Miracle-dz/UniFAR-FTUs huggingface dataset:
Extension of Cox Proportional Hazard Model for Estimation of Interrelated Age-Period-Cohort Effects on Cancer Survival

In the frame of the Cox proportional hazard (PH) model, a novel two-step procedure for estimating age-period-cohort (APC) effects on the hazard function of death from cancer was developed. In the first step, the procedure estimates the influence of joint APC effects on the hazard function, using Cox PH regression procedures from a standard software package. In the second step, the coefficients for age at diagnosis, time period and birth cohort effects are estimated. To solve the identifiability problem that arises in estimating these coefficients, an assumption that neighboring birth cohorts almost equally affect the hazard function was utilized. Using an anchoring technique, simple procedures for obtaining estimates of interrelated age at diagnosis, time period and birth cohort effect coefficients were developed. As a proof-of-concept these procedures were used to analyze survival data, collected in the SEER database, on white men and women diagnosed with LC in 1975–1999 and the age at diagnosis, time period and birth cohort effect coefficients were estimated. The PH assumption was evaluated by a graphical approach using log-log plots. Analysis of trends of these coefficients suggests that the hazard of death from LC for a given time from cancer diagnosis: (i) decreases between 1975 and 1999; (ii) increases with increasing the age at diagnosis; and (iii) depends upon birth cohort effects. The proposed computing procedure can be used for estimating joint APC effects, as well as interrelated age at diagnosis, time period and birth cohort effects in survival analysis of different types of cancer.
Coalitions Improve Performance in Data Swarming Systems

We present an argument in favor of forming coalitions of peers in a data swarming system consisting of peers with heterogeneous upload capacities. In this paper, a coalition refers to a set of peers that explicitly cooperate with other peers inside the coalition via choking, piece selection, and capacity allocation strategies. Furthermore, each peer in a coalition exchanges data with peers outside its coalition via distinct choking, piece selection, and capacity allocation strategies. We first propose a simple Random Choking strategy for peers inside a coalition and develop an analytical model for studying its performance. Our model accurately predicts a coalition's performance and shows that the proposed strategy helps a coalition achieve near-optimal performance. Furthermore, our model can be easily adapted to model a BitTorrent-like swarm. We show that our Random Choking strategy significantly outperforms Tit-for-Tat and Unchoke-All strategies proposed in prior work. We also introduce a simple piece selection strategy, which significantly improves data availability within a coalition as compared to Rarest-First strategy employed in BitTorrent systems. Using cooperative game theory, we prove the existence of stable coalitions when peer population is fixed and each peer has complete information of other peers' actions and payoffs. When peers are allowed to freely join or leave coalitions, we propose a Cooperation-Aware Better Response strategy that achieves convergence of the dynamic coalition formation process. Finally, using extensive simulations, we demonstrate that forming coalitions results in significant improvements in the overall performance of a data swarm.
Temporal and Spatial Locality: An Abstraction for Masquerade Detection

Most studies in masquerade detection focus mainly on the user action, ignoring the object upon which that action is performed. This may yield limited models, since, for example, command execution (an action) usually ends up in the transformation of a file (the object). The overall goal of this paper is to prove that the object is paramount to distinguishing a user from a masquerade. With this in mind, we have developed a new approach to masquerade detection, called file system navigation, and tested our ideas using the Windows-Users and Windows-Intruder simulations Logs Data set, (WUIL) which unlike other datasets of its kind includes close-to-real simulated attacks. We have shown that our approach makes it possible to capture computer behavior in an abstract way difficult to realize in a purely action-based approach. In this paper, we introduce an abstraction called locality, the tendency of programs to cluster references to memory. While temporal locality is applicable to both actions and objects, spatial locality is more suitable to objects, as it depends on a notion of position. We have successfully validated our working hypothesis: locality-based features better capture user behavior for masquerade detection. Particularly, results based on our approach report an Area Under the Curve (AUC) of the receiver operating characteristic curve value of 0.97 in average with 30% of users having an AUC equal to or above 0.99.
HPLC analysis of polyphenols and antioxidant capacity determination of Scirpus holoschoenus L. rhizome

The aim of the study was to analyse polyphenols compounds from vegetal product Holoschoeni rhizome and its antioxidant capacity in order to justify traditional therapeutic using (livery protect effect). The separation, identification and quantification of polyphenols compounds were made through High performance of liquid chromatography (HPLC), standardized method according USP30-NF25 Monograph. Total antioxidant activity was determined through photochemiluminiscence method as ACL (Antioxidant capacity of lipid soluble substances). In vegetal product Holoschoeni rhizome we identified the follows: E and Z resveratrol, vanillin and phenol carboxylic acids (chlorogenic, caffeic, cinnamic and gallic). From all of this we measured E - resveratrol, vanillin, and chlorogenic, caffeic and gallic acids. Antioxidant activity of alcoholic extract from Scirpus holoschoenus L rhizome is 23.402 mmols equivalent TROLOX/100 g vegetal product.
Spatially explicit fisheries simulation models for policy evaluation

