Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201

Bose-Einstein Condensation of Helium and Hydrogen inside Bundles of Carbon Nanotubes

Helium atoms or hydrogen molecules are believed to be strongly bound within the interstitial channels (between three carbon nanotubes) within a bundle of many nanotubes. The effects on adsorption of a nonuniform distribution of tubes are evaluated. The energy of a single particle state is the sum of a discrete transverse energy Et (that depends on the radii of neighboring tubes) and a quasicontinuous energy Ez of relatively free motion parallel to the axis of the tubes. At low temperature, the particles occupy the lowest energy states, the focus of this study. The transverse energy attains a global minimum value (Et=Emin) for radii near Rmin=9.95 Ang. for H2 and 8.48 Ang.for He-4. The density of states N(E) near the lowest energy is found to vary linearly above this threshold value, i.e. N(E) is proportional to (E-Emin). As a result, there occurs a Bose-Einstein condensation of the molecules into the channel with the lowest transverse energy. The transition is characterized approximately as that of a four dimensional gas, neglecting the interactions between the adsorbed particles. The phenomenon is observable, in principle, from a singular heat capacity. The existence of this transition depends on the sample having a relatively broad distribution of radii values that include some near Rmin.Comment: 21 pages, 9 figure

Implementation of 14 bits floating point numbers of calculating units for neural network hardware development

An important aspect of modern automation is machine learning. Specifically, neural networks are used for environment analysis and decision making based on available data. This article covers the most frequently performed operations on floating-point numbers in artificial neural networks. Also, a selection of the optimum value of the bit to 14-bit floating-point numbers for implementation on FPGAs was submitted based on the modern architecture of integrated circuits. The description of the floating-point multiplication (multiplier) algorithm was presented. In addition, features of the addition (adder) and subtraction (subtractor) operations were described in the article. Furthermore, operations for such variety of neural networks as a convolution network - mathematical comparison of a floating point ('less than' and 'greater than or equal') were presented. In conclusion, the comparison with calculating units of Atlera was made

Deep Learning for Single-Molecule Science

Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in Machine Learning (ML), so-called Deep Learning (DL) offers an interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional Machine Learning strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the 'internal workings' of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a Convolutional Neural Network, may be used for base calling in DNA sequencing applications. We compare it with a Support Vector Machine as a more conventional ML method, and and discuss some of the strengths and weaknesses of the approach. In particular, a 'deep' neural network has many features of a 'black box', which has important implications on how we look at and interpret data

Induced polarization of {\Lambda}(1116) in kaon electroproduction

We have measured the induced polarization of the ${\Lambda}(1116)$ in the reaction $ep\rightarrow e'K^+{\Lambda}$, detecting the scattered $e'$ and $K^+$ in the final state along with the proton from the decay $\Lambda\rightarrow p\pi^-$.The present study used the CEBAF Large Acceptance Spectrometer (CLAS), which allowed for a large kinematic acceptance in invariant energy $W$ ($1.6\leq W \leq 2.7$ GeV) and covered the full range of the kaon production angle at an average momentum transfer $Q^2=1.90$ GeV$^2$.In this experiment a 5.50 GeV electron beam was incident upon an unpolarized liquid-hydrogen target. We have mapped out the $W$ and kaon production angle dependencies of the induced polarization and found striking differences from photoproduction data over most of the kinematic range studied. However, we also found that the induced polarization is essentially $Q^2$ independent in our kinematic domain, suggesting that somewhere below the $Q^2$ covered here there must be a strong $Q^2$ dependence. Along with previously published photo- and electroproduction cross sections and polarization observables, these data are needed for the development of models, such as effective field theories, and as input to coupled-channel analyses that can provide evidence of previously unobserved $s$-channel resonances.Comment: 13 figure

Towards a resolution of the proton form factor problem: new electron and positron scattering data

