Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Invariant Angle-Range Dependent Directional Modulation Based on Time-Modulated Frequency Diverse Arrays

We first review the fundamentals of directional modulation (DM) based on frequency diverse arrays (FDA), which is capable of achieving angle-range dependent point-to-point physical layer secure communications where the phase shifters of the FDA-based DM must be optimized continuously with time by complex algorithms like genetic algorithm (GA). Thereafter, in order to overcome the time-variant drawbacks of conventional FDA-based DM, we propose a time-invariant angle-range dependent DM based on time-modulated logarithmically increasing frequency offset FDA (TDM-log-FDA). Different from the conventional FDA-based DM, where the frequency offsets are linear and determinate, the frequency offsets of TDM-log-FDA are elaborately designed as time-modulated nonlinear logarithmically increasing values guaranteeing the time-invariant characteristic of TDM-log-FDA. Additionally, an improved dot-shaped time-invariant DM based on time-modulated multicarrier frequency offset FDA (DTDM-mc-FDA) is put forward to achieve time-invariant spatial fine focusing point-to-point physical layer secure communications, where multiple carriers instead of a single carrier are transmitted through each antenna element and the corresponding frequency offsets are also time-modulated. Using GA, we obtain the optimized phase shifters for the proposed two time-invariant DMs once and for all, which reduces the realization complexity of phase shifters greatly. Besides, a new metric called secure area is proposed to evaluate the security performance of DM systems. The simulated results demonstrate that the proposed TDM-log-FDA can realize time-invariant angle-range dependent secure communications, while the proposed DTDM-mc-FDA can further enhance the security with more accurately focusing secure area despite a small penalty of complexity.
Mitochondrial Dynamics and Oxidative Stress Exposure to environmentally persistent free radicals during gestation lowers energy expenditure and impairs skeletal muscle mitochondrial function in adult mice

Erin J. Stephenson, Alyse Ragauskas, Sridhar Jaligama, JeAnna R. Redd, Jyothi Parvathareddy, Matthew J. Peloquin, Jordy Saravia, Joan C. Han, Stephania A. Cormier, and X Dave Bridges Department of Physiology, University of Tennessee Health Science Center, Memphis, Tennessee; Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee; and Children’s Foundation Research Institute, Le Bonheur Children’s Hospital, Memphis, Tennessee
A horizontal fat mesh interconnection network

The mesh interconnection network is the most popular topology for the network on chip architecture. The horizontal fat mesh topology is designed to keep all the advantages of the mesh topologies prime of which is the almost wire area and the simplicity of the topology. The designed topology also does not have the non-planar links as present in most of the topologies which requires multiple layers. The performance of the topology has been tested on the uniform traffic for the latency, throughput and the power of the network. The topology has shown the improvement of in latency, throughput and power of network.
A Unified Framework for Missing Data and Cold Start Prediction for Time Series Data

Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed. The latter challenge is the time series version of the cold start problem seen in recommender systems. Time series models are also often plagued by missing data and high-dimensionality (i.e., large collections of observed series), making them ill-suited to the typical structure of big data time series. We provide a unified framework for producing long-range forecasts even when the series has missing values or was previously unobserved; the same framework can be used to impute missing values. Key to the formulation and resulting performance is (1) leveraging repeated patterns over fixed periods of time and across series, and (2) metadata associated with the individual series; both of these assets are common features of big data time series. Our formulation leverages two low-rank decompositions: one to describe low-dimensional latent time series trends underlying our repeated patterns and another to handle the weightings matrix on potentially very high-dimensional metadata. In our simulated experiments, we demonstrate the importance of these two modeling components. We then provide an analysis of these methods on web traffic in a Wikipedia dataset.
Improving performance in a DBaaS environment through the use of resource reservation

Resource reservation provides a user with the requested resource (usually, CPU, memory, space in disk and bandwidth) at the requested time, allowing the user to have the expected performance in a defined time interval. In the context of databases systems, applications with long running time queries represent a big challenge when there is a requirement to know approximately how long a query will take to execute. This is a prediction that might be relevant for several reasons. For instance, by knowing that a query will take longer than expect to execute, resource reservation can be performed, which means reserving more resources in order to execute this query in a shorter time in a future execution. This paper presents an approach that conceives an advance reservation mechanism in a DBaaS environment using machine learning techniques. In a general way, the proposal is to use a prediction mechanism based on machine learning to give the user a resource recommendation regarding time and cost. In this paper, we present the proposed model, the machine learning configuration, as well as some experiments to indicate benefits and efficiency from our proposal.
Observability analysis and observer design for DC-DC converters: A generalized approach