This paper deals with the design of modelling tools suitable for investigating the consequences of alternative policies on the dynamics of resources and fisheries, such as the evaluation of marine protected areas (MPA). We first review the numerous models that have been developed for this purpose, and compare them from several standpoints: population modelling, exploitation modelling and management measure modelling. We then present a generic fisheries simulation model, Integration of Spatial Information for FISHeries simulation (ISIS-Fish). This spatially explicit model allows quantitative policy screening for fisheries with mixed-species harvests. It may be used to investigate the effects of combined management scenarios including a variety of policies: total allowable catch (TAC), licenses, gear restrictions, MPA, etc. Fisher's response to management may be accounted for by means of decision rules conditioned on population and exploitation parameters. An application to a simple example illustrates the relevance of this kind of tool for policy screening, particularly in the case of mixed fisheries. Finally, the reviewed models and ISIS-Fish are discussed and confronted in the light of the underlying assumptions and model objectives. In the light of this discussion, we identify desirable features for fisheries simulation models aimed at policy evaluation, and particularly MPA evaluation.
Structural Segmentation and Visualization of Sitar and Sarod Concert Audio

Hindustani classical instrumental concerts follow an episodic development that, musicologically, is described via changes in the rhythmic structure. Uncovering this structure in a musically relevant form can provide for powerful visual representations of the concert audio that is of potential value in music appreciation and pedagogy. We investigate the structural analysis of the metered section (gat) of concerts of two plucked string instruments, the sitar and sarod. A prominent aspect of the gat is the interplay between the melody soloist and the accompanying drummer (tabla). The tempo as provided by the tabla together with the rhythmic density of the sitar/sarod plucks serve as the main dimensions that predict the transition between concert sections. We present methods to access the stream of tabla onsets separately from the sitar/sarod onsets, addressing challenges that arise in the instrument separation. Further, the robust detection of tempo and the estimation of rhythmic density of sitar/sarod plucks are discussed. A case study of a fully annotated concert is presented, and is followed by results of achieved segmentation accuracy on a database of sitar and sarod gats across artists.
CIVSched: Communication-aware Inter-VM Scheduling in Virtual Machine Monitor Based on the Process

Server consolidation in Cloud Computing makes it possible for multiple servers or desktops to run on one physical server to get high resource utilization, low cost and less energy consumption. However, the scheduler in virtual machine monitor (VMM) is agnostic about the communication behavior between the guest operating systems. It leads to inefficient network communication in consolidated environment. In particular, the CPU resource management has a critical impact on the network latency between co-resident virtual machines (VMs) when there are CPU-bound and I/O-bound workloads existing simultaneously. It brings a negative impact on latency-sensitive VMs. In this paper, we present the design and implementation of CIVSched scheduling to make the VMM aware of the communication behavior between two inter-VMs running on the same virtual platform. CIVSched inspects the network packets transmitted between local domains and find the destination VM and the target process inside that will receive the packets. Then, the destination VM and the target process are preferentially scheduled by VMM scheduler and guest OS scheduler respectively. The cooperation of these two schedulers makes the network packets received by the target application timely. Experimental results show that the CIVSched scheduling can reduce the average response time of network traffic by up to 18% for the highly consolidated environment while keeping the fairness of the VMM scheduler.
The association of depressive symptoms with cardiovascular and all-cause mortality in Central and Eastern Europe: Prospective results of the HAPIEE study

