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In this paper, we show how volume rendering with a Programmable Transfer Function can be used for the effective and comprehensible visualization of WiFi signals. A traditional transfer function uses a low-dimensional lookup table to map the volumetric scalar field to color and opacity. In this paper, we present the concept of a Programmable Transfer Function. We then show how generalizing traditional lookup-based transfer functions to Programmable Transfer Functions enables us to leverage view-dependent and real-time attributes of a volumetric field to depict the data variations of WiFi surfaces with low and high-frequency components. Our Programmable Transfer Functions facilitate interactive knowledge discovery and produce meaningful visualizations.

Group testing idea is an efficient approach to detect prevalence of an infection in the test samples taken from a group of individuals. It is based on the idea of pooling the test samples and performing tests to the mixed samples. This approach results in possible reduction in the required number of tests to identify infections. Classical group testing works consider static settings where the infection statuses of the individuals do not change throughout the testing process. In our paper, we study a dynamic infection spread model, inspired by the discrete time SIR model, where infections are spread via non-isolated infected individuals, while infection keeps spreading over time, a limited capacity testing is performed at each time instance as well. In contrast to the classical, static group testing problem, the objective in our setup is not to find the minimum number of required tests to identify the infection status of every individual in the population, but to control the infection spread by detecting and isolating the infections over time by using the given, limited number of tests. In order to analyze the performance of the proposed algorithms, we focus on the average-case analysis of the number of individuals that remain non-infected throughout the process of controlling the infection. We propose two dynamic algorithms that both use given limited number of tests to identify and isolate the infections over time, while the infection spreads, while the first algorithm is a dynamic randomized individual testing algorithm, in the second algorithm we employ the group testing approach similar to the original work of Dorfman. By considering weak versions of our algorithms, we obtain lower bounds for the performance of our algorithms. Finally, we implement our algorithms and run simulations to gather numerical results and compare our algorithms and theoretical approximation results under different sets of system parameters.

This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.

In this paper, we study a slotted-time system where a base station needs to update multiple users at the same time. Due to the limited resources, only part of the users can be updated in each time slot. We consider the problem of minimizing the Age of Incorrect Information (AoII) when imperfect Channel State Information (CSI) is available. Leveraging the notion of the Markov Decision Process (MDP), we obtain the structural properties of the optimal policy. By introducing a relaxed version of the original problem, we develop the Whittle’s index policy under a simple condition. However, indexability is required to ensure the existence of Whittle’s index. To avoid indexability, we develop Indexed priority policy based on the optimal policy for the relaxed problem. Finally, numerical results are laid out to showcase the application of the derived structural properties and highlight the performance of the developed scheduling policies.

In this paper, we study the corrections to the Friedmann equations due to fast fluctuations in the universe scale factor. Such fast quantum fluctuations were recently proposed as a potential solution to the cosmological constant problem. They also induce strong changes to the current sign and magnitude of the average cosmological force, thus making them one of the potential probable causes of the modification of Newtonian dynamics in galaxy-scale systems. It appears that quantum fluctuations in the scale factor also modify the Friedmann equations, leading to a considerable modification of cosmological evolution. In particular, they give rise to the late-time accelerated expansion of the universe, and they may also considerably modify the effective universe potential.

This paper explores the use of fatty acids in silicone hydrogel contact lenses for extending the release duration of cationic drugs. Drug release kinetics was dependent on the carbon chain length of the fatty acid loaded in the lens, with 12-, 14- and 18-carbon chain length fatty acids increasing the uptake and the release duration of ketotifen fumarate (KTF) and tetracaine hydrochloride (THCL). Drug release kinetics from oleic acid-loaded lenses was evaluated in phosphate buffer saline (PBS) at different ionic strengths (I = 167, 500, 1665 mM); the release duration of KTF and THCL was decreased with increasing ionic strength of the release medium. Furthermore, the release of KTF and THCL in deionized water did not show a burst and was significantly slower compared to that in PBS. The release kinetics of KTF and THCL was significantly faster when the pH of the release medium was decreased from 7.4 towards 5.5 because of the decrease in the relative amounts of oleate anions in the lens mostly populated at the polymer–pore interfaces. The use of boundary charges at the polymer–pore interfaces of a contact lens to enhance drug partition and extend its release is further confirmed by loading cationic phytosphingosine in contact lenses to attract an anionic drug.

