dataesr/scientific-paragraphs-categorization huggingface dataset:
With the explosion of data volume and the deepening of model complexity, people began to separate the training and application of the model. Models are pre-trained based on large offline datasets, saved, and then applied to the problems using the transfer learning technique for quick solutions. BERT, proposed by Google AI Research Institute, and GPT-3, developed by OpenAI, are the two most well-known pre-trained models  [54, 55]. They have achieved great success in natural language processing  [56]. Artificial neural networks are key building blocks for deep learning (DL) systems, including deep reinforcement learning (DRL) systems. DRL systems use multi-layer neural networks to solve Markov decision problems (MDPs)  [57]. Both single-agent and multi-agent DRL models are increasingly being used to solve a variety of computing problems (e.
Perceptual Intelligence An intelligent system first starts intelligent perception before it starts to work. Thus perceptual intelligence plays a vital role in all intelligent systems. Perceptual intelligence focuses on multimodal perception, data fusion, smart signal extraction, and processing. Typical examples include smart city management, automatic diving system, smart defense system, and autonomous robots. A most consistent enthusiasm for perceptual intelligence is human-like five-sense capabilities, including vision, hearing, smelling, tasting, and tactile. In addition, intelligent sensing covers temperature, pressure, humidity, height, speed, gravity, etc. , whenever a significant effort in computing or data training is required to advance its performance. Significant advances have emerged in machine vision during the last several decades, emphasizing the creation of devices that can see and understand their surroundings independently.
Cognitive Intelligence Cognitive intelligence refers to machines having the capacity to logically understand and cognize like humans, especially the ability to think, comprehend, summarize, and actively apply knowledge. It describes the abilities and skills to process complex facts and situations in the real environment, like interpretation and planning. Data recognition is the core function of perceptual intelligence, which requires large-scale data collecting and feature extraction of images, videos, sounds, and other types of data to complete structured processing. In contrast, cognitive intelligence requires understanding the relationship between data elements, analyzing the logic within the structured data, and responding based on the distilled knowledge. Cognitive intelligent computing primarily studies the topics of natural language processing, causal inference, and knowledge reasoning of machines.
As an effective way to express knowledge, a knowledge graph connects entities through rich se-mantic relations and constructs a systematic and semi-structured knowledge base. Knowledge graphs have been widely used in vertical application fields such as medical care, e-commerce, finance, public security, transportation, and intelligent question-answering. Typical knowledge graphs include YAGO  [111] , DBpedia  [112] , Freebase  [113] , Wikidata  [114] , etc. These knowledge graphs extract, organize and manage knowledge from many data resources and then store and represent knowledge as triples. They help understand the semantics of search and provide accurate search answers. Knowledge reasoning based on knowledge graphs mainly focuses on relations, that is, inferring unknown facts or relations based on existing ones in the graph or identifying and correcting errors in existing entities, relations, and graph structures based on prior knowledge and experience.
Autonomous Learning Meta-learning can handle the general solution model of a specific type of task by learning from similar task sets and can transfer learning between tasks. However, this learning ability can only transfer between homogeneous tasks, where even the support and query set sizes of tasks are strictly aligned. Autonomous learning aims to transform from passive data acceptance and training to active learning and improve learning efficiency, which is the direction considered by Turing Award winner Yann LeCun  [134]. In addition to higher-level transfer learning capabilities, models of the external open world are incorporated into the design of autonomous intelligent architectures. Humans and other animals have always been able to learn a great deal of background knowledge about how everything works in an unsupervised manner through observation and a small amount of interaction.
Human-Machine Integration The theory of human-machine fusion intelligence focuses on a new form of intelligence produced through interaction between humans, machines, and environments. It is a brand-new generation of intelligent scientific systems that combine physical and biological characteristics. Human-machine fusion intelligence, which effectively mixes objective data collected by hardware sensors and subjective information perceived by human senses, integrates the profound cognitive way of people and the superior computing power of computers  [137, 138]. It utilizes human prior domain knowledge as important learning clues to construct new understanding approaches and enhances computerbased decision-making. With the new methodology, computers can manage complex problems in professional fields that neither humans nor machines are capable to handle alone.