Background Studies in western populations have shown a positive association between depression and cardiovascular disease (CVD) and all-cause mortality. The association with depressive symptoms seems to be graded, rather than limited to the presence versus the absence of depression. Evidence from populations with potentially different patterns of confounders helps to address the consistency of these findings. The objective of the study was to investigate the association between depressive symptoms and all-cause and CVD mortality in populations of Central and Eastern Europe. Study design This was a prospective cohort study. Methods A total of 24,542 participants aged 45–69 years, randomly selected from populations of Novosibirsk (Russia), Krakow (Poland) and six Czech towns, were included. Depressive symptoms, assessed by the 20-item Center for Epidemiologic Studies Depression (CES-D) scale, were used as both continuous and categorical variables. Data on deaths were obtained from local or national death registers. Associations between depression and mortality were assessed using Cox proportional hazards models. Results Over a median of 7 years, 2091 deaths from all causes and 850 CVD deaths occurred in the cohorts. There was a graded association between CES-D score and mortality; the hazard ratio (HR) of CVD mortality for a 1 SD increase in CES-D was 1.20 (95% confidence interval (CI): 1.16–1.24) in men and 1.23 (95% CI: 1.12–1.35) in women; for all-cause mortality, the HRs were 1.13 (95% CI: 1.09–1.18) and 1.17 (95% CI: 1.10–1.25), respectively. The results were similar across countries. Conclusions Depressive symptoms predicted CVD and all-cause mortality independently of a wide range of potential confounders. The association followed a gradient and increased mortality risks were associated with scores below the cut-offs that are commonly used to define ‘depression’.
Model-driven design flow for distributed control in reconfigurable FPGA systems

One of the most challenging and time-consuming design tasks for dynamically reconfigurable FPGA (Field Programmable Gate Array) systems is the design of runtime-adaptation control. This aspect covers various points such as runtime-monitoring, reconfiguration decision-making and reconfiguration realization. In this paper, we propose a control design flow aiming at facilitating the designers work and enhancing their productivity through high design reuse and automation. The proposed flow combines control distribution and Model-Driven-Engineering (MDE). The distributed control structure proposed in this flow aims at facilitating the reuse of the control design by using separate controllers for local and global control problems. The flow enables designers to move from high-level control models, using an extended version of the MARTE (Modeling and Analysis of Real-Time and Embedded Systems) UML standard profile, to an automatic generation of the corresponding VHDL code. The flow was validated through a video processing case study from modeling to implementation in FPGA.
Algorithm and Theoretical Analysis for Domain Adaptation Feature Learning with Linear Classifiers

Domain adaptation problem arises in a variety of applications where the training set (\textit{source} domain) and testing set (\textit{target} domain) follow different distributions. The difficulty of such learning problem lies in how to bridge the gap between the source distribution and target distribution. In this paper, we give an formal analysis of feature learning algorithms for domain adaptation with linear classifiers. Our analysis shows that in order to achieve good adaptation performance, the second moments of source domain distribution and target domain distribution should be similar. Based on such a result, a new linear feature learning algorithm for domain adaptation is designed and proposed. Furthermore, the new algorithm is extended to have multiple layers, resulting in becoming another linear feature learning algorithm. The newly introduced method is effective for the domain adaptation tasks on Amazon review dataset and spam dataset from ECML/PKDD 2006 discovery challenge.
Associations between objectively assessed and self-reported sedentary time with mental health in adults: an analysis of data from the Health Survey for England

Objective There is increasing interest in the association between sedentary behaviour and mental health, although most studies have relied solely on self-reported measures, thus making results prone to various biases. The aim was to compare associations between objectively assessed and self-reported sedentary time with mental health in adults. Setting Community dwelling population sample drawn from the 2008 Health Survey for England. Participants 11 658 (self-report analysis) and 1947 (objective data) men and women. Primary outcome The 12-item General Health Questionnaire was administered to assess psychological distress. Sedentary and physical activity (exposure) was objectively measured using accelerometers (Actigraph GT1M) worn around the waist during waking hours for seven consecutive days. Results The highest tertile of objective sedentary time was associated with higher risk of psychological distress (multivariate adjusted OR=1.74, 95% CI 1.07 to 2.83), as was the highest tertile of self-reported total sitting time (OR=1.34, 95% CI 1.15 to 1.56). Self-reported, but not objective, moderate-to-vigorous physical activity was associated with lower risk of psychological distress. Only objective light-intensity activity was associated with lower risk of psychological distress. Conclusions Sedentary time is associated with adverse mental health.
General linear models under Rician noise for fMRI data

When analyzing fMRI data to study the brain process, one faces two challenges: (i) the correct noise distribution and (ii) the brain dynamics. In general, the brain dynamics are modeled under the simplifying, but wrong assumption that the noise follows a Gaussian distribution. In this paper, we model the brain dynamics under the correct Rice distribution. We implement the hemodynamic response function into a Rice framework and apply the standard General Linear Model (GLM) which is linear-in-the-parameters and can easily be solved. Next, the statistical properties of the least squares estimator are investigated via a simulation experiment.
Tree Transducers, Machine Translation, and Cross-Language Divergences