Recent medical imaging technologies, specifically functional magnetic resonance imaging (fMRI), have advanced the diagnosis of neurological and neurodevelopmental disorders by allowing scientists and physicians to observe the activity within and between different regions of the brain. Deep learning methods have frequently been implemented to analyze images produced by such technologies and perform disease classification tasks; however, current state-of-the-art approaches do not take advantage of all the information offered by fMRI scans. In this paper, we propose a deep multimodal model that learns a joint representation from two types of connectomic data offered by fMRI scans. Incorporating two functional imaging modalities in an automated end-to-end autism diagnosis system will offer a more comprehensive picture of the neural activity, and thus allow for more accurate diagnoses. Our multimodal training strategy achieves a classification accuracy of 74% and a recall of 95%, as well as an F1 score of 0.805, and its overall performance is superior to using only one type of functional data.

In the Newtonian limit of general relativity a force acting on a test mass in a central gravitational field is conventionally defined by the attractive Newtonian gravity (inverse square) term plus a small repulsive cosmological force, which is proportional to the slow acceleration of the universe expansion. In this paper we considered the cosmological-force correction due to fast quantum oscillations of the universe scale factor as a potential solution of the cosmological constant problem. These fast fluctuations of the cosmological scale factor violate Lorentz invariance at the Planck scale, and they induce strong changes to the current sign and magnitude of the average cosmological force, thus making it one of the potential probable causes for the modification of Newtonian dynamics in galaxy-scale systems. The modified cosmological force may be responsible for the recently discovered “cosmic-clock” behavior of disk galaxies in the low-redshift universe. The obtained results have strong implications for astroparticle physics since they demonstrate that typical galaxy rotation curves may be obtained without (or almost without) dark-matter particles.

This paper presents several novel designs of small form factor underwater radio antennas operating in the 2 MHz, 50 MHz and 2.4 GHz bands. These antennas efficiently excite surface electromagnetic waves (SEW) which propagate along the surface of seawater. The antenna operation is made possible due to implementation of an impedance matching enclosure, which is filled with de-ionized water. Enhanced coupling to surface electromagnetic waves is enabled by the enhancement of the electromagnetic field at the antenna apex. These features allow us to make antenna dimensions considerably smaller compared to typical free space designs. They also considerably improve coupling of electromagnetic energy to the surrounding seawater. Since SEW propagation length is considerably larger than the skin depth in seawater, this technique is useful for underwater broadband wireless communication. We conclude that the developed broadband underwater radio communication technique will be useful in networking of unmanned underwater vehicles.

This paper focuses on extending drug release duration from contact lenses by incorporating catanionic aggregates. The aggregates consist of a long-chain cationic surfactant, i.e., cetalkonium chloride (CKC), and an oppositely charged anti-inflammatory amphiphilic drug. We studied three non-steroidal anti-inflammatory (NSAID) drugs with different octanol–water partition coefficients; diclofenac sodium (DFNa), flurbiprofen sodium (FBNa), and naproxen sodium (NPNa). Confirmation of catanionic aggregate formation in solution was determined by steady and dynamic shear rheology measurements. We observed the increased viscosity, shear thinning, and viscoelastic behavior characteristic of wormlike micelles; the rheological data are reasonably well described using a Maxwellian fluid model with a single relaxation time. In vitro release experiments demonstrated that the extension in the drug release time is dependent on the ability of a drug to form viscoelastic catanionic aggregates. Such aggregates retard the diffusive transport of drug molecules from the contact lenses. Our study revealed that the release kinetics depends on the CKC concentration and the alkyl chain length of the cationic surfactant. We demonstrated that more hydrophobic drugs such as diclofenac sodium show a more extended release than less hydrophobic drugs such as naproxen sodium.