve this by means of its construction, Novakovic told TechCrunch, explaining that OTel features a characteristic allowing anyone at any given time to see exactly which service, developer or software creates how much value on the observability side.
While some comparable companies label themselves as OTel-native, Dash0’s distinct value proposition has struck a chord with customers seeking innovative solutions. The startup secured $9.5 million in seed funding, spearheaded by Accel, alongside Dig Ventures, the venture arm of MuleSoft’s founder Ross Mason.
The Novakovic team’s detailed monitor file could have provided significant support. The company’s precursor, which also received backing from Accel, was reportedly acquired at the end of 2020 for $500 million – a transaction value previously kept confidential. Several former Instana employees have joined the Dash0 team.
When built on OpenTelemetry (OTel), Dash0 aims to further augment its capabilities. The framework has been around since 2019, but it’s not yet user-friendly, according to Novakovic. Distributors require minimal effort, akin to installing a Datadog agent, to guarantee the setup is as seamless as possible. That’s still the place where we’re struggling to catch up with our own potential.
Dash0 aims to harness the benefits of OTel’s vendor-agnostic standardized data, coupling it with a user-friendly interface, customizable dashboards, and seamless integrations with popular collaboration tools like Slack, email, and other instruments. The preliminary goal prospects are mid-sized corporations with a workforce of 50 to 5,000 employees.
The corporation is now venturing into public launch mode, but it won’t commit significant resources to sales and marketing efforts until it’s confident of achieving product-market fit. Meanwhile, Novakovic said that in the interim, her company’s resources would be allocated to enhance the technology and product aspects of its team, comprising 21 members, including 19 engineers, who work remotely.
The company’s future 10 recruits will include a developer relations specialist capable of fostering open-source innovation, potentially leading the charge in promoting OpenTelemetry as a robust alternative to proprietary solutions. The corporation plans to collaborate with various open-source telecommunications (OTel)-related startups while ensuring that “gaps” in dashboards and query languages are addressed through initiatives such as Perses and PromQL. “The success of this project is truly a collaborative achievement, with all parties involved working together seamlessly,”
The company has officially launched its flagship model, and we are now informed that a new, highly capable smartphone from vivo is nearing completion. According to reports from Digital Chat Station, the highly anticipated iQOO Neo10 Professional is poised to make its arrival shortly, with rumors suggesting that it will be fueled by a powerful Dimensity 9400 processor.
The leaked information also disclosed a significantly larger battery that supports accelerated charging capabilities and features a cutting-edge camera sensor on the rear, specifically designed for the primary shooter.
The Neo10 Pro will feature a 6.78-inch display with a resolution of 1.5K (tentatively equivalent to 1260 pixels) in China. The forthcoming smartphone is likely to feature an innovative 8T LTPO OLED display manufactured by BOE. The MediaTek chipset allegedly features 16GB of RAM and may be accompanied by 512GB of storage, with various memory configurations also expected.
Given that the iQOO Neo series prioritizes raw power over camera capabilities, vivo tends to allocate fewer resources towards developing advanced camera features. The newly launched system features a dual-camera setup, boasting a unique configuration that combines 50 megapixels from a 1/1.56-inch sensor and another 50 megapixels in a single entity.
While some may argue that the lack of curvature on four sides of the cellphone is inconsequential, the leakster’s suggestion that the device could feature a plastic body raises more significant questions. The panel below will likely feature an advanced ultrasonic fingerprint scanner from Goodix, a significant upgrade from the previous optical scanner found in the iQOO Neo9 Pro.
The advertised capacity of the battery is a vague “6×00” mAh, leaving room for interpretation: it could potentially range from approximately 6000 to 6900 milliampere-hours. The device’s value notwithstanding, this upgrade promises to be substantial, especially with the added benefit of 120W rapid charging capabilities.
The former head of Meta’s augmented reality glasses initiatives announced on Monday that she is joining OpenAI to lead robotics and customer hardware, according to reports. TechCrunch reports that OpenAI has revealed to the publication that journalist Caitlin Kalinowski is set to join the artificial intelligence startup’s team.
Kalinowski leads the government hardware team that spearheaded the development of Meta’s AR glasses project since March 2022. Meta recently confirmed the launch of Orion, a groundbreaking virtual reality experience showcased at its annual Join event, where it was unveiled to great fanfare by her team. Kalinowski spearheaded the hardware team responsible for developing Meta’s digital reality goggles for approximately nine years. Prior to that, she worked at Apple, responsible for designing the hardware for MacBook products.
Kalinowski announced with enthusiasm her decision to join OpenAI, where she will leverage her expertise to shape the development of robotics and hardware solutions for clients. “As my new role unfolds, I will focus on harnessing OpenAI’s robotics expertise and strategic partnerships to successfully integrate AI into the physical realm, unlocking its transformative potential and benefits for human society.”
Kalinowski is likely to collaborate with her former superior, Jony Ive, co-founder of LoveFrom, the startup he established after departing from Apple, on a novel AI hardware device being developed jointly by OpenAI and LoveFrom. In September, I’ve described this product as a revolutionary technology that harnesses the power of artificial intelligence to develop a computing expertise that is significantly less socially disruptive than the game-changing iPhone.