This paper mainly analyses the observability of large signal models of DC-DC converters and designs a nonlinear observer for the boost converter. A graph-theoretic method which has small computational complexity is proposed to verify observability properties of the boost converter and the superbuck converter. Furthermore, a full state nonlinear observer is designed based on the port-Hamiltonian system form of the large signal model of a DC-DC converter. The observer is applied to the boost converter and the observation of the inductor current tracks the actual value well.
Antisecretory Factor–Mediated Inhibition of Cell Volume Dynamics Produces Antitumor Activity in Glioblastoma

Interstitial fluid pressure (IFP) presents a barrier to drug uptake in solid tumors, including the aggressive primary brain tumor glioblastoma (GBM). It remains unclear how fluid dynamics impacts tumor progression and can be targeted therapeutically. To address this issue, a novel telemetry-based approach was developed to measure changes in IFP during progression of GBM xenografts. Antisecretory factor (AF) is an endogenous protein that displays antisecretory effects in animals and patients. Here, endogenous induction of AF protein or exogenous administration of AF peptide reduced IFP and increased drug uptake in GBM xenografts. AF inhibited cell volume regulation of GBM cells, an effect that was phenocopied in vitro by the sodium-potassium-chloride cotransporter 1 (SLC12A2/NKCC1) inhibitor bumetanide. As a result, AF induced apoptosis and increased survival in GBM models. In vitro, the ability of AF to reduce GBM cell proliferation was phenocopied by bumetanide and NKCC1 knockdown. Next, AF's ability to sensitize GBM cells to the alkylating agent temozolomide, standard of care in GBM patients, was evaluated. Importantly, combination of AF induction and temozolomide treatment blocked regrowth in GBM xenografts. Thus, AF-mediated inhibition of cell volume regulation represents a novel strategy to increase drug uptake and improve outcome in GBM. Mol Cancer Res; 16(5); 777–90. ©2018 AACR.
I act, therefore I judge: Network sentiment dynamics based on user activity change

The study of influence, persuasion, and user sentiment dynamics within online communities has recently emerged as a highly active area of research. In this paper, we focus on analyzing and modeling user sentiment dynamics within a real-world social media such as Twitter. Beyond text and connectivity, we are interested in exploring the level of topical user posting activity and its effect on sentiment change. We perform topic-wise analysis of tweeting behavior that reveals a strong relationship between users' activity acceleration and topic sentiment change. Inspired by this empirical observation, we develop a new generative and predictive model that extends classical neighborhood-based influence propagation with the notion of user activation. We fit the parameters of our model to a large, real-world Twitter dataset and evaluate its utility to predict future sentiment change. Our model outperforms significantly (1 order of magnitude in accuracy) existing alternatives in identifying the individuals who are most likely to change sentiment based on past information. When predicting the next sentiment of users who actually change their opinion (a relatively rare event), our model is twice more accurate than alternatives, while its overall network accuracy is 94% on average. We also study the effect of inactive users on consensus efficiency in the opinion dynamics process both analytically and in simulation within the context of our model.
Gonadotropins and their receptors: coevolution, genetic variants, receptor imaging, and functional antagonists

Abstract Gonadotropins belong to the family of dimeric glycoprotein hormones and regulate gonadal physiology mediated by G protein-coupled, seven-transmembrane receptors. These glycoprotein hormones are widely used in the clinic to promote ovarian follicle development and for treating some cases of male infertility. We traced the coevolution of dimeric gonadotropin hormones and their receptors, together with thyrotropin and its receptor. We updated recent findings on human genetic variants of these genes and their association with dizygotic twining, polycystic ovarian syndrome, primary ovarian insufficiency, male-limited precocious puberty, and infertility. In addition to the known physiological roles of gonadotropin-receptor signaling in gonadal tissues, we also discussed emerging understanding of extragonadal functions of gonadotropins in bones and adipose tissues, together with recent advances in in vivo imaging of gonadotropin receptors in live animals. Recent development of gonadotropin receptor agonists and antagonists were summarized with an emphasis on the development of functional antagonists for FSH receptors to alleviate osteoporosis and obesity associated with menopause. Summary Sentence Understanding of the co-evolution of gonadotropins and their cognate receptors, together with mutant phenotypes and receptor imaging, allow future design of functional antagonists to regulate different physiological and pathological processes.
A bootstrapped multi-view weighted Kernel fusion framework for cross-corpus integration of multimodal emotion recognition

Recently the development of robust emotion recognition has been increasingly emphasized in order to handle situations of different cultures and languages. This has become critical due to the potential applicability of emotion recognizers across a wide range of application scenarios. Instead of conventional approach in deriving a single universal emotion recognition module across all languages, we have previously demonstrated a method based on integrating other database's useful information to improve the emotion recognition of the current data with fusion of multiple emotion perspectives. In this paper, we present an improved framework, i.e., a bootstrapped multi-view weighted kernel fusion, to further advance the recognition accuracies. We have also extended the modeling of speech-only modality to include video information. In specifics, we utilize two emotional corpora of different languages. Our proposed framework obtains improved recognition in regressing activation and valence attributes using audio and video modalities across both of the databases. We not only demonstrate that the weighted kernel fusion can provide additional modeling power but also present analyses on the complementary emotionally-relevant acoustic and visual behaviors computed from the multiple emotion perspectives.
A new enforcement on declassification with reachability analysis

