This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. sievers-et-al-socs2022-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 7.0 for reproducing all experiments of the paper, under experiments/decoupled-abstractions. The scripts 2022-04-* contain configurations for running the experiments and the script paper-tables-*.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.) sievers-et-al-socs2022-ipc-benchmarks.zip contains the IPC benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2018 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks). sievers-et-al-socs2022-autoscale-benchmarks.zip contains the Autoscale 21.11 benchmarks (from https://github.com/AI-Planning/autoscale-benchmarks). sievers-et-al-socs2022-lab.tar.gz contains a copy of Lab 7.0 (https://github.com/aibasel/lab). sievers-et-al-socs2022-raw-data.zip and sievers-et-al-socs2022-processed-data.zip contain the experimental data. Directories without the "-eval" ending (sievers-et-al-socs2022-raw-data.zip) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" (sievers-et-al-socs2022-processed-data.zip) ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run. Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.

The System-on-Chip (SoC) market for IP cameras has become a critical component in the evolving landscape of security and surveillance technology. SoCs serve as the brain of IP cameras, integrating various functionalities such as image processing, compression, and transmission within a single chip. This innovation no

Automotive SoCs Market Outlook

The global automotive System on Chips (SoCs) market is poised for significant growth, with a projected compound annual growth rate (CAGR) of 6.8% from 2024 to 2032. The market size is anticipated to rise from $14.5 billion in 2023 to approximately $26.4 billion by 2032. This growth is propelled by the increasing adoption of advanced driver-assistance systems (ADAS) and the shift towards electric vehicles which demand more sophisticated computational power and connectivity. Moreover, the rising consumer demand for enhanced in-car infotainment systems also plays a crucial role in the expansion of the automotive SoCs market.

One of the primary growth factors driving the automotive SoCs market is the rapid technological advancements in ADAS and autonomous vehicle technology. The demand for AI-driven components and systems capable of real-time data processing and decision making is escalating, thereby increasing the reliance on highly efficient SoCs. These chips are integral to optimizing the performance of sensor arrays, image recognition systems, and communication frameworks within vehicles. Additionally, the integration of machine learning algorithms into SoCs facilitates advanced functionalities such as pedestrian detection, lane-keeping assistance, and adaptive cruise control, which are becoming standard features in modern vehicles.

Another significant growth factor is the proliferation of electric vehicles (EVs) worldwide. The transition from internal combustion engines to electric powertrains necessitates a different set of electronic requirements, where SoCs play a pivotal role. As electric vehicles require efficient energy management systems, the use of SoCs in regulating power usage, managing battery systems, and enhancing drive efficiency becomes indispensable. Furthermore, the electric vehicle sector is seeing a surge in demand due to environmental regulations and consumer preferences, which further accelerates the need for advanced SoCs tailored for these applications.

The growing consumer desire for enhanced infotainment systems is also a major contributor to the expansion of the automotive SoCs market. Modern consumers expect seamless connectivity, high-quality audio-visual interfaces, and integration with their smart devices. SoCs enable these features by supporting high-speed data transfer, connectivity solutions like Wi-Fi and Bluetooth, and robust processing capabilities for running multiple applications simultaneously. As vehicles evolve into more extensive smart devices, the demand for sophisticated infotainment systems, supported by advanced SoCs, continues to surge.

Car Infotainment SoCs are becoming increasingly vital as they serve as the backbone for modern in-car entertainment and connectivity systems. These SoCs are designed to handle a variety of tasks simultaneously, such as streaming music, providing navigation assistance, and integrating with smartphones. As vehicles become more connected, the demand for infotainment systems that offer seamless user experiences is on the rise. Car Infotainment SoCs are engineered to support high-speed data processing and connectivity, ensuring that drivers and passengers can enjoy a rich multimedia experience without interruptions. The integration of these SoCs not only enhances the entertainment value but also supports safety features by providing real-time information and alerts, making them an indispensable component in the automotive industry.

Regionally, Asia Pacific is expected to lead the automotive SoCs market due to its expansive automotive manufacturing industry and rapid technological adoption. The region is home to some of the largest automotive markets, including China and India, which have seen significant investments in EVs and ADAS technologies. North America and Europe also represent substantial markets, driven by the presence of major automotive OEMs and a consumer base keen on adopting the latest automotive technologies. These regions are heavily investing in autonomous vehicle research, creating further opportunities for the expansion of the automotive SoCs market.