OpenAI has recently launched an initiative focused on helping its partners integrate its multimodal AI technology into their hardware. OpenAI’s robotics initiative has been revived approximately four years after the startup discontinued its hardware development to concentrate exclusively on software. By 2018, researchers aimed to develop a robotic system capable of grasping and manipulating objects independently.
Several corporations have already integrated OpenAI’s innovations into their hardware. Apple’s latest offering is expected to launch later this year, with the obvious choice being a prominent player in the tech industry. One notable example is the robotics company, Determine, which specializes in natural language processing and enables pure spoken conversations.
A novel marketing campaign targets npm builders by creating numerous typosquatted variations of well-known packages, designed to deceive developers into installing cross-platform malicious software.
The attack stands out for utilizing Ethereum smart contracts as a command-and-control (C2) server for distribution, following revelations from multiple sources, including,,,, and, published over the preceding days.
The exercise was initially flagged on October 31, 2024, despite being in progress for at least seven days beforehand. At least 287 typosquats packages have been registered in the npm package deal registry.
“As the marketing campaign began to take shape, it became evident that an attacker was in the early stages of a typosquatting effort targeting developers seeking to utilize popular tools like Puppeteer, Bignum.js, and various cryptocurrency libraries,” Phylum said.
The packages consist of obfuscated JavaScript code that is executed during the setup process, ultimately leading to the retrieval of a subsequent-stage binary from a remote server, dependent on the operating system used.
The malicious binary, having infiltrated a device for half of its duration, exhibits persistence by maintaining a foothold on the compromised system, then secretly exfiltrates sensitive information related to the affected machine back to the same server.
In a surprising yet intriguing development, the JavaScript code seamlessly communicates with an Ethereum smart contract via the ethers.js library to retrieve the IP address. Here: It’s crucial to note that a marketing campaign, dubbed “Operation Lemonade,” leveraged the same tactic by deploying Binance’s BSC smart contracts to execute the next phase of the attack chain effectively.
As the blockchain’s decentralised architecture makes it challenging to contain a malicious campaign, the perpetrator can dynamically update the contract-served IP addresses in real-time, ensuring the malware effortlessly connects to newly available IP addresses as older ones become inaccessible due to blocking or takedowns?
“According to Checkmarx researcher Yehuda Gelb, attackers gain two significant advantages by leveraging the blockchain: their infrastructure becomes virtually impervious to takedown due to the blockchain’s immutable nature, and the decentralized architecture renders it extremely challenging to block these communications.”
While the identity of those driving the marketing campaign remains unclear, an intriguing discovery by the Socket Risk Analysis Team has shed some light on the matter: error messages written in Russian were found to be embedded in the code for exception handling and logging functions, hinting that the risk actor may possess a working knowledge of the Russian language.
The latest incident underscores the alarming frequency with which malicious actors exploit vulnerabilities in the open-source community, underscoring the imperative for developers to remain acutely aware of potential threats when accessing software packages from public repositories.
“The deployment of blockchain technology in C2 infrastructure enables novel methods of perpetuating chain attacks within the npm ecosystem, rendering the attack infrastructure more resistant to takedown attempts and obscuring detection capabilities,” Gelb noted.
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In January 2024, a severe weather event ravaged large swaths of Brazil, with the southern and northeastern regions bearing the brunt of the impact. Heavy rainfall caused widespread flooding, leaving a trail of destruction in its wake and claiming lives. As local weather patterns shift in response to global climate changes, the likelihood of extreme events like devastating droughts and catastrophic floods is expected to rise, making it crucial to develop proactive contingency plans and conduct thorough risk assessments to mitigate the impact of these occurrences.
A Python and Jupyter-based pocketbook workflow facilitates flood danger assessments in rural and small-town settings within a northeastern Brazilian state, focusing on effective emergency planning and response strategies. The process commences by leveraging a digital elevation model (DEM), ultimately culminating in a visualization of flood risk assessment across diverse regions via the Topographic Above Nearest Drainage (HAND) methodology. The strategy aims to assess flood risk in urban cores promptly, leveraging limited data and computational resources.
Overview
To accurately assess flood inundation danger, one must first procure high-quality Digital Elevation Model (DEM) data that captures the terrain’s topography. This can be achieved by consulting online repositories such as NASA’s Shuttle Radar Topography Mission (SRTM), the National Geophysical Data Center (NGDC), or the United States Geological Survey (USGS).
To set the programming setting with the required packages for the evaluation, first ensure that your Python environment is properly configured by installing necessary packages through pip or conda. For instance, to install a specific package named ‘package_name’, execute the following command in your terminal:
DEM data processing and preprocessing involve several steps to ensure accurate drainage extraction. These include:
Data quality assessment and filtering; Removing no-data values or null pixels; Applying spatial filters, such as median or mean, to reduce noise and improve data quality; Correcting for atmospheric effects, such as topographic shadows; Ensuring the DEM has sufficient resolution and accuracy for drainage extraction;
To accurately assess the risk of flooding in different regions using a HAND mannequin, it’s essential to categorize areas into five labels: “extremely high danger”, “high danger”, “moderate danger”, “low danger”, and “very low danger”.
