dataesr/scientific-paragraphs-categorization huggingface dataset:
TinyML Framework Today, many deep learning open source frameworks are available to ease the development of AI applications for server or cloud servers running on supercomputers (unlimited computational resources and memory) based on general purpose CPUs, tensor processing units (TPUs) and graphic processing units (GPUs): PyTorch (Meta), TensorFlow (Google), SageMaker (Amazon), Azure Machine Learning (Microsoft), Core ML (Apple), Caffe (BAIR UC Berkeley), etc. These frameworks are a valuable source of workloads for hardware researchers to explore hardware-software trade-offs. Since 2019, an emerging technology, named tiny machine learning (TinyML), has been addressing the challenges in designing power-efficient (mW range and below), deep learning models, to be integrated into embedded systems such as AIoT/IIoT/IoT devices  [60].
h5 file), the TensorFlow Lite framework converter generates three optimized models: int8 quantization, integer dynamic quantization and float16. One of the three files (e. g. , int8 quantization tflite file) can be used in another tool such as the X-CUBE-AI  [61]  extension pack of STM32CubeMx  [62]  to generate C codes to be implemented on the ST MCU portfolio. Similar to the TensorFlow for the microcontroller framework, the Apache micro tensor virtual machine (µTVM) deep learning framework is based on two main components (Figure  10 ): µTVM runtime and AutoTVM. AutoTVM extracts operator requirements automatically from a neural network computation graph, and then leverages a machine learning guided optimizer to tune operator implementations for performance on real hardware. The µTVM runtime bloc is mainly a cross compiler to generate binary code to be executed by a target MCU  [63].
The µTVM runtime bloc is mainly a cross compiler to generate binary code to be executed by a target MCU  [63]. The authors show that µTVM enables development of ML applications to be embedded on bare-metal devices such as STM Nucleo-F746ZG, STM STM32F746 Discovery and nRF 5340 Development Kit  [63, 64]. It should be noted that, in general, MCU manufacturers such as ST (X-CUBE-AI), DXP (eIQ ML) and ARM (ARM NN), provide TinyML tools that accept TensorFlow Lite converter file (tflite) to target their MCU portfolios. Figure  11  presents the workflow using the TensorFlow framework to train and validate a deep learning model and to convert the validated model by using a TensorFlow Lite converter: Integer 8-bit Quantized TFLite Model. The generated tflite model is evaluated by using the X-CUBE-AI expansion pack of STM32CubeMX to target the ST MCU portfolio.
00000032). It should be noted that STM32CubeMx generates the model C code to be built and debugged on the STM32CubeIDE, as shown in Figure  15. The input image files of the inference program running on the STM32B-L4S5i board are manually downloaded one by one through the serial port using the Ymodem protocol. Figure  16  shows the results of the inference running on the STL32B-L4S5i discovery kit. The obtained results validate the feasibility of the AIoT device based on the STM32L4 microcontroller portfolio. However, the accuracy of strawberry diseases detection still needs to be validated in cultivated fields where the environment is continuously changing. Online training can continuously improve detection accuracy, but the lack of resources (computing power and memory) is the main obstacle.
One of the solutions is that the CNN training model can be updated remotely, taking into account the evolution of the environment. Another solution is to implement an unsupervised learning CNN algorithm on the target MCU. According to the desktop and target validation results, the accuracy of the model does not change when the model is executed on the STM32B-L4S5i board, the difference between the desktop l2r and the target l2r is near zero (0. 00000032). It should be noted that STM32CubeMx generates the model C code to be built and debugged on the STM32CubeIDE, as shown in Figure  15. The input image files of the inference program running on the STM32B-L4S5i board are manually downloaded one by one through the serial port using the Ymodem protocol. Figure  16  shows the results of the inference running on the STL32B-L4S5i discovery kit.