The HBCI system has two essential features: information fusion and control strategy. Information fusion includes data level fusion, feature level fusion, and decision level fusion according to the level of information representation. Data level fusion directly fuses the signal data obtained by different sensors. Feature level fusion combines the feature vector extracted from the data obtained by each sensor. Decision level fusion outputs the decision results of the overall system according to voting or weight calculation of classified decisions, which are processed by each sensor separately. According to the control strategies, HBCI system processes input signals by adopting the simultaneous mode  [148, 149]  or sequential mode  [150, 151]. A collaborative BCI (CBCI), which can be applied to group collaboration to improve system performance by increasing the user dimension, is proposed  [152].
35 Downloaded from https://spj. science. org on January 26, 2023 • IDrug [174] by Tencent offers a drug development platform that integrates the strengths of the latest algorithms, databases as well as hardware. The operating time for computer-aided drug search iteration is significantly reduced by utilizing powerful computing resources (e. g. , NVIDIA GPU). IDrug facilitates creating and aggregating fresh data while integrating several current databases. IDrug offers services related to preclinical drug development, covering protein structure prediction, visualization, synthesis routing, and molecular design. • EasyDL  [175]  features a thresholdless deep learning platform from Baidu Brain that utilizes P4 and P40 GPUs from NVIDIA's Tesla series for the majority of machine learning workloads.
For fundamental tasks, the PaddlePaddle framework and the AI workflow engine are combined  [176]. Typically, business researchers with training in AI development should use the EasyDL. • Amazon AI [177] (AWS) leverages Amazon Web Services via the cloud. The major characteristics of Amazon AI are flexibility, configurable, and simplicity of installation. AWS supplies a full range of resources, including a variety of popular Python tools and libraries, besides security features. • VenusAI  [173]  is a supercomputer-based method that extends the virtualization and containerization of primary hardware. VenusAI provides a technology mechanism for aggregating and allocating diverse resources. VenusAI also has a uniform interface for resources at the layer of application services. The abovementioned platforms range from commercial cloud deployments to industry-specific platforms requiring complex integration with scientific investigations.
In-Memory Computing It's clear that the way computers are used is rapidly evolving. According to von Neumann's model of computation, a computer retrieves the data and code it needs to carry out its instructions from a central repository called memory. Nevertheless, the performance gap between the memory and processor is widening despite improvements in memory devices. Breakthroughs like deep learning and IoT have a particularly severe case of this issue. Since handling such massive datasets exceeds the capabilities of the von Neumann architecture, such applications provide significant difficulty. When memory cells are given the ability to execute elementary logic operations, computing-inmemory (CIM) becomes a viable option since it can compute independently of a central processor unit  [192] [193] [194] [195].
One subclass of neuromorphic approaches relies on generating and manipulating "spikes" in analog neural networks. The frequency that spikes appear, their magnitude, and their shape can be employed to store numerical data in neuromorphic computers, whereas von Neumann computers encode information as binary values. The conversion of binary values into spikes and vice versa is still a subject of study in neuromorphic computing  [226]. Figure  15 : Structures of conventional computing systems and brain-inspired computing systems  [227]. The figure illustrates that based on the three-tiered structure of conventional computing systems (right), a brain-inspired computing system (left) consisting of software (on the top), a compiler (in the center), and hardware (at the bottom) were proposed. Applications and Turing-complete programming languages (like JAVA and Python) make up the software layer of the architecture of a conventional computer system.
Intermediate software representations, such as the abstract syntax tree, are transformed into hardware representations, such as instructions, throughout the compilation process. The instructions are executed by CPUs or GPUs that adhere to the von Neumann architecture at a hardware level. ALU, CPU, ROM, RAM, and I/O are all von Neumann architecture components. Turing completeness guarantees the exact equality of all stacks. The neuromorphic applications and frameworks for their development constitute the software layer of a computer system inspired by the human brain (such as Nengo and PyTorch). The POG represents software at this stage, and the EPG represents hardware at this stage (CFG, control-flow graph). The POG is presented before the compilation tools are used to convert it to the EPG.
