Miracle-dz/UniFAR-FTUs huggingface dataset:
REX: Explaining Relationships between Entity Pairs

Knowledge bases of entities and relations (either constructed manually or automatically) are behind many real world search engines, including those at Yahoo! , Microsoft, and Google. Those knowledge bases can be viewed as graphs with nodes representing entities and edges representing (primary) relationships, and various studies have been conducted on how to leverage them to answer entity seeking queries. Meanwhile, in a complementary direction, analyses over the query logs have enabled researchers to identify entity pairs that are statistically correlated. Such entity relationships are then presented to search users through the "related searches" feature in modern search engines. However, entity relationships thus discovered can often be "puzzling" to the users because why the entities are connected is often indescribable. In this paper, we propose a novel problem called entity relationship explanation, which seeks to explain why a pair of entities are connected, and solve this challenging problem by integrating the above two complementary approaches, i.e., we leverage the knowledge base to "explain" the connections discovered between entity pairs. More specifically, we present REX, a system that takes a pair of entities in a given knowledge base as input and efficiently identifies a ranked list of relationship explanations. We formally define relationship explanations and analyze their desirable properties. Furthermore, we design and implement algorithms to efficiently enumerate and rank all relationship explanations based on multiple measures of "interestingness. " We perform extensive experiments over real web-scale data gathered from DBpedia and a commercial search engine, demonstrating the efficiency and scalability of REX. We also perform user studies to corroborate the effectiveness of explanations generated by REX.
Youth alcohol and drug good practice guide 1 : a framework for youth alcohol and other drug practice

This Guide outlines a framework for working with young people whose AOD use creates significant vulnerability to current or future harm. The target audience is practitioners who work with young people who have problematic AOD use and the managers of these practitioners. Areas of content include the elements of a framework for AOD practice, an appreciation of the developmental, social and institutional location of young people, key concepts and understandings regarding good youth centered context responsive practice, and key policy constructs and directions.
Natural Compounds Modulating Mitochondrial Functions

Mitochondria are organelles responsible for several crucial cell functions, including respiration, oxidative phosphorylation, and regulation of apoptosis; they are also the main intracellular source of reactive oxygen species (ROS). In the last years, a particular interest has been devoted to studying the effects on mitochondria of natural compounds of vegetal origin, quercetin (Qu), resveratrol (RSV), and curcumin (Cur) being the most studied molecules. All these natural compounds modulate mitochondrial functions by inhibiting organelle enzymes or metabolic pathways (such as oxidative phosphorylation), by altering the production of mitochondrial ROS and by modulating the activity of transcription factors which regulate the expression of mitochondrial proteins. While Qu displays both pro- and antioxidant activities, RSV and Cur are strong antioxidant, as they efficiently scavenge mitochondrial ROS and upregulate antioxidant transcriptional programmes in cells. All the three compounds display a proapoptotic activity, mediated by the capability to directly cause the release of cytochrome c from mitochondria or indirectly by upregulating the expression of proapoptotic proteins of Bcl-2 family and downregulating antiapoptotic proteins. Interestingly, these effects are particularly evident on proliferating cancer cells and can have important therapeutic implications.
Multi-step motion planning for climbing robots with kinodynamic constraints

This paper presents a kinodynamic multi-step motion planner that aims to investigate the influence and mutual dependencies of different kinematic robot configurations and 3D environments. The planner is able to plan the motion for a defined robot that can move along known docking points placed at walls and ceiling. This planner is based on the available multi-step planner of Bretl [1] and adds extended kinodynamic awareness into the planning process. The robot kinematic and dynamic properties, as well as the environment, can be easily changed and thus allows an investigation on the influence of different designs on the movement capabilities. The focus of the planner is the usage as a design tool that allows to compare different robot properties and their resulting influence on the movement capabilities as well the maximum joint forces/torques for desired movements.
The impact of learning on unemployed, low-qualified adults: a systematic review

This report presents the findings of a systematic review, commissioned by the Department for Work and Pensions (DWP). The review question was: What is the impact of learning on employment outcomes for low-qualified adults who are out-of-work or at risk of losing their job? The aim of the review was ‘to contribute towards a better understanding of whether training or learning works and leads to positive employment outcomes for those claiming benefits’ [and] … what sort of training or learning produces such outcomes, and in what circumstances’. Sixteen documents, representing 12 studies, were included in the final research synthesis. The following implications are drawn: that training has a role to play in helping low-qualified, out-of-work adults into employment, but that other factors are important, including work experience, job search, and broader support and advice structures; the importance of, where possible, looking at individual circumstances and tailoring support to these is emphasised; and there is a need to understand better the interaction between being low-qualified and having poor basic skills and the relative importance of addressing each of these for employment outcomes.
The Dispersion of Lossy Source Coding