Setting the Surroundings
The workflow employed in this research, as presented, is readily available. The workflow is a Jupyter notebook that runs on Python 3.8 and utilizes the following packages:
Numpy – array manipulation.
Geospatial Data Evaluation and Manipulation with Whitebox Tools.
GDAL – Geospatial information manipulation.
RichDEM: A suite of DEM and hydrological evaluation instruments for spatial analysis and modeling applications.
Matplotlib – Knowledge Visualization.
Knowledge Preparation and Acquisition
Flood risk assessment requires a systematic approach, involving the following information preparation and acquisition steps:
Step 1: Knowledge Acquisition
Acquiring comprehensive elevation data is the inaugural step in fostering a sense of wonder and exploration in the world of curiosity. This study leverages high-quality topographic data from the publicly accessible Forest and Buildings Eliminated Copernicus Digital Elevation Model (FABDEM) dataset. The FABDEM is a globally comprehensive topographic dataset that mitigates the effects of construction and tree canopy height artefacts present in the Copernicus GLO-30 Digital Elevation Model, providing an accurate representation of terrain morphology. The information is provided with an accuracy of 1 arc second, which corresponds to approximately 30 meters on the equator, covering the entire globe.
The focus of this study lies in the northeastern region of Brazil. The DEM file encompasses a 1° by 1° area, spanning from 6°S 39°W to 5°S 38°W, referencing the World Geodetic System 1984 (EPSG: 4326) coordinate system. The illustration of this area is presented below in Figure 1.
Within Brazil’s most parched biome, a highlighted space is nestled. Characterized by sporadic and infrequent rainfall patterns, this region experiences precipitation in short intervals, typically occurring only a few times within a 12-month period. Despite being a normal year by all accounts, 2024 saw an extraordinary and intense bout of rainfall in this region, resulting in far-reaching consequences that resonated globally?
Step 2: Knowledge Preparation
The information preparation process involves populating the DEM file’s sink structures. Sinks refer to regions where elevation values indicate a sense of melancholy; in other words, they represent pixels or units of pixels that have neighboring pixels with higher elevation. Water is collected in sinks as an alternative to flowing in traditional hydrological assessments.
While sinks may appear as innocuous features akin to lakes and basins, they are often generated by digital elevation model (DEM) inaccuracies, mirroring poor data quality or collection decisions. To ensure accurate hydrological modeling, subsequent depressions or sinks are filled during preprocessing, either by removing them or raising their elevation to allow for unobstructed water flow. This crucial step in flood danger evaluation stands out among various hydrological research studies.
Using Python libraries akin to NumPy and SciPy, we’ll process the DEM to ensure it’s characterized in a way that enables precise calculations of flow routes and accumulation.
Additionally learn:
Movement Course and Movement Accumulation
Calculating Movement Instructions
The next step involves determining the path of circulation for each pixel in the image. This process generates a novel raster image where each pixel’s value corresponds precisely to the circulation pathway. Three key strategies exist: D8, the Number of Movement Courses (MFC), and D-Infinity (DINF).
Using the D8 method, this study identifies flow paths by determining the steepest downhill neighbour for each pixel. The resulting raster contains pixel values ranging from 1 to 128, representing the optimal path calculated by the steepest-descent algorithm. If the steepest slope points to the right, the pixel’s value could be 1, while a slope oriented towards the top-right corner would yield a value of 128, as depicted in the accompanying diagram.
Calculating Movement Accumulation
Once the circulation route has been computed, the next step involves calculating the circulation accumulation? Movement accumulation identifies the regions where water is more prone to collect based on the flow pathway. The desired output quality is ensured by accurately determining the diverse upstream pixels that collectively contribute to each pixel’s spread.
The resulting raster displays the cumulative flow values at each pixel location, where the value of each pixel corresponds to the total accumulated flow at that point. Pixels with unusually high accumulation values often represent streams, rivers, or drainage networks, which gather runoff from multiple upstream regions. Inversely, pixels exhibiting low accumulation values indicate regions evoking the characteristics of ridges or elevated terrain, where minimal water accumulation is evident, as depicted in Figure 3.
Using Python and the WhiteboxTools library, the calculation of circulation-based accumulation can be accomplished via the d8_flow_accumulation method.
By leveraging the circulate accumulation raster, it is possible to identify areas characterizing watercourses, including rivers, streams, and drainage systems. Pixels exceeding a predetermined threshold are classified as part of the streaming community, utilizing a buildup value system.
The choice of a suitable Brinkworth number relies heavily on various factors, including the hydrological conditions of the study area and the digital elevation model (DEM) used. In this region, a predominantly semi-arid climate prevails, with a Digital Elevation Model (DEM) having a spatial resolution of 30 meters. After conducting extensive trials and refining the process, a threshold value of 15 was ultimately determined to be optimal for effectively identifying larger-scale drainage networks.
HAND Mannequin Evaluation
The HAND (Hydrologic Area Near Dryness) methodology, initially introduced by , serves as a reliable technique for assessing the likelihood of an area experiencing flood inundation. To achieve this, professionals employ a Digital Elevation Model (DEM) in tandem with a stream community raster, calculating the deviation of each DEM pixel from its corresponding value in the stream community dataset by measuring the absolute difference between the two values. The resulting image features a novel raster where each pixel value corresponds to the vertical distance between its elevation and that of the nearest drainage level, as depicted in Figure 4.
