Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilience Protected against Suicidal Behavior for Men But Not Women in a Community Sample of Older Adults in Korea

Suicide prevention efforts in reducing risk factors have been found to be more beneficial to older women than men, suggesting potential gender differences in effective prevention. The study aimed to examine gender difference in resilience for suicidal behavior in a community sample of older adults in Korea. A community-based survey was conducted to investigate resilience and risk factors of suicidal behavior using the Suicidal Behaviors Questionnaire-Revised, Connor-Davidson Resilience Scale, Center for Epidemiologic Studies Depression Scale (CES-D), as well as questions regarding physical illness and depression history. After accounting for well-known risk factors, resilience was inversely associated with suicidal behavior, but this protective role of resilience was applicable to men only. The findings of this study indicated gender difference in resilience against suicidal behavior in the elderly population. Gender-specific preventive intervention strategies need to be developed for community-based suicide prevention for older adults.
Relaxations of Approximate Linear Programs for the Real Option Management of Commodity Storage

The real option management of commodity conversion assets gives rise to intractable Markov decision processes (MDPs), in part because of the use of high-dimensional models of commodity forward curve evolution, as commonly done in practice. Focusing on commodity storage, we identify a deficiency of approximate linear programming (ALP), which we address by developing a novel approach to derive relaxations of approximate linear programs. We apply our approach to obtain a class of tractable ALP relaxations, also subsuming an existing method. We provide theoretical support for the use of these ALP relaxations rather than their associated approximate linear programs. Applied to existing natural gas storage instances, our ALP relaxations significantly outperform their corresponding approximate linear programs. Our best ALP relaxation is both near optimal and competitive with, albeit slower than, state-of-the-art methods for computing heuristic policies and lower bounds on the value of commodity storage, but is more directly applicable for dual (upper) bound estimation than these methods. Our approach is potentially relevant for the approximate solution of MDPs that arise in the real option management of other commodity conversion assets, as well as the valuation of real and financial options that depend on forward curve dynamics. This paper was accepted by Dimitris Bertsimas, optimization.
Cancelable PalmCode generated from randomized Gabor Filters for palmprint protection

The security and protection of biometric template has been the bottleneck of its applications due to permanent appearance of biometrics. We propose “Cancelable PalmCode” based on coding approaches to protect texture features of palmprints. Cancelable PalmCode templates are generated from Gabor filters whose parameters are randomized by the user-specific tokenised pseudo-random number (PRN). The effects of seven randomization schemes are studied and compared. Three of the randomization schemes are proved feasible and practicable for cancelable PalmCode. Cancelable PalmCode is able to be revoked and reissued conveniently and the transform is non-invertible. The selected three schemes avoid performance degradation caused by PRN. The experimental results on PolyU palmprint database show the feasibility and efficiency of cancelable PalmCode and it can be easily generalized to other palmprint codes for biometric security and protection.
Modeling protein tandem mass spectrometry data with an extended linear regression strategy

Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomics owing in part to robust spectral interpretation algorithm. The intensity patterns presented in mass spectra are useful information for identification of peptides and proteins. However, widely used algorithms can not predicate the peak intensity patterns exactly. We have developed a systematic analytical approach based on a family of extended regression models, which permits routine, large scale protein expression profile modeling. By proving an important technical result that the regression coefficient vector is just the eigenvector corresponding to the least eigenvalue of a space transformed version of the original data, this extended regression problem can be reduced to a SVD decomposition problem, thus gain the robustness and efficiency. To evaluate the performance of our model, from 60,960 spectra, we chose 2,859 with high confidence, non redundant matches as training data, based on this specific problem, we derived some measurements of goodness of fit to show that our modeling method is reasonable. The issues of overfitting and underfitting are also discussed. This extended regression strategy therefore offers an effective and efficient framework for in-depth investigation of complex mammalian proteomes.
Shallow Semantics with Shallow Syntax

Assigning semantic roles to the constituents of a natural language sentence is an important first step in translating natural language into a logical form for further processing. I present a statistical classifier which can perform this task using minimal syntactic cues. I use the syntactic and the semantic head of each constituent as the only features and present simple rules for extracting these heads. I also show how a hierarchical tree of semantic roles can be exploited to better estimate the probabilities in the model. On the FrameNet 1.3 corpus, my system can label pre-segmented constituents with an accuracy of 79.6%.
Arabidopsis CSN5B Interacts with VTC1 and Modulates Ascorbic Acid Synthesis[W]

