Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring frequency diversity with interference alignment in cognitive radio networks

The available spectrum in cognitive radio networks is usually discontinuous but wide, which provides abundant frequency domain diversity. In this paper, we identify the opportunity of leveraging the newly-emerged technique interference alignment to exploit such diversity to support concurrent transmission and improve the network throughput in secondary networks. To enable interference alignment, independent-fading subcarriers should be grouped together to provide sufficient dimensions for intended signals and non-intended interferences at the receiver side. We formulate the subcarrier grouping problem for interference alignment to maximize the number of concurrent transmissions, and propose a greedy-based algorithm to solve it, which is proved to be optimal. Simulation results show that using the proposed scheme, the total throughput in cognitive radio networks can be greatly improved.
E- and √E-model too conservative to describe low field time dependent dielectric breakdown

Extremely low field time dependent dielectric breakdown measurements were performed on single damascene structures with 90 and 50 nm ½ pitch integrated in a porous low-k material (k=2.5). We found with statistical significance that the E-model and the √E-model were too conservative to extrapolate our high field data to these low fields. Also, while soft breakdown does not occur at higher fields and wider spacings, we demonstrate that the time difference between soft and hard breakdown becomes significant in the 50 nm ½ pitch structures at normal operating fields. Besides this, we detected a change in distributional shape at these low fields and we argue that an extrinsic failure mode could be driving these failures or that the role of spacing variations across the wafer becomes more significant at lower fields.
A Time-Efficient and Accurate Open Circuit Voltage Estimation Method for Lithium-Ion Batteries

The open circuit voltage (OCV) of lithium-ion batteries is widely used in battery modeling, state estimation, and management. However, OCV is a function of state of charge (SOC) and battery temperature (Tbat) and is very hard to estimate in terms of time efficiency and accuracy. This is because two problems arise in normal operations: (1) Tbat changes with the current (I), which makes it very hard to obtain the data required to estimate OCV—terminal voltage (U) data of different I under the same Tbat; (2) the difference between U and OCV is a complex nonlinear function of I and is very difficult to accurately calculate. Therefore, existing methods have to design special experiments to avoid these problems, which are very time consuming. The proposed method consists of a designed test and a data processing algorithm. The test is mainly constant current tests (CCTs) of large I, which is time-efficient in obtaining data. The algorithm solves the two problems and estimates OCV accurately using the test data. Experimental results and analyses showed that experimental time was reduced and estimation accuracy was adequate.
Establishment and Assessment of a New GNSS Precipitable Water Vapor Interpolation Scheme Based on the GPT2w Model

With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological departments. GNSS stations are distributed in the form of scatters, generally, these separations range from a few kilometers to tens of kilometers. Therefore, the spatial resolution of GNSS-PWV can restrict some applications such as interferometric synthetic aperture radar (InSAR) atmospheric calibration and regional atmospheric water vapor analysis, which inevitably require the spatial interpolation of GNSS-PWV. This paper explored a PWV interpolation scheme based on the GPT2w model, which requires no meteorological data at an interpolation station and no regression analysis of the observation data. The PWV interpolation experiment was conducted in Hong Kong by different interpolation schemes, which differed in whether the impact of elevation was considered and whether the GPT2w model was added. In this paper, we adopted three skill scores, i.e., compound relative error (CRE), mean absolute error (MAE), and root mean square error (RMSE), and two approaches, i.e., station cross-validation and grid data validation, for our comparison. Numerical results showed that the interpolation schemes adding the GPT2w model could greatly improve the PWV interpolation accuracy when compared to the traditional schemes, especially at interpolation points away from the elevation range of reference stations. Moreover, this paper analyzed the PWV interpolation results under different weather conditions, at different locations, and on different days.
A vis-à-vis evaluation of MT paradigms for linguistically distant languages

Neural Machine Translation is emerging as the de facto standard for Machine Translation across the globe. Statistical Machine Translation has been the state-of-the-art for translation among Indian languages. This paper probes into the effectiveness of NMT for Indian languages and compares the strengths and weaknesses of NMT with SMT through a vis-a-vis qualitative estimation on different linguistic parameters. We compare the outputs of both models for the languages English, Malayalam and Hindi; and test them on various linguistic parameters. We conclude that NMT works better in most of the settings, however there is still immense scope for the betterment of accuracy for translation of Indian Languages. We describe the challenges faces especially when dealing with languages from different language families.
CHANGES IN FOREST STRUCTURE : Time-varying parameter sensitivity analysis of the Two Source Balance Model clarifies model behavior over a gradient of canopy complexity