Pixel values in the subsequent HAND (Height Above Nearest Drainage) raster represent the relative elevation above the nearest drainage level. Higher values indicate zones farther away from the drainage, which are significantly less prone to flooding, whereas lower values pinpoint locations closer to the drainage, increasing their susceptibility to flooding.
The HAND (Height Above Nearest Drainage) raster was created using the WhiteboxTools Python library and the elevation above stream technique. This subsequent grid encompasses a range of pixel values spanning from 0 to 330 metres, depicting the elevation of each DEM pixel relative to its nearest drainage point within the study area.
What’s the probability that floodwater poses an immediate threat?
Pixels from the HAND (Height Above Nearby Drainage) raster can inform danger zones, where lower values suggest an increased risk of flooding compared to areas with higher elevations. The following desk outlines the thresholds (in meters) for categorizing areas according to varying levels of danger.
What intervals are employed to stratify perilous zones?
Danger Stage
Threshold (m)
Class Worth
Very Excessive
0 – 1
5
Excessive
1 – 2
4
Medium
2 – 6
3
Low
6 – 10
2
Very Low
10
1
The thresholds established in Desk 1 were determined through empirical testing and validation. By leveraging the NumPy package, it is possible to assign class values to distinct regions of a unique digital elevation model (DEM), ultimately generating a new raster file that reflects the classification results.
Thresholds introduced in Desk 1 were determined through rigorous empirical testing. By leveraging the capabilities of the NumPy package, it is possible to assign class values to distinct regions of a unique DEM, thereby generating a novel raster file featuring the classification results.
Outcomes and Discussions
After processing is complete, it’s now necessary to visualize the results and draw meaningful inferences. This package deal enables visualization of the outcome predictions from the risk classification model applied to the HAND raster file, as depicted in Figure 5.
To raise awareness of which areas are particularly vulnerable to flooding, the GDAL Python library can be employed to convert the labeled array into a GeoTIFF file for further analysis and visualization. The file can be subsequently uploaded into GIS software programs like QGIS to visualize the higher-risk zones, as depicted in Figure 6 below.
In Determinant 6, a rural landscape is depicted with a small city situated at its center. On the correct side of the determination, high-risk areas highlighted in yellow and extremely high-risk areas highlighted in purple are designated for identification purposes. The stream community, depicted in a calming blue hue, provides a visual representation of the regions proximal to the waterways susceptible to flooding.
Conclusion
The HAND mannequin is particularly useful for swiftly and accurately assessing the risk of flooding in areas, allowing for efficient computational assessments. By leveraging a DEM file exclusively from an area of interest, it is possible to create detailed maps that accurately identify flood-prone zones and enable the development of effective contingency plans aimed at minimizing the devastating effects of inundation. The workflow outlined in this study possesses broad applicability across diverse areas and scenarios, yielding significant benefits for civil safety organizations.
References
Nathan, Smiti & Harrower, Michael. (2023). Assessing the spatial arrangement of Bronze Age towers in Oman in relation to water circulation patterns. Arabian Archaeology and Epigraphy. 34. n/a-n/a. 10.1111/aae.12237.
Hu, Anson & Demir, Ibrahim. (2021). Flood mapping in real-time utilizing consumer-aspect network techniques with the Handheld Automated Neural Network Device (HAND). Hydrology. 8. 65. 10.3390/hydrology8020065.
Top-Down, a novel terrain model for hydrologic applications, Journal of Hydrology (2011), doi: 10.1016/j.jhydrol.2011.03.051
Lindsay, J.B. (2023). Introducing WhiteboxTools: A Comprehensive Guide to the Command-Line Interface and Python API for Efficient Geospatial Processing. Whitebox Geospatial Inc. Retrieved from .
Barnes, R. (2023). Rich DEM: A High-Performance Terrain Evaluation Library for Digital Elevation Models? Retrieved from .
Esri. (2024). Understanding Drainage Techniques. ArcGIS Professional. Retrieved from .
Often Requested Questions
Ans. The existing investigation employed FABDEM, opting for a threshold of 30 metres. While low-resolution digital elevation models (DEMs) can be beneficial for larger regions, they tend to oversimplify terrain complexities, leading to inaccuracies during the stream extraction process and, subsequently, incorrect classification of areas susceptible to flooding risks.
Ans. We investigated diverse approaches to determine the optimal threshold value for extracting streams from communities in our research. While higher threshold values yield more compact drainage areas, resulting in a drastically altered stream community compared to satellite imagery? Therefore, selecting a suitable threshold is crucial for identifying a drainage network that accurately reflects the water patterns depicted in the imagery, thereby fostering a deeper understanding of the world’s complexities.
Ans. The hand mannequin highlights areas of exceptionally high risk (yellow) and extremely high risk (purple), underscoring the heightened vulnerability to flooding due to their proximity to the drainage system. Notwithstanding, determining a space as flooded necessitates taking into account factors such as land use, precipitation patterns, and historical flood data.
