Miracle-dz/UniFAR-FTUs huggingface dataset:
Vascular depression: An early warning sign of frailty

Objectives: Frailty is a common geriatric disorder associated with activities of daily living (ADL) impairment, hospitalization, and death. Phenomenological evidence suggests that late-life depression (Katz, 2004), particularly vascular depression, may be a risk factor for frailty. This study tests that hypothesis. Methods: We identified a sample of stroke-free women over the age of 80 from the Health and Retirement Survey. The sample included 984 respondents in 2000 (incidence sample). Of these, 459 were non-frail at baseline and still alive in 2004 (prevalence sample). Frail respondents experienced at least three of the following: wasting, exhaustion, weakness, slowness, and falls. Vascular depression was represented using two dummy variables. The first represented respondents with either high cerebrovascular burden (CVB; at least two cerebrovascular risk factors) or probable depression (score ≥3 on the 8-item Center for Epidemiological Studies Depression Scale (CES-D)), and the second represented respondents with both high CVB and probable depression. Results: At baseline, the prevalence of frailty was 31.5%. Over four years the incidence of frailty was 31.8%. After controlling for age, education, ADL and IADL disability, arthritis, pulmonary disorders, cancer, and self-rated health, respondents with either high CVB or probable depression were more likely to be frail at baseline, and those with both were at even higher risk. Of those who were not frail at the 2000 wave, respondents who reported both high CVB and probable depression were more likely to become frail by 2004. Discussion: These findings suggest that vascular depression is a prodrome for frailty.
Oestrogen confers cardioprotection by suppressing Ca2+/calmodulin‐dependent protein kinase II

Background and purpose: Oestrogen confers cardioprotection by down‐regulating the β1‐adrenoceptor and suppressing the expression and activity of protein kinase A. We hypothesized that oestrogen may also protect the heart by suppressing Ca2+/calmodulin‐dependent protein kinase II (CaMKII), another signalling messenger activated by the β1‐adrenoceptor, that enhances apoptosis.
Machine Learning for Automatic Physical DBMS Tuning

Tuning a DBMS that experiences varying workload is challenging. Database administrators cannot be expected to monitor the workload and react with appropriate tunings, therefore automation is essential. In this report we outline a new method for automatic physical DBMS tuning that uses machine learning to model and predict workloads, and tune for the future. Our method builds on previous approaches by exploiting predictability of DBMS workload without assuming full knowledge. We present results on a set of synthetic workloads generated from the TPCH query templates.
Comparative Implementation of PicoSoC System-on-Chip in X-Fab 180 nm CMOS Technology

This paper presents the physical implementation of the PicoSoC System-on-Chip (SoC) using commercial EDA tools, to use it as a comparison source for future advances in open-source EDA tools for digital implementation flows. The PICORV32 is a simple and versatile microcontroller core that can be used for different applications (e.g., Internet of Things). The whole process entails logical and physical synthesis, design goals aspects, and reports by tools under different working conditions. The Logical and Physical synthesis of the PicoSoC for the X-Fab 180 nm node technology, presented in this work, relies on using the Cadence EDA tools. The microcontrollercore was fully synthesized with and without pads, resulting in a SoC that, including the pads, presents an estimated energy consumption of about 695.3 pJ per operation under nominal conditions.
The CoachAI Badminton Environment: A Novel Reinforcement Learning Environment with Realistic Opponents (Student Abstract)

The growing demand for precise sports analysis has been explored to improve athlete performance in various sports (e.g., basketball, soccer). However, existing methods for different sports face challenges in validating strategies in environments due to simple rule-based opponents leading to performance gaps when deployed in real-world matches. In this paper, we propose the CoachAI Badminton Environment, a novel reinforcement learning (RL) environment with realistic opponents for badminton, which serves as a compelling example of a turn-based game. It supports researchers in exploring various RL algorithms with the badminton context by integrating state-of-the-art tactical-forecasting models and real badminton game records. The Badminton Benchmarks are proposed with multiple widely adopted RL algorithms to benchmark the performance of simulating matches against real players. To advance novel algorithms and developments in badminton analytics, we make our environment open-source, enabling researchers to simulate more complex badminton sports scenarios based on this foundation. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI%20Badminton%20Environment.
Excess Sludge Production and Costs Due to Phosphorus Removal