Conclusions and Perspectives Smart everything is under way, driven by the development of digital twins, metaverse, Industry 4. 0 and autonomous vehicles, and it has enjoyed an incredible synergy of advancements in its core hardware and software technologies such as firmware (Zephyr and OpenThread), deep learning for microcontroller tools (e. g. , X-CUBE-AI), VLSI and MCU (ultra-low power subthreshold and new memory technologies and new asymmetric multicore RISC-V architecture), MEMS/NEMS (fusion of MEMS/NEMS and CMOS processes) and wireless technologies (e. g. , low-energy high-bandwidth and medium-range Wi-Fi Halow). These advances enable the implementation of low-cost, low-power AIoT devices for Industry 4. 0, smart homes, etc. Moreover, the interoperability of AIoT devices will soon be solved (e.
Top: during the training, a visual predictive model is trained on a dataset of executed pick-and-place actions (e. g. , grasping a toy to a specific location). Bottom: using the visual predictive model to optimize appropriate actions for the specific manipulation tasks (e. g. , replacing a sponge's position in the workspace) foresight  [12]  or its follow-up works  [7, 9, 39] , robot actions in this paradigm are usually prescribed to be low level, such as small differential displacements of the robot end-effector. Take the example in Fig. 1. Even to relocate a sponge within a quite short distance, a robot using visual foresight will typically have to apply a fairly long sequence of low-level displacements, while a human expert can come up with more efficient solutions with higher level manipulation actions, such as simply picking (grasping) the sponge up and placing it to the target location directly.
This allows our model to learn both high-level and low-level robot actions from the interval of consecutive frames. The main contributions of this paper are summed up as follows: -We propose a novel visual predictive model for highlevel robot actions containing an action decomposer and a video prediction model. -We present a sampling-based optimization method to utilize this visual predictive model for planning high-level pick-and-place actions in real robot tasks. -We contribute a novel vision dataset that contains a rich set of real robot pick-and-place actions. We evaluate our method in terms of the accuracy of the predicted outcomes of high-level actions and the overall performance of using the predictive model in real robot downstream tasks. The results demonstrate that our approach can substantially learn to understand high-level robot actions and can promisingly be utilized in planning for real robot manipulation tasks.
To train the model, we gather a dataset D = {ξ i } N i=1 of N high-level robot of actions, where each example ξ i contains of a pick-and-place action (P pick , P place ), its low-level decomposition {s 0 , a (low) 0 , a (low) 1 ,. , a (low) T -1 }, and the corresponding visual frames {I 0 , I 1 ,. , I T }.
Dataset We collect a real robot dataset named PandaGrasp-Pick&Place, to train and evaluate the proposed method. While RoboNet  [4]  introduced an autonomous data collection strategy to obtain data on interactions between the robot and objects in openworld environments and released a promising dataset, actions in RoboNet are mostly low-level displacements of the robot's end-effector. To address this limitation, we introduce highlevel pick-and-place action on our dataset. Specifically. [4]  provides a large open dataset containing 150K trajectories of robot manipulation from several robots. Each trajectory in RoboNet records a sequence of visual observations and low-level actions defined as the displacements of the robot end-effector. Despite RoboNet providing a large number of examples, the babbling-like exploration strategy results in a scarcity of high-level actions in the provided examples.
PandaGrasp-Pick&Place As its name indicates, it is a dataset containing the pickand-place actions of a Franka Panda robot. Concretely, as shown in Fig. 7 , each example in the dataset records the robot performing a random pick-and-place action. The robot executes the actions according to the primitives defined in Fig. 3 , which involve approaching the picking position, grasping the object, and moving to the placing position. We record the visual observation of each action as a sequence of 21 frames in length, according to the high-level action decomposer proposed in the section "Method".
This is because the CVAE network learns the pick-and-place actions only from mapping pixels between the initial and the last frames. Our method, however, learns pick-and-place actions through a sequence of intermediate frames, which have more semantic information about the actions. This enables us to correctly predict the sequence of frames up to the last frame we are interested in. We also conduct a comparison with two baselines to evaluate whether or not the visual predictive model still has the ability to learn high-level actions in the absence of such actions. Specifically, the baseline models are trained using only low-level actions (i. e. , the end-effector's displacements), similar to those in RoboNet. To mitigate the bias from the specific robots and environments in our dataset, we acquire a dataset with our setup but adopt the babblinglike methodology in RoboNet.