Evapotranspiration (ET), which includes evaporation and vegetation transpiration, is an important component of the earth’s energy balance that influences water availability and energy partitioning at the land surface. Two-source energy balance (TSEB) models are widely used for estimating ET, however selecting an appropriate modeling scheme often depends on the understanding of the system, data availability, and modeling objectives. Therefore, understanding how the development of TSEB models influences their process-level behavior is a necessary next step to advance the field. To this end, I developed a comprehensive exploration of the dominant parameters in the TSEB model structure. Model controls are isolated using time-varying Sobols Sensitivity analysis over time series data accounting for spatial and temporal variability environmental conditions in order to assess the time-dependent nature of parameter sensitivity. Sensitivity indices are visualized along gradients to identify key behavioral differences between TSEB models and to connect these back to the models underlying assumptions. The results highlight model differences in performance controls. Understanding the links between model formulation and behavior can be an important diagnostic approach in applications where dominate model controls change over time.
High risk of cardiovascular disease in patients with type 1 diabetes in the U.K.: a cohort study using the general practice research database.

OBJECTIVE To estimate the absolute and relative risk of cardiovascular disease (CVD) in patients with type 1 diabetes in the U.K. RESEARCH DESIGN AND METHODS Subjects with type 1 diabetes (n = 7,479) and five age- and sex-matched subjects without diabetes (n = 38,116) and free of CVD at baseline were selected from the General Practice Research Database (GPRD), a large primary care database representative of the U.K. population. Incident major CVD events, comprising myocardial infarction, acute coronary heart disease death, coronary revascularizations, or stroke, were captured for the period 1992-1999. RESULTS The hazard ratio (HR) for major CVD was 3.6 (95% CI 2.9-4.5) in type 1 diabetic men compared with those without diabetes and 7.7 (5.5-10.7) in women. Increased HRs were found for acute coronary events (3.0 and 7.6 in type 1 diabetic men and women, respectively, versus nondiabetic subjects), coronary revascularizations (5.0 in men, 16.8 in women), and for stroke (3.7 in men, 4.8 in women). Type 1 diabetic men aged 45-55 years had an absolute CVD risk similar to that of men in the general population 10-15 years older, with an even greater difference in women. CONCLUSIONS Despite advances in care, these data show that absolute and relative risks of CVD remain extremely high in patients with type 1 diabetes. Women with type 1 diabetes continue to experience greater relative risks of CVD than men compared with those without diabetes.
Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling

Latent Dirichlet Allocation (LDA) models trained without stopword removal often produce topics with high posterior probabilities on uninformative words, obscuring the underlying corpus content. Even when canonical stopwords are manually removed, uninformative words common in that corpus will still dominate the most probable words in a topic. In this work, we first show how the standard topic quality measures of coherence and pointwise mutual information act counter-intuitively in the presence of common but irrelevant words, making it difficult to even quantitatively identify situations in which topics may be dominated by stopwords. We propose an additional topic quality metric that targets the stopword problem, and show that it, unlike the standard measures, correctly correlates with human judgements of quality. We also propose a simple-to-implement strategy for generating topics that are evaluated to be of much higher quality by both human assessment and our new metric. This approach, a collection of informative priors easily introduced into most LDA-style inference methods, automatically promotes terms with domain relevance and demotes domain-specific stop words. We demonstrate this approach's effectiveness in three very different domains: Department of Labor accident reports, online health forum posts, and NIPS abstracts. Overall we find that current practices thought to solve this problem do not do so adequately, and that our proposal offers a substantial improvement for those interested in interpreting their topics as objects in their own right.
Sedentary Behaviors among Hispanic Children: Influences of Parental Support in a School Intervention Program

Purpose. To examine the effect of parental support on sedentary behaviors among Hispanic children. Design and Setting. A longitudinal quasi-experimental design with five waves of data collection was used to examine the effect of parental support on children's sedentary behaviors in a school-based intervention program in west Texas. Subjects. Hispanic low-income parents and their children of 5 to 9 years (N = 416 child-parent dyads) over a 22-month period (1217 observations). Intervention. Transformation Para Salud was a multicomponent intervention program aimed at prevention and control of childhood overweight and obesity. It used a community-based participatory research approach, including nutrition education, physical exercise, gardening, and family involvement. Measures. Sedentary behaviors were measured by parents' report of their children's daily screen time per week, including TV/DVD, computer, Internet, and video games. Parental support was measured with parents' reported support for active living. Analysis. Growth curve analysis was used to examine trajectories of sedentary behaviors of children. Results. Response rate was over 70%. Parental support reduced children's sedentary behaviors. Girls were less sedentary than boys, but girls were less affected by parental support. The intervention was effective in reducing children's sedentary behaviors over time. Conclusion. It is important for school intervention programs to mobilize parents to provide more support to reduce sedentary behaviors.
The application of musculoskeletal modeling to investigate gender bias in non-contact ACL injury rate during single-leg landings

