Do Small-mass Neutrinos participate in Gauge Transformations?

Neutrino oscillation experiments presently suggest that neutrinos have a small but finite mass. If neutrinos are to have mass, there should be a Lorentz frame in which they can be brought to rest. This paper discusses how Wigner's little groups can be used to distinguish between massive and massless particles. We derive a representation of the SL(2,c) group which separates out the two sets of spinors contained therein. One set is gauge dependent. The other set is gauge-invariant and represents polarized neutrinos. We show that a similar calculation can be done for the Dirac equation. In the large-momentum/zero-mass limit, the Dirac spinors can be separated into large and small components. The large components are gauge invariant, while the small components are not. These small components represent spin-$\frac{1}{2}$ non-zero mass particles. If we renormalize the large components, these gauge invariant spinors again represent the polarization of neutrinos. Massive neutrinos cannot be invariant under gauge transformations.Comment: 15 page

A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks

There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not been investigated thoroughly. This paper proposes a novel biologically plausible supervised learning algorithm for learning precisely timed multiple spikes in a multilayer SNNs. Based on the spike-timing-dependent plasticity learning rule, the proposed learning method trains an SNN through the synergy between weight and delay learning. The weights of the hidden and output neurons are adjusted in parallel. The proposed learning method captures the contribution of synaptic delays to the learning of synaptic weights. Interaction between different layers of the network is realized through biofeedback signals sent by the output neurons. The trained SNN is used for the classification of spatiotemporal input patterns. The proposed learning method also trains the spiking network not to fire spikes at undesired times which contribute to misclassification. Experimental evaluation on benchmark data sets from the UCI machine learning repository shows that the proposed method has comparable results with classical rate-based methods such as deep belief network and the autoencoder models. Moreover, the proposed method can achieve higher classification accuracies than single layer and a similar multilayer SNN

Low cost fault-tolerant routing algorithm for Networks-on-Chip

A novel adaptive routing algorithm - Efficient Dynamic Adaptive Routing (EDAR) is proposed to provide a fault-tolerant capability for Networks-on-Chip (NoC) via an efficient routing path selection mechanism. It is based on a weighted path selection strategy, which exploits the status of real-time NoC traffic made available via monitor modules. The key performance goal is to maintain throughput under congested and faulty conditions via effective routing path decisions. In the proposed EDAR, port weights are calculated in real-time according to the channel status – Idle/Busy/Congested/Faulty, and the port with the lowest weighting is ranked as the near-optimal route to forward packets. This mechanism enables the router to bypass congested ports and tolerate faulty ports. To assess the latency and throughput of the proposed routing algorithm, several traffic patterns for both fault-free and faulty NoCs were evaluated. Results show that EDAR can achieve higher throughput compared to other state of the art routing algorithms under various traffic patterns and levels of injected faults. In addition, the hardware area overhead for EDAR is demonstrated to have a reasonably low cost which maintains scalability for large NoC implementations

Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach

Background:Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagnosis. Methods:This study aims to fill this void by utilizing a Bayesian network (BN) modelling approach. A K2 learning algorithm and statistical computation methods are used to construct BN structure and assess the obtained BN model. The data used in this study were collected from a clinical ultrasound dataset derived from a Chinese local hospital and a fine-needle aspiration cytology (FNAC) dataset from UCI machine learning repository. Results: Our study suggested that, in terms of ultrasound data, cell shape is the most significant feature for breast cancer diagnosis, and the resistance index presents a strong probabilistic dependency on blood signals. With respect to FNAC data, bare nuclei are the most important discriminating feature of malignant and benign breast tumours, and uniformity of both cell size and cell shape are tightly interdependent. Contributions: The BN modelling approach can support clinicians in making diagnostic decisions based on the significant features identified by the model, especially when some other features are missing for specific patients. The approach is also applicable to other healthcare data analytics and data modelling for disease diagnosis

Tratado práctico de las enfermedades de los riñones y de las alteraciones de la orina incluyendo los cálculos urinarios

Ilustrado con 81 grab. y una lám. cromolitografiada

Quantitative secondary electron imaging for work function extraction at atomic level and layer identification of graphene