Language-based information flow security aims to decide whether an action-observable program can unintentionally leak confidential information if it has the authority to access confidential data. Recent concerns about declassification polices have provided many choices for practical intended information release, but more precise enforcement mechanism for these policies is insufficiently studied. In this paper, we propose a security property on the where-dimension of declassification and present an enforcement based on automated verification. The approach automatically transforms the abstract model with a variant of self-composition, and checks the reachability of illegal-flow state of the model after transformation. The self-composition is equipped with a store-match pattern to reduce the state space and to model the equivalence of declassified expressions in the premise of property. The evaluation shows that our approach is more precise than type-based enforcement.
Simultaneous determination of polyamines and acetylpolyamines in human urine by capillary electrophoresis with fluorescence detection

There has been evidence linking elevated polyamines (PAs) and acetylpolamines (AcPAs) level and cancer. So the simultaneous analysis of these compounds has become important task for cancer diagnosis and antitumor drug monitoring. A simple, fast and inexpensive CZE‐LIF method has been developed for the determination of cadaverine (CAD), putrescine (PUT), spermine (SPM), spermidine (SPD), acetylspermine (ASPM), and acetylspermidine (ASPD) in human urine using 4‐chloro‐7‐nitro‐2,1,3‐benzooxadiazole as a fluorescent reagent. Labeling reaction conditions were systematically investigated and were found to be 20 mM borate buffer at pH 7.4, labeling reaction time, and temperature were 10 min and 70°C, respectively. Under these optimized conditions the four PAs, two AcPAs and the internal standard were separated in 6 min. An Exactive‐MS with an ESI source was used for identification of the bis‐derivative of the ASPM. The method was validated in term of linearity, LODs, repeatability, intra‐ and interday assays, recovery, and selectivity. The LODs for CAD, PUT, SPM, SPD, ASPM, and ASPD were found to be 7.6, 10.0, 9.0, 8.8,7.8, and 3.3 nM, respectively. The method was successfully applied for the analysis of PAs and AcPAs in healthy human urine samples.
Using the length of the speech to measure the opinion

This article describes an automated technique that allows to differentiate texts expressing a positive or a negative opinion. The basic principle is based on the observation that positive texts are statistically shorter than negative ones. From this observation of the psycholinguistic human behavior, we derive a heuristic that is employed to generate connoted lexicons with a low level of prior knowledge. The lexicon is then used to compute the level of opinion of an unknown text. Our primary goal is to reduce the need of the human implication (domain and language) in the generation of the lexicon in order to have a process with the highest possible autonomy. The resulting adaptability would represent an advantage with free or approximate expression commonly found in social networks environment.
Evaluating the Expressiveness of Domain Specific Modeling Languages Using the Bunge-Wand-Weber Ontology

Business Process Management is becoming an ever more important aspect for organizations alongside with Business Process Diagrams as a tool to describe business processes. So far process modeling has been mainly performed with generic process modeling languages. These approaches have however limitations when it comes to the needs of specific problem domains or automated process analysis. Semantic building block based languages (SBBL) aim to overcome those limitations by integrating domain semantics in the modeling language. However, this class of languages is only useful if they exhibit the same expressiveness as generic languages. In this paper we strive to answer this question by comparing the expressiveness of the SBBL language PICTURE with ARIS as a generic language based on the Bunge-Wand-Weber ontology, showing that PICTURE has hardly construct deficits compared to ARIS while showing less construct redundancy and construct overload in its constructs.
A Quantum Implementation Model for Artificial Neural Networks

The learning process for multi layered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow-Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, this iterative formulas result in terms formed by the principal components of the weight matrix: i.e., the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase estimation algorithm is known to provide speed-ups over the conventional algorithms for the eigenvalue-related problems. Combining the quantum amplitude amplification with the phase estimation algorithm, a quantum implementation model for artificial neural networks using the Widrow-Hoff learning rule is presented. The complexity of the model is found to be linear in the size of the weight matrix. This provides a quadratic improvement over the classical algorithms.
A Hesitant Fuzzy Based Security Approach for Fog and Mobile-Edge Computing