Tree transducers are formal automata that transform trees into other trees. Many varieties of tree transducers have been explored in the automata theory literature, and more recently, in the machine translation literature. In this paper I review T and xT transducers, situate them among related formalisms, and show how they can be used to implement rules for machine translation systems that cover all of the cross-language structural divergences described in Bonnie Dorr's influential article on the topic. I also present an implementation of xT transduction, suitable and convenient for experimenting with translation rules.
TTS evaluation: Double-ended objective quality measures

We address the problem of TTS speech quality evaluation and propose a double-ended objective measure in the form of average spectral distortion between time-aligned reference and synthesized speech, where the reference signal is made available as the speech of the text input to the TTS spoken by the same speaker as the unit-database. We detail the time-aligned spectral distortion measure calculated via dynamic time-warping and apply this measure for comparison of the effectiveness of 5 different automatic segmentation techniques for annotating the unit-database for two Indian languages, Tamil and Kannada. We also show that the proposed measure yields more meaningful scores than the PESQ measure which is popular for codec evaluation, but likely to be less suited for TTS evaluation due to the lack of a rigorous time-alignment as is done in the proposed spectral distortion measure here.
Robust discrimination between EEG responses to categories of environmental sounds in early coma

Humans can recognize categories of environmental sounds, including vocalizations produced by humans and animals and the sounds of man-made objects. Most neuroimaging investigations of environmental sound discrimination have studied subjects while consciously perceiving and often explicitly recognizing the stimuli. Consequently, it remains unclear to what extent auditory object processing occurs independently of task demands and consciousness. Studies in animal models have shown that environmental sound discrimination at a neural level persists even in anesthetized preparations, whereas data from anesthetized humans has thus far provided null results. Here, we studied comatose patients as a model of environmental sound discrimination capacities during unconsciousness. We included 19 comatose patients treated with therapeutic hypothermia (TH) during the first 2 days of coma, while recording nineteen-channel electroencephalography (EEG). At the level of each individual patient, we applied a decoding algorithm to quantify the differential EEG responses to human vs. animal vocalizations as well as to sounds of living vocalizations vs. man-made objects. Discrimination between vocalization types was accurate in 11 patients and discrimination between sounds from living and man-made sources in 10 patients. At the group level, the results were significant only for the comparison between vocalization types. These results lay the groundwork for disentangling truly preferential activations in response to auditory categories, and the contribution of awareness to auditory category discrimination.
Estimation of tremor parameters and extraction tremor from recorded signals for tremor suppression

Pathological tremor is defined as a roughly sinusoidal movement and usually occurs in the upper limb impacting individuals activities of daily livings. Functional electrical stimulation (FES) is proposed as a potential alternative for cancelling the pathological tremor. However, the feasibility and accuracy of FES depends on the estimation of amplitude and frequency of tremor signals measured by sensors. In this study, a novel algorithm incorporating a sliding fast Fourier transform (SFFT), an interpolation procedure and a limitation module of frequency range is developed to estimate tremor frequency and separate the tremor components from raw data. Based on the artificial signals and the actual tremor signals, the performance of the proposed algorithm is evaluated. The experimental results indicate that the developed algorithm could quickly adapt to the unknown dominant frequency and extract the tremor components with high accuracy. Therefore, this method could be employed in the tremor suppression by FES without affecting the voluntary movement.
Social Cohesion, Civic Culture, and Urba Development in Russia

Social cohesion and other ingredients of social capital are shown to be conducive for economic development, institutional performance, and quality of governance. No such analyses were available for Russia so far, and the present paper fills this gap. We propose a model which differentiates the impact on economic welfare of open (bridging) and closed (bonding) forms of social capital. By using 2007 survey data collected in the Geo-Rating project, we establish a significant positive relationship between open social capital and urban development in Russia. Closed social capital has a negative impact on development. It is further shown that the transmission mechanism between social capital and economic outcomes is based on municipal governance. Closed social capital is deployed to seek isolated protection from bad institutions and poor public sector governance; as such it reduces the political costs of malfeasance and facilitates the abuse of power.
Identifying and ranking critical success factors of customer experience in banks

Purpose – The purpose of this paper is to identify and evaluate the critical success factors which make the dimensions for measuring customer experience (CE) in banking organizations more effective and purposeful. These critical success factors have been prioritized in order to find out which is more important for the five threshold dimensions to achieve favourable CE.Design/methodology/approach – The paper examines the critical success factors of CE which have been identified from the literature survey and through expert's opinion have finally confirmed in context of this study on banking organizations. Further, analytic hierarchy process (AHP) has been used in order to evaluate the identified 14 critical factors of CE and to find their priorities for success in banking organizations.Findings – On the basis of the proposed hierarchical model, the findings suggest that the factors such as convenience, employees, online functional elements, and servicescape are critical for measuring CE in banking organiza.