The central tenet of this study was to develop, validate and apply various individualised 3D musculoskeletal models of the human body for application to single-leg landings over increasing vertical heights and horizontal distances. While contributing to an understanding of whether gender differences explain the higher rate of non-contact anterior cruciate ligament (ACL) injuries among females, this study also correlated various musculoskeletal variables significantly impacted by gender, height and/or distance and their interactions with two ACL injury-risk predictor variables; peak vertical ground reaction force (VGRF) and peak proximal tibia anterior shear force (PTASF). Kinematic, kinetic and electromyography data of three male and three female subjects were measured. Results revealed no significant gender differences in the musculoskeletal variables tested except peak VGRF (p = 0.039) and hip axial compressive force (p = 0.032). The quadriceps and the gastrocnemius muscle forces had significant correlations with peak PTASF (r = 0.85, p < 0.05 and r = − 0.88, p < 0.05, respectively). Furthermore, hamstring muscle force was significantly correlated with peak VGRF (r = − 0.90, p < 0.05). The ankle flexion angle was significantly correlated with peak PTASF (r = − 0.82, p < 0.05). Our findings indicate that compared to males, females did not exhibit significantly different muscle forces, or ankle, knee and hip flexion angles during single-leg landings that would explain the gender bias in non-contact ACL injury rate. Our results also suggest that higher quadriceps muscle force increases the risk, while higher hamstring and gastrocnemius muscle forces as well as ankle flexion angle reduce the risk of non-contact ACL injury.
DIVA: DOMAIN INVARIANT VARIATIONAL AUTOEN-

We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the Domain Invariant Variational Autoencoder (DIVA), a generative model that tackles this problem by learning three independent latent subspaces, one for the domain, one for the class, and one for any residual variations. We highlight that due to the generative nature of our model we can also incorporate unlabeled data from known or previously unseen domains. To the best of our knowledge this has not been done before in a domain generalization setting. This property is highly desirable in fields like medical imaging where labeled data is scarce. We experimentally evaluate our model on the rotated MNIST benchmark and a malaria cell images dataset where we show that (i) the learned subspaces are indeed complementary to each other, (ii) we improve upon recent works on this task and (iii) incorporating unlabelled data can boost the performance even further.
Three‐week beta‐adrenergic blockade does not impair or improve general intellectual function in young healthy males

The widespread use of beta blockers in treatment of both cardiovascular and nonvascular conditions has generated interest in changes in functions of the central nervous system during treatment. We studied the effect of 3 weeks of beta blockade on learning and memory ability, concentration, and verbal abstraction in 32 young normotensive healthy men. We chose healthy males to exclude the possible influence of changes related to a hypertensive state. Subjects were randomized into a 3‐week treatment protocol with either atenolol 50 mg × 2 (cardioselective, hydrophilic), metoprolol 100 mg × 2 (cardioselective, lipophilic), propranolol 80 mg × 2 (noncardioselective, lipophilic), or placebo × 2. Each subject underwent two neuropsychological testing sessions. We found no significant enhancement or impairment of intellectual or psychomotor performances after the 3‐week treatment with beta‐adrenergic‐blocking agents compared to a placebo‐treated control group. Differences in pharmacokinetic profiles of the drugs (e.g., central nervous system penetrability, lipophilicity, or membranestabilizing effect) did not influence the test outcome. Antihypertensive treatment with beta blockers over a prolonged period does not affect young peoples' learning and memory abilities or reasoning powers, nor their ability to concentrate and perform psychometric tasks.
Towards an Agricultural IoT-Infrastructure for Micro-climate Measurements

Modeling, analysis, and control of crop diseases in a minimally invasive way continues to be a grand challenge in agriculture. While some diseases are well understood and possess accurate prediction models, there are many other diseases where not enough information is available yet. This precludes the construction of predictive models, and therefore the protection of such plants. In order to study these diseases it is necessary to acquire sufficient information from the field, that is, the plants’ environment and the plants themselves. This in turn allows one to determine the environmental factors influencing or triggering a disease, and the way the disease spreads among the plants. We tackle this problem by building an agricultural Internet of Things (IoT) infrastructure which we call AIoT, for measuring the micro-climate in vineyards, which abound around Vienna. In this paper we present the overall architecture of AIoT, how it combines the swarm, the fog and the cloud, and describe in some detail the sensors we have developed for the swarm.
In Search for Holistic ICT 4 D Research : A Systematic Literature Review

The technologist vs. contextualist debate has divided Information and Communications Technology for Development (ICT4D) research. Today, the field is disconnected across three distinct research streams, which should be consolidated into a more holistic discourse. However, this endeavor remained unfeasible to date, because the similarities and differences of these ICT4D streams had not been clearly conceptualized. Our present work addresses this gap in knowledge through a systematic review of extant ICT4D research from 2007-2016. We provide an indepth analysis of 48 articles, identify and discuss the characteristics of each research stream, and offer new insights on how knowledge in the field might be consolidated into a more holistic fashion.
SELPLG Gene Polymorphisms in Relation to Plasma SELPLG Levels and Coronary Artery Disease