Vehicle Type Analysis

The segmentation of the automotive SoCs market by vehicle type highlights significant trends and opportunities across passenger cars, commercial vehicles, and electric vehicles. Passenger cars account for a substantial portion of the market, primarily due to the widespread adoption of infotainment systems and ADAS features. With an increasing focus on en

The global Automotive System-on-Chips (SoCs) market size was valued at USD 10.36 billion in 2022 and is projected to grow from USD 11.98 billion in 2023 to USD 23.41 billion by 2029, exhibiting a CAGR of 10.1% during the forecast period. The market growth is primarily driven by the increasing demand for advanced driver assistance systems (ADAS), autonomous vehicles, and electric vehicles, which require high-performance and energy-efficient SoCs. The growth of the market is also fueled by the increasing adoption of 5G technology, which enables faster data transfer rates and lower latency, essential for autonomous driving and other advanced automotive applications. The market is segmented by application into passenger cars and commercial vehicles, with passenger cars accounting for the larger share due to the higher demand for advanced features in these vehicles.

Signal strength of ADEs at the SOC level in FAERS database.

According to a survey conducted in July 2024, Gen Z was the generation most likely to wear a combination of socks and sandals. Millennials followed closely with just over 20 percent.

Protein-Protein, Genetic, and Chemical Interactions for Babon JJ (2008):The SOCS box domain of SOCS3: structure and interaction with the elonginBC-cullin5 ubiquitin ligase. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Suppressor of cytokine signalling 3 (SOCS3) is responsible for regulating the cellular response to a variety of cytokines, including interleukin 6 and leukaemia inhibitory factor. Identification of the SOCS box domain led to the hypothesis that SOCS3 can associate with functional E3 ubiquitin ligases and thereby induce the degradation of bound signalling proteins. This model relies upon an interaction between the SOCS box, elonginBC and a cullin protein that forms the E3 ligase scaffold. We have investigated this interaction in vitro using purified components and show that SOCS3 binds to elonginBC and cullin5 with high affinity. The SOCS3-elonginBC interaction was further characterised by determining the solution structure of the SOCS box-elonginBC ternary complex and by deletion and alanine scanning mutagenesis of the SOCS box. These studies revealed that conformational flexibility is a key feature of the SOCS-elonginBC interaction. In particular, the SOCS box is disordered in isolation and only becomes structured upon elonginBC association. The interaction depends upon the first 12 residues of the SOCS box domain and particularly on a deeply buried, conserved leucine. The SOCS box, when bound to elonginBC, binds tightly to cullin5 with 100 nM affinity. Domains upstream of the SOCS box are not required for elonginBC or cullin5 association, indicating that the SOCS box acts as an independent binding domain capable of recruiting elonginBC and cullin5 to promote E3 ligase formation.

Welcome to the ultimate Android vs iOS battle with this Smartphone SoC dataset!

Includes three .csv files. Any analysis is appreciated, even if it is a short one 😎

Context

Benchmarks allow for easy comparison between multiple devices by scoring their performance on a standardized series of tests, and they are useful in many instances: When buying a new phone or tablet

Content

smartphone cpu_stats.csv is the main data. Updated performance rating of smartphone SoCs as of 2022. Includes summary of Geekbench 5 and AnTuTu v9 scores. Includes CPU specs such as clock speed, core count, core config, and GPU.

ML ALL_benchmarks.csv is the Geekbench ML Benchmark data. This tells you how well each smartphone device performs when performing Machine Learning tasks. The data is gathered from user-submitted Geekbench ML results from the Geekbench Browser. To make sure the results accurately reflect the average performance of each device, the dataset only includes devices with at least five unique results in the Geekbench Browser.

antutu android vs ios_v4.csv is the AnTuTu benchmarks data. It includes information about CPU, GPU, MEM, UX and Total score.

Antutu Benchmarks

1. Total Score

Benchmark apps gives your device an overall numerical score as well as individual scores for each test it performs. The overall score is created by adding the results of those individual scores. These score numbers don't mean much on their own, they're just helpful for comparing different devices. For example, if your device's score is 300000, a device with a score of 600000 is about twice as fast. You can use individual test scores to compare the relative performance of specific parts of different devices. For example, you could compare how fast your phone's storage performs compared to another phone's storage.

