Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving Object Detection Algorithm Based on Variance Analysis

A novel object detection approach based on variance analysis is presented in this paper. Firstly, the variance of the frame difference information is analyzed and the background model is built. Then, the variance is used as threshold to separate the frame difference image into two parts, moving region and background region. Thirdly, moving region is further analyzed by comparing it with background; the moving object and uncovering region are detected. Lastly, the background is updated with uncovering area for detecting the moving object in next video frame. The experiment results show that the proposed method is practical and with fast detection speed, the proposed approach could detect clear motion objects and meet the need of the real time intelligent video surveillance and monitoring system.
Racket control for robot playing table tennis ball

This paper proposes a racket control method for returning a table tennis ball to a reference position with a desired translational/ rotational velocity. The method determines the racket's state, i.e., the racket's striking posture and translational velocity by using two physical models: racket rebound model and aerodynamics model. The paper makes clear what the racket can control among the reference position, translational and rotational velocities. The algorithm of determining the racket's state is derived by solving nonlinear equations and solving two points problems of differential equation. Two approximate aerodynamic models are proposed for real-time treatments. Then numerical simulations show effectiveness of the proposed methods.
Semantic caching for pervasive grids

Recently, grids and pervasive systems have been drawing increasing attention in order to coordinate large-scale resources enabling access to small and smart devices. In this paper, we propose a caching approach enabling to improve querying on pervasive grids. Our proposal, called semantic pervasive dual cache, follows a semantics oriented approach. It is based on the one hand on a clear separation between the analysis and the evaluation process, and on the other hand on the cooperation between client caches considering light analysis and proxy caches including evaluation capabilities. Such an approach helps load balancing, making the system more scalable. We have validated semantic pervasive dual cache using analytic models and simulations. Results obtained show that our approach is quite promising.
The deliberate practice: the sports excellence versus the musical excellence

RESUMEN.2 INTRODUCTION.4 THEORETICAL FRAMEWORK.5 AIM OF THE PRESENT PROJECT.13 MATERIALS AND METHODS.14 PARTICIPANTS.14 DATA COLLECTION.14 QUESTIONNAIRE.17 RESULTS AND DISCUSSION (Conclusions). 18 FUTURES PERSPECTIVES.26 DIFFICULTIES.27 REFERENCES.28 ANNEX.30
Analysis physiological signals for emotion recognition

The emotion recognition is one of the great challenges in human-human and human-computer interaction. In this paper, an approach for the emotions recognition based on physiological signals is proposed. Six basic emotions: joy, sadness, fear, disgust, neutrality and amusement are analysed using physiological signals. These emotions are induced through the presentation of IAPS pictures (International Affecting Picture System) to the subjects. Also, the physiological signals of interest in this analysis are: electromyogram signal (EMG), respiratory volume (RV), skin temperature (SKT), skin conductance (SKC), blood volume pulse (BVP) and heart rate (HR). These are selected to extract some characteristic parameters, which will be used for classifying the emotions. The SVM (support vector machines) technique is used for classifying these parameters. The experimental results show that the proposed methodology provides a recognition rate of 85% for different emotional states.
Linear-time enumeration of maximal K-edge-connected subgraphs in large networks by random contraction

Capturing sets of closely related vertices from large networks is an essential task in many applications such as social network analysis, bioinformatics, and web link research. Decomposing a graph into k-core components is a standard and efficient method for this task, but obtained clusters might not be well-connected. The idea of using maximal k-edge-connected subgraphs was recently proposed to address this issue. Although we can obtain better clusters with this idea, the state-of-the-art method is not efficient enough to process large networks with millions of vertices. In this paper, we propose a new method to decompose a graph into maximal k-edge-connected components, based on random contraction of edges. Our method is simple to implement but improves performance drastically. We experimentally show that our method can successfully decompose large networks and it is thousands times faster than the previous method. Also, we theoretically explain why our method is efficient in practice. To see the importance of maximal k-edge-connected subgraphs, we also conduct experiments using real-world networks to show that many k-core components have small edge-connectivity and they can be decomposed into a lot of maximal k-edge-connected subgraphs.
Augmenting appearance-based localization and navigation using belief update

