Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosted Multifeature Learning for Cross-Domain Transfer

Conventional learning algorithm assumes that the training data and test data share a common distribution. However, this assumption will greatly hinder the practical application of the learned model for cross-domain data analysis in multimedia. To deal with this issue, transfer learning based technology should be adopted. As a typical version of transfer learning, domain adaption has been extensively studied recently due to its theoretical value and practical interest. In this article, we propose a boosted multifeature learning (BMFL) approach to iteratively learn multiple representations within a boosting procedure for unsupervised domain adaption. The proposed BMFL method has a number of properties. (1) It reuses all instances with different weights assigned by the previous boosting iteration and avoids discarding labeled instances as in conventional methods. (2) It models the instance weight distribution effectively by considering the classification error and the domain similarity, which facilitates learning new feature representation to correct the previously misclassified instances. (3) It learns multiple different feature representations to effectively bridge the source and target domains. We evaluate the BMFL by comparing its performance on three applications: image classification, sentiment classification and spam filtering. Extensive experimental results demonstrate that the proposed BMFL algorithm performs favorably against state-of-the-art domain adaption methods.
Influence of different sewage sludges and composts on growth, yield, and trace elements accumulation in rice and wheat

The use of sewage sludge (SS) and solid waste composts in agriculture is considered as one of the best disposal options. In this study, impact of different composts and SS was evaluated on the soil quality, growth, and yield performance of rice and wheat crops. Four types of composts and SS were collected from different sources and applied in soil at 0.5% (5 g kg−1 soil) and 1.0% (10 g kg−1 soil) in comparison with chemical fertilizer (no SS/compost). The application of all types of composts at all the tested rates increased the straw and grain yields of both the crops compared with control. Nevertheless, application of Lahore compost and all SS (1.0%) resulted in a significant increase in the concentration of cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) in plant parts. The Cd concentration in rice grains with Lahore compost and Kasur SS applied at the rate of 1.0% was above the safe limit. The trace elements in wheat grains with all composts and SS at all the tested rates remained within safe limits. The release of amendment bound trace elements in soil solution was highly dependent upon dissolved organic carbon and concentration of these trace elements especially for Cu and Zn. Application of SS at low level (0.5%) and compost (except Lahore compost) at both levels could be a better strategy to exploit their benefits in terms of crop performance and soil quality. However, application of SS and compost at high rates may lead to accumulation of trace elements in rice grains limiting its suitability for human consumption.
Automated generation of fuzzy rules from large-scale network traffic analysis in digital forensics investigations

This paper describes ongoing study and first results on the application of Neuro-Fuzzy (NF) to support large-scale forensics investigation in the domain of Network Forensics. In particular we focus on patterns of benign and malicious activity that can be find in network traffic dumps. We propose several improvements to the NF algorithm that results in proper handling of large-scale datasets, significantly reduces number of rules and yields a decreased complexity of the classification model. This includes better automated extraction of rules parameters as well as bootstrap aggregation for generalization. Experimental results show that such optimization gives a smaller number of rules, while the accuracy increases in comparison to existing approaches. In particular, it showed an accuracy of 98% when using only 39 rules. In our research we contribute to forensics science by increasing awareness and bringing more comprehensive fuzzy rules. During the last decade many cases related to network forensics resulted in data that can be related to Big Data due to its complexity. Application of Soft Computing methods, such that Neuro-Fuzzy may bring not only sufficient classification accuracy of normal and attack traffic, yet also facilitate in understanding traffic properties and developing a decision-support mechanism.
Work Stealing in a Shared Virtual-Memory Heterogeneous Environment: A Case Study with Betweenness Centrality

This paper uses betweenness centrality as a case study to research efficient work stealing in a heterogeneous system environment. Betweenness centrality is an important algorithm in graph processing. It presents multiple-level parallelism and is an interesting problem to exploit various optimizations. We investigate queue-based work stealing to distribute its tasks across GPU compute units (CUs) and across the CPU and the GPU, which has not been done by prior work. In particular, we demonstrate how to leverage the new platform-atomic operations on AMD Accelerated Processing Units (APUs) to operate cross-device queues in a lock-free manner in shared virtual memory. To make the work stealing runtime and the application more efficient, we apply new architectural features, including atomic operations with different memory scopes and or-derings for different synchronization scenarios. We implement our solution using heterogeneous system architecture (HSA). Our results show that betweenness centrality with CPU-GPU work stealing achieves an average of 15% (up to 30%) performance improvement over GPU-only execution for diverse graph inputs. Our work stealing solution can be applied widely to other applications too. Finally, we analyze important parameters critical for queuing and stealing.
TraceLink: A model of amnesia and consolidation of memory