2. CPU Score

The first part of the overall score is your CPU score. The CPU score in turn includes the output of CPU Mathematical Operations, CPU Common Algorithms, and CPU Multi-Core. In simpler words, the CPU score means how fast your phone processes commands. Your device's central processing unit (CPU) does most of the number-crunching. A faster CPU can run apps faster, so everything on your device will seem faster. Of course, once you get to a certain point, CPU speed won't affect performance much. However, a faster CPU may still help when running more demanding applications, such as high-end games.

3. GPU Score

The second part of the overall score is your GPU score. This score is comprised of the output of graphical components like Metal, OpenGL or Vulkan, depending on your device. The GPU score means how well your phone displays 2D and 3D graphics. Your device's graphics processing unit (GPU) handles accelerated graphics. When you play a game, your GPU kicks into gear and renders the 3D graphics or accelerates the shiny 2D graphics. Many interface animations and other transitions also use the GPU. The GPU is optimized for these sorts of graphics operations. The CPU could perform them, but it's more general-purpose and would take more time and battery power. You can say that your GPU does the graphics number-crunching, so a higher score here is better.

4. MEM score

The third part of the overall score is your MEM score. The MEM score includes the results of the output of RAM Access, ROM APP IO, ROM Sequential Read and Write, and ROM Random Access. In simpler words, the MEM score means how fast and how much memory your phone possesses. RAM stands for random-access memory; while ROM stands for read-only memory. Your device uses RAM as working memory, while flash storage or an internal SD card is used for long-term storage. The faster it can write to and read data from its RAM, the faster your device will perform. Your RAM is constantly being used on your device, whatever you're doing. While RAM is volatile in nature, ROM is its opposite. RAM mostly stores temporary data, while ROM is used to store permanent data like the firmware of your phone. Both the RAM and ROM make up the memory of your phone, helping it to perform tasks efficiently.

5. UX Score

The fourth and final part of the overall score is your UX score. The UX score is made up of the results of the output of the Data Security, Data Processing, Image Processing, User Experience, and Video CTS and Decode tests. The UX score means an overall score that represents how the device's "user experience" will be in the real world. It's a number you can look at to get a feel for a device's overall performance without digging into the above benchmarks or relying too much on the overall score.

Acknowledgements

Sourced from Geekbench and AnTuTu.

If you enjoyed this dataset, here's some similar datasets you may like 😎

The Automotive System-on-Chips (SoCs) market is a rapidly evolving segment within the automotive industry, driven by the increasing demand for advanced vehicle functionalities and the rise of electric and autonomous vehicles. SoCs serve as the brains of modern vehicles, integrating multiple components like processor

The RISC-V Based System on Chips (SoCs) market is rapidly evolving, driven by the increasing demand for customizable and efficient computing solutions across various industries, including consumer electronics, automotive, artificial intelligence, and IoT. RISC-V, an open standard instruction set architecture (ISA),

The RISC-V System on Chip (SoC) market is an evolving and dynamic segment of the semiconductor industry that leverages the RISC-V architecture to create highly efficient and flexible computing solutions. RISC-V, an open-source instruction set architecture (ISA), has gained significant traction among developers and m

Protein-Protein, Genetic, and Chemical Interactions for Babon JJ (2009):The SOCS box encodes a hierarchy of affinities for Cullin5: implications for ubiquitin ligase formation and cytokine signalling suppression. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The SOCS (suppressors of cytokine signalling) family of proteins inhibits the cytokine-induced signalling cascade in part by promoting the ubiquitination of signalling intermediates that are then targeted for proteasomal degradation. This activity relies upon an interaction between the SOCS box domain, the adapter complex elonginBC and a member of the Cullin family, the scaffold protein of an E3 ubiquitin ligase. In this study, we dissected this interaction in vitro using purified components.We found that all eight SOCS proteins bound Cullin5 but required prior recruitment of elonginBC. Neither SOCS nor elonginBC bound Cullin5 when in isolation. Interestingly, the affinity of each SOCS-elonginBC complex for Cullin5 varied by 2 orders of magnitude across the SOCS family. Unexpectedly, the most potent suppressors of signalling, SOCS-1 and SOCS-3, bound most weakly to the E3 ligase scaffold, with affinities 100- and 10-fold lower, respectively, than the rest of the family. The remaining six SOCS proteins all bound Cullin5 with high affinity (K(d) of ~10 nM) due to a slower off-rate and hence a longer halflife of the complex. This difference in affinity may reflect a difference in mode of action as only SOCS-1 and SOCS-3 have been shown to suppress signalling using both SOCS box-dependent and SOCS box-independent mechanisms. This is not the case with the other six SOCS proteins, and our data imply the existence of two distinct subclasses of SOCS proteins with a high affinity for Cullin5, the E3 ligase scaffold, possibly reflecting complete dependence upon ubiquitination for suppression of cytokine signalling.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The global market for application processors and system-on-a-chip (SOCs) is expected to reach USD xx billion by 2028. The report forecasts that the market will grow at a CAGR of 5.7% during the forecast period. The growth of this market can be attributed to the increasing demand for smart devices and the growing trend of miniaturization in electronics. In addition, the rising demand for 5G technology is also contributing to the growth of this market.