Appearance-based localization compares the current image taken from a robot's camera to a set of pre-recorded images in order to estimate the current location of the robot. Such techniques often maintain a graph of images, modeling the dynamics of the image sequence. This graph is used to navigate in the space of images. In this paper we bring a set of techniques together, including Partially-Observable Markov Decision Processes, hierarchical state representations, visual homing, human-robot interactions, and so forth, into the appearance-based approach. Our approach provides a complete solution to the deployment of a robot in a relatively small environment, such as a house, or a work place, allowing the robot to robustly navigate the environment after minimal training. We demonstrate our approach in two environments using a real robot, showing how after a short training session, the robot is able to navigate well in the environment.
Drug Addicts' Mental Health Conditions and Their Relations with Relapse Reasons

Objective: To examine the drug addicts' mental health condition,such as self-esteem,depression and anxiety,and explore the relapse reasons.Methods: 260 drug addicts and economical criminals under reform were chosen for the study.They are asked to complete SES,CRS,STAI and relapse reason Questionnaire.Results: ①Drug addicts' self-esteem was significantly negatively correlated with anxiety and depression.The mental health conditions of criminals under different reform styles were significantly different;②According to addicts' choices,mental factor was the main reason of relapse,and mental health conditions showed significant correlation with relapse reasons.Conclusion: ①Drug addicts'mental health conditions,such as self-esteem,depression,and anxiety,were significantly related to each other.Drug addicts',esp.females depression and anxiety degree were higher than those of other criminals under reform. ②Mental factor was the main reason of relapse.Drug addicts' mental health conditions were correlated with relapse reasons.
A generalized fusion approach for segmenting dermoscopy images using Markov random field

Malignant melanoma is among the most rapidly increasing cancers in the world. Image border detection is often the first step to characterize skin lesion for the follow-up computer-aided diagnosis. Existing approaches lack robustness in the face of dermoscopy images varying in size, color, texture, and structure. In this paper, a generalized Markov random field (MRF) framework is proposed to fuse the results obtained from segmentation algorithms, by taking full advantages of characteristics of different methods and making them work synergistically to acquire more reliable results. The experimental results on the real dermoscopy image set demonstrate that the proposed fusion method is capable of improving the overall performance in terms of both accuracy and robustness.
QSO 0957+561 and other large-separated double quasars: some new results and a future observational project

We collected from literature the information about large-separated pairs of QSOs, which however once were suspected as gravitationally lensed system. We discuss some new results on time delay determinations including optical-radio correlations for QSO 0957+561. We considered some possible observational effects of gravitational lensing by a cosmic string. A future international project for observations of the gravitational lens system UM425 is briefly discussed.
Relationship between ApoE, MRI findings, and cognitive function in the Cardiovascular Health Study.