Based on data collected from 35 French wastewater treatment plants and on published data, excess sludge production and chemical consumption associated with phosphorus removal is estimated for the three following phosphorus removal processes: chemical precipitation, Enhanced Biological Phosphorus Removal and hybrid process. The influence of wastewater characteristics on excess sludge production are assessed. Chemical costs and costs associated with sludge disposal were calculated and results for the three phosphorus removal processes are compared. The global costs for phosphorus removal are then estimated.
Saliency Detection based on Global Color Distribution and Active Contour Analysis

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.
A Geometric Particle Filter for Template-Based Visual Tracking

Existing approaches to template-based visual tracking, in which the objective is to continuously estimate the spatial transformation parameters of an object template over video frames, have primarily been based on deterministic optimization, which as is well-known can result in convergence to local optima. To overcome this limitation of the deterministic optimization approach, in this paper we present a novel particle filtering approach to template-based visual tracking. We formulate the problem as a particle filtering problem on matrix Lie groups, specifically the three-dimensional Special Linear group SL(3) and the two-dimensional affine group Aff(2). Computational performance and robustness are enhanced through a number of features: (i) Gaussian importance functions on the groups are iteratively constructed via local linearization; (ii) the inverse formulation of the Jacobian calculation is used; (iii) template resizing is performed; and (iv) parent-child particles are developed and used. Extensive experimental results using challenging video sequences demonstrate the enhanced performance and robustness of our particle filtering-based approach to template-based visual tracking. We also show that our approach outperforms several state-of-the-art template-based visual tracking methods via experiments using the publicly available benchmark data set.
Differentiable Spectrum Partition for Fractional Frequency Reuse in Multi-Cell OFDMA Networks

To mitigate inter-cell interference (ICI) and achieve higher spectrum efficiency, fractional frequency reuse (FFR) has been widely adopted by the next generation wireless systems, wherein different frequency reuse factors are applied to cell center and cell edge zones. In such conventional FFR, a contiguous radio spectrum is partitioned in a fixed fashion across all cells for edge and cell center zones. This approach evidently lacks the flexibility of inter-cell and/or intra-cell resource allocation adjustment and the capability of dealing with traffic load fluctuation and quality of service (QoS) requirements variations. This paper models the implementation of FFR in a multi-cell network environment and proposes a scheme called D-FFR that can adaptively partition radio spectrum in a distributed manner to achieve different FFR configuration among different cells. Resource demands and various inter-cell/intracell allocation constraints are accounted in D-FFR to enable differentiable inter-cell and/or intra-cell throughput and deliver higher spectrum efficiency. Analytical and extensive simulation results are provided in the paper to validate the effectiveness of our proposed scheme.
Tools for Gamification Analytics: A Survey

Application and gamification data contains valuable information about users and their behavior. This data can be used to measure the success of gamification projects, to analyze user behavior, and to continuously improve gamification designs. Existing software solutions promise support for analyzing game- and gamification-related data. However, research shows that most gamification projects do not monitor and analyze this data in an automated way, even though gamification experts are aware of its potential. In this survey paper we identify relevant software solutions and assesses them with regards to their fulfillment of user requirements in the gamification analytics domain. The survey results can be used by practitioners to make tool decisions. Furthermore, we identify gaps of current solutions that should be addressed by future work in the field of gamification analytics.
Deriving a global identification tool of sandy beaches using satellite images

The objective of this research is to explore the feasibility of detecting sandy beaches at a high spatial level, using satellite images. A pixel-based classification scheme of the four classes selected (sand, mud, vegetation and gravel) is conducted, using Landsat 8 Top-of-Atmosphere images (years 2014 and 2015) and Google Earth Engine. Moreover, a case study of the N-W European coastline (from Gibraltar to Denmark) is conducted, showing the applicability of the tool developed.
Carbamylated Erythropoietin: A Prospective Drug Candidate for Neuroprotection

Carbamylated erythropoietin (cEpo), which is neuroprotective but lacks hematopoietic activity, has been attracting rising concerns. However, the cellular and molecular mechanisms involved in the process of neuroprotection of cEpo are not well known. Based on several recent reports, the neuroprotective effects of cEpo are illustrated, and signaling pathways involved in the different effects of erythropoietin and cEpo are discussed. These newly reported researches may shed new light on the development and application of cEpo, a prospective drug candidate for neuroprotection.
Organic carbon, nutrient, and salt dynamics in saline soil and switchgrass (Panicum virgatum L.) irrigated with treated municipal wastewater

