Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic sports video genre categorization for broadcast videos

A novel sports genre categorization algorithm based on representative shot extraction and geometry visual phrase(GVP) is presented in this paper. Performance of sports classification can be observably improved by generating reduced image set containing representative information and encoding spatial information into bag-of-words (BOW) model. Firstly, Shots containing significant information of videos are chosen by key-frame clustering. Secondly, GVP are searched by the co-occurrence of visual words in a spatial layout based on scale invariant feature transform (SIFT). Then visual words and GVP are concatenated to form enhanced histograms before SVM based classifying procedure. Compared with most existing methods, our algorithm is domain knowledge free as well as fully automatic and thus provides better extensibility. Experiments on a database of 10 sport genres with over 10257 minutes of videos from different sources achieved an average accuracy of 87.3%, which validates the robustness of our proposed algorithm over large-scale database.
Outsourcing and Firm-level Performance

Using firm-level panel data from the German cost structure survey over the period 1992 to 2000, our empirical analysis shows that firms that increased material inputs relative to internal labor costs performed better in terms of gross operating surplus than other firms. However, firms that increased external services relative to internal labor costs, thus outsourcing service functions previously provided within the firm, performed worse. In sum, our findings support the view that firms tend to overestimate the benefits accruing from outsourcing of services previously provided internally.
Angiotensin-converting enzyme gene polymorphism in Kuwaiti patients with systemic lupus erythematosus.

OBJECTIVE To investigate the frequency of angiotensin-converting enzyme (ACE) gene insertion/deletion (I/D) polymorphism genotypes in patients with systemic lupus erythematosus (SLE), and to study the correlation between I/D polymorphism of the ACE gene and the clinical manifestations of SLE, especially vascular involvement, lupus nephritis and disease severity. METHODS The frequency of ACE gene I/D polymorphism genotypes was determined in 92 patients with SLE from Kuwait, and compared to that in 100 ethnically matched healthy controls using the polymerase chain reaction. RESULTS The distribution of ACE I/D polymorphism and allele frequencies in SLE patients was not significantly different from controls. Further analyses of SLE patients showed that there was a significant association between DD genotype and Raynaud's phenomenon (p=0.008, odd ratio=5.4, 95% confidence interval: 1.6-18.6). However, there was no significant association between the ACE genotype and lupus nephritis or disease severity. CONCLUSION No difference was found between the distribution of the ACE genotype in SLE patients and the general pop-ulation in Kuwait. However, the presence of the DD genotype may confer susceptibility to the development of vascular morbidity.
Bag-of-Words Based Deep Neural Network for Image Retrieval

This work targets image retrieval task hold by MSR-Bing Grand Challenge. Image retrieval is considered as a challenge task because of the gap between low-level image representation and high-level textual query representation. Recently further developed deep neural network sheds light on narrowing the gap by learning high-level image representation from raw pixels. In this paper, we proposed a bag-of-words based deep neural network for image retrieval task, which learns high-level image representation and maps images into bag-of-words space. The DNN model is trained on the large scale clickthrough data, and the relevance between query and image is measured by the cosine similarity of query's bag-of-words representation and image's bag-of-words representation predicted by DNN, the visual similarity of images is computed by high-level image representation extracted via the DNN model too. Finally, PageRank algorithm is used to further improve the ranking list by considering visual similarity of images for each query. The experimental results achieved state-of-the-art performance and verified the effectiveness of our proposed method.
Narrowband active noise control using adaptive delay filter

An adaptive delay filter (ADF) consisting of one variable delay and gain is developed for narrowband active noise control. The ADF estimates the time delay and the gain difference between the primary and secondary paths at a given frequency. Multiple ADF systems are connected in parallel to control periodic noise with several narrowband components. Theoretical analysis shows that the amount of noise reduction is determined by the delay difference and the sampling rate.
Some differentials on Khovanov–Rozansky homology

In [12; 13], Khovanov and Rozansky introduced a new class of homological knot invariants which generalize the original construction of the Khovanov homology [9]. In this paper, we investigate these KR–homologies and the relations between them. Our motivation was to give some substance to the conjectures made in Dunfield, Gukov and Rasmussen [3] about the behavior of these theories and their relation to the knot Floer homology. Although we are unable to say anything about the latter problem, we hope that we can at least shed some light on the structure of KR–homology.
Treatment Outcomes in Patients with Internet Addiction: A Clinical Pilot Study on the Effects of a Cognitive-Behavioral Therapy Program