A TIA is used to amplify and voltage-convert the photocurrent i sum. Adjusting the supply light makes it possible to produce the optical output of neurons. Optical neural networks (ONNs) offer many benefits over electrical neural networks, including ultra-high bandwidth, fast calculation speed, and high parallelism, all of which are realized by using photonic hardware acceleration to calculate complicated matrix-vector multiplication  [210, 238, 239]. To keep up with the ever-increasing complexity of data processing techniques and the volume of datasets, we need deeply integrated and scalable ONN systems with compact sizes and decreased energy usage. Light's superposition and coherence features allow ONN neurons to be naturally coupled by interference  [240]  or diffraction  [241]  in diverse contexts, while a wide range of nonlinear optical effects  [242]  may be used to implement the activation function of the neurons physically.
Their work also demonstrates the promising future of DeepMD in the study of transport properties of solid-state electrolytes. Recently, He et al. studied the structure phase transitions of SrTiO3 using DeepMD  [269]. The temperature-driven phase transition characters under different in-plane strains were studied via the model build using DeepMD. Figure  18 : High-level comparison of paradigms for materials/molecular sciences  [270]. The conventional paradigm is described on the left and illustrated using organic redox flow batteries in the middle. On the right is a model of a closed-loop system. Inverse engineering, intelligent software, AI/machine learning, embedded systems, and robotics are all necessary components of a closed-loop system. Phase field simulations can illustrate the microstructure evolution over time at the continuum thermodynamic and kinetical levels.
Data are collected from several calculations of the physical model with different input parameters and applied in ML-model training. Usually, the ML surrogate model can be evaluated far faster than the original physical model, while both models have nearly the same accuracy. The several orders acceleration on computational speed allows for a global search in the design and optimization space. Typical ML algorithms that can be applied to train a multi-fidelity surrogate model include Kriging/Gaussian process  [283] , LSTM Networks  [271, 284] , Physics-informed Neural 52 Downloaded from https://spj. science. org on January 26, 2023 Networks  [285] , and CNNs  [286]. For cases without available physical models, a surrogate model can be trained directly from experimental data to substitute a yet-unknown physical model.
Computing for Astronomy Astronomy has gathered vast amounts of data in history as one of the most ancient observational sciences. Thanks to the breakthroughs in telescopic technologies that generate digital outputs, there has recently been a tremendous data explosion. The field of astronomy and astrophysics is characterized by a wealth of data and a variety of ground-based telescopes with big apertures, for example, the upcoming large synoptic survey telescope and the space-based telescopes  [292]. Data collection is now more efficient and largely automated using high-resolution cameras and associated tools. The system will collect roughly 15TB of data daily  [293]. With respect to effective decisionmaking, it is imperative to have more effective data analysis. Hence, intelligent computing techniques are needed to interpret and evaluate that dataset.
According to the latest CASP14 analysis, AlphaFold provides the most precise estimation of 3D protein structures  [311]. In addition to considering different constraints (evolutionary, physical, and geometric) related to protein structures, AlphaFold implements a neural network architecture based on protein data banks. • SwissDrugDesign  [312] , a product of the Swiss Institute of Bioinformatics, is one of the most widely used AI platforms for drug design. • Synthia by Merck, an upgraded version of Chematica, suggests potential synthesis routes based on compound information. The AI application can provide multiple synthetic routes for the target molecule by adjusting the search options. Chematica was developed by Klucznik et al. to generate synthetic procedures for eight commonly occurring compounds and subsequently conduct experiments on them.
The detection of active compounds through huge chemical libraries is one of the initial stages in the drug development process  [319]. High-throughput screening (HTS) now rules this phase  [320]. Large chemical libraries are screened via HTS using assays relevant to the study. Instead of calculating behaviors in silico, it offers the benefit of testing them empirically. HTS is not constantly necessary yet. Large libraries are expensive to experimentally screen because they only contain a tiny portion of the chemical space. Additionally, not all assays can be carried out on a large enough scale; generally, negotiation must be made between the amount and quality of experimental data acquired for each test to get the best possible results. Virtual screening (VS) is an alternate method that may be used in addition to or instead of HTS  [321, 322].