Ans. The proposed workflow enables real-time mapping of flood-vulnerable regions, thereby augmenting situational awareness, disaster mitigation efforts, and swift emergency responses. The HAND (Height Above Nearest Drainage) mannequin provides a practical, scalable solution utilising only DEM data and open-source Python libraries, making it an excellent choice for regions with limited technological resources to assess flood risks reliably?
Additionally, the certification exam assesses an individual’s ability to deploy machine learning models using established methodologies. Candidates are thoroughly evaluated on their ability to design and implement effective monitoring strategies that identify instances of knowledge drift. Individuals passing the certification exam are expected to perform exceptional machine learning engineering tasks utilizing Databricks’ machine learning capabilities.
Cloud-Skilled Machine Learning Engineer at Google
A seasoned machine learning engineer constructs, assesses, refines, and streamlines AI models using Google Cloud technologies, leveraging expertise in tried-and-tested frameworks and methodologies, as stated by Google Cloud. The certification guarantees that a machine learning engineer can effectively handle massive, intricate datasets, develop repeatable and reusable code, consider responsible AI and equity throughout the machine learning model development process, and collaborate seamlessly with other job roles to ensure the long-term success of machine learning-based applications.
Licensed engineers in Google Cloud’s machine learning specialty require a strong foundation in programming skills and experience working with data platforms and distributed data processing tools, as specified by the company. This individual is expected to demonstrate expertise in designing model architectures, developing data pipelines, and interpreting key performance indicators. Through expert guidance, skills reinvigoration, deployment strategy, timely scheduling, diligent oversight, and continuous refinement of fashion-forward approaches, the machine learning engineer crafts and develops high-caliber solutions with scalability in mind.
This submission will outline a method for utilizing smartphone accelerometer and gyroscope data to predict human physical activities based on phone usage patterns. Information provided about this submission originates from the University of California, Irvine. Thirty individuals have been assigned to conduct a range of fundamental actions while using a connected smartphone equipped with an accelerometer and gyroscope, which records their motion.
Before proceeding, let’s import the necessary libraries used throughout the assessment.
The information used in this submission comes from sources distributed by the University of California, Irvine.
Upon downloading the linked information, the data consists of two distinct elements: one that has undergone preprocessing through various feature extraction methods, including Fourier transform, and another RawData The raw data from sensors requires processing to provide meaningful insights into the movement. Despite the absence of conventional noise filtering techniques or function extraction methods typically employed with accelerometer data, What’s the purpose of this information?
The primary objective of processing unstructured data within this submission is to facilitate a seamless transformation of codes and ideas into chronologically organized formats, ultimately enabling effective application across various, albeit lesser-known, domains. While an accurate model can be constructed by leveraging clean data, the process of filtering and transformation varies significantly across activities, necessitating substantial guidance and domain expertise. One of the most striking aspects of deep learning is that feature extraction is achieved internally from the data, rather than relying on external information.
Exercise labels
While the model’s encoding system isn’t crucial to its functionality, these integer values are valuable for observation purposes. Let’s load them first.
Subsequently, we retrieve data from the ‘labels’ dictionary. RawData. The file contains comprehensive listings of individual exercise recordings and corresponding observational data from the dataset. The key considerations for column design stem directly from available data. README.txt.
The files utilize a consistent three-part naming convention. The primary aspect of this type of file is the type of data it contains: both acc for accelerometer or gyro for gyroscope. Subsequent quantities are listed here, accompanied by their corresponding consumer IDs for each recording. Let’s load these right into a pandas DataFrame for ease of use and manipulation later.
Before leveraging the provided information, we need to convert it into a format amenable to modeling. It is intended that we compile a register featuring recordings, their respective classifications or exercise labels, as well as supplementary data corresponding to each observation.
To obtain this, we will scrutinize all existing recording data. dataFilesThe audio recordings were analyzed for content, and the following observations were extracted:
# Learn contents of single file to a dataframe with accelerometer and gyro information. readInData <- perform(experiment, userId){ genFilePath = perform(sort) { paste0("information/RawData/", sort, "_exp",experiment, "_user", userId, ".txt") } bind_cols( learn.desk(genFilePath("acc"), col.names = c("a_x", "a_y", "a_z")), learn.desk(genFilePath("gyro"), col.names = c("g_x", "g_y", "g_z")) ) } # Operate to learn a given file and get the observations contained alongside # with their lessons. loadFileData <- perform(curExperiment, curUserId) { # load sensor information from file into dataframe allData <- readInData(curExperiment, curUserId) extractObservation <- perform(startPos, endPos){ allData[startPos:endPos,] } # get commentary places on this file from labels dataframe dataLabels <- labels %>% filter(userId == as.integer(curUserId), experiment == as.integer(curExperiment)) # extract observations as dataframes and save as a column in dataframe. dataLabels %>% mutate( information = map2(startPos, endPos, extractObservation) ) %>% choose(-startPos, -endPos) } # scan via all experiment and userId combos and collect information right into a dataframe. allObservations <- map2_df(fileInfo$experiment, fileInfo$userId, loadFileData) %>% right_join(activityLabels, by = c("exercise" = "quantity")) %>% rename(activityName = label) # cache work. write_rds(allObservations, "allObservations.rds") allObservations %>% dim()
Exploring the info
Now that we have consolidated all the relevant data into a single platform, experiment, userId, and exercise Labels enable us to uncover the underlying data structure.