This study was conducted to evaluate the effects of treated municipal wastewater irrigation on soil organic carbon (SOC), soil macronutrients, Na, Cl, and SO4 dynamics in soil as well as switchgrass (Panicum virgatum L.) shoots under greenhouse conditions. Average SOC concentrations in upper 60 cm increased significantly from the prestudy level of 18 Mg ha−1 to between 21 and 23 Mg ha−1 at the end of the study (2009–2015) under different treatments. Annual average rate of increase for all four depths (upper 60 cm) ranged from 0.6 to 0.8 Mg ha−1 y−1. Nutrient concentration in soil samples was positively influenced by fertilizer application. N, P, and K concentrations in soil and switchgrass shoots were significantly positively correlated. On average, Alamo switchgrass cultivar under irrigated conditions removed 28.5 to 35.7 g N m−2, 2.52 to 3.48 g P m−2, and 29.6 to 38.4 g K m−2, and differences between freshwater and wastewater treatments were not significant. This indicated that wastewater irrigation did not result in excessive accumulation of nutrients in switchgrass tissue. Although salt constituents such as Na, Cl, and SO4 concentrations in soil increased over time as a result of wastewater irrigation, their concentration did not increase in switchgrass shoots, indicating a salt exclusion mechanism. The results of this greenhouse study indicated that the use treated municipal wastewater for irrigation can improve SOC contents of salt affected lands and extend the availability of freshwater in arid regions.
Performance analysis of DF relaying with optimum combining in a poisson field of interferers

The performance of optimum combining (OC) in decode-and forward (DF) relaying in the presence of a Poisson field of interferers is analyzed. An approximation for the outage probability of the OC receiver is derived by considering the temporal correlation of the interference. Despite the randomness of the number and the locations of the interferers, the OC receiver suppresses the interference at the destination node and diversity gains are achieved, provided that the relay nodes are noise limited. For single-antenna relay nodes, the end-to-end performance is vulnerable to interference at the relay nodes which is not canceled, resulting in zero diversity gains.
The annealing robust backpropagation (ARBP) learning algorithm

Multilayer feedforward neural networks are often referred to as universal approximators. Nevertheless, if the used training data are corrupted by large noise, such as outliers, traditional backpropagation learning schemes may not always come up with acceptable performance. Even though various robust learning algorithms have been proposed in the literature, those approaches still suffer from the initialization problem. In those robust learning algorithms, the so-called M-estimator is employed. For the M-estimation type of learning algorithms, the loss function is used to play the role in discriminating against outliers from the majority by degrading the effects of those outliers in learning. However, the loss function used in those algorithms may not correctly discriminate against those outliers. In this paper, the annealing robust backpropagation learning algorithm (ARBP) that adopts the annealing concept into the robust learning algorithms is proposed to deal with the problem of modeling under the existence of outliers. The proposed algorithm has been employed in various examples. Those results all demonstrated the superiority over other robust learning algorithms independent of outliers. In the paper, not only is the annealing concept adopted into the robust learning algorithms but also the annealing schedule k/t was found experimentally to achieve the best performance among other annealing schedules, where k is a constant and is the epoch number.
Rate and Synchrony in Feedforward Networks of Coincidence Detectors: Analytical Solution

We provide an analytical recurrent solution for the firing rates and cross-correlations of feedforward networks with arbitrary connectivity, excitatory or inhibitory, in response to steady-state spiking input to all neurons in the first network layer. Connections can go between any two layers as long as no loops are produced. Mean firing rates and pairwise cross-correlations of all input neurons can be chosen individually. We apply this method to study the propagation of rate and synchrony information through sample networks to address the current debate regarding the efficacy of rate codes versus temporal codes. Our results from applying the network solution to several examples support the following conclusions: (1) differential propagation efficacy of rate and synchrony to higher layers of a feedforward network is dependent on both network and input parameters, and (2) previous modeling and simulation studies exclusively supporting either rate or temporal coding must be reconsidered within the limited range of network and input parameters used. Our exact, analytical solution for feedforward networks of coincidence detectors should prove useful for further elucidating the efficacy and differential roles of rate and temporal codes in terms of different network and input parameter ranges.
2D Channel Defect States Properties Extraction Using DLTFS in AlGaN/GaN HEMTs

In this work, we extracted the defect state properties of a AlGaN/GaN high mobility electron transistor (GH25) commercialzed by United Monolithic Semiconductors (UMS). We measured the capacitance as a function of the gate voltage. This characterization was correlated with Deep-Level Transient Fourier Spectroscopy (DLTFS) measurements. Two defect states, located in the GaN 2D channel, were observed with energies of about 0.25 eV and 0.44 eV.
LCIndex: A Local and Clustering Index on Distributed Ordered Tables for Flexible Multi-dimensional Range Queries