Large-city population development has brought the additional challenge of managing more complicated road networks. Because of this, traffic management also necessitates analysis, forecasting, and smart action  [350]. For instance, cities have been developing integrated traffic management systems for the optimization of traffic flow in real-time, such as City Brain in Hangzhou, China. These technologies take advantage of copious urban monitoring data captured by a variety of sensors. In addition, the difficulty of simulating the urban brain system increases with the complexity of traffic networks. Important considerations include  [351]  (1) speeding up computations on enormous synchronized heterogeneous network configurations through parallel heterogeneous computing; (2) visually simulating an urban setting and building algorithms for flexible perception with strong environmental robustness.
Acknowledgement This work was partially supported by the  Key Research Project  (No. K2022PD1BB01 , No. 2020NB2GA01 , No. U21A20488 , No. 2022PI0AC01 ), the  Exploratory Research  project (No. 2022KG0AN01 ), and the  Center-initiated Research Project  (No. 2022ME0AL02 ) of  Zhejiang Lab , the  National Key Research and Development Program of China  (No. 2022YFB4500300 ), the  National Natural Science Foundation of China  under Grant (No. 62271452 , No. 62172372 ), and the  Natural Science Foundation of Zhejiang Province, China  (No. LZ21F030001 ).
Hydrostatics Hydrostatic forces can be computed using a fully nonlinear model, integrating the hydrostatic pressure over a 3D surface mesh of the hull. Alternatively, and for small motion around the equilibrium position, hydrostatic forces can be computed using a 6 × 6 linear hydrostatic stiffness matrix K HS  (13). F HS = K HS (X u -X eq ) (13) Where non-zero coefficients can be easily computed with  (14)  and all other coefficients are equal to zero for a symmetric hull. K HS 3,3 = ρ∇gA wp K HS 4,4 = ρ∇gGM T K HS 5,5 = ρ∇gGM L K HS 3,5 = ρgx B S f = K HS 5,3 (14) 3 Numerical method for steady ship behavior The present method was implemented in the open-source program xWASP CN, licensed under the weak-copyleft Eclipse Public License v2. 0 (EPLv2). It relies heavily on the ship simulator xdyn -developed by Sirehna (Naval Group) -for the computation of forces and the time integration (for dynamic computations).
Experimental data points A set of static data points was obtained from tests with the Farwind prototype. Each static data point was obtained by averaging times series over a dynamic steady state period. Unfortunately, the tests did not produce results for a range of TWA at fixed TWS or for a range of TWS at fixed TWA, so the 15 data points have to be studied separately. These data points are presented in Table  3. HDG and TWA denote the two control strategies used on the prototype. The HDG strategy is for heading control while TWA is for True Wind Angle control. Note that the standard variation of the True Wind Angle is not significantly lower in TWA control mode.
Then, in Section III, we propose a unified framework allowing to estimate the tail parameter of the elliptical distribution using the proposed scale statistic η. In the case of the MVT distribution, this leads to a new estimate of the d. o. f. parameter based on Tyler's weights. Finally, Section IV demonstrates the relevance of the proposed approach on simulated data, with concluding remarks in Section V. In the R package fitHeavyTail  [16] , the function fit_Tyler implements this method.
5 T superconducting solenoid and provides energy and timing measurements for photons and electrons. A K 0 L and muon detection subsystem is installed in the iron flux return of the solenoid. The z axis of the laboratory frame is defined as the central axis of the solenoid, with its positive direction defined as the direction opposite the positron beam. Simulation is used to train the binary classifier that provides the tagging prediction. The simulation uses KKMC  [20]  to generate quark-antiquark pairs from e + e -collisions, Pythia8  [21]  to simulate the quark hadronization, EvtGen  [22]  to decay the hadrons, and GEANT4  [23]  to simulate the detector response. Events are reconstructed using the Belle II software  [24, 25].