Light regulates ascorbic acid (AsA) synthesis, which is reduced in the dark. This work identifies the mechanism of this regulation, an interaction between the AsA synthesis enzyme VTC1 and CSN5B, a component the COP9 signalosome. This interaction promotes VTC1 destruction and results in inactivation of AsA synthesis in the dark. Light regulates ascorbic acid (AsA) synthesis, which increases in the light, presumably reflecting a need for antioxidants to detoxify reactive molecules produced during photosynthesis. Here, we examine this regulation in Arabidopsis thaliana and find that alterations in the protein levels of the AsA biosynthetic enzyme GDP-Man pyrophosphorylase (VTC1) are associated with changes in AsA contents in light and darkness. To find regulatory factors involved in AsA synthesis, we identified VTC1-interacting proteins by yeast two-hybrid screening of a cDNA library from etiolated seedlings. This screen identified the photomorphogenic factor COP9 signalosome subunit 5B (CSN5B), which interacted with the N terminus of VTC1 in yeast and plants. Gel filtration profiling showed that VTC1-CSN5B also associated with the COP9 signalosome complex, and this interaction promotes ubiquitination-dependent VTC1 degradation through the 26S proteasome pathway. Consistent with this, csn5b mutants showed very high AsA levels in both light and darkness. Also, a double mutant of csn5b with the partial loss-of-function mutant vtc1-1 contained AsA levels between those of vtc1-1 and csn5b, showing that CSN5B modulates AsA synthesis by affecting VTC1. In addition, the csn5b mutant showed higher tolerance to salt, indicating that CSN5B regulation of AsA synthesis affects the response to salt stress. Together, our data reveal a regulatory role of CSN5B in light-dark regulation of AsA synthesis.
Predictive performance modelling of parallel component composition

Large-scale scientific computing applications frequently make use of closely-coupled distributed parallel components. The performance of such scientific applications is therefore dependent on the component parts and their interaction at run-time. This paper describes a methodology for predictive performance modelling of parallel applications composed of multiple interacting components. In this paper, the fundamental steps and required operations involved in the modelling process are identified - including inter-component dataflow analysis, M/spl times/N communication performance evaluation and composite performance model evaluation. A case study is presented to illustrate the modelling process and the methodology is verified through experimental analysis.
Inferring Data Flow in Semantic Web Service Composition

Automation of web service composition is one of the most interesting challenges facing the semantic web today. Despite approaches which are able to infer partial order on services, data flow (i.e., the way data is exchanged among services) remains implicit and difficult to be inferred and automatically generated. Since web services have been enhanced with formal semantic descriptions, it becomes conceivable to exploit and reason on their semantic links (i.e., semantic matching between their functional output and input parameters) to infer data flow. Our approach has been directed to meet the main challenges facing the latter problem i.e., how to effectively i) guarantee whether a data flow is well-formed and ii) infer data flow between services based on their Description Logics (DL) descriptions. To this end, we apply constructive DL reasoning abduction, contraction and introduce the non standard DL reasoning join to model and infer data flow in compositions. The preliminary evaluation results showed high efficiency and effectiveness of the proposed approach.
Object‐sensitive cost analysis for concurrent objects

This article presents a novel cost analysis framework for concurrent objects. Concurrent objects form a well‐established model for distributed concurrent systems. In this model, objects are the concurrency units that communicate among them via asynchronous method calls. Cost analysis aims at automatically approximating the resource consumption of executing a program in terms of its input parameters. While cost analysis for sequential programming languages has received considerable attention, concurrency and distribution have been notably less studied. The main challenges of cost analysis in a concurrent setting are as follows. First, inferring precise size abstractions for data in the program in the presence of shared memory. This information is essential for bounding the number of iterations of loops. Second, distribution suggests that analysis must infer the cost of the diverse distributed components separately. We handle this by means of a novel form of object‐sensitive recurrence equations that use cost centres in order to keep the resource usage assigned to the different components separate. We have implemented our analysis and evaluated it on several small applications that are classical examples of concurrent and distributed programming. Copyright © 2015 John Wiley & Sons, Ltd.
Low p 27 Kip 1 Expression Is an Independent Adverse Prognostic Factor in Patients with Multiple Myeloma 1

