Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment

Convolution neural network is being used in field of autonomous driving vehicles or driver assistance systems (ADAS), and has achieved great success. Before the convolution neural network, traditional machine learning algorithms helped the driver assistance systems. Currently, there is a great exploration being done in architectures like MobileNet, SqueezeNext & SqueezeNet. It improved the CNN architectures and made it more suitable to implement on real-time embedded systems. This paper proposes an efficient and a compact CNN to ameliorate the performance of existing CNN architectures. The intuition behind this proposed architecture is to supplant convolution layers with a more sophisticated block module and to develop a compact architecture with a competitive accuracy. Further, explores the bottleneck module and squeezenext basic block structure. The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture. The proposed architecture is further trained on the CIFAR-10 dataset from scratch. All the training and testing results are visualized with live loss and accuracy graphs. Focus of this paper is to make an adaptable and a flexible model for efficient CNN performance which can perform better with the minimum tradeoff between model accuracy, size, and speed. Having a model size of 0.595MB along with accuracy of 92.60% and with a satisfactory training and validating speed of 9 seconds, this model can be deployed on real-time autonomous system platform such as Bluebox 2.0 by NXP

Plan de negocio para la implementaci?n de una soluci?n tecnol?gica integradora de servicios para mascotas

Las mascotas siempre han formado parte de la vida de las personas, pero en los ?ltimos a?os han adquirido mayor relevancia dentro de las familias. Esto ha permitido que el mercado de servicios para mascotas tenga un r?pido crecimiento, en donde los proveedores de servicios se esfuerzan por ampliar la variedad y alcance de su oferta; y los due?os de mascotas por satisfacer una oferta m?s variada y accesible de servicios de calidad para sus engre?dos, que no s?lo les permita cubrir sus necesidades b?sicas sino adem?s mejorar su calidad de vida e incluso poder engre?rlo con servicios especializados. En este contexto, el plan de negocio desarrollado plantea una soluci?n que ayuda a conectar a proveedores de servicios (veterinarias y paseadores) con los due?os de mascotas en un entorno digital. Es as? como la empresa ?Entre Patas? propone poner a disposici?n de los proveedores de servicios un canal efectivo que les permita publicar su oferta de servicios, aumentar la exposici?n de su marca y cautivar un p?blico objetivo m?s amplio; y a los due?os de mascotas, tener acceso a una oferta variada y centralizada de servicios de calidad, que puedan ser contratados de manera f?cil, con mayor confianza y seguridad

Dopamine Signaling and Oxytocin Administration in a Rat Model of Empathy

The rat model is commonly used to study prosocial and empathetic behavior. However, the neural underpinnings of such behavior are unknown. We investigated the potential roles of two neurotransmitters, dopamine (DA) and oxytocin (OT), in prosocial behavior of rats. Our first experiment used a Pavlovian association task with two rats to investigate how DA release was modulated by social context. This experiment used fast-scan cyclic voltammetry (FSCV) to measure subsecond DA release in the nucleus accumbens (NAc). Consistent with previous work, cues that predicted reward were associated with increased DA release, and cues that predicted shock inhibited DA release non-discriminately across trial types. However, during shock trials, DA release was modulated by social context in two ways. First, reductions in DA release during shock trials were weaker in the presence of the conspecific, suggesting a consoling effect which was supported by behavioral indicators. Second, DA release during shock trials increased when shock was administered to the conspecific, suggesting that recording rats used the reactions of the conspecific to verify personal safety. We concluded that DA release is modulated by social context in that rats use social cues to optimize predictions about their own well-being. In our second experiment, we investigated the influence of oxytocin on prosocial behavior. Oxytocin was administered intranasally prior to a distress task in which a lever press resulted in reward delivery and one of three additional outcomes: no shock (‘reward-only’), shock to engaged rat (‘shock-self’), or shock to the conspecific (‘shock-other’). Results demonstrated that oxytocin did not significantly increase prosocial behaviors

Muon (g-2) Technical Design Report

The Muon (g-2) Experiment, E989 at Fermilab, will measure the muon anomalous magnetic moment a factor-of-four more precisely than was done in E821 at the Brookhaven National Laboratory AGS. The E821 result appears to be greater than the Standard-Model prediction by more than three standard deviations. When combined with expected improvement in the Standard-Model hadronic contributions, E989 should be able to determine definitively whether or not the E821 result is evidence for physics beyond the Standard Model. After a review of the physics motivation and the basic technique, which will use the muon storage ring built at BNL and now relocated to Fermilab, the design of the new experiment is presented. This document was created in partial fulfillment of the requirements necessary to obtain DOE CD-2/3 approval

Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm

We present a new measurement of the positive muon magnetic anomaly, a_{μ}≡(g_{μ}-2)/2, from the Fermilab Muon g-2 Experiment using data collected in 2019 and 2020. We have analyzed more than 4 times the number of positrons from muon decay than in our previous result from 2018 data. The systematic error is reduced by more than a factor of 2 due to better running conditions, a more stable beam, and improved knowledge of the magnetic field weighted by the muon distribution, ω[over ˜]_{p}^{'}, and of the anomalous precession frequency corrected for beam dynamics effects, ω_{a}. From the ratio ω_{a}/ω[over ˜]_{p}^{'}, together with precisely determined external parameters, we determine a_{μ}=116 592 057(25)×10^{-11} (0.21 ppm). Combining this result with our previous result from the 2018 data, we obtain a_{μ}(FNAL)=116 592 055(24)×10^{-11} (0.20 ppm). The new experimental world average is a_{μ}(exp)=116 592 059(22)×10^{-11} (0.19 ppm), which represents a factor of 2 improvement in precision

Design and construction of the MicroBooNE detector

This paper describes the design and construction of the MicroBooNE liquid argon time projection chamber and associated systems. MicroBooNE is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics. In this document details of design specifications, assembly procedures, and acceptance tests are reported

Measurement of the Positive Muon Anomalous Magnetic Moment to 0.46 ppm

We present the first results of the Fermilab Muon g-2 Experiment for the positive muon magnetic anomaly $a_\mu \equiv (g_\mu-2)/2$. The anomaly is determined from the precision measurements of two angular frequencies. Intensity variation of high-energy positrons from muon decays directly encodes the difference frequency $\omega_a$ between the spin-precession and cyclotron frequencies for polarized muons in a magnetic storage ring. The storage ring magnetic field is measured using nuclear magnetic resonance probes calibrated in terms of the equivalent proton spin precession frequency ${\tilde{\omega}'^{}_p}$ in a spherical water sample at 34.7$^{\circ}$C. The ratio $\omega_a / {\tilde{\omega}'^{}_p}$, together with known fundamental constants, determines $a_\mu({\rm FNAL}) = 116\,592\,040(54)\times 10^{-11}$ (0.46\,ppm). The result is 3.3 standard deviations greater than the standard model prediction and is in excellent agreement with the previous Brookhaven National Laboratory (BNL) E821 measurement. After combination with previous measurements of both $\mu^+$ and $\mu^-$, the new experimental average of $a_\mu({\rm Exp}) = 116\,592\,061(41)\times 10^{-11}$ (0.35\,ppm) increases the tension between experiment and theory to 4.2 standard deviationsComment: 10 pages; 4 figure

Deployment of SE-SqueezeNext on NXP BlueBox 2.0 and NXP i.MX RT1060 MCU

Convolution neural system is being utilized in field of self-governing driving vehicles or driver assistance systems (ADAS), and has made extraordinary progress. Before the CNN, conventional AI calculations helped ADAS. Right now, there is an incredible investigation being done in DNNs like MobileNet, SqueezeNext & SqueezeNet. It improved the CNN designs and made it increasingly appropriate to actualize on real-time embedded systems. Due to the model size complexity of many models, they cannot be deployed straight away on real-time systems. The most important requirement will be to have less model size without a tradeoff with accuracy. Squeeze-and-Excitation SqueezeNext which is an efficient DNN with best model accuracy of 92.60% and with least model size of 0.595MB is chosen to be deployed on NXP BlueBox 2.0 and NXP i.MX RT1060. This deployment is very successful because of its less size and better accuracy. The model is trained and validated on CIFAR-10 dataset

Micro-fibre based Porous Composite Propellants with High Regression Rates

Harnessing energy at micro-scale from high energy sources has gained significance in recent times for space propulsion and other applications. Conventional solid rocket propellants have advantages in terms of being efficient, compact and safe to handle, though with much lower regression rates as compared to solid explosives. An approach to high regression rates in composite propellants is demonstrated in the present work by the enhancement of fuel-oxidiser interaction, and by the incorporation of micro-scale porosity into the propellant grain. The porous polystyrene-ammonium perchlorate grain designed in this work, based on electrospun micro-fibres and aqueous impregnation, exhibits burning rates more than 25 times as compared to the non-porous grain. Such high regression rates using insensitive propellant compositions have practical implications in the development of micro-thrusters, and in gas generating devices such as MAV launch systems and turbine starters. Detailed preparatory procedure, characterisation techniques, and flame regression studies are included in this paper