A model of amnesia is introduced, called TraceLink, that consists of three systems: 1) a trace system (neocortex), 2) a link system (hippocampus), and 3) a modulatory system (hippocampus/fornix/basal forebrain). It aims to explain salient aspects of the neuropsychology of amnesia, such as Ribot gradients in retrograde amnesia, patterns of dissociation between anterograde and retrograde amnesia, recovery from amnesia, and a newly discovered form of amnesia (semantic dementia) that results from certain temporal lobe lesions that do not affect the hippocampus. The model, furthermore, offers a new explanation for the global neuroanatomy of the hippocampus and neocortex based on the assumption that the brain aims to minimize connectivity volume. It also offers various strategies for the consolidation of memory, the effects of which are explored in computer simulations. The paper concludes with ten, largely untested, predictions derived from the TraceLink model. © 1997 Wiley‐Liss, Inc.
Practical Application of Order-preserving Encryption in Wide Column Stores

Order-preserving encryption (OPE) produces ciphertexts that preserve the relative order of the underlying plaintexts. Thus, it is very suitable for range queries over encrypted outsourced data, as it is a popular case in cloud database scenarios. Unfortunately, most schemes suffer from infeasibility in practice due to requirements like hardly maintainable data structures or additional architectural components. While OPE is a widely discussed topic in theory, to our knowledge only one OPE scheme received noticeable practical attention ((Boldyreva et al., 2009) for SQL-based systems in (Popa et al., 2011; Tu et al., 2013)). Therefore, our work identifies the practical requirements for utilizing OPE in real world usage with focus on existing NoSQL cloud database technologies. We evaluate a variety of popular schemes and propose improvements for two of them in order to further improve their practicability. Then we assess the performance of our modifications in comparison to the approach of (Boldyreva et al., 2011) (which can be considered the successor of (Boldyreva et al., 2009) by a runtime analysis in combination with two popular NoSQL wide column store databases.
Diagnostic Performance of a Smartphone‐Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting

Background Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera–based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real‐world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. Methods and Results Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single‐lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77–99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51–87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97–99%] versus 99.4% [95% CI 99–100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38–67%] versus 76.9% [95% CI 56–91%]); both had a very high negative predictive value (99.8% [95% CI 99–100%] versus 99.2% [95% CI 98–100%]). Conclusions The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population‐based screening for AF.
Performance Analysis of Underlay Cognitive Radio Systems: Estimation-Throughput Tradeoff

In this letter, we study the performance of cognitive Underlay Systems (USs) that employ power control mechanism at the Secondary Transmitter (ST). Existing baseline models considered for the performance analysis either assume the knowledge of involved channels at the ST or retrieve this information by means of a feedback channel, however, such situations hardly exist in practice. Motivated by this fact, we propose a novel approach that incorporates the estimation of the involved channels at the ST, in order to characterize the performance of USs under realistic scenarios. Moreover, we apply an outage constraint that captures the impact of imperfect channel knowledge, particularly on the interference power received at the primary receiver. Besides this, we employ a transmit power constraint at the ST to determine an operating regime for the US. Finally, we analyze an interesting tradeoff between the estimation time and the secondary throughput allowing an optimized performance of the US.
Multipath TCP: Analysis and Design

Multi-path TCP (MP-TCP) has the potential to greatly improve application performance by using multiple paths transparently. We propose a fluid model for a large class of MP-TCP algorithms and identify design criteria that guarantee the existence, uniqueness, and stability of system equilibrium. We clarify how algorithm parameters impact TCP-friendliness, responsiveness, and window oscillation and demonstrate an inevitable tradeoff among these properties. We discuss the implications of these properties on the behavior of existing algorithms and motivate a new design that generalizes existing algorithms and strikes a good balance among TCP-friendliness, responsiveness, and window oscillation. We illustrate our analysis and the behavior of the new algorithm using ns2 simulations.
Predictors of a Favorable Outcome in Patients Treated by Chiropractors for Neck Pain