In this work we investigate the behavior of the minimal rate needed in order to guarantee a given probability that the distortion exceeds a prescribed threshold, at some fixed finite quantization block length. We show that the excess coding rate above the rate-distortion function is inversely proportional (to the first order) to the square root of the block length. We give an explicit expression for the proportion constant, which is given by the inverse Q-function of the allowed excess distortion probability, times the square root of a constant, termed the excess distortion dispersion. This result is the dual of a corresponding channel coding result, where the dispersion above is the dual of the channel dispersion. The work treats discrete memoryless sources, as well as the quadratic-Gaussian case.
A cepstrum prefiltering approach for DOA estimation of speech signal in reverberant environments

A time-quefrency dereverberation method for reducing the minimum-phase component (MPC) of the room impulse response (RIR) is presented. This paper shows that the vocaltract and glottal components and the MPC of the RIR can be eliminated from observed signals convolving the unknown RIR with an unknown voiced speech signal. The liftered sequence is applied to direction-of-arrival (DOA) estimator with the multiple signal classification (MUSIC) procedure. Computer simulations demonstrate the superiority of the our cepstral prefiltering approach.
Psoriatic Arthritis A Systematic Review

Psoriatic arthritis is an inflammatory disorder of unknown etiology generally with negative rheumatoid factor and associated with patients having psoriasis. The prevalence of uncomplicated psoriasis is between 1-3% in the general population. Arthritis is found in increased frequency in psoriatic patients and its incidence is estimated to be 5-7%. In severe psoriasis, arthritis can occur in up to 30-40% of the patients and can go on to develop usually within 610 years of cutaneous onset of disease. Dermatologist are in a better position to detect psoriatic arthritis earlier once the skin lesions are found through regular screening and proper follow-up. The clinical course of peripheral and axial psoriatic arthritis is usually less severe than rheumatoid arthritis and Ankylosing spondylitis, respectively. There are five recognized presentations of psoriatic arthritis Asymmetric arthritis, Symmetric arthritis, Arthritis mutilans, Ankylosing spondylitis, Special form. Here we review the literature to identify the clinical types and genetic factors most highly associated with development of psoriatic arthritis, with the goal of assisting dermatologists in risk-stratifying their psoriasis patients. Local corticosteroid injections and non-steroidal anti-inflammatory drugs are recommended in milder forms. Sulphasalazine and methotrexate are effective in peripheral psoriatic arthritis. Recent studies have provided evidence on the efficacy of anti-tumor necrosis factor-α drugs to control symptoms and to slow or arrest radiological disease
Mesh median filter for smoothing 3-D polygonal surfaces

In this paper we introduce a new mesh filtering method: a mesh median filter. This is an application of the median filter to smoothen 3-D noisy shapes given by triangle meshes. An algorithm of the mesh median filter is realized by applying the median filter to face normals on triangle meshes and updating mesh vertex positions to make them fit to the filtered normals. As an advanced modification of the mesh median filter we further introduce a weighted mesh median filter. The weighted mesh median filter has a reinforced feature preservation effect. The weighted mesh median filter with positive weighting has the smoothing effect, and the one with negative weighting has the enhancing effect. The two kinds of mesh median filters are compared with two conventional mesh filtering methods: the Laplacian smoothing flow and the mean curvature flow. Experimental results demonstrate that the mesh median filter does not induce oversmoothing.
Based on Data Fusion Multi- Data Sources Intrusion Detection Model

In this paper, aiming at the characteristics of intrusion detection methods' limitations and the low detection accuracy, we analyze the course of intrusion and classify the detection data sources, and then establish a common intrusion detection model based on data fusion of multi- data sources. Through analyze each part's function of the intrusion detection model, summarizes its characteristics, using multiple detection methods and data integration technology, In theory to achieve the feasibility. In the end we bring up the future research direction.
A communication conflict removing method in collaborative project systems

In this paper observed data objects replicated at multiple computers that can be synchronized using synchronization messages exchanged over a network. The messages include a current operation parameter and a previous operation parameter that are processed to determine how and when the replicated data objects should be manipulated. Data in the message can also indicate a unique priority level of the sending computer with respect to the other computers.
TRIST: Circumventing Censorship with Transcoding-Resistant Image Steganography