Purpose: To determine the clinical relevance of p27 in multiple myeloma (MM), we examined the relationship between p27 expression at diagnosis and clinical as well as laboratory parameters, including response to chemotherapy and overall survival in 74 previously untreated patients with MM. Experimental Design: Expression of p27 was assessed by immunohistochemistry on formalin-fixed, paraffin-embedded bone marrow biopsies. p27 expression was classified as low (<5% p27-positive myeloma cells) or high (>5% p27-positive myeloma cells). Results: Low p27 expression was observed in 23 (31%) patients. The response rate to standard dose chemotherapy (including vincristine, doxorubicin, and dexamethasone induction before high-dose chemotherapy) was 70%, with no significant difference between patients with low or high p27 expression (83 versus 65%; P 0.1). KaplanMeier analysis of all 74 patients revealed that patients with low p27 expression had a significantly shorter overall survival (median, 3.7 years versus 4.7 years; P 0.03) than those with high p27 expression. Patients with high p27 expression receiving high-dose chemotherapy experienced prolonged overall survival as compared with those with low p27 expression (median not yet reached versus 2.9 years; P 0.008). By multivariate Cox regression analyses, low p27(P 0.03), deletion of chromosome 13q14 (P 0.02), and 2-microglobulin (P 0.01) were identified as independent adverse prognostic factors for overall survival. According to the number of independent unfavorable prognostic factors present in each patient, low-risk, intermediate-risk, and high-risk patients with different overall survival times were defined (median overall survival, 6.3 versus 4.2 versus 1.8 years; P < 0.001). Conclusions: Low p27 expression is an independent adverse prognostic factor in patients with MM. The proposed risk score might be useful for risk-adapted treatment in the future.
Design and Analysis of a Greedy Pursuit for Distributed Compressed Sensing

We consider a distributed compressed sensing scenario where many sensors measure correlated sparse signals and the sensors are connected through a network. Correlation between sparse signals is modeled by a partial common support-set. For such a scenario, the main objective of this paper is to develop a greedy pursuit algorithm. We develop a distributed parallel pursuit (dipp) algorithm based on exchange of information about estimated support-sets at sensors. The exchange of information helps to improve estimation of the partial common support-set, that in turn helps to gradually improve estimation of support-sets in all sensors, leading to a better quality reconstruction performance. We provide restricted isometry property (RIP) based theoretical analysis on the algorithm's convergence and reconstruction performance. Under certain theoretical requirements (i.e., under certain assumptions) on the quality of information exchange over the network and RIP parameters of sensor nodes, we show that the dipp algorithm converges to a performance level that depends on a scaled additive measurement noise power (convergence in theory) where the scaling coefficient is a function of RIP parameters and information processing quality parameters. Using simulations, we show practical reconstruction performance of dipp vis-a-vis amount of undersampling, signal-to-measurement-noise ratios and network-connectivity conditions.
A Web-based Kernel Function for Matching Short Text Snippets

Determining the similarity of short text snippets, such as search queries, works poorly with traditional document similarity measures (e.g., cosine), since there are often few, if any, terms in common between two short text snippets. We address this problem by introducing a novel method for measuring the similarity between short text snippets (even those without any overlapping terms) by leveraging web search results to provide greater context for the short texts. In this paper, we dene such a similarity kernel function and provide examples of its ecacy . We also show the use of this kernel function in a large-scale system for suggesting related queries to search engine users.
Mediating chance encounters through opportunistic social matching

MEDIATING CHANCE ENCOUNTERS THROUGH OPPORTUNISTIC SOCIAL MATCHING by Julia M. Mayer Chance encounters, the unintended meeting between people unfamiliar with each other, serve as an important social lubricant helping people to create new social ties, such as making new friends or finding an activity, study or collaboration partner. Unfortunately, social barriers often prevent chance encounters in environments where people do not know each other and people have to rely on serendipity to meet or be introduced to interesting people around them. Little is known about the underlying dynamics of chance encounters and how systems could utilize contextual data to mediate chance encounters. This dissertation addresses this gap in research literature by exploring the design space of opportunistic social matching systems that aim to introduce relevant people to each other in the opportune moment and the opportune place in order to encourage face-to-face interaction. A theoretical framework of relational, social and personal context as predictors of encounter opportunities is proposed and validated through a mixed method approach using interviews, experience sampling and a field study of a design prototype. Key contributions of the field interview study (n=58) include novel context-aware social matching concepts such as: sociability of others as an indicator of opportune social context; activity involvement as an indicator of opportune personal context; and contextual rarity as an indicator of opportune relational context. The following study combining Experience Sampling Method (ESM) and participant interviews extends prior research on social matching by providing an empirical foundation for the design of opportunistic social matching systems. A generalized linear mixed model analysis (n=1781) shows that personal context (mood and busyness) together with the sociability of others nearby are the strongest predictors of people’s interest in a social match. Interview findings provide novel approaches on how to operationalize relational context
Manipulation of stretch-induced atriopeptin prohormone release and processing in the perfused rat heart.