Study Design. Prospective, multicenter, cohort study. Objective. To examine which clinical and sociodemographic baseline variables can predict a favorable outcome in subjects with neck pain treated by chiropractors. Summary of Background Data. Relatively little is known on predictors of neck pain, particularly for those subjects undergoing chiropractic care. No previous study has examined predictors of outcome for subjects with neck pain by modeling the trajectories of subjects in a longitudinal design. Methods. All new, consecutive patients, between 18 and 65 years of age with neck pain of any duration, who had not undergone chiropractic or manual therapy in the prior 3 months, were recruited. Questionnaires were administered at the first 3 visits, and at 3 and 12 months. In all, 29 putative prognostic baseline variables were evaluated. Multivariate multilevel longitudinal regression analyses were conducted using neck pain, neck disability, and perceived recovery as outcomes. Results. In total, 529 patients fulfilled the inclusion criteria. The response rate at 12-months was 92%. In the multivariate analyses, 14 (48%) of the prognostic variables examined were retained in at least one of the models. Shorter duration of neck pain at the first visit was the only variable retained in all 3 final regression models. The following were predictive of a favorable outcome for any 2 of the 3 outcome measures examined: intermittent neck pain, those not on sick-leave or receiving workers compensation at baseline, a higher level of education, less tiredness, higher expectations that the treatment would be beneficial, lack of morning pain, and worse perceived general health. Conclusion. On the basis of the patient’s history, the clinician can identify a number of determinants, which are predictive of a favorable outcome. Shorter duration of neck pain at the first visit was the only variable consistently found to be predictive of a favorable outcome for all 3 outcome measures examined.
Decline of Quercus robur forests in northwestern Italy: current situation and tentative aetiology

Quercus robur decline is a widespread phenomenon in northwestern Italy. In order to determine the incidence, the severity and the evolution of the decline, Q. robur crown transparency was assessed starting from 2007 in 11 study sites. In 2007, the mean crown transparency ranged from 35% to 74% depending on sites. No significant variation was observed among crown transparency levels in the 3 sampling periods (2007-2008-2009) at any sites. Soil and climate conditions of study sites were investigated in order to find possible correlations with crown transparency. Drought in spring may be the main factor triggering off oak decline in northwestern Italy. Ongoing experiments indicate that the availability of water and light may have effect only in the medium and long time period.
Circadian variability of insulin sensitivity: physiological input for in silico artificial pancreas.

BACKGROUND Closed-loop control clinical research trials have been considerably accelerated by in silico trials using the Food and Drug Administration-accepted type 1 diabetes mellitus (T1DM) simulator. We have recently demonstrated that postprandial insulin sensitivity (SI) in T1DM subjects was lower at breakfast (B) than lunch (L) and dinner (D), but not significantly, because of the small population size. The goal of this study was therefore to incorporate this novel information into the University of Virginia/Padova T1DM simulator and to reproduce in silico the observed circadian variability. SUBJECTS AND METHODS Twenty T1DM subjects received an identical mixed meal at B, L, and D. SI was calculated for each meal using the oral glucose minimal model. Seven SI daily patterns were identified, and their probabilities were estimated. Each in silico subject was linked to a time-varying SI profile, while random deviations of up to 40% were allowed. RESULTS Simulations were compared with experimental data. The integrated area above the basal glucose curve values were 2.60 ± 0.91 (B), 1.38 ± 0.91 (L), and 1.44 ± 1.07 (D) 10(4) min · mg/dL in silico versus 2.87 ± 1.65 (B), 1.98 ± 1.56 (L), and 2.16 ± 2.00 (D) 10(4) min · mg/dL in vivo. Incremental peak glucose values were 109 ± 33 (B), 80 ± 29 (L), and 81 ± 30 (D) mg/dL in silico versus 136 ± 39 (B), 126 ± 37 (L), and 125 ± 48 (D) mg/dL in vivo. CONCLUSIONS The incorporation of a time-varying SI into the simulator makes this technology suitable for running multiple-meal scenarios, thus enabling a more robust design of artificial pancreas algorithms.
Real or Virtual Laboratories in Science Teaching - is this Actually a Dilemma?