BACKGROUND AND PURPOSE We determined the relationship between apolipoprotein (Apo)E, MRI, and low cognitive scores. METHODS The relationship between age, education, ApoE genotype, MRI examination of the brain, subclinical and clinical cardiovascular disease, and low (<80) score on the Modified Mini-Mental State Examination (3MSE, as modified by Teng and Chui) was evaluated for 3469 black and white participants in the Cardiovascular Health Study (CHS) in years 5 and 6 of the study. The participants were followed for up to 3 years. RESULTS The prevalence of scores <80 in years 5 and 6 of the CHS was 8.2% for participants without and 20.4% for those with prior history of stroke. Age, race, and education were important determinants of low 3MSE scores. The prevalence of ApoE-4 (odds ratio [OR], 1.6 [1.1 to 2.1]) was directly related to scores <80, as was high ventricular volume (OR, 1.6 [1.2 to 2.3]), high white matter grade (OR, 1.4 [1.1 to 1.9]), and infarctlike lesions (OR, 1.6 [1.2 to 2.1]) on the MRI in the multivariate analysis. A five-point or greater decline in scores over up to 3 years was more often observed for participants with low 3MSE scores at year 5, at older ages, with lower education, and experiencing incident stroke (OR, 3.6 [1.2 to 10.6]), ApoE-4 genotype (OR, 1.8 [1.4 to 2.3]), and with MRI findings of high ventricular volume (OR, 2.0 [1.5 to 2.7]), and infarctlike lesions (OR, 1.2 [0.9 to 1.5]). CONCLUSIONS These results demonstrate that vascular changes on MRI, measures of brain atrophy, ApoE-4, and age, education, and race are associated with low cognitive scores among older individuals. The MRI of the brain provides valuable information related to cognitive tests and decline over time. The potential exists for using MRI measurements to identify high-risk individuals for dementia and to test potential interventions to reduce the risk of dementia.
Sediment Delivery Ratio of Single Flood Events and the Influencing Factors in a Headwater Basin of the Chinese Loess Plateau

Little is known about the sediment delivery of single flood events although it has been well known that the sediment delivery ratio at the inter-annual time scale is close to 1 in the Chinese Loess Plateau. This study examined the sediment delivery of single flood events and the influencing factors in a headwater basin of the Loess Plateau, where hyperconcentrated flows are dominant. Data observed from plot to subwatershed over the period from 1959 to 1969 were presented. Sediment delivery ratio of a single event (SDR e) was calculated as the ratio of sediment output from the subwatershed to sediment input into the channel. It was found that SDR e varies greatly for small events (runoff depth <5 mm or rainfall depth <30 mm) and remains fairly constant (approximately between 1.1 and 1.3) for large events (runoff depth >5 mm or rainfall depth >30 mm). We examined 11 factors of rainfall (rainfall amount, rainfall intensity, rainfall kinetic energy, rainfall erosivity and rainfall duration), flood (area-specific sediment yield, runoff depth, peak flow discharge, peak sediment concentration and flood duration) and antecedent land surface (antecedent precipitation) in relation to SDR e. Only the peak sediment concentration significantly correlates with SDR e. Contrary to popular belief, channel scour tends to occur in cases of higher peak sediment concentrations. Because small events also have chances to attain a high sediment concentration, many small events (rainfall depth <20 mm) are characterized by channel scour with an SDR e larger than 1. Such observations can be related to hyperconcentrated flows, which behave quite differently from normal stream flows. Our finding that large events have a nearly constant SDR e is useful for sediment yield predictions in the Loess Plateau and other regions where hyperconcentrated flows are well developed.
Carbon Ion Radiotherapy for Oligo-Recurrence in the Lung

The clinical results after carbon ion radiotherapy for the metastatic lung tumors believed to be in the state of oligo-recurrence were evaluated. One hundred and sixteen lesions in 91 patients with lung cancer metastasis were treated with carbon ion radiotherapy at our institute from April 1997 to February 2011. Regarding the prescribed dose, total dose ranged between 40 gray equivalents (GyE) and 80 GyE, and fraction size ranged from 1 to 16 fractions. After a median followup period of 2.3 years (range, 0.3–13.1 years), the statistical overall survival rate and local control rate were 71.2% and 91.9% at 2 years after treatment, respectively. Treatment-related side effects were not a clinical problem. When classified by the primary organ, there were 49 cases of lung cancer, 20 cases of colorectal cancer, and 22 cases of others. The overall survival rate and local control rate for lung metastasis cases from lung cancer at 2 years after treatment were 81.5% and 92.4%, respectively, and 65.0% and 92.0% regarding lung metastasis from colorectal cancer. Carbon ion beam therapy for the metastatic lung tumors is a safe therapy, and the therapeutic effect is comparable to the outcome obtained from reported surgical resections.
Controlled information destruction: the final frontier in preserving information security for every organisation