Fog and mobile-edge computing (FMEC) is a sustainable and innovative mobile networking framework that enables the offloading of cloud services and resources at the edge of mobile cellular networks to provide high bandwidth and ultra-low latency. Nonetheless, how to handle several dynamically varying security services with the mobile user’s requirements efficiently is a critical problem that hinders the development of FMEC. To address this problem, we sought to introduce an approach to selecting an appropriate security service as per the mobile user requirements in FMEC. The problem of appropriate security service selection with hesitant fuzzy information is a multi-criteria decision making problem. In this paper, we introduce a soft hesitant fuzzy rough set (SHFRS) to solve multi-criteria decision making problems. SHFRS is introduced as an innovative extension of the hesitant fuzzy rough set theory by fusing it with the hesitant fuzzy soft set. We describe the inverse hesitant fuzzy soft set that defines the inverse hesitant fuzzy relation to determine the SHFRS upper and lower approximation operators of any hesitant fuzzy subset in the given set of parameters. We also present different special cases of SHFRS upper and lower approximation operators and discuss some fundamental theorems based on approximation operators. In addition, we propose a novel solution to multi-criteria decision making problems based on SHFRS. Finally, we assess the proposed solution by applying it to a real-time multi-criteria decision making problem of appropriate security service selection for FMEC in the existence of multi-observer hesitant fuzzy information.
Effect of 1-mg dose of finasteride on spermatogenesis and pregnancy.

Note: Postings to Androlog have been lightly edited before publication. The effect of finasteride on spermatogenesis and its use by male partners during pregnancy is a topic that has garnered interest recently. Although finasteride has been approved to treat benign prostatic hyperplasia since 1992, a 1-mg dose has been used to treat male pattern baldness since 1997. This newer treatment indication has resulted in far more men who are of reproductive age using the drug. This has caused some to consider finasteride’s effect on both fertility and pregnancy as the below string of Androlog entries highlights. The first posting came from Dr Mark Jutras (United States) who wrote:
Enhance cell-edge rates by amplify-forward shared relays in dense cellular networks

This paper explores the benefits of deploying multi-antenna half-duplex amplify-and-forward shared relays at the cell-edge to assist the downlink transmission in a multiple-input multiple-output wireless cellular network. We design the relay node to provide extra spatial dimensions to multiple receivers at the same time for interference mitigation and signal enhancement. This paper proposes an efficient algorithm to solve the non-convex problem of jointly optimizing the transmit beamforming and relay combining matrices to a stationary point by extending the celebrated weighted minimum mean squared error (WMMSE) algorithm. We show that the optimized relaying strategy can significantly improve the long-term average rates of cell-edge users in a cellular network, even after accounting for the extra bandwidth required for halfduplex relaying.
The Cerebral Network of Parkinson's Tremor: An Effective Connectivity fMRI Study.

UNLABELLED Parkinson's resting tremor has been linked to pathophysiological changes both in the basal ganglia and in a cerebello-thalamo-cortical motor loop, but the role of those circuits in initiating and maintaining tremor remains unclear. Here, we test whether and how the cerebello-thalamo-cortical loop is driven into a tremor-related state by virtue of its connectivity with the basal ganglia. An internal replication design on two independent cohorts of tremor-dominant Parkinson patients sampled brain activity and tremor with concurrent EMG-fMRI. Using dynamic causal modeling, we tested: (1) whether activity at the onset of tremor episodes drives tremulous network activity through the basal ganglia or the cerebello-thalamo-cortical loop and (2) whether the basal ganglia influence the cerebello-thalamo-cortical loop through connectivity with the cerebellum or motor cortex. We compared five physiologically plausible circuits, model families in which transient activity at the onset of tremor episodes (assessed using EMG) drove network activity through the internal globus pallidus (GPi), external globus pallidus, motor cortex, thalamus, or cerebellum. In each family, we compared two models in which the basal ganglia and cerebello-thalamo-cortical loop were connected through the cerebellum or motor cortex. In both cohorts, cerebral activity associated with changes in tremor amplitude (using peripheral EMG measures as a proxy for tremor-related neuronal activity) drove network activity through the GPi, which effectively influenced the cerebello-thalamo-cortical loop through the motor cortex. We conclude that cerebral activity related to Parkinson's tremor first arises in the GPi and is then propagated to the cerebello-thalamo-cortical circuit. SIGNIFICANCE STATEMENT Parkinson's resting tremor has been linked to pathophysiological changes both in the basal ganglia and in a cerebello-thalamo-cortical motor loop, but the role of those circuits in initiating and maintaining tremor remains unclear. Using dynamic causal modeling of concurrently collected EMG-fMRI data in two cohorts of Parkinson's patients, we showed that cerebral activity associated with changes in tremor amplitude drives network activity through the basal ganglia. Furthermore, the basal ganglia effectively influenced the cerebello-thalamo-cortical circuit through the motor cortex (but not the cerebellum). Out findings suggest that Parkinson's tremor-related activity first arises in the basal ganglia and is then propagated to the cerebello-thalamo-cortical circuit.