Miracle-dz/UniFAR-FTUs huggingface dataset:
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction

Chinese spelling correction (CSC) is a task to detect and correct spelling errors in texts. CSC is essentially a linguistic problem, thus the ability of language understanding is crucial to this task. In this paper, we propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) for CSC, which jointly learns how to understand language and correct spelling errors. To this end, PLOME masks the chosen tokens with similar characters according to a confusion set rather than the fixed token “[MASK]” as in BERT. Besides character prediction, PLOME also introduces pronunciation prediction to learn the misspelled knowledge on phonic level. Moreover, phonological and visual similarity knowledge is important to this task. PLOME utilizes GRU networks to model such knowledge based on characters’ phonics and strokes. Experiments are conducted on widely used benchmarks. Our method achieves superior performance against state-of-the-art approaches by a remarkable margin. We release the source code and pre-trained model for further use by the community (https://github.com/liushulinle/PLOME).
How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis

Recently, there has been a trend to investigate the factual knowledge captured by Pre-trained Language Models (PLMs). Many works show the PLMs’ ability to fill in the missing factual words in cloze-style prompts such as ”Dante was born in [MASK]. ” However, it is still a mystery how PLMs generate the results correctly: relying on effective clues or shortcut patterns? We try to answer this question by a causal-inspired analysis that quantitatively measures and evaluates the word-level patterns that PLMs depend on to generate the missing words. We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred. Our analysis shows: (1) PLMs generate the missing factual words more by the positionally close and highly co-occurred words than the knowledge-dependent words; (2) the dependence on the knowledge-dependent words is more effective than the positionally close and highly co-occurred words. Accordingly, we conclude that the PLMs capture the factual knowledge ineffectively because of depending on the inadequate associations.
A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

Predicting where a person is looking is a complex task, requiring to understand not only the person’s gaze and scene content, but also the 3D scene structure and the per-son’s situation (are they manipulating? interacting or observing others? attentive? ) to detect obstructions in the line of sight or apply attention priors that humans typically have when observing others. In this paper, we hypothesize that identifying and leveraging such priors can be better achieved through the exploitation of explicitly derived multimodal cues such as depth and pose. We thus propose a modular multimodal architecture allowing to combine these cues using an attention mechanism. The architecture can naturally be exploited in privacy-sensitive situations such as surveillance and health, where personally identifiable information cannot be released. We perform extensive experiments on the GazeFollow and VideoAttentionTarget public datasets, obtaining state-of-the-art performance and demonstrating very competitive results in the privacy setting case.1
Limit theorems on large deviations for Markov stochastic processes

0.1 Problems on large deviations for stochastic processes. - 0.2 Two opposite types of behaviour of probabilities of large deviations. - 0.3 Rough theorems on large deviations the action functional. - 0.4 Survey of work on large deviations for stochastic processes. - 0.5 The scheme for obtaining rough theorems on large deviations. - 0.6 The expression: k (? ) S (? ) is the action functional uniformly over a specified class of initial points. - 0.7 Chapters 3 - 6: a survey. - 0.8 Numbering. - 1. General Notions, Notation, Auxiliary Results. - 1.1. General notation. Legendre transformation. - 1.2. Compensators. Levy measures. - 1.3. Compensating operators of Markov processes. - 2. Estimates Associated with the Action Functional for Markov Processes. - 2.1. The action functional. - 2.2. Derivation of the lower estimate for the probability of passing through a tube. - 2.3. Derivation of the upper estimate for the probability of going far from the sets $$ {{\Phi }_{{{{x}_{0}} \left[ {0,T} \right]}}}\left( i \right),{{\bar{\Phi }}_{{{{x}_{0}} \left[ {0,T} \right]}}}\left( i \right) $$. - 2.4. The truncated action functional and the estimates associated with it. - 3. The Action Functional for Families of Markov Processes. - 3.1. The properties of the functional $$ {{S}_{{{{T}_{1}},{{T}_{2}}}}}\left( \phi \right) $$. - 3.2. Theorems on the action functional for families of Markov processes in Rr. The case of finite exponential moments. - 3.3. Transition to manifolds. Action functional theorems associated with truncated cumulants. - 4. Special Cases. - 4.1. Conditions A - E of 3.1. - 3.3. - 4.2. Patterns of processes with frequent small jumps. The cases of very large deviations, not very large deviations, and super-large deviations. - 4.3. The case of very large deviations. - 4.4. The case of not very large deviations. - 4.5. Some other patterns of not very large deviations. - 4.6. The case of super-large deviations. - 5. Precise Asymptotics for Large Deviations. - 5.1. The case of the Wiener process. - 5.2. Processes with frequent small jumps. - 6. Asymptotics of the Probability of Large Deviations Due to Large Jumps of a Markov Process. - 6.1. Conditions imposed on the family of processes. Auxiliary results. - 6.2. Main theorems. - 6.3. Applications to sums of independent random variables. - References.
B-Jump: Roller length, sequent depth, and relative energy loss using Artificial Neural Networks