Size of recordings
The initial analysis begins with examining the dimensions of the exercise-related audio files.
While acknowledging potential disparities in recording length among distinct exercise types, it’s essential to exercise caution in our approach. To avoid inefficient processing and minimize data loss, it’s crucial that we standardize the length of our mannequin inputs by padding each observation to match the longest one, thereby ensuring that the vast majority of data remains intact. Given that our current understanding is that due to this, we plan to synchronize our mannequin with the key group of observations on size actions, which comprises STAND_TO_SIT, STAND_TO_LIE, SIT_TO_STAND, SIT_TO_LIE, LIE_TO_STAND, and LIE_TO_SIT.
By developing a futuristic training program, we can leverage innovative architectures such as recurrent neural networks (RNNs) capable of processing variable-size inputs, thereby empowering it to learn from an entire dataset? While using a mannequin without proper consideration may lead to the risk of it solely studying from lengthy commentary, it’s crucial to note that such a scenario would likely generalize poorly to real-world data streams where this model would be applied in a real-time setting, specifically with 4 longest lessons failing to provide any meaningful insights.
Filtering actions
Building upon our previous discussions, we can distill the key takeaways into a concise framework focused solely on the manifestations of curiosity.
Following our rigorous data culling process, we are likely to retain a substantial amount of relevant information for our model’s analysis.
Coaching/testing break up
Before diving deeper into our model’s data, it is crucial to separate the information into a training set and test set to ensure accurate performance measurements. Since each consumer completed all tasks instantly – with one exception being the individual who performed only 10 of the 12 actions – upon splitting into userId We will ensure our mannequin is viewed by new individuals entirely when we examine it.
# get all customers userIds <- allObservations$userId %>% distinctive() # randomly select 24 (80% of 30 people) for coaching set.seed(42) # seed for reproducibility trainIds <- pattern(userIds, measurement = 24) # set the remainder of the customers to the testing set testIds <- setdiff(userIds,trainIds) # filter information. trainData <- filteredObservations %>% filter(userId %in% trainIds) testData <- filteredObservations %>% filter(userId %in% testIds)
Visualizing actions
With the information streamlined by eliminating actions and separating into distinct test sets, we can now gain a clearer view of the data for each class, allowing us to identify any inherent patterns that our model may be able to detect naturally?
Let’s transform our data from its current wide format in the Pandas DataFrame into a more organized, long-form structure that’s easier to analyze and visualize.
Patterns of positive emergence are consistently evident within accelerometer data. Given the limitations imposed on me, I will make the improvement.
It’s reasonable to assume that the mannequin would be perplexed by the disparities between… LIE_TO_SIT and LIE_TO_STAND As their online profiles share a striking similarity. The identical goes for SIT_TO_STAND and STAND_TO_SIT.
Preprocessing
Before we can effectively engage with the neural network, we must first take a few crucial steps to preprocess the data.
Padding observations
To standardize sequence lengths, let us first determine the 98th percentile size by examining the distribution of existing sequence sizes. To avoid disrupting the system’s functionality, we recommend keeping commentaries concise to prevent lengthy outliers from skewing the overall padding.
To streamline our analysis, we can efficiently transform our observational data into matrices, leveraging the capabilities of Keras to seamlessly pad each observation and convert it into a 3D tensor, thereby facilitating further processing.
The processing and representation of the data are greatly facilitated as the information is now available in a suitable format for analysis by sophisticated algorithms and models, being a well-structured 3D tensor with dimensions that can be easily manipulated and operated on. (<num obs>, <sequence size>, <channels>).
One-hot encoding
Before we can practice with our mannequin, there’s a crucial step remaining – converting our commentary lessons from integer values to one-hot, or dummy-encoded, vectors. Fortunately, Keras provides us again with a highly useful function that enables us to accomplish this task.
oneHotClasses <- . %>% {. - 7} %>% # deliver integers right down to 0-6 from 7-12 to_categorical() # One-hot encode trainY <- trainData$exercise %>% oneHotClasses() testY <- testData$exercise %>% oneHotClasses()
Modeling
Structure
Given our dataset’s temporal density, we will leverage 1D convolutional layers for effective feature extraction. While processing temporally-dense data, recurrent neural networks (RNNs) must analyze complex dependencies over extended periods to identify patterns, whereas convolutional neural networks (CNNs) can efficiently build larger feature representations by stacking multiple convolutional layers. Given that we’re solely seeking to assign a solitary type of exercise to each comment, we can employ pooling to condense the CNN’s perception of the data into a single, interpretable output via a dense layer.
To strengthen the model’s robustness, we will employ a combination of techniques, including stacking two layers, incorporating batch normalization within the convolutional layers, and applying dropout regularization not only on the convolutional layers but also on the densely connected ones.
Now, with our training data and guidelines, we’re ready to hone the mannequin’s skills. Be aware that we use callback_model_checkpoint() To ensure that we conserve the most superior variant of the model, it’s crucial, as it may eventually overfit or stagnate during training.