There is a significant discrepancy between the values of the proton electric form factor, $G_E^p$, extracted using unpolarized and polarized electron scattering. Calculations predict that small two-photon exchange (TPE) contributions can significantly affect the extraction of $G_E^p$ from the unpolarized electron-proton cross sections. We determined the TPE contribution by measuring the ratio of positron-proton to electron-proton elastic scattering cross sections using a simultaneous, tertiary electron-positron beam incident on a liquid hydrogen target and detecting the scattered particles in the Jefferson Lab CLAS detector. This novel technique allowed us to cover a wide range in virtual photon polarization ($\varepsilon$) and momentum transfer ($Q^2$) simultaneously, as well as to cancel luminosity-related systematic errors. The cross section ratio increases with decreasing $\varepsilon$ at $Q^2 = 1.45 \text{ GeV}^2$. This measurement is consistent with the size of the form factor discrepancy at $Q^2\approx 1.75$ GeV$^2$ and with hadronic calculations including nucleon and $\Delta$ intermediate states, which have been shown to resolve the discrepancy up to $2-3$ GeV$^2$.Comment: 6 pages, 4 figures, submitted to PR

Precision measurements of g1g_1 of the proton and the deuteron with 6 GeV electrons

The inclusive polarized structure functions of the proton and deuteron, g1p and g1d, were measured with high statistical precision using polarized 6 GeV electrons incident on a polarized ammonia target in Hall B at Jefferson Laboratory. Electrons scattered at lab angles between 18 and 45 degrees were detected using the CEBAF Large Acceptance Spectrometer (CLAS). For the usual DIS kinematics, Q^2>1 GeV^2 and the final-state invariant mass W>2 GeV, the ratio of polarized to unpolarized structure functions g1/F1 is found to be nearly independent of Q^2 at fixed x. Significant resonant structure is apparent at values of W up to 2.3 GeV. In the framework of perturbative QCD, the high-W results can be used to better constrain the polarization of quarks and gluons in the nucleon, as well as high-twist contributions

Isotopic and spin selectivity of H_2 adsorbed in bundles of carbon nanotubes

Due to its large surface area and strongly attractive potential, a bundle of carbon nanotubes is an ideal substrate material for gas storage. In addition, adsorption in nanotubes can be exploited in order to separate the components of a mixture. In this paper, we investigate the preferential adsorption of D_2 versus H_2(isotope selectivity) and of ortho versus para(spin selectivity) molecules confined in the one-dimensional grooves and interstitial channels of carbon nanotube bundles. We perform selectivity calculations in the low coverage regime, neglecting interactions between adsorbate molecules. We find substantial spin selectivity for a range of temperatures up to 100 K, and even greater isotope selectivity for an extended range of temperatures,up to 300 K. This isotope selectivity is consistent with recent experimental data, which exhibit a large difference between the isosteric heats of D_2 and H_2 adsorbed in these bundles.Comment: Paper submitted to Phys.Rev. B; 17 pages, 2 tables, 6 figure

HAPI: Hardware-Aware Progressive Inference

Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks. Despite their popularity, CNN inference still comes at a high computational cost. A growing body of work aims to alleviate this by exploiting the difference in the classification difficulty among samples and early-exiting at different stages of the network. Nevertheless, existing studies on early exiting have primarily focused on the training scheme, without considering the use-case requirements or the deployment platform. This work presents HAPI, a novel methodology for generating high-performance early-exit networks by co-optimising the placement of intermediate exits together with the early-exit strategy at inference time. Furthermore, we propose an efficient design space exploration algorithm which enables the faster traversal of a large number of alternative architectures and generates the highest-performing design, tailored to the use-case requirements and target hardware. Quantitative evaluation shows that our system consistently outperforms alternative search mechanisms and state-of-the-art early-exit schemes across various latency budgets. Moreover, it pushes further the performance of highly optimised hand-crafted early-exit CNNs, delivering up to 5.11x speedup over lightweight models on imposed latency-driven SLAs for embedded devices.Comment: Accepted at the 39th International Conference on Computer-Aided Design (ICCAD), 202