The phenomenon of the hydraulic jump is so complex that despite considerable laboratory and prototype studies, estimation of its main characteristics in a generalized and accurate form is still difficult. The Artificial Neural Network (ANN) approach aims at limiting the needs for costly and timeconsuming experiments. In this study, twoANN models, multi-layer perceptron using back propagation algorithm (MLP/BP) and radial basis function using orthogonal least-squares algorithm (RBF/OLS), were used to predict the roller length, sequent depth, and the relative energy loss of the B-jump. Based on a pre-specified range of jump parameters, the input vectors include: upstream bed slope (tan ? ), inflow depth h1, approach velocity V1 and elevation of jump toe from the datum plane z1, generated from the experimental data of Hager (J. Hydraul. Res., IAHR, 26(5), 539–558, 1988) and Kawagoshi and Hager (J. Hydraul. Res., IAHR, 28(4), 461–480, 1990). Once the network is trained to an acceptable level of accuracy, it produces an output of jump roller length, sequent depth, and relative energy loss for any input vector. The predicted values agree well with measurements. Sensitivity analysis was performed to investigate the importance of each input neuron. Finally a matrix of weights was specified for use at any given location
The Saga of the Ojo Alamo Sandstone; or The Rock-Stratigrapher and the Paleontologist Should be Friends

Abstract The Ojo Alamo was named by Brown in 1910 for exposures south of the store at Ojo Alamo Arroyo. Brown said that the Ojo Alamo was a dinosaur-bearing shale unit overlain by a conglomerate bed. Brown put no lower boundary on the Ojo Alamo. In 1914 Sinclair and Granger raised the upper contact of the Ojo Alamo to include the overlying conglomerate because they found “the centrum of a dinosaurian caudal vertebra . loose on the surface . ”of the “conglomeratic sandstone with much fossil wood. ” They also described a lower conglomerate, 6 to 8 feet thick, 58 feet below the upper conglomerate. Sinclair and Granger, also, did not place a lower boundary on the Ojo Alamo. In 1916 Bauer redefined the Ojo Alamo as “a sandstone including lenses of shale and conglomerate. ” He agreed with Sinclair and Granger that the top of the Ojo Alamo was the top of the conglomeratic sandstone, but he placed the base of the Ojo Alamo at the base of the lower conglomerate of Sinclair and Granger. Reeside accepted Bauer’s redefinition in 1924 and mapped the Ojo Alamo as a conglomeratic sandstone throughout the western San Juan Basin. Reeside, however, designated the Ojo Alamo as Tertiary (? ). Dane discussed the Ojo Alamo in 1936, and also agreed with Bauer’s definition of it, but suggested that it “should be classified as Cretaceous. ” Dane mapped the Ojo Alamo throughout the southeastern San Juan Basin as a coarse-grained, in places conglomeratic, sandstone. In 1966 Baltz, Ash, and Anderson suggested that “the name Ojo Alamo Sandstone be restricted to apply only to the upper conglomerate of Bauer’s type Ojo Alamo Sandstone on Ojo Alamo Arroyo. ” This redefinition of the Ojo Alamo ironically eliminated the last remnant of Brown’s original Ojo Alamo on Ojo Alamo Arroyo from the restricted Ojo Alamo of Baltz, Ash, and Anderson. In 1973 Powell recommended that the revision of the Ojo Alamo by Baltz, Ash, and Anderson be dropped and that the original rock-stratigraphic definition of the Ojo Alamo be revived. Powell further divided the Ojo Alamo into a lower member, the Naashoibito Member which was defined by Baltz, ash, and Anderson (1966) as a member of the Kirtland Shale and an upper member, the Kimbeto Member. During the time that the definition of the Ojo Alamo was evolving, as briefly outlined above, the fossil vertebrate remains collected by Barnum Brown, C.W. Gilmore, and others in the Ojo Alamo Arroyo area were being referred to in paleontological papers in publications throughout the world. Nearly all these publications referred to the Ojo Alamo as a dinosaur-bearing shale unit in much the same sense as the original definition of Brown. Thus, today the name Ojo Alamo has two distinct meanings: to rock-stratigraphers, it is a conglomeratic sandstone and shale sequence; to biostratigraphers, it is a shale unit containing a distinctive vertebrate fauna.
Search for a muonic dark force at BABAR