Internet addiction is regarded as a growing health concern in many parts of the world with prevalence rates of 1-2% in Europe and up to 7% in some Asian countries. Clinical research has demonstrated that Internet addiction is accompanied with loss of interests, decreased psychosocial functioning, social retreat, and heightened psychosocial distress. Specialized treatment programs are needed to face this problem that has recently been added to the appendix of the DSM-5. While there are numerous studies assessing clinical characteristics of patients with Internet addiction, the knowledge about the effectiveness of treatment programs is limited. Although a recent meta-analysis indicates that those programs show effects, more clinical studies are needed here. To add knowledge, we conducted a pilot study on the effects of a standardized cognitive-behavioral therapy program for IA. 42 male adults meeting criteria for Internet addiction were enrolled. Their IA-status, psychopathological symptoms, and perceived self-efficacy expectancy were assessed before and after the treatment. The results show that 70.3% of the patients finished the therapy regularly. After treatment symptoms of IA had decreased significantly. Psychopathological symptoms were reduced as well as associated psychosocial problems. The results of this pilot study emphasize findings from the only meta-analysis conducted so far.
RC Variability of Short-Range Interconnects

Line edge roughness (LER) in end-of-the-roadmap integrated circuit interconnects causes variability in their resis- tance R, capacitance C and hence also their RC delay. We present an analysis of LER-induced variability of resistance, capacitance and delay of short-range interconnects within standard cells at the 32, 22 and 18 nm technology nodes using both a commercial RC extraction tool as well as a fast quasi-analytical (QA) method. Our QA method includes size dependent resistivity which, when coupled with LER, reveals increased resistance variability and total resistance in interconnects at these technology nodes. For example, the QA method predicts variability of 52% in resistance, 16% in capacitance and 36% in RC delay. When LER is the dominant source of variability there is a correlation of �0.8 between resistance and capacitance. Our results indicate interconnect variability is a significant and worsening problem, which should be included in statistical models of standard cells.
Upper bounds for the value of Bermudan options

2 Interest rate derivatives 6 2.1 Zero coupon bonds . 6 2.1. 1 Valuation of zero coupon bonds . 6 2.2 Interest rates . 6 2.2.1 Forward rates . 6 2.2.2 LIBOR market model . 7 2.3 Interest rate swaps . 7 2.3. 1 Valuation of swaps . 8 2.3. 2 Swap rate . 8 2.4 Options . 9 2.4. 1 Swap options . 9
From Canonical Poses to 3D Motion Capture Using a Single Camera

We combine detection and tracking techniques to achieve robust 3D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to infer 3D poses between consecutive detections, and finally refining them over the whole sequence using a generative model. We demonstrate our approach in the cases of golf motions filmed using a static camera and walking motions acquired using a potentially moving one. We will show that our approach, although monocular, is both metrically accurate because it integrates information over many frames and robust because it can recover from a few misdetections.
Legal and Statistical Framework of Climate Change from the EU and International Point of View

Climate change is not only ecological circumstance but also related with economy, law, energy, industrial processes and daily life habits. Within the framework of our work, we will focus on the factors affecting the climate like greenhouse effect, carbon emissions, methane effect and on different climate change scenarios. A literature review will be given on the first part. A big effort is made on International and European Law basis for facing the Climate Change and the rapid environmental consequences. EU member countries accept urgent need for the protection of the environment which plays a great role in Climate Change. A comprehensive legal approach will be done from the side of EU law and International law on Climate Change and environmental law. An extensive information will be given about different conventions and agreements like UN Climate Convention, Paris Convention, Kyoto Protocol, UNFCCC Meetings, the Aarhus Convention and other international Fora. Special attention will be given to the human Rights related with the Climate Change and to the sanctions provided. The Study will explore the legal framework on the European and International Jurisprudence. Second aim of our work is also to use statistical methods and tools about climate change. Chosen statistical methods with current statistical data will help to evaluate countries. We also focus on making prediction for the future and to show the trend of global warming as long as the current activities continue. On this view, we will examine if the recommendations that we will develop will be taken in consideration how this trend will be affected. The legal recommendations and statistical studies will be an important step to prevent Climate Change.
Identification of the differentially expressed genes associated with familial combined hyperlipidemia using bioinformatics analysis