An Application Processor, or app processor, is a type of microprocessor that controls the operation of a mobile device. It usually performs calculations and tasks related to the running of the device's software and user interface. Application processors are distinct from central processing units (CPUs), which typically handle more general-purpose computations in laptops and desktop computers.

On the basis of Types, the market is segmented into EEPROM, Flash, ROM, ROMLESS, RREM.

EEPROM:

EEPROM is an acronym for Electrically Erasable Programmable Read-Only Memory. It is a type of flash memory that can be erased and reprogrammed electronically. EEPROMs are used in a wide variety of applications, including computer memories, cell phones, printers, medical devices, and automobiles.

Flash:

Flash is a type of EEPROM that retains its contents when power is turned off. This makes it suitable for devices such as digital cameras, where photos need to be preserved even if the device is turned off or loses power. Flash also has a higher read/write speed than other types of EEPROM, making it a better choice for devices that require faster data access. For these reasons, flash has become the standard storage medium for portable devices.

ROM:

A ROM is a microchip that stores permanent data. The term "ROM" stands for Read-Only Memory, which means the data can only be read and not written or erased. This type of memory is used to store programs and other essential system information that needs to be accessed quickly and reliably.

On the basis of Application, the market is segmented into Consumer Electronics, Industrial Control Electronics, Medical Electronics, Communication Equipment, Other.

Consumer Electronics:

Application Processors and SOCs Sales are widely used in Consumer Electronics. They are used to manage and operate the various functions of a consumer electronics device. Some of the most common applications include Smartphone Processors, tablet processors, and digital media players.

Medical Electronics:

One of the most important applications for Application Processors and SOCs Sales is in medical electronics. In this sector, they are used to control and monitor a wide range of devices and systems, from simple patient monitors to sophisticated MRI machines. They play an essential role in ensuring that patients receive safe and effective treatment. Additionally, advances in medical technology are often driven by the development of new Application Processor and SOC products.

Communication Equipment:

Application Processors and SOCs Sales are used in communication equipment to send and receive signals between two or more points. These processors play a major role in the proper functioning of telecommunication systems. They are also used in other electronic devices such as smartphones, laptops, tablets, etc. that need to communicate with each other wirelessly.

On the basis of Region, the market is segmented into North America, Latin America, Europe, Asia Pacific, and Middle East & Africa.

North America: The North American region is a major market for Application Processors and SOCs Sales. This is due to the high level of development in the region's electronics industry, as well as the presence of several leading players. The United States is the dominant country in this market, followed by Canada and Mexico.

Latin America: Latin America is also a key market for these products, with Brazil being the largest player. This region has seen strong growth in recent years thanks to rising consumer demand and increasing investment in electronic infrastructure.

Europe: In Europe, Germany is the largest market for Application Processors and SOCs Sales. This can be attributed to that country's strong industrial sector, which makes heavy use of these products. Other key markets in Europe include France, the United Kingdom, and Italy.

Asia Pacific: The Asia Pacific region is the fastest-growing market for these products.