The inanimate object is actually observing a single phenomenon. We achieve a respectable 94.4% accuracy rate on our validation data, offering users six practical lesson options to engage with. Let’s scrutinize the validation efficiency more closely to pinpoint where the model is faltering.
Analysis
Now that we have an educated model, let’s examine the mistakes it made on our test data?
We will select the top-performing model based on validation accuracy, and then examine each comment, the predicted outcome, the probability assigned by the model, and the actual exercise label.
The mannequin’s assuredness depended on whether the prediction proved accurate.
predictionPerformance %>% mutate(outcome = ifelse(appropriate, 'Right', 'Incorrect')) %>% ggplot(aes(highestProb)) + geom_histogram(binwidth = 0.01) + geom_rug(alpha = 0.5) + facet_grid(outcome~.) + ggtitle("Chances related to prediction by correctness")
While initially seeming uncertain about incorrect categorizations, the mannequin surprisingly demonstrated greater trepidation regarding mistaken conclusions than accurate ones. Although the pattern measurement is insufficiently precise to warrant a definitive conclusion.
Can we accurately determine which actions the mannequin struggled with most utilizing a confusion matrix?
Since we observe that preliminary visualization guided the mannequin in understanding the difference, it experienced some difficulty distinguishing between LIE_TO_SIT and LIE_TO_STAND lessons, together with the SIT_TO_LIE and STAND_TO_LIEWhich also possess distinct visible profiles.
Future instructions
One potential direction for further development would be to strive for greater realism by incorporating more diverse and effective training methods. By presenting recordings as a single, uninterrupted stream rather than segmented ‘observations’, we can create a scenario mirroring real-world deployments of models, where they process continuous data to classify and detect changes in behavior, thereby gauging their effectiveness?
Gal, Yarin, and Zoubin Ghahramani. 2016. “Bayesian Dropout: Quantifying Mannequin Uncertainty in Deep Learning,” Journal of Artificial Intelligence, pp. 1050-9.
Graves, Alex. 2012. “Supervised sequence labelling: A review of the concept in, pp. 5-13.” Springer.
Kononenko, Igor. 1989. Bayesian neural networks—A probabilistic take on deep learning. Springer: 361–70.
This revolutionary trio: LeCun, Yann, Bengio, Yoshua, and Hinton, Geoffrey. 2015. “Deep Studying.” 521 (7553). Nature Publishing Group: 436.
Reyes-Ortiz, J.-L., Oneto, L., Samà, A., Parra, X., & Anguita, D. 2016. “Smartphone-Based Detection of Human Transitions for Enhanced Activity Recognition.” Elsevier: 754–67.
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., & Bregler, C. 2014. “Object Localization via Environmentally Conscious CNNs: A Novel Approach.” .
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Our Prime Air Drone Car Design and Take a look at group inside Flight Sciences is in search of an impressive Aerodynamic Take a look at & Analysis Engineer who combines hands-on technical expertise with take a look at planning and coordination expertise. This individual will personalize our wind tunnel testing system and course, offering alternative solutions to further personalize flight test instrumentation, and assist our acoustic testing methodologies. The ideal candidate will thrive in a dynamic, collaborative environment, providing seamless support to a diverse team of design engineers and scientists working together in a high-energy testing atmosphere? We’re seeking someone who embodies resilience, possessing the aptitude to navigate diverse challenges while remaining open-minded and receptive to evolving circumstances. Work tirelessly, enjoy yourself, and you’ll make history naturally.
Key job duties Conduct comprehensive wind tunnel testing and analysis of complete automobiles, propellers, and various test specimens, incorporating meticulous planning, development, and execution. Design and configure wind tunnel test rig components, including mannequins and testing accessories, as well as develop and implement testing protocols. Leads the combination and testing of recent research articles and prototypes? Design and manufacture custom wiring harnesses for energy electronics applications, while simultaneously inspecting and testing instrumentation systems. Conduct diagnostic tests on energy electronics and examine instrumentation systems. Full calibrations are conducted on all instrumentation, including load cells, stress transducers, and others. Establish standardized protocols for experimental execution and provide clear guidelines for conduct. Coordinate logistics for transporting test equipment to and from testing zones.
In regards to the group Our Flight Sciences Car Design & Take a look at group contains groups that span the next disciplines: Aerodynamics, Efficiency, Stability & Management, Configuration & Spatial Integration, Masses, Buildings, Mass Properties, Multi-disciplinary Optimization (MDO), Wind Tunnel Testing, Noise Testing, Flight Take a look at Instrumentation, and Speedy Prototyping.
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Bachelor’s degree in aerospace engineering, mechanical engineering, or electrical engineering, with a minimum of three years’ experience. Professional-grade expertise in developing, building, and implementing high-performance aerodynamics testing strategies. Expertise in wind tunnel testing and instrumentation, encompassing a comprehensive understanding of aerodynamic principles, experimental design, and data acquisition techniques. With expertise in designing and constructing mechanical and electrical systems, we examine and assess various styles and configurations of mechanisms, machinery, and equipment.