J. P. Lees, V. Poireau, V. Tisserand, E. Grauges, A. Palano, G. Eigen, D. N. Brown, Yu. G. Kolomensky, H. Koch, T. Schroeder, C. Hearty, T. S. Mattison, J. A. McKenna, R. Y. So, V. E. Blinov, A. R. Buzykaev, V. P. Druzhinin, V. B. Golubev, E. A. Kravchenko, A. P. Onuchin, S. I. Serednyakov, Yu. I. Skovpen, E. P. Solodov, K. Yu. Todyshev, A. J. Lankford, J. W. Gary, O. Long, A. M. Eisner, W. S. Lockman, W. Panduro Vazquez, D. S. Chao, C. H. Cheng, B. Echenard, K. T. Flood, D. G. Hitlin, J. Kim, T. S. Miyashita, P. Ongmongkolkul, F. C. Porter, M. Röhrken, Z. Huard, B. T. Meadows, B. G. Pushpawela, M. D. Sokoloff, L. Sun, ∗ J. G. Smith, S. R. Wagner, D. Bernard, M. Verderi, D. Bettoni, C. Bozzi, R. Calabrese, G. Cibinetto, E. Fioravanti, I. Garzia, E. Luppi, V. Santoro, A. Calcaterra, R. de Sangro, G. Finocchiaro, S. Martellotti, P. Patteri, I. M. Peruzzi, M. Piccolo, A. Zallo, S. Passaggio, C. Patrignani, † B. Bhuyan, U. Mallik, C. Chen, J. Cochran, S. Prell, H. Ahmed, A. V. Gritsan, N. Arnaud, M. Davier, F. Le Diberder, A. M. Lutz, G. Wormser, D. J. Lange, D. M. Wright, J. P. Coleman, E. Gabathuler, D. E. Hutchcroft, D. J. Payne, C. Touramanis, A. J. Bevan, F. Di Lodovico, R. Sacco, G. Cowan, Sw. Banerjee, D. N. Brown, C. L. Davis, A. G. Denig, M. Fritsch, W. Gradl, K. Griessinger, A. Hafner, K. R. Schubert, R. J. Barlow, ‡ G. D. Lafferty, R. Cenci, A. Jawahery, D. A. Roberts, R. Cowan, R. Cheaib, S. H. Robertson, B. Dey, N. Neri, F. Palombo, L. Cremaldi, R. Godang, § D. J. Summers, P. Taras, G. De Nardo, C. Sciacca, G. Raven, C. P. Jessop, J. M. LoSecco, K. Honscheid, R. Kass, A. Gaz, M. Margoni, M. Posocco, M. Rotondo, G. Simi, F. Simonetto, R. Stroili, S. Akar, E. Ben-Haim, M. Bomben, G. R. Bonneaud, G. Calderini, J. Chauveau, G. Marchiori, J. Ocariz, M. Biasini, E. Manoni, A. Rossi, G. Batignani, S. Bettarini, M. Carpinelli, ¶ G. Casarosa, M. Chrzaszcz, F. Forti, M. A. Giorgi, A. Lusiani, B. Oberhof, E. Paoloni, M. Rama, G. Rizzo, J. J. Walsh, A. J. S. Smith, F. Anulli, R. Faccini, F. Ferrarotto, F. Ferroni, A. Pilloni, G. Piredda, C. Bünger, S. Dittrich, O. Grünberg, M. Heß, T. Leddig, C. Voß, R. Waldi, T. Adye, F. F. Wilson, S. Emery, G. Vasseur, D. Aston, C. Cartaro, M. R. Convery, J. Dorfan, W. Dunwoodie, M. Ebert, R. C. Field, B. G. Fulsom, M. T. Graham, C. Hast, W. R. Innes, P. Kim, D. W. G. S. Leith, S. Luitz, V. Luth, D. B. MacFarlane, D. R. Muller, H. Neal, B. N. Ratcliff, A. Roodman, M. K. Sullivan, J. Va’vra, W. J. Wisniewski, M. V. Purohit, J. R. Wilson, A. Randle-Conde, S. J. Sekula, M. Bellis, P. R. Burchat, E. M. T. Puccio, M. S. Alam, J. A. Ernst, R. Gorodeisky, N. Guttman, D. R. Peimer, A. Soffer, S. M. Spanier, J. L. Ritchie, R. F. Schwitters, J. M. Izen, X. C. Lou, F. Bianchi, F. De Mori, A. Filippi, D. Gamba, L. Lanceri, L. Vitale, F. Martinez-Vidal, A. Oyanguren, J. Albert, A. Beaulieu, F. U. Bernlochner, G. J. King, R. Kowalewski, T. Lueck, I. M. Nugent, J. M. Roney, B. Shuve, N. Tasneem, T. J. Gershon, P. F. Harrison, T. E. Latham, R. Prepost, and S. L. Wu
Operating system support for modern applications