The Application Processors and Systems-on-Chips (SoCs) market is experiencing robust growth, driven by the increasing demand for sophisticated functionalities in diverse electronic devices. The market, estimated at $50 billion in 2025, is projected to expand significantly over the next decade, fueled by several key factors. The burgeoning consumer electronics sector, particularly smartphones, wearables, and smart home devices, constitutes a major driver, demanding increasingly powerful and energy-efficient processors. Simultaneously, the industrial automation and medical electronics sectors are adopting SoCs at an accelerating rate, leveraging their capabilities for advanced control systems and medical imaging equipment. Furthermore, the expansion of 5G and IoT networks is creating a surge in demand for high-performance SoCs capable of handling large data volumes and complex communication protocols. The market segmentation reveals a strong preference for Flash memory types due to their high density and fast read/write speeds. However, the EEPROM segment is also experiencing growth, driven by its suitability for applications requiring non-volatile data storage with high endurance. Competition among major players like AMD, Intel, and Qualcomm (implicitly suggested by the list of companies) is intense, leading to continuous innovation and price reductions, which benefits end-users. The market's growth trajectory is expected to be influenced by several trends. The increasing adoption of artificial intelligence (AI) and machine learning (ML) in various applications is driving demand for high-compute SoCs. Miniaturization and power efficiency are also critical considerations, leading to the development of advanced process technologies. However, the market faces certain constraints. Supply chain disruptions, geopolitical uncertainties, and the potential for over-capacity in certain segments pose challenges. The rising cost of research and development for advanced SoCs could also limit entry for smaller players. Regional analysis indicates strong growth in Asia Pacific, driven by the rapid expansion of electronics manufacturing in China and India. North America and Europe continue to be significant markets, driven by technological advancements and high adoption rates in various sectors. Sustained investment in R&D, strategic partnerships, and addressing supply chain vulnerabilities will be crucial for players to succeed in this dynamic market.

This repository provides the data and code necessary to reproduce the manuscript "Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation".This repository also contains sample datasets for running the code and bounding box and keypoint annotations. Collection of large behavioral data-sets on wild animals in natural habitats is vital in ecology and evolution studies. Recent progress in machine learning and computer vision, combined with inexpensive microcomputers, have unlocked a new frontier of fine-scale markerless measurements. Here, we leverage these advancements to develop a 3D Synchronized Outdoor Camera System (3D-SOCS): an inexpensive, mobile and automated method for collecting behavioral data on wild animals using synchronized video frames from Raspberry Pi controlled cameras. Accuracy tests demonstrate 3D-SOCS’ markerless tracking can estimate postures with a 3mm tolerance. To illustrate its research potential, we place 3D-SOCS ..., We develop and use a markerless 3D tracking system to estimate the posture of wild passerine birds (great tits and blue tits) in the field. We demonstrate the capabilities of this system using a stimulus-display experiment. 3D tracking pipeline and system accuracy validation were performed in Python, and any questions related to these should be directed to Alex Chan. Bayesian statistical analysis, figures and tables were all peformed in R, and any questions related to these, along with those related to the Python scripts that control the Raspberry Pis should be directed to Michael Chimento. We provide required packages, directory contents and column descriptions for all analyses below., , # Data and code to reproduce "3D-SOCS: synchronized video capture for posture estimation"

This repository provides the data and code necessary to reproduce the manuscript "Peering into the world of wild passerines with 3D-SOCS: synchronized video capture for posture estimation" by Michael Chimento, Alex Hoi Hang Chan, Lucy M. Aplin & Fumihiro Kano. Bold denotes co-first authorship. Note: This is separate from the code necessary to run 3D-SOCS yourself, which can be found at this github repository.

3D tracking pipeline and system accuracy validation were performed in Python, and any questions related to these should be directed to Alex Chan (hoi-hang.chan at uni-konstanz.de). Bayesian statistical analysis, figures and tables were all performed in R, and any questions related to these (or the Python scripts that control the data collection system) should be directed to Michael Chimento (mchimento at ab.mpg.de). We provide required pa...,

Time-to-onset analysis using the Weibull distribution test.

Protein-Protein, Genetic, and Chemical Interactions for Zhang JG (1999):The conserved SOCS box motif in suppressors of cytokine signaling binds to elongins B and C and may couple bound proteins to proteasomal degradation. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The suppressors of cytokine signaling (SOCS) family of proteins act as intracellular inhibitors of several cytokine signal transduction pathways. Their expression is induced by cytokine activation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway and they act as a negative feedback loop by subsequently inhibiting the JAK/STAT pathway either by direct interaction with activated JAKs or with the receptors. These interactions are mediated at least in part by the SH2 domain of SOCS proteins but these proteins also contain a highly conserved C-terminal homology domain termed the SOCS box. Here we show that the SOCS box mediates interactions with elongins B and C, which in turn may couple SOCS proteins and their substrates to the proteasomal protein degradation pathway. Analogous to the family of F-box-containing proteins, it appears that the SOCS proteins may act as adaptor molecules that target activated cell signaling proteins to the protein degradation pathway.

The Automotive Infotainment System-on-Chip (SoCs) market is experiencing significant growth, driven by the rising demand for advanced connectivity and entertainment solutions in vehicles. These SoCs serve as the brain of automotive infotainment systems, integrating multimedia, navigation, and communication features