Information security represents the cornerstone of every data processing system that resides in an organisation’s trusted network, implementing all necessary protocols, mechanisms and policies to be one step ahead of possible threats. Starting from the need to strengthen the set of security services, in this article we introduce a new and innovative process named controlled information destruction (CID) that is meant to secure sensitive data that are no longer needed for the organisation’s future purposes but would be very damaging if revealed. The disposal of this type of data has to be controlled carefully in order to delete not only the information itself but also all its splinters spread throughout the network, thus denying any possibility of recovering the information after its alleged destruction. This process leads to a modified model of information assurance and also reconfigures the architecture of any information security management system. The scheme we envisioned relies on a reshaped information lifecycle, which reveals the impact of the CID procedure directly upon the information states.
Using hybrid Neural Network to address Chinese Named Entity Recognition

Many machine learning methods have been applied on Named Entity Recognition (NER). Such methods generally build on a large manually-annotated training set. However, the training set is usually limited as human labeling is costly and time consuming. Compare to the training set, the unlabeled corpus is usually much bigger and contains rich information about language. In this paper, a hybrid Deep Neural Network (DNN) is proposed to take advantage of the implicit information embedded in the un-labeled corpus. The experiments show that F1-score is improved from 85% to 90% (person name), from 75% to 81% (location name), and from 74% to 78% (organization name), compared with Conditional Random Fields (CRFs).
Ten Simple Rules for Designing and Interpreting ERP Experiments

Overview This chapter discusses some of the central issues in the design and interpretation of ERP experiments. Many design and interpretation issues are unique to a given content area, but many principles apply to virtually all ERP studies and these common principles are the focus of this chapter. The chapter begins with a discussion of the nature of ERP components, an issue that lies at the heart of the design and interpretation of ERP experiments. It then discusses some general principles of design and interpretation. Finally, it discusses a simple and yet vexing issue that must be addressed by any experimental design, namely the number of trials per subject. Throughout the chapter, the most significant points are distilled into a set of ten simple rules for designing and interpreting ERP experiments.
High-Accuracy Stereo Matching Based on Adaptive Ground Control Points

This paper proposes a novel high-accuracy stereo matching scheme based on adaptive ground control points (AdaptGCP). Different from traditional fixed GCP-based methods, we consider color dissimilarity, spatial relation, and the pixel-matching reliability to select GCP adaptively in each local support window. To minimize the global energy, we propose a practical solution, named as alternating updating scheme of disparity and confidence map, which can effectively eliminate the redundant and interfering information of unreliable pixels. The disparity values of those unreliable pixels are reassigned with the information provided by local plane model, which is fitted with GCPs. Then, the confidence map is updated according to the disparity reassignment and the left-right consistency. Finally, the disparity map is refined by multistep filers. Quantitative evaluations demonstrate the effectiveness of our AdaptGCP scheme for regularizing the ill-posed matching problem. The top ranks on Middlebury benchmark with different error thresholds show that our algorithm achieves the state-of-the-art performance among the latest stereo matching algorithms. This paper provides a new insight toward high-accuracy stereo matching.
An L-infinity Norm Visual Classifier

We introduce a mathematical framework, based on the L-infinity norm distance metric, to describe human interactions in a visual data mining environment. We use the framework to build a classifier that involves an algebra on hyper-rectangles. Our classifier, called VisClassifier, generates set-wise rules from simple gestures in an exploratory visual GUI. Logging these rules allows us to apply our analysis to a new sample or batch of data so that we can assess the predictive power of our visual-processing motivated classifier. The accuracy of this classifier on widely-used benchmark datasets rivals the accuracy of competitive classifiers.
Algebraic Characterization of Edible Powerdomains