We explore the viability of extending state-of-the-art image steganography techniques for bypassing censorship. Our quest for a scalable steganographic technique, which is robust against automated transcoders that reformat images in-flight, led to the implementation of a prototype system called TRIST1 that embeds data by selectively modifying bits in the frequency domain of the image. By choosing heavily quantized frequency components at low JPEG quality values, we can robustly embed information within images, and demonstrate how this information survives a number of transformations, including transcoding to higher JPEG quality levels and other perturbations, such as image resizing (within bounds). We evaluate our system by building a prototype of a transcoding-resistant steganography library that we integrate with StegoTorus [36]. Our evaluations demonstrate that StegoTorus integrated with TRIST provides reasonable bandwidth capable of supporting basic web surfing along with transcoding resilience. Finally, we describe how our system can be adapted to counter state-of-theart statistical attacks such as blockiness detectors.
SysPTM 2.0: an updated systematic resource for post-translational modification

Post-translational modifications (PTMs) of proteins play essential roles in almost all cellular processes, and are closely related to physiological activity and disease development of living organisms. The development of tandem mass spectrometry (MS/MS) has resulted in a rapid increase of PTMs identified on proteins from different species. The collection and systematic ordering of PTM data should provide invaluable information for understanding cellular processes and signaling pathways regulated by PTMs. For this original purpose we developed SysPTM, a systematic resource installed with comprehensive PTM data and a suite of web tools for annotation of PTMs in 2009. Four years later, there has been a significant advance with the generation of PTM data and, consequently, more sophisticated analysis requirements have to be met. Here we submit an updated version of SysPTM 2.0 (http://lifecenter.sgst.cn/SysPTM/), with almost doubled data content, enhanced web-based analysis tools of PTMBlast, PTMPathway, PTMPhylog, PTMCluster. Moreover, a new session SysPTM-H is constructed to graphically represent the combinatorial histone PTMs and dynamic regulation of histone modifying enzymes, and a new tool PTMGO is added for functional annotation and enrichment analysis. SysPTM 2.0 not only facilitates resourceful annotation of PTM sites but allows systematic investigation of PTM functions by the user. Citation details: Li,J., Jia,J., Li,H. et al. SysPTM 2.0: an updated systematic resource for post-translational modification. Database (2014) Vol. 2014: article ID bau025; doi:10.1093/database/bau025. Database URL: http://lifecenter.sgst. cn/SysPTM/
Edge Caching at Base Stations With Device-to-Device Offloading

Content caching at network edge nodes, such as base stations (BSs) and user equipments (UEs), can significantly reduce the traffic load in future cellular networks. Considering the limited caching space, the contents cached at BSs should be selected carefully for improving caching efficiency. In this paper, we study the edge caching at BSs to minimize the transmission cost by considering traffic offloading via the device-to-device (D2D) communications. The traffic offloading reduces the traffic via cellular transmission and thus changes the utility achieved by content caching at BSs. We model the edge caching problem as a Markov decision process and propose a distributed cache replacement strategy based on Q-learning. The proposed strategy further needs the calculations of the following two key parameters: 1) To describe the effect of D2D offloading to the cellular traffic, we define the cellular serving ratio, which is calculated by the iterative maximum weighted independent sets problem for static networks and the stochastic geometry for high-dynamic networks; 2) The cache replacement rewards are calculated by analyzing the relationship between the requested and cached amounts of content data, which are obtained from the messages of the previous data request and transmissions, ignoring any extra information exchange between the BSs. Furthermore, the convergence of the proposed distributed cache replacement strategy is proved by the sequential stage game model. Simulation results verify the convergence of the proposed cache replacement strategy and show its performance gain compared with conventional strategies.
Treatment outcomes of curative radiotherapy in patients with vulvar cancer: results of the retrospective KROG 1203 study

