A Message-Passing Algorithm for Counting Short Cycles in a Graph

A message-passing algorithm for counting short cycles in a graph is presented. For bipartite graphs, which are of particular interest in coding, the algorithm is capable of counting cycles of length g, g +2,..., 2g - 2, where g is the girth of the graph. For a general (non-bipartite) graph, cycles of length g; g + 1, ..., 2g - 1 can be counted. The algorithm is based on performing integer additions and subtractions in the nodes of the graph and passing extrinsic messages to adjacent nodes. The complexity of the proposed algorithm grows as $O(g|E|^2)$, where $|E|$ is the number of edges in the graph. For sparse graphs, the proposed algorithm significantly outperforms the existing algorithms in terms of computational complexity and memory requirements.Comment: Submitted to IEEE Trans. Inform. Theory, April 21, 2010

An Efficient Algorithm for Finding Dominant Trapping Sets of LDPC Codes

This paper presents an efficient algorithm for finding the dominant trapping sets of a low-density parity-check (LDPC) code. The algorithm can be used to estimate the error floor of LDPC codes or to be part of the apparatus to design LDPC codes with low error floors. For regular codes, the algorithm is initiated with a set of short cycles as the input. For irregular codes, in addition to short cycles, variable nodes with low degree and cycles with low approximate cycle extrinsic message degree (ACE) are also used as the initial inputs. The initial inputs are then expanded recursively to dominant trapping sets of increasing size. At the core of the algorithm lies the analysis of the graphical structure of dominant trapping sets and the relationship of such structures to short cycles, low-degree variable nodes and cycles with low ACE. The algorithm is universal in the sense that it can be used for an arbitrary graph and that it can be tailored to find other graphical objects, such as absorbing sets and Zyablov-Pinsker (ZP) trapping sets, known to dominate the performance of LDPC codes in the error floor region over different channels and for different iterative decoding algorithms. Simulation results on several LDPC codes demonstrate the accuracy and efficiency of the proposed algorithm. In particular, the algorithm is significantly faster than the existing search algorithms for dominant trapping sets

FEELS: a full-spectrum enhanced emotion learning system for assisting individuals with autism spectrum disorder

Autism Spectrum Disorder (ASD) is a developmental disorder thatcan lead to a variety of social and communication challenges, andindividuals with ASD are at a higher risk of loneliness and depres-sion as a result of the disconnect and isolation they may feel fromthe rest of society as a result of their ASD. Interventions targetingimproved emotional detection has been clinically shown to be quitepromising; however, there are considerable barriers that make itchallenging to incorporate emotion detection within daily life sce-narios. Motivated by the need to fill this gap, we introduce theconcept of FEELS, a full-spectrum enhanced emotion learning sys-tem which could be useful as a tool to assist individuals with ASD.FEELS facilitates enhanced emotion detection by capturing a livevideo stream of individuals in real-time, then leveraging deep con-volutional neural networks to detect facial landmarks and a customhybrid neural network consisting of a time distributed feed-forwardneural network and a LTSM neural network to determine the emo-tional state of the individuals based on a sequence of facial land-marks over time. The feasibility of such an approach was exploredthrough the construction of a proof-of-concept FEELS system thatcan detect between five different basic emotional states: neutral,sad, happy, surprise, and anger. Future work will include extend-ing the proof-of-concept FEELS system to detect more emotionalstates and evaluate the system in more natural settings

Analytical development and optimization of a graphene-solution interface capacitance model

Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs) is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs) along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accurac

Measuring the orbital angular momentum spectrum of an electron beam

Electron waves that carry orbital angular momentum (OAM) are characterized by a quantized and unbounded magnetic dipole moment parallel to their propagation direction. When interacting with magnetic materials, the wavefunctions of such electrons are inherently modified. Such variations therefore motivate the need to analyse electron wavefunctions, especially their wavefronts, to obtain information regarding the material’s structure. Here, we propose, design and demonstrate the performance of a device based on nanoscale holograms for measuring an electron’s OAM components by spatially separating them. We sort pure and superposed OAM states of electrons with OAM values of between −10 and 10. We employ the device to analyse the OAM spectrum of electrons that have been affected by a micron-scale magnetic dipole, thus establishing that our sorter can be an instrument for nanoscale magnetic spectroscopy

Towards a holographic approach to spherical aberration correction in scanning transmission electron microscopy

Recent progress in phase modulation using nanofabricated electron holograms has demonstrated how the phase of an electron beam can be controlled. In this paper, we apply this concept to the correction of spherical aberration in a scanning transmission electron microscope and demonstrate an improvement in spatial resolution. Such a holographic approach to spherical aberration correction is advantageous for its simplicity and cost-effiectiveness

Chitosan-based nanoscale systems for doxorubicin delivery:Exploring biomedical application in cancer therapy

Abstract Green chemistry has been a growing multidisciplinary field in recent years showing great promise in biomedical applications, especially for cancer therapy. Chitosan (CS) is an abundant biopolymer derived from chitin and is present in insects and fungi. This polysaccharide has favorable characteristics, including biocompatibility, biodegradability, and ease of modification by enzymes and chemicals. CS‐based nanoparticles (CS‐NPs) have shown potential in the treatment of cancer and other diseases, affording targeted delivery and overcoming drug resistance. The current review emphasizes on the application of CS‐NPs for the delivery of a chemotherapeutic agent, doxorubicin (DOX), in cancer therapy as they promote internalization of DOX in cancer cells and prevent the activity of P‐glycoprotein (P‐gp) to reverse drug resistance. These nanoarchitectures can provide co‐delivery of DOX with antitumor agents such as curcumin and cisplatin to induce synergistic cancer therapy. Furthermore, co‐loading of DOX with siRNA, shRNA, and miRNA can suppress tumor progression and provide chemosensitivity. Various nanostructures, including lipid‐, carbon‐, polymeric‐ and metal‐based nanoparticles, are modifiable with CS for DOX delivery, while functionalization of CS‐NPs with ligands such as hyaluronic acid promotes selectivity toward tumor cells and prevents DOX resistance. The CS‐NPs demonstrate high encapsulation efficiency and due to protonation of amine groups of CS, pH‐sensitive release of DOX can occur. Furthermore, redox‐ and light‐responsive CS‐NPs have been prepared for DOX delivery in cancer treatment. Leveraging these characteristics and in view of the biocompatibility of CS‐NPs, we expect to soon see significant progress towards clinical translation