Atriopeptin (AP) is stored as the prohormone AP-126 [atrial natriuretic factor-(1-26)] in atrial granules. Cultured atrial myocytes synthesize and release only prohormone into the medium. HPLC analysis of the coronary venous effluent of media from perfused rat hearts subjected to right atrial stretch indicated the presence of the C-terminal mature hormone AP-28 [atrial natriuretic factor-(99-126)] and little or no prohormone. Absence of calcium from the perfusion medium increased total AP release and surprisingly blocked the proteolytic cleavage of the prohormone. Similarly, addition of the proteolytic inhibitor aprotinin to the perfusion medium suppressed the processing of the endogenous AP-126 released by atrial stretch. Aprotinin would be restricted to the extracellular space, which is therefore implicated as the site of prohormone processing. This suggestion was validated by the demonstration that the perfused rat heart could readily cleave exogenous prohormone to mature hormone, a process blocked by aprotinin. Hypothetically, the stimulus-release-processing event initiated by atrial stretch may require the concerted action of the synthetic cell (i.e., atrial myocyte) and a processing cell or site (e.g., the adjacent atrial mesenchymal cell) for the production of the mature AP-28, which is the circulating molecular form of this endocrine system.
Semi-Automatic Geometry-Driven Reassembly of Fractured Archeological Objects

3D laser scanning of broken cultural heritage content is becoming increasingly popular, resulting in large col- lections of detailed fractured archeological 3D objects that have to be reassembled virtually. In this paper, we present a new semi-automatic reassembly approach for pairwise matching of the fragments, that makes it possible to take into account both the archeologist's expertise, as well as the power of automatic geometry-driven match- ing algorithms. Our semi-automatic reassembly approach is based on a real-time interaction loop: an expert user steadily specifies approximate initial relative positions and orientations between two fragments by means of a bimanual tangible user interface. These initial poses are continuously corrected and validated in real-time by an algorithm based on the Iterative Closest Point (ICP): the potential contact surface of the two fragments is identi- fied by efficiently pruning insignificant areas of a pair of two bounding sphere hierarchies, that is combined with a k-d tree for closest vertex queries. The locally optimal relative pose for the best match is robustly estimated by taking into account the distance of the closest vertices as well as their normals. We provide feedback to the user by a visual representation of the locally optimal best match and its associated error. Our first results on a concrete dataset show that our system is capable of assisting an expert user in real-time during the pairwise matching of downsampled 3D fragments.
In vivo Antitumor Activity of MEK and Phosphatidylinositol 3-Kinase Inhibitors in Basal-Like Breast Cancer Models

Purpose: The pathways underlying basal-like breast cancer are poorly understood, and as yet, there is no approved targeted therapy for this disease. We investigated the role of mitogen-activated protein kinase kinase (MEK) and phosphatidylinositol 3-kinase (PI3K) inhibitors as targeted therapies for basal-like breast cancer. Experimental Design: We used pharmacogenomic analysis of a large panel of breast cancer cell lines with detailed accompanying molecular information to identify molecular predictors of response to a potent and selective inhibitor of MEK and also to define molecular mechanisms underlying combined MEK and PI3K targeting in basal-like breast cancer. Hypotheses were confirmed by testing in multiple tumor xenograft models. Results: We found that basal-like breast cancer models have an activated RAS-like transcriptional program and show greater sensitivity to a selective inhibitor of MEK compared with models representative of other breast cancer subtypes. We also showed that loss of PTEN is a negative predictor of response to MEK inhibition, that treatment with a selective MEK inhibitor caused up-regulation of PI3K pathway signaling, and that dual blockade of both PI3K and MEK/extracellular signal–regulated kinase signaling synergized to potently impair the growth of basal-like breast cancer models in vitro and in vivo. Conclusions: Our studies suggest that single-agent MEK inhibition is a promising therapeutic modality for basal-like breast cancers with intact PTEN, and also provide a basis for rational combination of MEK and PI3K inhibitors in basal-like cancers with both intact and deleted PTEN.
Physical Human Activity Recognition Using Wearable Sensors

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.