We refer to this dataset as Panda-Babbling. The compared baselines and our model are as follows: (1) A visual predictive model trained on RoboNet. (2) A visual predictive model pre-trained on RoboNet and fine-tuned on Panda-Babbling. (3) A visual predictive model pre-trained on RoboNet and fine-tuned on PandaGrasp-Pick&Place. Table  2  shows the average quantitative results over the prediction of the entire sequence of pick-and-place actions. The model trained on pick-and-place action data outperforms other models trained only on low-level actions. We then evaluate the models on different stages of pick-and-place actions (Table  3 ). Although the model trained on Panda-Babbling performs better in the short horizon, such as the first two stages, it fails to predict in the long horizon, which is important for pick-and-place actions.
Conclusion and discussion We propose a visual predictive model that learns the highlevel pick-and-place actions in the real robot manipulation environment. The predictive model combines a high-level action decomposer and a video prediction network to learn the intrinsic semantic information of high-level actions. We also expand our previous work  [27]  and contribute a new dataset. PandaGrasp-Pick&-Place contains 5K examples of a Franka Panda robot executing pick-and-place actions. In our experiments, we find that our method outperforms a CVAE network to predict the target frame conditional on the initial frame and the pick-and-place action. By comparing different visual predictive models that are trained on high-level action or not, we find that our proposed method can substantially learn the pick-and-place actions.
The collision interaction mechanism between a vibrating string and a non-resonant obstacle is at the heart of many musical instruments. This paper focuses on the identification of perceptually salient auditory features related to this phenomenon. The objective is to design a signal-based synthesis process, with an eye towards developing intuitive control strategies. To this end, a database of synthesized sounds is assembled through physics-based emulation of a string/obstacle collision, in order to characterize the effect of collisions on time-frequency content. The investigation of this database reveals characteristic timefrequency patterns related to the position of the obstacle during the interaction. In particular, a frequency shift of certain modes is apparent for strong interactions, which, alongside the generation of new frequency components, leads to increased perceived roughness and inharmonicity.
For example, Gan proposes an interactive multimodal simulation platform based on impact sounds  [30]   [31]  that could be compatible with the model proposed in our study. The outcomes of such a study can be useful for sonic interaction design  [32]  and the synthesis of new sounds  [33]. Lately, various studies have used data-driven methods for sound generation with convincing results  [34]   [35]. Our approach is complementary to these approaches: the direct modelling of sound morphologies is fully interpretable and transparent but requires more information a priori and more care in the model design. In future studies it would be interesting to link the two approaches using physical and signal models to create large deep-learning training datasets. Our approach is inspired by the ecological approach to auditory events  [36]   [37].
We believe that the sound signal of these two situations can be modeled in the same way even if the control issues should be considered differently. A sound database that is representative of sounds produced with this type of nonlinear interaction has been assembled. To produce the sounds, we have chosen a physical approach based on recent investigations of numerical modeling of collisions in musical instruments  [42]  [43] that generates realistic sounds defined by physical parameters  [20]  (see Section II). The use of a physical model rather than recorded sounds allows us to generate an infinite number of samples under perfectly controlled conditions. We have nevertheless added a few recorded sounds to the corpus for verification purposes. A reduced number of sounds considered characteristic of the particular phenomenon studied in this paper are presented.
Further, the proposed signal-based synthesis process allows a low-parameter alternative to physical constraints in terms of control while retaining the perceptual characteristic of interacting with an obstacle. In practice, the sound morphologies reproduced by the signal model and characterizing the non linear interaction could be applied to different virtual objects or sound textures by modifying the initial signal (defined in section III. A). Hence, new tools could be developed for sound designers, giving them an alternative to databases of recorded sounds for various applications such as video games. This would lead to real-time event-driven synthesis of sounds in virtual or augmented reality environments. Also, the possibility emerges of generating entirely new sounds that carry information contained in the sound invariants.