Considerable pedagogical advantage may be gained by the integration of the different ICT tools commonly used in teaching science and technology, particularly by integrating "real" and "virtual" laboratory activities. In the context of this paper, "real" laboratories involve benchtop experiments utilizing data acquisition systems while "virtual" laboratories entail interactive simu- lations and animations. Examples of such integrated activities are described; namely, (i) the study of wave phenomena using sound and (ii) a study of motion in one dimension. Such integrated com- puterized teaching tools also provide an opportunity for a greater level of integration of different science and technology disciplines.
Time-Domain Transfer Coupled Response of Antennas—Reciprocity Theorem Approach

A complete time-domain network representation of a transmit–receive two-port perfect electric conducting antenna system in an arbitrary distance is presented. We utilize a scattering matrix setting, which, as the impedance matrix formulation, is a proper setting for the time-domain formulation in antenna system representations. The formulation is derived directly in the time domain using both the convolution-type and the correlation-type reciprocity relations. We derive the general expressions for the matrix, in particular the transfer coupled coefficients between the antennas (transfer coupled response), followed by closed-form example of parallel staggered dipole antennas that provides a clear and transparent physical interpretation for the time-domain phenomenology.
Zr‐Metal Organic Framework and Derivatives for Adsorptive and Photocatalytic Removal of Acid Dyes

To investigate effects of modification of MOFs on removal of acid dyes via adsorption and photodegradation, zirconium‐based MOF, UiO‐66, and its derivatives were synthesized. UiO‐66 derivatives were prepared by using amine (NH2)‐containing ligand and incorporating carbon nanotubes (CNTs) and reduced graphene oxide (RGO). During the synthesis UiO‐66‐NH2, UiO‐66‐CNT and UiO‐66‐RGO, were obtained, respectively. While UiO‐66‐NH2 showed the enhanced adsorption capacity for acid dyes owing to the electrostatic attraction, CNTs were found to be the most effective addition to enhance the adsorption of acid dyes. However, the addition of RGO in UiO‐66 (to form UiO‐66‐RGO) exhibited the highest removal efficiency via photodegradation compared to UiO‐66 and other derivatives probably attributed to its unique layered morphology. The presence of NH2, CNTs and RGO not only significantly improved the adsorption capacity for acid dyes but also enabled these UiO‐66 derivatives to exhibit photocatalytic activity under visible light irradiation.
ATMOSPHERIC PHASE DELAY IN SENTINEL SAR INTERFEROMETRY

Abstract. The repeat-pass Synthetic Aperture Radio Detection and Ranging (RADAR) Interferometry (InSAR) has been a widely used geodetic technique for observing the Earth’s surface, especially for mapping the Earth’s topography and deformations. However, InSAR measurements are prone to atmospheric errors. RADAR waves traverse the Earth’s atmosphere twice and experience a delay due to atmospheric refraction. The two major layers of the atmosphere (troposphere and ionosphere) are mainly responsible for this delay in the propagating RADAR wave. Previous studies have shown that water vapour and clouds present in the troposphere and the Total Electron Content (TEC) of the ionosphere are responsible for the additional path delay in the RADAR wave. The tropospheric refractivity is mainly dependent on pressure, temperature and partial pressure of water vapour. The tropospheric refractivity leads to an increase in the observed range. These induced propagation delays affect the quality of phase measurement and introduce errors in the topography and deformation fields. The effect of this delay was studied on a differential interferogram (DInSAR). To calculate the amount of tropospheric delay occurred, the meteorological data collected from the Spanish Agencia Estatal de Meteorologia (AEMET) and MODIS were used. The interferograms generated from Sentinel-1 carrying C-band Synthetic Aperture RADAR Single Look Complex (SLC) images acquired on the study area are used. The study area consists of different types of scatterers exhibiting different coherence. The existing Saastamoinen model was used to perform a quantitative evaluation of the phase changes caused by pressure, temperature and humidity of the troposphere during the study. Unless the phase values due to atmospheric disturbances are not corrected, it is difficult to obtain accurate measurements. Thus, the atmospheric error correction is essential for all practical applications of DInSAR to avoid inaccurate height and deformation measurements.
Towards an Implementation of the OpenMP Collector API

Parallel programming languages/libraries including OpenMP, MPI, and UPC are either in the process of defining or have already established standard performance profiling interfaces. The OpenMP Architecture Review Board (ARB) recently sanctioned an interface specification for profiling/tracing tools that defines a protocol for two-way communications and control between the OpenMP runtime library and performance tools, known as the collector API. Reference implementations of the collector are sparse and are primarily closed-source. We provide a description of our efforts towards a full implementation of an open-source performance monitoring tool for OpenMP based on the collector API. This efforta evaluates the collector’s approach to performance measurement, assesses what is necessary to implement a performance tool based on the collector interface, and also provides information useful to performance tool developers interested in interfacing with the collector for performance measurements.