PREFERRED QUALIFICATIONS
– Possesses over three years of experience in conducting wind tunnel tests for air-powered vehicles and propellers. Expertise in quantifying system uncertainty through methodologies such as propagation of errors, Monte Carlo simulations, and sensitivity analysis; skilled in calibrating models to experimental data and implementing corrections for biases, systematic errors, and other sources of uncertainty. With extensive expertise in assembling, designing, and modifying mechanical constructions and fixtures. Familiarity with debugging and operating excessive energy electrical systems. Mastering skills to interpret and apply knowledge from technical documentation including engineering drawings, circuit diagrams, and operating manuals. Familiarity with cutting-edge computer-aided design (CAD) software programs, including CATIA and 3DX. Expertise in developing or leveraging information procurement software methodologies.
Amazon is committed to fostering a diverse and inclusive workplace culture. Amazon is a committed equal opportunity employer and does not discriminate based on race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or any other legally protected characteristic. If you have a disability and would like to apply for accommodations at Amazon, kindly visit our dedicated webpage at https://www.amazon.jobs/en/incapacity/us.
Our compensation data presents the cost of labor across various US geographic markets. The compensation package at this organization spans a range of $105,600 to $185,000 per annum, dependent on the specific geographic location within our network. Pay varies significantly based on multiple factors, including market location and job-specific information, skills, and experience. Amazon is a leading e-commerce company, not a compensation firm. Depending on the location, fairness is considered alongside sign-on funds and various types of compensation, forming a comprehensive remuneration package that may include a broad range of medical, financial, and other benefits. Amazon’s comprehensive benefits package can be found at https://www.aboutamazon.com/office/employee-benefits? Until it’s fully booked? Applicants are invited to submit their applications through either our internal or external professional website.
One of the world’s most advanced humanoid robots has been indulging in leisure activities to the detriment of its productivity. Boston Dynamics’ Atlas is renowned for its humanoid appearance, impressive mobility, and remarkable capabilities in navigating challenging terrain. These exceptional robotic systems demand meticulous oversight, yet they’re also primarily engaging experimental prototypes.
Six months on from the groundbreaking launch of the renowned robotics laboratory, the team is showcasing even more astounding innovations that have emerged from their cutting-edge work. In a groundbreaking innovation, a cutting-edge video showcases Atlas deftly selecting automotive parts from one inventory cabinet and seamlessly transferring them to another, a task typically handled by factory staff.
Aside from its electric powertrain, the all-new Atlas boasts a unique automatic transmission system that sets it apart from other vehicles in its class. Its head, higher physique, pelvis, and legs are capable of swiveling independently. Its head may spin around to face the opposite direction of its legs and torso, with the rest of its body twisting to follow suit later.
The newly released demo showcases a fundamental transformation for Atlas. Boston Dynamics’ latest video showcases a remarkable shift: whereas the company previously controlled the robotic’s most impressive movements, the new footage features an entirely autonomous Atlas in action.
“In this robotic system, autonomous online motion generation is enabled without any pre-programmed or remote-controlled actions.”
Why launch this video now? Humanoid robots are suddenly gaining momentum. What potential breakthroughs await us in the realm of artificial general intelligence? Boston Dynamics dominated the field for a long time, yet it took its sweet time bringing Atlas to market for industrial applications. The technology has not incorporated substantial amounts of artificial intelligence into its framework. It appears to be a matter of fact.
Last month, Hyundai’s laboratory collaborated with Toyota Research Institute (TRI) to integrate synthetic intelligence, TRI’s area of expertise, into its Atlas platform. The partnership aims to transform Atlas, alongside rigorous analysis, into a versatile humanoid robot capable of performing various tasks.
It’s an intriguing growth. Atlas is renowned globally for its exceptional performance in pure robotics applications. TRI is developing massive habits of fashion, akin to monumental language styles for robotic movement and control. The idea posits that as AI models are trained on increasingly vast amounts of real-world data, they may eventually develop the capacity to think and respond in a manner akin to a fully autonomous robotic intelligence, requiring minimal explicit programming to adapt to novel situations.
Google DeepMind has collaborated on a vision-language-action model called RT-X, teaming up with 33 research labs to create a massive new AI training dataset for robotics. Recently, a TR35-listed MIT venture, backed by the same funding that spawned ChatGPT, launched its inaugural robot-focused platform.
“Our vision is to develop a universally accessible robotic AI platform that requires no training whatsoever, allowing users to seamlessly integrate it into their robots.” “While we’re still in the early stages, we’ll continue to push forward and leverage the scaling effect to drive a breakthrough in robotic policies, just as we saw with large language models.”
Boston Dynamics isn’t alone in its innovative pursuits. Though tardy, it arrives at the social event. Companies founded in recent years, including numerous startups, unite under a common objective. The following companies embody innovative approaches: Tesla, Determined, and 1X, among others.
Within Boston Dynamics, Scott Kuindersma highlighted that this development could be “one of the most exhilarating aspects” in the field’s history. At the same time, he recognized that the industry was witnessing significant excitement and enthusiasm, yet still faced a substantial amount of work left to be accomplished. Obstacles arise from aggregating adequate accurate data and fine-tuning the most effective methods for programming robotic algorithms.
That doesn’t rule out the possibility of additional Boston Dynamics films being released soon. Russ Tedrake of TRI emphasized the importance of fostering enthusiasm for forthcoming results among individuals, while also instilling confidence through tangible evidence.
