Stabilization of Periodic Piecewise Time-Varying Systems With Time-Varying Delay Under Multiple Cyber Attacks: An Augmented Lyapunov Functional Approach.

This article investigates the asymptotic stabilization of periodic piecewise time-varying systems with time-varying delay under various cyber attacks, particularly deception and DoS attacks. The addressed system is reformed into a number of time-varying subsystems based on the time interval for each period. Following that, a state-feedback controller with periodic time-varying gain parameters is developed to solve the stabilization problem. The control design depicts the possibility of the aforementioned cyber attacks with two mutually exclusive stochastic Bernoulli distributed parameters. Then, an augmented Lyapunov-Krasovskii functional with periodically varying matrices is used to determine the conditions for designing the proposed controller that ensures the mean-square asymptotic stability of the addressed system. The results of numerical examples support the conclusion that the proposed method is effective and superior, regardless of the cyber attacks involved.

How to handle noisy labels for robust learning from uncertainty.

Most deep neural networks (DNNs) are trained with large amounts of noisy labels when they are applied. As DNNs have the high capacity to fit any noisy labels, it is known to be difficult to train DNNs robustly with noisy labels. These noisy labels cause the performance degradation of DNNs due to the memorization effect by over-fitting. Earlier state-of-the-art methods used small loss tricks to efficiently resolve the robust training problem with noisy labels. In this paper, relationship between the uncertainties and the clean labels is analyzed. We present novel training method to use not only small loss trick but also labels that are likely to be clean labels selected from uncertainty called "Uncertain Aware Co-Training (UACT)". Our robust learning techniques (UACT) avoid over-fitting the DNNs by extremely noisy labels. By making better use of the uncertainty acquired from the network itself, we achieve good generalization performance. We compare the proposed method to the current state-of-the-art algorithms for noisy versions of MNIST, CIFAR-10, CIFAR-100, T-ImageNet and News to demonstrate its excellence.

A comparative clinical trial to evaluate efficacy and safety of the 308-nm excimer laser and the gain-switched 311-nm titanium:sapphire laser in the treatment of vitiligo.

BACKGROUND/PURPOSE: Ultraviolet B (UVB) laser irradiation in a targeted manner is a reasonable treatment option for localized vitiligo. Recently, narrow-band UVB gain-switched 311-nm titanium:sapphire lasers (TSL) were developed for the treatment of localized vitiligo. We aimed to compare efficacy, patient satisfaction, and safety between the conventional 308-nm excimer laser (EL) and gain-switched 311-nm TSL in patients with vitiligo. METHODS: The 13-paired lesions from 10 patients who had small vitiligo patches were included in this prospective intra-patient comparison trial. Each pair was randomly assigned to each laser treatment group and treated twice weekly for 12 weeks. The global photographic assessments by dermatologists, objective numerical assessments by imaging analyzer, and patient's satisfaction were used to evaluate the effectiveness. Adverse effects were also investigated at every visit. RESULTS: All treated lesions showed improvement of about 50% after 12 weeks. There was no significant difference between EL- and TSL-treated groups. Patient satisfaction and preference among the groups were also similar. Regarding safety, there were no serious adverse effects requiring cessation of the treatments; however, the severity score for persistent erythema (lasting >24 hours) was significantly lower in the TSL group than in the EL group. CONCLUSIONS: The gain-switched 311-nm TSL exhibited similar efficacy to the 308-nm EL in treating vitiligo as well as improved safety. Therefore, the 311-nm TSL is considered as a candidate device to replace the EL as a new and promising treatment option for localized vitiligo.

Neuropeptides Profile and Increased Innervation in Becker's Nevus.

BACKGROUND: Melanocytes are derived from neural crest, and various pigmentary disorders may accompany abnormalities in nerve system or develop following dermatome, suggesting that melanocyte and pigmentation may be closely related to neural factors. There are reports of Becker's nevus (BN) showing linear and segmental configuration, suggesting the association of BN with nerve system. However, there are no studies regarding the expression of neuropeptides in BN. OBJECTIVE: We investigated the expression of neuropeptides and innervation in BN. METHODS: Polymerase chain reaction (PCR) array of 84 genes related to neuronal process was done. Among the genes with 10-fold or more increase in lesional, real-time PCR was performed for neuropeptide Y (NPY), galanin, neurotensin (NTS) and their receptors skin compared to normal skin. IHC stain was done to look for the expression of NPY, galanin, NTS and their receptors and the distribution of protein gene products (PGP) 9.5 immunoreactive nerve fibers. RESULTS: PCR array revealed that 16 out of 84 genes related to neuronal process were increased by 10-fold or more in lesional skin. In real-time PCR of NPY, galanin, NTS and their receptors, statistically significant increase of NPY1R (p<0.05) and marginally significant increase of NPY2R, GAL2R, and NTS2R (p<0.1) was verified in lesional skin. In immunohistochemistry, NPY, NPY1R NPY2R, and NTS2R were highly expressed in lesional skin and increased PGP 9.5 immunoreactive linear nerve fibers were found in the epidermis of BN. CONCLUSION: NPY, galanin, NTS and their receptors and increased innervation may play a role in the pathogenesis of BN.

Giant Plexiform Neurofibroma of the Perineum and Pelvic Cavity Manifesting as Segmental Neurofibromatosis.

Segmental neurofibromatosis (SN) is rare form of neurofibromatosis characterized that cutaneous or neural changes are limited to one region of the body. SN present neurofibroma and less frequently, café au lait macules (CALMs) on usually unilateral or rarely bilateral of the body region. SN seems to have fewer systemic complications than neurofibromatosis type I or II, except patients with plexiform neurofibromas (PNFs). PNFs are rare benign peripheral nerve sheath tumors which arise from single or multiple nerves. PNFs can easily become aggressive growth particularly during puberty or pregnancy and leading to disfigurement and functional impairment. Also, PNFs can transform to malignant peripheral nerve sheath tumor, higher rate than classic neurofibroma. So, it is important to decide appropriate treatment modalities and time to intervention.

A Case of Ostraceous Psoriasis with Psoriatic Arthritis in an AIDS Patient.

Human immunodeficiency virus (HIV) infection may present with severe and atypical cutaneous diseases. Psoriasis also can develop in HIV patients associated with immune dysfunction and be presented as more severe and atypical manifestation. Furthermore, treatment of psoriasis in HIV patients can be complex and challenging. Herein, we report the case of a 50-year-old male with a 9-year history of HIV infection who developed an uncommon clinical variant of psoriasis with psoriatic arthritis and we discuss a possible pathogenesis of this autoimmune disease and possible treatment.

Extended dissipative analysis for neural networks with time-varying delays.

In this brief, an extended dissipativity analysis was conducted for a neural network with time-varying delays. The concept of the extended dissipativity can be used to solve for the H∞, L2-L∞, passive, and dissipative performance by adjusting the weighting matrices in a new performance index. In addition, the activation function dividing method is modified by introducing a tuning parameter. Examples are provided to show the effectiveness and less conservatism of the proposed method.

Stability for neural networks with time-varying delays via some new approaches.

This paper considers the problem of delay-dependent stability criteria for neural networks with time-varying delays. First, by constructing a newly augmented Lyapunov-Krasovskii functional, a less conservative stability criterion is established in terms of linear matrix inequalities. Second, by proposing novel activation function conditions which have not been proposed so far, further improved stability criteria are proposed. Finally, three numerical examples used in the literature are given to show the improvements over the existing criteria and the effectiveness of the proposed idea.