Two-dimensional (2D) materials usually have a layer-dependent work function, which require fast and accurate detection for the evaluation of their device performance. A detection technique with high throughput and high spatial resolution has not yet been explored. Using a scanning electron microscope, we have developed and implemented a quantitative analytical technique which allows effective extraction of the work function of graphene. This technique uses the secondary electron contrast and has nanometre-resolved layer information. The measurement of few-layer graphene flakes shows the variation of work function between graphene layers with a precision of less than 10meV. It is expected that this technique will prove extremely useful for researchers in a broad range of fields due to its revolutionary throughput and accuracy

A multi-wavelength investigation of the radio-loud supernova PTF11qcj and its circumstellar environment

We present the discovery, classification, and extensive panchromatic (from radio to X-ray) follow-up observations of PTF11qcj, a supernova discovered by the Palomar Transient Factory. PTF11qcj is located at a distance of dL ~ 124 Mpc. Our observations with the Karl G. Jansky Very Large Array show that this event is radio-loud: PTF11qcj reached a radio peak luminosity comparable to that of the famous gamma-ray-burst-associated supernova 1998bw (L_{5GHz} ~ 10^{29} erg/s/Hz). PTF11qcj is also detected in X-rays with the Chandra observatory, and in the infrared band with Spitzer. Our multi-wavelength analysis probes the supernova interaction with circumstellar material. The radio observations suggest a progenitor mass-loss rate of ~10^{-4} Msun/yr x (v_w/1000 km/s), and a velocity of ~(0.3-0.5)c for the fastest moving ejecta (at ~10d after explosion). However, these estimates are derived assuming the simplest model of supernova ejecta interacting with a smooth circumstellar material characterized by radial power-law density profile, and do not account for possible inhomogeneities in the medium and asphericity of the explosion. The radio light curve shows deviations from such a simple model, as well as a re-brightening at late times. The X-ray flux from PTF11qcj is compatible with the high-frequency extrapolation of the radio synchrotron emission (within the large uncertainties). An IR light echo from pre-existing dust is in agreement with our infrared data. Our analysis of pre-explosion data from the Palomar Transient Factory suggests that a precursor eruption of absolute magnitude M_r ~ -13 mag may have occurred ~ 2.5 yr prior to the supernova explosion. Based on our panchromatic follow-up campaign, we conclude that PTF11qcj fits the expectations from the explosion of a Wolf-Rayet star. Precursor eruptions may be a feature characterizing the final pre-explosion evolution of such stars.Comment: 43 pages, 15 figures; this version matches the one published in ApJ (includes minor changes that address the Referee's comments.

CO_2 on Titan

A sharp stratospheric emission feature at 667 cm^(−1) in the Voyager infrared spectra of Titan is associated with the ν_2 Q branch of CO_2. A coupling of photochemical and radiative transfer theory yields an average mole fraction above the 110 mbar level of ƒCO_2 = 1.5 ± ^(1.5)_(0.8) x 10^(-9), with most of the uncertainty being due to imprecise knowledge of the vertical distribution. CO_2 is found to be in a steady state, with its abundance being regulated principally by the ∼72 K cold trap near the tropopause and secondarily by the rate at which water-bearing meteoritic material enters the top of the atmosphere. An influx of water about 0.4 times that at the top of the terrestrial atmosphere is consistent with a combination of the observed CO_2 abundance and a steady state CO mole fraction of 1.1×10^(−4); the theoretical value for CO is close to the value observed by Lutz et al. (1983), although there are large margins for error in both numbers. If steady state conditions for CO prevail, little information is available regarding the evolution of Titan's atmosphere

A catalytically active oscillator made from small organic molecules

Oscillatory systems regulate many biological processes, including key cellular functions such as metabolism and cell division, as well as larger-scale processes such as circadian rhythm and heartbeat. Abiotic chemical oscillations, discovered originally in inorganic systems, inspired the development of various synthetic oscillators for application as autonomous time-keeping systems in analytical chemistry, materials chemistry and the biomedical field. Expanding their role beyond that of a pacemaker by having synthetic chemical oscillators periodically drive a secondary function would turn them into significantly more powerful tools. However, this is not trivial because the participation of components of the oscillator in the secondary function might jeopardize its time-keeping ability. We now report a small molecule oscillator that can catalyse an independent chemical reaction in situ without impairing its oscillating properties. In a flow system, the concentration of the catalytically active product of the oscillator shows sustained oscillations and the catalysed reaction is accelerated only during concentration peaks. Augmentation of synthetic oscillators with periodic catalytic action allows the construction of complex systems that, in the future, may benefit applications in automated synthesis, systems and polymerization chemistry and periodic drug delivery. </p