The aim of the present study was to screen the differentially expressed genes (DEGs) associated with familial combined hyperlipidemia (FCHL) and examine the changing patterns. The transcription profile of GSE18965 was obtained from the NCBI Gene Expression Omnibus database, including 12 FCHL samples and 12 control specimens. The DEGs were identified using a linear models for microarray data package in the R programming language. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was also performed. Protein-protein interaction (PPI) networks of the DEGs were constructed using the EnrichNet online tool. In addition, cluster analysis of the genes in networks was performed using ClusterONE. A total of 879 DEGs were screened, including 394 upregulated and 485 downregulated genes. Enrichment analysis identified four important KEGG pathways associated with FCHL: One carbon pool by folate, α-linolenic acid metabolism, asthma and the glycosphingolipid biosynthesis-globo series. GO annotation identified 12 enriched biological processes, including one associated with hematopoiesis and four associated with bone cell differentiation. This identification was in accordance with clinical data and experiments into hyperlipidemia and bone lesions. Based on PPI networks, these DEGs had a close association with immune responses, hormone responses and cytokine-cytokine receptors. In conclusion, these DEGs may be used as specific therapeutic molecular targets in the treatment of FCHL. The present findings may provide the basis for understanding the pathogenesis of FCHL in future studies. However, further experiments are required to confirm these results.
Learning Robot Skills Through Motion Segmentation and Constraints Extraction

Learning skills while interacting with human users allows robots to work in human environments and become efficient when performing everyday tasks. This paper presents a method of analyzing data obtained from human demonstrations of a task in order to: (a) extract specific constraints for each part of the task (important variables, the frame of reference to be used, and the most suitable controller for reproducing the task); (b) use these task features as continuously embeddable constraints in the learned robot motion (c) properly segment into subtask; The proposed method has been tested on a common kitchen task and its performance was compared against standard control modes.
Scalable numerical approach for the steady-state ab initio laser theory

We present an efficient and flexible method for solving the non-linear lasing equations of the steady-state ab initio laser theory. Our strategy is to solve the underlying system of partial differential equations directly, without the need of setting up a parametrized basis of constant flux states. We validate this approach in one-dimensional as well as in cylindrical systems, and demonstrate its scalability to full-vector three-dimensional calculations in photonic-crystal slabs. Our method paves the way for efficient and accurate simulations of microlasers which were previously inaccessible.
Modelling and Detecting Changes in User Satisfaction

Informational needs behind queries, that people issue to search engines, are inherently sensitive to external factors such as breaking news, new models of devices, or seasonal changes as 'black Friday'. Mostly these changes happen suddenly and it is natural to suppose that they may cause a shift in user satisfaction with presented old search results and push users to reformulate their queries. For instance, if users issued the query 'CIKM conference' in 2013 they were satisfied with results referring to the page cikm2013.org and this page gets a majority of clicks. However, the confernce site has been changed and the same query issued in 2014 should be linked to the different page cikm2014.fudan. edu.cn. If the link to the fresh page is not among the retrieved results then users will reformulate the query to find desired information. In this paper, we examine how to detect changes in user satisfaction if some events affect user information goals but search results remained the same. We formulate a problem using concept drift detection techniques. The proposed method works in an unsupervised manner, we do not rely on any labelling. We report results of a large scale evaluation over real user interactions, that are collected by a commercial search engine within six months. The final datasets consist of more than sixty millions log entries. The results of our experiments demonstrate that by using our method we can accurately detect changes in user behavior. The detected drifts can be used to enhance query auto-completion, user satisfaction metrics, and recency ranking.
Macroalbuminuria and Renal Pathology in First Nation Youth With Type 2 Diabetes

OBJECTIVE To determine the prevalence of macroalbuminuria and to describe the clinical and renal pathological changes associated with macroalbuminuria in a population of Canadian First Nation children and adolescents with type 2 diabetes. RESEARCH DESIGN AND METHODS We conducted a retrospective chart review at a single tertiary care pediatric diabetes center, and a case series was constructed. We collected data on microalbuminuria (≥3 mg/mmol creatinine [26.5 mg/g]) and macroalbuminuria (≥28 mg/mmol creatinine [247.5 mg/g]), estimated glomerular filtration rate, renal pathology, and aggravating risk factors (poor glycemic control, obesity, hypertension, glomerular hyperfiltration, hypercholesterolemia, smoking, and exposure to diabetes in utero). RESULTS We reviewed 90 charts of children and adolescents with type 2 diabetes. A total of 53% had at least one random urine albumin-to-creatinine ratio ≥3 mg/mmol (26.5 mg/g). There were 14 of 90 (16%) who had persistent macroalbuminuria at or within 8 years of diagnosis of diabetes. Of these 14 subjects, 1 had orthostatic albuminuria and 3 had spontaneous resolution of albuminuria. A total of 10 had renal biopsies performed. There were 9 of 10 who exhibited immune complex disease or glomerulosclerosis, and none had classic diabetic nephropathy. CONCLUSIONS This study suggests that the diagnosis of renal disease in children with type 2 diabetes cannot be reliably determined by clinical and laboratory findings alone. Renal biopsy is necessary for accurate diagnosis of renal disease in children and adolescents with type 2 diabetes and macroalbuminuria. The additional burden of nondiabetic kidney disease may explain the high rate of progression to end-stage kidney failure in this population.
Turkish Validity and Reliability Study of the Leisure Constraint Questionnaire