VI. SUMMARY We developed a novel charm-flavor tagging algorithm for Belle II that determines the production flavor of a signal neutral D meson. The algorithm exploits the correlation between the production flavor and the electric charges of particles reconstructed in the rest of the event, i. e. , those originating from the decay of the other charmed hadron produced in the e + e -→ cc process and those produced in association with the signal D meson (e. g. , in the decay of a parent D * ± meson). The tagger uses boosted decision trees trained on simulated data. Its response is calibrated and evaluated using several self-tagged decays of charmed hadrons reconstructed in 362 fb -1 of Belle II data. The effective tagging efficiency is measured in data to be (47. 91 ± 0. 07(stat) ± 0. 51(syst))%, independent of the signal neutral-D decay mode.
2020. 02/0257 , and  Ministry of Education and Science of Ukraine ; the  U. S. National Science Foundation  and research Grants No. PHY-1913789  and No. PHY-2111604 , and the  U. S. Department of Energy  and research Awards No. DE-AC06-76RLO1830 , No. DE-SC0007983 , No. DE-SC0009824 , No. DE-SC0009973 , No. DE-SC0010007 , No. DE-SC0010073 , No. DE-SC0010118 , No. DE-SC0010504 , No. DE-SC0011784 , No. DE-SC0012704 , No. DE-SC0019230 , No. DE-SC0021274 , No. DE-SC0022350 ; and the  Vietnam Academy of Science and Technology (VAST)  under Grant No. DL0000. 05/21-23. These acknowledgements are not to be interpreted as an endorsement of any statement made by any of our institutes, funding agencies, governments, or their representatives. We thank the  SuperKEKB team  for delivering high-luminosity collisions; the  KEK cryogenics group  for the efficient operation of the detector solenoid magnet; the KEK computer group and the  NII  for on-site computing support and SINET6 network support; and the raw-data centers at  BNL ,  DESY ,  GridKa ,  IN2P3 ,  INFN , and the  University of Victoria  for offsite computing support.
2019, was supported by Science Committee of the Republic of Armenia Grant No. 20TTCG-1C010; Australian Research Council and research Grants No. DE220100462, No. DP180102629, No. DP170102389, No. DP170102204, No. DP150103061, No. FT130100303, No. FT130100018, and No. FT120100745; Austrian Federal Ministry of Education, Science and Research, Austrian Science Fund No. P 31361-N36 and No. J4625-N, and Horizon 2020 ERC Starting Grant No. 947006 "InterLeptons"; Natural Sciences and Engineering Research Council of Canada, Compute Canada and CANARIE; Chinese Academy of Sciences and research Grant No. QYZDJ-SSW-SLH011, National Natural Science Foundation of China and research Grants No. 11521505, No. 11575017, No. 11675166, No. 11761141009, No. 11705209, and No. 11975076, LiaoNing Revitalization Talents Program under Contract No.
XLYC1807135, Shanghai Pujiang Program under Grant No. 18PJ1401000, Shandong Provincial Natural Science Foundation Project ZR2022JQ02, and the CAS Center for Excellence in Particle Physics (CCEPP); the Ministry of Education, Youth, and Sports of the Czech Republic under Contract No. LTT17020 and Charles University Grant No. SVV 260448 and the Czech Science Foundation Grant No. 22-18469S; European Research Council, Seventh Framework PIEF-GA-2013-622527, Horizon 2020 ERC-Advanced Grants No. 267104 and No. 884719, Horizon 2020 ERC-Consolidator Grant No. 819127, Horizon 2020 Marie Sklodowska-Curie Grant Agreement No. 700525 "NIOBE" and No. 101026516, and Horizon 2020 Marie Sklodowska-Curie RISE project JENNIFER2 Grant Agreement No. 822070 (European grants); L'Institut National de Physique Nucléaire et de Physique des Particules (IN2P3) du CNRS (France); BMBF, DFG, HGF, MPG, and AvH Foundation (Germany); Department of Atomic Energy under Project Identification No.