VI. CONCLUSION & PERSPECTIVES In this paper, we have indentified the sound morphologies responsible for the recognition of nonlinear interactions such as a string colliding with a rigid barrier. We have proposed a signal-based sound synthesis process allowing to retranscribe the disturbance undergone by a vibrating object when it collides with a rigid obstacle. We have determined typical signal behaviors that carry the perceptual information of the nature of the interaction with respect to the location of the rigid barrier by investigating a sound data bank synthesized with a physical model. We have observed a generation of frequency components during the interaction due to harmonic distortion and mode coupling and a frequency shift of some modes for strong interactions which, added to the generation of frequency components, causes a rough and inharmonic signal.
The major element composition of volcanic rocks carries important information about the source and differentiation processes affecting the magma, the physical properties that allow it to erupt, and its eruptive style. Although global rock geochemical databases exist, these are not linked to volcanic eruption history which hampers our global understanding of the relationship between magma composition and eruption dynamics. Here, we integrate two global databases, the Geochemistry of Rocks of the Oceans and Continents (GEOROC) and the Holocene volcanoes of the world of the Global Volcanism Program (VOTW-GVP). The integration is based on matching the location name, geographic position and eruption time, which is automated by a tool called DashVolcano. The tool is open-source, accessible at https://github.
com/feog/DashVolcano, and gives access to the integrated datasets via an interactive dashboard. DashVolcano is based on more than 138,000 volcanic rock samples and provides the basis for the identification of global relationships between eruption styles, volcano types, and rock composition for more than 700 volcanoes and their eruptions for the last 10,000 years. The combined record of the eruptive history and its corresponding geochemical rock composition that DashVolcano provides can be used for characterizing global geochemical differences between volcanoes, and should also prove useful for improved long-term hazard and risk evaluations.
Thus, a comprehensive record of the eruptive histories and their corresponding geochemical rock compositions is critical for long-term evaluations of hazard and risk. The multifaceted and multiparametric nature of volcanic phenomena means that many different databases have been created, but each typically only targets one of the aspects of volcanology  (Andrews et al. , 2022). For instance, much of the information about the composition of volcanic rocks can be gathered from the Geochemistry of Rocks of the Oceans and Continents (GEOROC, https://georoc. eu;  Sarbas and Nohl, 2008 ; DIGIS Team, 2022a; DIGIS Team, 2022b; DIGIS Team, 2022c; DIGIS Team, 2022d; DIGIS Team, 2022e; DIGIS Team, 2022f; DIGIS Team, 2022g; DIGIS Team, 2022h; DIGIS Team, 2022i; DIGIS Team, 2022j) of the Digital Geochemistry Infrastructure (DIGIS), and the Volcanoes of the World (VOTW) of the Smithsonian's Global Volcanism Program (GVP, https://volcano.
si. edu/; Global Volcanism Program, 2013) provides a wealth of background and eruption chronology data for Holocene events. However, much more could be learnt if the individual global volcanological datasets could be jointly explored, potentially unveiling correlations unseen so far. The GEOROC database contains more than 380,000 volcanic rock analyses, with the concentration of their major oxides. Linking the compositional information to their corresponding volcanic episode, would enrich the knowledge provided by GVP database, which includes over 1,400 volcanoes and around 9,800 eruptions. In this paper, we present a tool that automates the integration of GVP and GEOROC databases. We describe 1) how the geochemical and volcanological data integration was done by matching parameters from both databases, and linking their data contents, 2) the complexity and challenges encountered in processing the available datasets, and in automating the linkage between the two resources, 3) data preparation and preconditioning, and 4) the type of information obtained from this integration and the complementarity that it offers.
We created the interactive data exploration dashboard called DashVolcano which provides an intuitive way to visually explore, select, compare, and download datasets. DashVolcano gives access to both GVP and GEOROC datasets, allowing applications for exploring the data available for volcanoes of interest (addition of other datasets is also possible as discussed in Supplementary Appendix II), comparing the rock compositions and eruptive histories of volcanoes, and thus gather evidence of expected behaviors, or form new hypotheses. We also provide a prospective look at potential applications to utilize the integrated datasets, with selected case examples to illustrate how to perform data query, visualization, plot, and download.