Purpose We evaluated the prognostic factors and clinical outcomes of 56 patients with vulvar cancer treated with curative radiotherapy (RT) or concurrent chemoradiotherapy. Materials and Methods Overall survival (OS) and disease-free survival (DFS) were assessed retrospectively. Prognostic factors evaluated included age, International Federation of Gynecology and Obstetrics (FIGO) stage, TNM classification, tumor size, treatment modality, RT duration, and RT field. The association between the tumor human papillomavirus (HPV) status and survival was analyzed in 35 patients. Results During the median follow-up of 2.8 years (range, 0.3 to 18.9 years), 21 patients (37.5%) experienced treatment failure. Fifteen patients (27%) had local failure: nine (16%) local failure only, three (5%) locoregional failure, two (4%) local and distant failure, and one (2%) locoregional and distant failure. Of 56 patients, seven (13%) had persistent disease at the first follow-up at 2 months and all but one died within a year after completing RT. The 5-year OS and DFS were 51.6% and 44.0%, respectively. In multivariate analysis, clinical size ≥3 cm predicted a poor prognostic factor for DFS (p = 0.040) and age (≥70 years) was poor prognostic for DFS (p = 0.032) and OS (p = 0.048). Patients with HPV-positive tumors tended to have better 5-year OS and DFS, but the differences were not significant statistically. Conclusion Clinical size ≥3 cm was a significant prognostic factor for DFS. However, age was the most important prognostic factor for DFS and OS in patients treated with curative RT. Further studies are needed to determine which treatment should be considered for old age ≥70 years.
Cardiovascular risk markers in obstructive sleep apnoea syndrome and correlation with obesity

Background: High C-reactive protein (CRP) and homocysteine levels are risk factors for cardiovascular disease. Some, but not all, previous studies have reported increased levels of CRP and homocysteine in patients with obstructive sleep apnoea syndrome (OSAS). A study was undertaken to investigate the levels of these factors in carefully selected patients with OSAS and matched normal controls. Methods: CRP and homocysteine levels were measured in 110 subjects following polysomnography (PSG). Non-OSAS patients (group 1) were compared with two patient groups (mild/moderate OSAS (group 2) and severe OSAS (group 3)) group-matched for body mass index (BMI), and a fourth group of patients with severe OSAS who were more obese (group 4). All were free of other disease and similar in age, smoking habits and cholesterol levels. 50 suitable patients were commenced on continuous positive airway pressure (CPAP) treatment after PSG and 49 were reassessed 6 weeks later. Results: CRP levels were similar in groups 1, 2 and 3 (median (interquartile range (IQR)) 1.11 (0.76–2.11) mg/l vs 1.82 (1.20–3.71) mg/l vs 2.20 (1.16–3.59) mg/l; p = 0.727, Kruskal-Wallis test), but were significantly higher in group 4 than in the other groups (5.36 (2.42–9.17) mg/l, p<0.05 by individual group comparisons). In multivariate analysis of all subjects, BMI was an independent predictor for CRP levels (β = 0.221; p = 0.006) but apnoea-hypopnoea index and other measures of OSAS were not. There was no difference in homocysteine levels between all four groups (p = 0.1). CPAP did not alter CRP (2.29 (1.32–4.10) vs 2.84 (1.13–5.40) mg/l; p = 0.145) or homocysteine levels (8.49 (3.66) vs 9.90 (4.72) μmol/l; p = 0.381). Conclusion: CRP and homocysteine levels are not associated with OSAS severity in men but CRP is independently associated with obesity.
Learning Convolutional Action Primitives from Multimodal Timeseries Data *

Fine-grained action recognition is important for many applications such as human-robot interaction, automated skill assessment, and surveillance. The goal is to predict what action is occurring at any point in a timeseries sequence. While recent work has improved recognition performance in robotics applications, these methods often require hand-crafted features or large use of domain knowledge. Furthermore these methods tend to model actions using pointwise estimates of individual frames or statistics on collections of frames. In this work we develop a notion of an action primitive that models how generic features transition over the course of an action. Our Latent Convolutional Skip Chain Conditional Random Field (LC-SCCRF) model learns a set of interpretable and composable action primitives. We apply our model to the cooking and robotic surgery domains using the University of Dundee 50 Salads dataset and the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Each consists of multimodal timeseries data including video, robot kinematics, and/or kitchen object accelerations. Our recognition performance on 50 Salads and JIGSAWS are 18.0% and 5.3% higher than the state of the art. Our model has the advantage of being more general than many recent approaches and performs well without requiring hand-crafted features or intricate domain knowledge. Upon publication we will release our LC-SC-CRF code and the features used on the two datasets.
Multi-level anomaly detection on time-varying graph data

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labeled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into the graphs' structure and helps contextualized detected event. For evaluation, two new hierarchical detectors are tested against a baseline Gaussian method on a synthetic graph sequence with seeded anomalies. We demonstrate that in a labeled setting with community structure, our graph statistics-based approach outperforms both a distribution-based detector and the baseline, accurately detecting anomalies at the node, subgraph, and graph levels.