Computer systems now run drastically different workloads than they did two decades ago. The enormous advances in hardware power, such as processor speed, memory and storage capacity, and network bandwidth, enable them to run new kinds as well as a large number of applications simultaneously. Software technologies, such as garbage collection and multi-threading, also reshape applications and their behaviors, introducing more challenges to system resource management. However, existing general-purpose operating systems do not provide adequate support for these modern applications. These operating systems were designed over two decades ago, when garbage-collected applications were not prevalent and users interacted with systems using consoles and command lines, rather than graphical user interfaces. As a result, they fail to allow necessary coordinations among resource management components to ensure consistent performance guarantees. For example, garbage-collected applications cannot adjust themselves to maintain high throughput under dynamic memory pressure, simply because existing virtual memory managers do not collect and expose enough information to them. Furthermore, despite the increasing demand of supporting co-existing interactive applications in desktop environment, resource managers (especially memory and disk I/O) mostly focus on optimizing throughput. They each work independently, ignoring the response time requirements that the CPU scheduler attempts to satisfy. Consequently, pressure on any of these resources can significantly degrade application responsiveness. In order to deliver robust performance to these modern applications, an operating system has to coordinate its resource managers (e.g., CPU, memory, and disk I/O), as well as cooperate with resource managers in the user space, such as the garbage collector and the thread manger. To support garbage-collected applications, we present CRAMM, a system that enables them to predict an appropriate heap size using information supplied by the underlying operating system, allowing them to maintain high throughput in the face of changing memory pressure. To support highly interactive workloads, we present Redline, a system that manages CPU, memory, and disk I/O in an integrated manner. It uses lightweight specifications to drive CPU scheduling and to coordinate memory and disk I/O management to serve the needs of interactive applications. Such coordination enables it to maintain responsiveness in the face of extreme resource contention, without sacrificing resource utilization. We also show that Redline can be used to support response time sensitive multi-threaded server applications. Our experiences and extensive experiments show that we can coordinate resource managers, both inside and outside the operating system, efficiently without destroying the modularity of the existing system. Such coordination prevents resource managers from working at cross purposes, and dramatically improve the performance of applications when facing heavy resource contention, sometimes by orders of magnitude.
Inter-Brain EEG Feature Extraction and Analysis for Continuous Implicit Emotion Tagging During Video Watching

