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The present investigation reports the design and development of an advanced material with a high figure of merit (FoM) for beampipe applications in particle accelerators by bringing synergy between computational and experimental approaches. Machine learning algorithms have been used to predict the phase(s), low density, and high radiation length of the designed Al-Ti-V alloys. Al-Ti-V alloys with various compositions for single-phase and dual-phase mixtures, liquidus temperature, and density values are obtained using the Latin hypercube sampling method in TC Python Thermo-Calc software. The obtained dataset is utilized to train the machine-learning algorithms. Classification algorithms such as XGBoost and regression models such as Linear Regression and Random Forest regressor have been used to compute the number of phases, radiation length, and density respectively. The XGBoost algorithms show an accuracy of $98\%$, the Linear regression model shows an accuracy of $94\%$, and the Random Forest regressor model is accurate up to $99\%$. The developed Al-Ti-V alloys exhibit high radiation length as well as a good combination of high elastic modulus and toughness due to the synergistic effect of the presence of hard $Al_3Ti$ phase along with a minor volume fraction of FCC $(Al)_{ss}$ solid solution phase mixture. The comparison of our alloys, alloy-1 ($Al_{75.2}Ti_{22.8}V_{2}$) and alloy-2 ($Al_{89}Ti_{10}V_{1}$) shows an increase in the radiation length by seven-times and a decrease in the density by two to three times as compared to stainless steel 304, the preferred material for constructing beampipes in low-energy particle accelerators. Further, we experimentally verify the elastic modulus of the alloy-1 and compute the FoM equal to 0.416, which is better than other existing materials for beampipes in low-energy experiments., Comment: 9 pages and 4 captioned figures. Submitted for publication

Relativistic heavy-ion collisions produce quark-gluon plasma (QGP), which is locally thermalized. QGP with higher thermal conductivity advances towards global thermalization. Being made of electrically charged particles (quarks), QGP exhibits interesting thermoelectric phenomena during its evolution, resulting in the induction of an electric field in the medium. For the first time, we estimate the induced electric field in the QGP due to thermoelectric effect. This can be seen even in the QGP produced in the head-on collisions. The Seebeck coefficient is essential in determining the induced field. However, spectator current can produce a magnetic field in peripheral heavy-ion collisions. This breaks the isotropy of the thermoelectric coefficient matrix and introduces magneto-Seebeck and Nernst coefficients that contribute to the induced electric field in peripheral collisions. We have taken care of the temperature evolution of QGP with different hydrodynamic cooling rates to calculate the transport coefficients. The induced electric field is estimated with the cooling rate obtained from Gubser hydrodynamic flow. We estimated the space-time profile of the induced field and found that it is zero at the center and increases as we go away from the center. At the early time of evolution, the electric field can reach a maximum value of $eE \approx 1~m_\pi^2$, and the strength decreases in time. Moreover, we estimated the transport coefficients in the presence of the external time-varying magnetic field. The effect of the intensity and decay parameter of the magnetic field on the induced electric field is also explored., Comment: 38 pages and 9 figures. Submitted for publication

The effect of the temperature evolution of QGP on its thermal conductivity and elliptic flow is investigated here in the presence of a time-varying magnetic field. Thermal conductivity plays a vital role in the cooling rate of the medium or its temperature evolution. The magnetic field produced during the early stages of (non-central) heavy-ion collisions decays with time, where electrical conductivity plays a significant role. As the medium expands, the electrical and thermal properties change, reflecting the effect in various observables. In this study, we have calculated the thermal conductivity of the QGP medium, incorporating the effects of temperature and magnetic field evolution. We discovered that conductivity significantly depends on the cooling rate, and its value increases due to temperature evolution. Furthermore, the influence of these evolutions on the elliptic flow coefficient is measured, and elliptic flow decreases due to the evolution. We also extend our study for the case of Gubser flow, where, along with the longitudinal Bjorken expansion, the radially transverse expansion is also present., Comment: Same as the published version in Phys. Rev D

We have estimated the electrical and thermal conductivity of a hadron resonance gas for a time-varying magnetic field, which is also compared with constant and zero magnetic field cases. Considering the exponential decay of electromagnetic fields with time, a kinetic theory framework can provide the microscopic expression of electrical and thermal conductivity in terms of relaxation and decay times. In the absence of the magnetic field, only a single time scale appears, and in the finite magnetic field case, their expressions carry two-time scales, relaxation time and cyclotron time period. Estimating the conductivities for HRG matter in three cases -- zero, constant, and time-varying magnetic fields, we have studied the validity of the Wiedemann-Franz law. We noticed that at a high-temperature domain, the ratio saturates at a particular value, which may be considered as Lorenz number of the hadron resonance gas. With respect to the saturation values, the deviation of the Wiedemann-Franz law has been quantified at the low-temperature domain. For the first time, the present work sketches this quantitative deviation of the Wiedemann-Franz law for hadron resonance gas at a constant and a time-varying magnetic field., Comment: Same as the published version in Phys. Rev. D

The state of the art in video understanding suffers from two problems: (1) The major part of reasoning is performed locally in the video, therefore, it misses important relationships within actions that span several seconds. (2) While there are local methods with fast per-frame processing, the processing of the whole video is not efficient and hampers fast video retrieval or online classification of long-term activities. In this paper, we introduce a network architecture that takes long-term content into account and enables fast per-video processing at the same time. The architecture is based on merging long-term content already in the network rather than in a post-hoc fusion. Together with a sampling strategy, which exploits that neighboring frames are largely redundant, this yields high-quality action classification and video captioning at up to 230 videos per second, where each video can consist of a few hundred frames. The approach achieves competitive performance across all datasets while being 10x to 80x faster than state-of-the-art methods., Comment: Submitted to ECCV 2018. 17 pages, 7 figures, Supplementary Material, https://github.com/mzolfaghari/ECO-efficient-video-understanding