The purpose of this study to adapt to Turkish version by applying validity and reliability test of Leisure Constraint Questionnaire (LCQ) developed by Alexander and Carroll (1997). 214 (62.4%) men and 129 (37.6%) women, total of 343 people was participated to the study working as public officers in Iğdır. Exploratory Factor Analysis (EFA), Reliability Analysis and Confirmatory Factor Analysis (CFA) was applied to Turkish version of the scale after translated to Turkish. When the EFA results are examined Anti Imaj Correlation (AIC) cross correlation coefficients of all items greater than 0.5 and It has been decided to use all items in the analysis. After Principal Component Analaysis (PCA), there are 7 factors with eigenvalue greater than 1 and the contribution of these factors to the total variance is 56.806% were determined. The factors belonging to the items were determined by Rotated Component Matrix (VARIMAX). The tests of Cronbach’s Alpha (CA), Spearman-Brown Correlation (SBC) and Guttman Split Half Correlation (GSHC) were performed for reliability of the scale. The value of CA: 0.876, SBC: 0.754 and GSHC: 0.754 were found for the all items. Comparative Fit Index (CFI), Goodness-of-fit index (GFI) and he Normed Fit Index (NFI) were used for the CFA. As a result of CFA analysis; the value of CFI: 0.94, GFI: 0.96 and NFI: 0.93 were found. It has been concluded that the scale of adaptation to Turkish is valid and reliable and also it was composed of 7 factors and 29 items like original scale.
A scalable multi-datacenter layer-2 network architecture

Cloud today is evolving towards multi-datacenter deployment, with each datacenter serving customers in different geographical areas. The independence between datacenters, however, prohibits effective inter-datacenter resource sharing and flexible management of the infrastructure. In this paper, we propose WL2, a Software-Defined Networking (SDN) solution to an Internet-scale Layer-2 network across multiple datacenters. In WL2, a logically centralized controller handles control-plane communication and configuration in each datacenter. We achieve scalability in three ways: (1) eliminating Layer-2 broadcast by rerouting control-plane traffic to the controller; (2) introducing a layered addressing scheme for aggregate Layer-2 routing; and (3) creating an overlay abstraction on top of physical topology for fast flow setup. WL2 is fault-tolerant against controller and gateway failures. We deployed and evaluated WL2 in a 2,250-VM testbed across three datacenters. The results indicate high performance and robustness of the system.
Structural keypoints voting for global visual tracking

This paper proposes a patch-based keypoints clustering method for long term robust visual tracking. We plan to employ a parallel framework with keypoints matching and estimation for tracking purpose. Patch-based method is implemented in our algorithm to improve the flexibility of system. The template is divided into patches to ensure the spatial constraint of local keypoints. The motion cue of patches is calculated with optical flow for consensus clustering and the outliers are suppressed for the final voting. To eliminate the error, we propose a two-step voting from global to local scope. The effective keypoints in global vote for a center and estimate the patch centers which will be compared with the voting centers from each individial patch keypoints. The final voting is determined by the voting with minimum error, which could robustly reduce the error due to the misclassified outliers. Finally, the experiments will be followed to validate the performance of proposed algorithm on the public benchmark.
Hybrid Multi-Objective PSO with Solution Diversity Extraction for job-shop scheduling management

The Multi-Objective Flexible Job-Shop Scheduling Problem (FJSP), which concerned with allocating limited resources to optimize some performance criteria, is difficult to find optimal scheduling solutions because of NP-hard complexity. In this paper, the particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) is proposed to always produce feasible candidate solutions for the FJSP. Then a solution searching strategy called Solution Diversity Extraction is adopted to improve the Particle Swarm Optimization (PSO) to deal with the diversity in Pareto-optimal solutions. To test the performance of the proposed method, the experiments contain six representative benchmarks and to compare the proposed method with the published algorithms. The simulation results indicate the proposed method can find more wide range potential solutions, and outperform related methods.