Methods We start by summarizing the features of interest from the GVP and GEOROC databases. The GVP database allows to query both volcano and eruption data (see https://volcano. si. edu/). We are using Holocene volcano datasets with known eruption data downloaded from the GVP volcano search URL (https://volcano. si. edu/search_volcano. cfm), including the volcano name, major and minor rocks types, the location, and the tectonic setting (Table  1 ). Hereafter, we only considered confirmed eruptions of GVP (https:// volcano. si. edu/search_eruption. cfm) which typically include the eruption date(s), the volcanic explosivity index (VEI;  Newhall and Self, 1982) , among other data (Table  2 ). As classified by GVP. A detailed explanation of how eruption dates are handled depending on the different types of available data is provided in Supplementary Appendix I.
Overall, GVP contains data for about 1,400 volcanoes and 9,800 confirmed eruptions. The GEOROC database contains rock compositional data, including major element oxides in mass % (wt%), trace elements, and a range of isotope compositions (https://georoc. eu/georoc/newstart. asp) of about 384,000 volcanic sample analyses. It is possible to query the database, or to download precompiled files by locations, which are categorized by tectonic settings. Hereafter we will identify the features in GEOROC in capital letters to make it easier to distinguish between GEOROC features from those from GVP. The features of a rock sample from a precompiled file can include sample name, rock type, eruption date, sample location, and the geochemical data (only a subset of the available chemicals is reported here; Table  3 ).
Linking GEOROC and GVP To link the two databases we need to match GEOROC rock samples to GVP volcanoes and eruptions, for which we matched the geographical location and temporal features as described below (Figure  1 ). In the GVP database, volcanoes are identified by their name and a volcano number. The volcano number is necessary because there are different volcanoes with the same name (e. g. , Cerro Azul is the name of both a volcano in Chile, and also one in the Galapagos islands in Ecuador). Moreover, there are volcanoes which have several names (synonyms), or several spellings of the same name (e. g. , Rinjani/Rindjani in Indonesia). A GVP volcano can contain multiple eruption records, but each eruption belongs to only one volcano. In the GEOROC database, a sample has a LOCATION, a LOCATION COMMENT, a range of latitudes (LATITUDE MIN, LATITUDE MAX) and a range of longitudes (LONGITUDE MIN, LONGITUDE MAX).
The ranges can be reduced to a single point, depending on the precision. The LOCATION is of the form REGION/SUBREGION/SUBSUBREGION; depending on the record, it can have from 1 to 9 (sub) region(s). The tectonic settings use different conventions in the two databases. There is no convention for regions and sub-regions either. Linking the two databases requires matching the volcano names (or locations) in GVP with names appearing in the LOCATION field (or LOCATION COMMENT) of the GEOROC files. The overall process is summarized in Figure  2. Using only the names appearing in the LOCATION field gave too many errors, due to the same or similar names being present in separate places. We decided to use instead the latitude and longitude data first. We used the ranges provided by the GEOROC files, allowing a margin of error of 0.
5 °, and then retained only those GEOROC records whose range contains a latitude-longitude pair corresponding to a GVP volcano, and discarded the rest (Figure  2 ). We then automatically matched names by finding a GVP volcano name in the LOCATION field (or in the LOCATION COMMENT field) of shortlisted samples. Complications arise from 1) having different spellings for the same volcano [e. g. , in the GEOROC records, the names TJERIMAI (TJAREME), CEREME, CIREMAI are found for the GVP name Cereme], 2) having different names used in both databases (e. g. , the GEOROC name TERNATE corresponds to the GVP name Gamalama), 3) linking several GEOROC location names to the same GVP volcano, 4) identifying samples belonging to the same volcano even though they may appear in different GEOROC files. Once the automatic matching was completed, part of the resulting data were checked manually.