How to efficiently tag the emotional experience of multimedia contents is an important and challenging problem in the field of affective computing. This paper presents an EEG-based real-time emotion tagging approach, by extracting inter-brain features from a group of participants when they watch the same emotional video clips. First, the continuous subjective reports on both the arousal and valence dimensions of emotion were obtained by employing a three-round behavioral rating paradigm. Second, the inter-brain features were systematically explored in both spectral and temporal domain. Finally, regression analyses were performed to evaluate the effectiveness of inter-brain amplitude and phase features. The inter-brain amplitude feature showed significantly better prediction performance than the inter-brain phase feature, as well as another two conventional features (spectral power and inter-subject correlation). By combining the four types of features, regression values (R<sup>2</sup>) were obtained for the prediction of arousal (<inline-formula><tex-math notation="LaTeX">${{0.61}}\pm {{0.01}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mrow><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>61</mml:mn></mml:mrow><mml:mo>±</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>01</mml:mn></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq1-2849758.gif"/></alternatives></inline-formula>) and valence (<inline-formula><tex-math notation="LaTeX">${{0.70}}\pm {{0.01}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mrow><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>70</mml:mn></mml:mrow><mml:mo>±</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>01</mml:mn></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq2-2849758.gif"/></alternatives></inline-formula>), corresponding to prediction errors of <inline-formula><tex-math notation="LaTeX">${{1.01}}\pm {{0.02}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>01</mml:mn></mml:mrow><mml:mo>±</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>. </mml:mo><mml:mn>02</mml:mn></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq3-2849758.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">${{0.78}}\pm {{0.02}}$</tex-math><alternatives><mml:math><mml:mrow><mml:mrow><mml:mn>0</mml:mn><mml:mo>. </mml:mo><mml:mn>78</mml:mn></mml:mrow><mml:mo>±</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>. </mml:mo><mml:mn>02</mml:mn></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq4-2849758.gif"/></alternatives></inline-formula> (unit on 9-point scales), respectively. The contributions of different electrodes and frequency bands were also analyzed. Our results show promising potentials of inter-brain EEG features in real-time emotion tagging applications.
Tokenizing micro-blogging messages using a text classification approach

The automatic processing of microblogging messages may be problematic, even in the case of very elementary operations such as tokenization. The problems arise from the use of non-standard language, including media-specific words (e.g. "2day", "gr8", "tl;dr", "loool"), emoticons (e.g. "(ò_ó)", "(=^-^=)"), non-standard letter casing (e.g. "dr. Fred") and unusual punctuation (e.g. ". .", "! ", ",,,"). Additionally, spelling errors are abundant (e.g. "I;m"), and we can frequently find more than one language (with different tokenization requirements) in the same short message. For being efficient in such environment, manually-developed rule-based tokenizer systems have to deal with many conditions and exceptions, which makes them difficult to build and maintain. We present a text classification approach for tokenizing Twitter messages, which address complex cases successfully and which is relatively simple to set up and maintain. For that, we created a corpus consisting of 2500 manually tokenized Twitter messages -- a task that is simple for human annotators -- and we trained an SVM classifier for separating tokens at certain discontinuity characters. For comparison, we created a baseline rule-based system designed specifically for dealing with typical problematic situations. Results show that we can achieve F-measures of 96% with the classification-based approach, much above the performance obtained by the baseline rule-based tokenizer (85%). Also, subsequent analysis allowed us to identify typical tokenization errors, which we show that can be partially solved by adding some additional descriptive examples to the training corpus and re-training the classifier.
Research on Complete Coverage Path Planning Algorithms based on A*Algorithms

�� � � � � � � � �� �� �� �� �� �� �� �� �� � �� �� �� �� �� �� �� �� �� � �� �� �� �� �� �� �� �� �� � �� �� �� �� �� �� �� �� �� � �� �� �� �� �� �� �� �� �� � �� �� �� �� �� �� �� �� �� � �� �� �� �� �� �� �� �� �� � �� �� �� �� �� �� �� �� �� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