Powerdomains like mixes, sandwiches, snacks and scones are typically used to provide semantics of collections of descriptions of partial data. In particular, they were used to give semantics of databases with partial information. In this paper we argue that to be able to put these constructions into the context of a programming languages it is necessary to characterize them as free (ordered) algebras. Two characterizations — for mixes and snacks — are already known, and in the first part of the paper we give characterizations for scones and sandwiches and provide an alternative characterization of snacks. The algebras involved have binary and unary operations and relatively simple equational theories. We then define a new construction, which is in essence all others put together (hence called salad) and give its algebraic characterization. It is also shown how all algebras considered in the paper are related in a natural way, that is, in a way that corresponds to embeddings of their powerdomains. We also discuss some semantic issues such as relationship between the orderings and the semantics and justification for choosing the orderings. Finally, we outline prospects for further research. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-70. This technical report is available at ScholarlyCommons: http://repository.upenn. edu/cis_reports/494 Algebraic Characterization of Edible Powerdomains MS-CIS-93-70 LOGIC & COMPUTATION 71
Translational Therapeutic Opportunities in Ductal Adenocarcinoma of the Pancreas

Pancreatic ductal adenocarcinoma (PDA) remains a devastating disease with nearly equal incidence and mortality rates. Over the past few decades, a litany of randomized clinical trials has failed to improve the outcome of this disease. More recently, the combination chemotherapy regimen FOLFIRINOX has shown improvement in overall survival over the single agent gemcitabine, and nab-paclitaxel (an albumin-coated formulation of paclitaxel) in combination with gemcitabine has shown promising results in phase II studies. Despite limited impact on patient care as of yet, the molecular and biologic understanding of PDA has advanced substantially. This includes understanding the genomic complexity of the disease, the potential importance of the tumor microenvironment, the metabolic adaptation of PDA cells to obtain nutrients in a hypoxic environment, and the role of pancreatic cancer stem cells. These fundamental discoveries are starting to be translated into clinical studies. In this overview, we discuss the implications of biologic understanding of PDA in clinical research and provide insights for future development of novel approaches and agents in this disease. Clin Cancer Res; 18(16); 4249–56. ©2012 AACR.
SyncPlayer 1.5.2 Public Demo

The SyncPlayer is a prototypical software framework that integrates various Music Information Retrieval (MIR) techniques such as music synchronization, audio structure analysis and content-based retrieval into a powerful, multimodal system [1, 2]. The SyncPlayer system basically consists of three software components: a server component, a client component, and some tools for data administration. These three components can be summarized as follows:
XPF protein levels determine sensitivity of malignant melanoma cells to oxaliplatin chemotherapy: Suitability as a biomarker for patient selection

As the options for systemic treatment of malignant melanoma (MM) increase, the need to develop biomarkers to identify patients who might benefit from cytotoxic chemotherapy becomes more apparent. In preclinical models, oxaliplatin has activity in cisplatin‐resistant cells. In this study, we have shown that oxaliplatin forms interstrand crosslinks (ICLs) in cellular DNA and that loss of the heterodimeric structure‐specific endonuclease XPF‐ERCC1 causes hypersensitivity to oxaliplatin in mammalian cells. XPF deficiency resulted in late S‐phase arrest and persistence of double‐strand breaks following oxaliplatin treatment. In a panel of 12 MM cell lines, oxaliplatin sensitivity correlated with XPF and ERCC1 protein levels. The knockdown of ERCC1 and XPF protein levels by RNA interference increased sensitivity of cancer cells to oxaliplatin; overexpression of exogenous ERCC1 significantly decreased drug sensitivity. Following immunohistochemical optimization, XPF protein levels were quantified in MM tissue samples from 183 patients, showing variation in expression and no correlation with prognosis. In 57 patients with MM treated with cisplatin or carboplatin, XPF protein levels did not predict the likelihood of clinical response. We propose that oxaliplatin should not be discarded as a potential treatment for MM on the basis of the limited activity of cisplatin in unselected patients. Moreover, we show that XPF‐ERCC1 protein levels are a key determinant of the sensitivity of melanoma cells to oxaliplatin in vitro. Immunohistochemical detection of XPF appears suitable for development as a tissue biomarker for potentially selecting patients for oxaliplatin treatment in a prospective clinical trial.