P‐selectin and P‐selectin glycoprotein ligand (SELPLG, selectin P ligand) constitute a receptor/ligand complex that is likely to be involved in the development of atherosclerosis and its complications. While the genetic variability of P‐selectin has already been investigated in depth, that of the SELPLG gene has not yet been extensively explored. The coding and regulatory sequences of the SELPLG were screened and nine polymorphisms were identified. The identified polymorphisms were genotyped in the AtheroGene study, a case‐control study of coronary artery disease (CAD). Haplotype analysis revealed that two polymorphisms of SELPLG, the M62I and the VNTR, independently influenced plasma SELPLG levels. Conversely, haplotypes of SELPLG were not associated with CAD risk.
Repeatable Motion Planning for Redundant Robots Over Cyclic Tasks

We consider the problem of repeatable motion planning for redundant robotic systems performing cyclic tasks in the presence of obstacles. For this open problem, we present a control-based randomized planner, which produces closed collision-free paths in configuration space and guarantees continuous satisfaction of the task constraints. The proposed algorithm, which relies on bidirectional search and loop closure in the task-constrained configuration space, is shown to be probabilistically complete. A modified version of the planner is also devised for the case in which configuration-space paths are required to be smooth. Finally, we present planning results in various scenarios involving both free-flying and nonholonomic robots to show the effectiveness of the proposed method.
DISENTANGLED STATE SPACE MODELS: UNSUPER-

Sequential data often originates from diverse environments. Across them exist both shared regularities and environment specifics. To learn robust crossenvironment descriptions of sequences we introduce disentangled state space models (DSSM). In the latent space of DSSM environment-invariant state dynamics is explicitly disentangled from environment-specific information governing that dynamics. We empirically show that such separation enables robust prediction, sequence manipulation and environment characterization. We also propose an unsupervised VAE-based training procedure to learn DSSM as Bayesian filters. In our experiments, we demonstrate state-of-the-art performance in controlled generation and prediction of bouncing ball video sequences across varying gravitational influences.
In Vitro and In Vivo Activities of Pterostilbene against Candida albicans Biofilms

ABSTRACT Pterostilbene (PTE) is a stilbene-derived phytoalexin that originates from several natural plant sources. In this study, we evaluated the activity of PTE against Candida albicans biofilms and explored the underlying mechanisms. In 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide (XTT) reduction assays, biofilm biomass measurement, confocal laser scanning microscopy, and scanning electron microscopy, we found that ≤16 μg/ml PTE had a significant effect against C. albicans biofilms in vitro, while it had no fungicidal effect on planktonic C. albicans cells, which suggested a unique antibiofilm effect of PTE. Then we found that PTE could inhibit biofilm formation and destroy the maintenance of mature biofilms. At 4 μg/ml, PTE decreased cellular surface hydrophobicity (CSH) and suppressed hyphal formation. Gene expression microarrays and real-time reverse transcription-PCR showed that exposure of C. albicans to 16 μg/ml PTE altered the expression of genes that function in morphological transition, ergosterol biosynthesis, oxidoreductase activity, and cell surface and protein unfolding processes (heat shock proteins). Filamentation-related genes, especially those regulated by the Ras/cyclic AMP (cAMP) pathway, including ECE1, ALS3, HWP1, HGC1, and RAS1 itself, were downregulated upon PTE treatment, indicating that the antibiofilm effect of PTE was related to the Ras/cAMP pathway. Then, we found that the addition of exogenous cAMP reverted the PTE-induced filamentous growth defect. Finally, with a rat central venous catheter infection model, we confirmed the in vivo activity of PTE against C. albicans biofilms. Collectively, PTE had strong activities against C. albicans biofilms both in vitro and in vivo, and these activities were associated with the Ras/cAMP pathway.
Co-teaching + : Towards Training of Robust Deep Networks with Noisy Labels

Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach “Co-teaching” that cross-trains two deep neural networks using the small-loss trick. However, with the increase of epochs, two networks converge to a consensus and Co-teaching reduces to the self-training MentorNet. To tackle this issue, we propose a robust learning paradigm called Co-teaching+, which bridges the “Update by Disagreement” strategy with the original Co-teaching. First, two networks feed forward and predict all data, but keep prediction disagreement data only. Then, among such disagreement data, each network selects its small-loss data, but back propagates the small-loss data from its peer network and updates its own parameters. Empirical results on benchmark datasets demonstrate that Coteaching+ is much superior to many state-of-theart methods in the robustness of trained models.