Aharonov-Bohm differential conductance modulation in defective metallic single-wall carbon nanotubes

Using a perturbative approach, the effects of the energy gap induced by the Aharonov-Bohm (AB) flux on the transport properties of defective metallic single-walled carbon nanotubes (MSWCNTs) are investigated. The electronic waves scattered back and forth by a pair of impurities give rise to Fabry-Perot oscillations which constitutes a coherent backscattering interference pattern (CBSIP). It is shown that, the CBSIP is aperiodically modulated by applying a magnetic field parallel to the nanotube axis. In fact, the AB-flux brings this CBSIP under control by an additional phase shift. As a consequence, the extrema as well as zeros of the CBSIP are located at the irrational fractions of the quantity $\Phi_\rho={\Phi}/{\Phi_0}$, where $\Phi$ is the flux piercing the nanotube cross section and $\Phi_{0}=h/e$ is the magnetic quantum flux. Indeed, the spacing between two adjacent extrema in the magneto-differential conductance (MDC) profile is decreased with increasing the magnetic field. The faster and higher and slower and shorter variations is then obtained by metallic zigzag and armchair nanotubes, respectively. Such results propose that defective metallic nanotubes could be used as magneto-conductance switching devices based on the AB effect.Comment: 11 pages, 4 figure

Environmental, developmental, and genetic factors controlling root system architecture

A better understanding of the development and architecture of roots is essential to develop strategies to increase crop yield and optimize agricultural land use. Roots control nutrient and water uptake, provide anchoring and mechanical support and can serve as important storage organs. Root growth and development is under tight genetic control and modulated by developmental cues including plant hormones and the environment. This review focuses on root architecture and its diversity and the role of environment, nutrient, and water as well as plant hormones and their interactions in shaping root architecture

Influence of phonons on exciton-photon interaction and photon statistics of a quantum dot

In this paper, we investigate, phonon effects on the optical properties of a spherical quantum dot. For this purpose, we consider the interaction of a spherical quantum dot with classical and quantum fields while the exciton of quantum dot interacts with a solid state reservoir. We show that phonons strongly affect the Rabi oscillations and optical coherence on first picoseconds of dynamics. We consider the quantum statistics of emitted photons by quantum dot and we show that these photons are anti-bunched and obey the sub-Poissonian statistics. In addition, we examine the effects of detuning and interaction of quantum dot with the cavity mode on optical coherence of energy levels. The effects of detuning and interaction of quantum dot with cavity mode on optical coherence of energy levels are compared to the effects of its interaction with classical pulse

Android application collusion demystified

Application collusion is an emerging threat to Android based devices. In app collusion, two or more apps collude in some manner to perform a malicious action that they are unable to do independently. Detection of colluding apps is a challenging task. Existing commercial malware detection systems analyse each app separately, hence fail to detect any joint malicious action performed by multiple apps through collusion. In this paper, we discuss the current state of research on app collusion and open challenges to the detection of colluding apps. We compare existing approaches and present an integrated approach to effectively detect app collusion

Dynamics of threads and polymers in turbulence: power-law distributions and synchronization

We study the behavior of threads and polymers in a turbulent flow. These objects have finite spatial extension, so the flow along them differs slightly. The corresponding drag forces produce a finite average stretching and the thread is stretched most of the time. Nevertheless, the probability of shrinking fluctuations is significant and is known to decay only as a power-law. We show that the exponent of the power law is a universal number independent of the statistics of the flow. For polymers the coil-stretch transition exists: the flow must have a sufficiently large Lyapunov exponent to overcome the elastic resistance and stretch the polymer from the coiled state it takes otherwise. The probability of shrinking from the stretched state above the transition again obeys a power law but with a non-universal exponent. We show that well above the transition the exponent becomes universal and derive the corresponding expression. Furthermore, we demonstrate synchronization: the end-to-end distances of threads or polymers above the transition are synchronized by the flow and become identical. Thus, the transition from Newtonian to non-Newtonian behavior in dilute polymer solutions can be seen as an ordering transition.Comment: 13 pages, version accepted to Journal of Statistical Mechanic

Achieving Fair Inference Using Error-Prone Outcomes

Recently, an increasing amount of research has focused on methods to assess and account for fairness criteria when predicting ground truth targets in supervised learning. However, recent literature has shown that prediction unfairness can potentially arise due to measurement error when target labels are error prone. In this study we demonstrate that existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest, when an error-prone proxy target is used. As a solution to this problem, we suggest a framework that combines two existing fields of research: fair ML methods, such as those found in the counterfactual fairness literature and measurement models found in the statistical literature. Firstly, we discuss these approaches and how they can be combined to form our framework. We also show that, in a healthcare decision problem, a latent variable model to account for measurement error removes the unfairness detected previously

Fair inference on error-prone outcomes

Fair inference in supervised learning is an important and active area of research, yielding a range of useful methods to assess and account for fairness criteria when predicting ground truth targets. As shown in recent work, however, when target labels are error-prone, potential prediction unfairness can arise from measurement error. In this paper, we show that, when an error-prone proxy target is used, existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest. To remedy this problem, we suggest a framework resulting from the combination of two existing literatures: fair ML methods, such as those found in the counterfactual fairness literature on the one hand, and, on the other, measurement models found in the statistical literature. We discuss these approaches and their connection resulting in our framework. In a healthcare decision problem, we find that using a latent variable model to account for measurement error removes the unfairness detected previously.Comment: Online supplementary code is available at https://dx.doi.org/10.5281/zenodo.370815

An Illustrative Case of Subcutaneous Panniculitis-Like T-Cell Lymphoma

Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a very rare form of skin lymphoma that is localized primarily to the subcutaneous adipose tissue without palpable involvement of the lymph nodes. Diagnosis of SPTCL is a challenge, especially during its early phases when symptoms mimic other, more common conditions, such as benign panniculitis, eczema, dermatitis, psoriasis and cellulitis. Clinical and systemic features are nonspecific and can include fever, chills, and weight loss. Further complicating diagnosis is the high number of false negatives provided by biopsy. Here we present a case of SPTCL that illustrates the full course of the disease, from presentation and multiple misdiagnoses to correct disease recognition and successful treatment. A review of the challenges of diagnosis is provided with recommendations for more accurate and timely recognition of SPTCL

Correlation Between Mucosal IL-6 mRNA Expression Level and Virulence Factors of Helicobacter pylori in Iranian Adult Patients With Chronic Gastritis

Background: Helicobacter pylori infection is associated with gastritis and marked infiltration of the gastric mucosa by several cytokines secreting inflammatory cells that contribute to sustained local inflammation. In this study, we sought to examine IL-6 expression in H. pylori-infected and uninfected gastric mucosa and elucidate the implication in the pathogenesis of H. pylori-associated gastritis in human. Objectives: The current study aimed to determine mucosal IL-6 mRNA expression level and their correlation with virulence factors and the grade of chronic gastritis among H. pylori infected patients with chronic gastritis from Shahrekord, Iran. Patients and Methods: Mucosal IL-6 mRNA levels was measured by real-time PCR using endoscopic biopsies taken from the gastric antrum of 58 subjects infected with H. pylori and 44 uninfected subjects. Presence of vacA and cagA virulence factors was evaluated using PCR. Results: The IL-6 mRNA expression levels were significantly more elevated in H. pylori-positive patients than uninfected individuals and expression of this cytokine was independent from the virulence factors. There was a correlation between IL-6 expression level and the grade of chronic gastritis. Conclusions: Enhanced induction of IL-6 may be involved in the pathogenesis of H. pylon-associated gastritis