A lot of Not Only SQL (NoSQL) databases have been proposed in the era of big data. Distributed Ordered Table (DOT) is one kind of No SQL database that has attracted lots of attention. It horizontally partitions table into regions and distributes regions to region servers according to the keys. Multi-Dimensional Range Query (MDRQ) is a common operation over DOTs. Many indexing techniques have been proposed to improve the performance of MDRQ, but they cannot guarantee high performance on both insert and flexible MDRQ at the same time. In this paper, we propose a novel indexing technique named LC Index, short for Local and Clustering Index, to solve this issue. Experimental results confirm that LC Index can achieve high performance on both insert operations and flexible MDRQ.
Post-Stroke Epilepsy in Young Adults: A Long-Term Follow-Up Study

Background Little is known about the incidence and risk of seizures after stroke in young adults. Especially in the young seizures might dramatically influence prognosis and quality of life. We therefore investigated the long-term incidence and risk of post-stroke epilepsy in young adults with a transient ischemic attack (TIA), ischemic stroke (IS) or intracerebral hemorrhage (ICH). Methods and Findings We performed a prospective cohort study among 697 consecutive patients with a first-ever TIA, IS or ICH, aged 18–50 years, admitted to our hospital between 1-1-1980 till 1-11-2010. The occurrence of epilepsy was assessed by standardized questionnaires and verified by a neurologist. Cumulative risks were estimated with Kaplan-Meier analysis. Cox proportional hazard models were used to calculate relative risks. After mean follow-up of 9.1 years (SD 8.2), 79 (11.3%) patients developed post-stroke epilepsy and 39 patients (5.6%) developed epilepsy with recurrent seizures. Patients with an initial late seizure more often developed recurrent seizures than patients with an initial early seizure. Cumulative risk of epilepsy was 31%, 16% and 5% for patients with an ICH, IS and TIA respectively (Logrank test ICH and IS versus TIA p<0.001). Cumulative risk of epilepsy with recurrent seizures was 23%, 8% and 4% respectively (Logrank ICH versus IS p = 0.05, ICH versus TIA p<0.001, IS versus TIA p = 0.01). In addition a high NIHSS was a significant predictor of both epilepsy and epilepsy with recurrent seizures (HR 1.07, 95% CI 1.03–1.11 and 1.08, 95% CI 1.02–1.14). Conclusions Post-stroke epilepsy is much more common than previously thought. Especially patients with an ICH and a high NIHSS are at high risk. This calls upon the question whether a subgroup could be identified which benefits from the use of prophylactic antiepileptic medication. Future studies should be executed to investigate risk factors and the effect of post-stroke epilepsy on quality of life.
Introduction to the Special Topic Embodied and Grounded Cognition

textabstractIntroduction: In the last 10–15 years, the embodied and grounded (E and G) cognition approach has become widespread in all fields related to cognitive (neuro) science, and a lot of evidence has been collected. The approach proposes that cognitive activity is grounded in sensory–motor processes and situated in specific contexts and situations. This special topic had two aims: first, give an idea of the field in its broadness. Second, focus on some challenges for E and G theories. The first important challenge is to account for understanding abstract concepts and words. Evidence on the representation of concrete concepts is compelling, whereas evidence on abstract concepts is still scarce and limited to restricted domains. A second important challenge concerns the role of computational models. E and G theories of cognition need to formulate more precise hypotheses, and models help to constrain and specify in more detail the predictions and the claims advanced.
Structured Search Result Differentiation

Studies show that about 50% of web search is for information exploration purpose, where a user would like to investigate, compare, evaluate, and synthesize multiple relevant results. Due to the absence of general tools that can effectively analyze and differentiate multiple results, a user has to manually read and comprehend potentially large results in an exploratory search. Such a process is time consuming, labor intensive and error prone. With meta information embedded, keyword search on structured data provides the potential for automating or semi-automating the comparison of multiple results. In this paper we present an approach for differentiating search results on structured data. We define the differentiability of query results and quantify the degree of difference. Then we define the problem of identifying a limited number of valid features in a result that can maximally differentiate this result from the others, which is proved to be NP-hard. We propose two local optimality conditions, namely single-swap and multi-swap. Efficient algorithms are designed to achieve local optimality. To show the applicability of our approach, we implemented a system XRed for XML result differentiation. Our empirical evaluation verifies the effectiveness and efficiency of the proposed approach.