One can derive a large class of new $mathcal{N}=1$ SCFTs by turning on $mathcal{N}=1$ preserving deformations for $mathcal{N}=2$ Argyres-Dougals theories. In this work, we use $mathcal{N}=2$ superconformal indices to get indices of $mathcal{N}=1$ SCF
Ts, then use these indices to derive chiral rings of $mathcal{N}=1$ SCFTs. For a large class of $mathcal{N}=2$ theories, we find that the IR theory contains only free chirals if we deform the parent $mathcal{N}=2$ theory using the Coulomb branch operator with smallest scaling dimension. Our results provide interesting lessons on studies of $mathcal{N}=1$ theories, such as $a$-maximization, accidental symmetries, chiral ring, etc.
Objective: A patients activity patterns can be informative about her/his health status. Traditionally, this type of information has been gathered using patient self-report. However, these subjective self-report data can suffer from bias, and the surv
eys can become burdensome to patients over long time periods. Smartphones offer a unique opportunity to address these challenges. The smartphone has built-in sensors that can be programmed to collect data objectively, unobtrusively, and continuously. Due to their widespread adoption, smartphones are also accessible to most of the population. A main challenge in smartphone-based activity recognition is in extracting information optimally from multiple sensors to identify different activities. Materials and Methods: We analyze data collected by two sensors in the phone, the accelerometer and gyroscope, which measure the phones acceleration and angular velocity, respectively. We propose an extension to the movelet method that jointly incorporates both data types. We apply this proposed method to a dataset we collected and compare the joint-sensor results to those from using each sensor separately. Results: The findings show that the joint-sensor method reduces errors of the gyroscope-only method in distinguishing between standing and sitting. Also, the joint-sensor method reduces errors of the accelerometer-only method in classifying vigorous activities, such as walking, ascending stairs, and descending stairs. Conclusion: Across activities, for the given method, combining data from the two sensors performs as well as or better than using data from a single sensor. The method is transparent, personalized to the individual user, and requires less training data than competitor methods.
We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized. We first decompose an HDR image into a normalized Laplacian pyramid, and use two deep neural networks (DNNs) to esti
mate the Laplacian pyramid of the desired tone-mapped image from the normalized representation. We then end-to-end optimize the entire method over a database of HDR images by minimizing the normalized Laplacian pyramid distance (NLPD), a recently proposed perceptual metric. Qualitative and quantitative experiments demonstrate that our method produces images with better visual quality, and runs the fastest among existing local tone mapping algorithms.
Inspired by the excellent control of single photons realized by the atom-photon-chiral couplings, we propose a novel potential photonic-quantum-computation scheme. The single-photon rotating and phase-shift operations, which can be controlled by anot
her single photon, are realized by properly designed atom-photon-chiral couplings. The operations can be integrated into a chiral quantum network to realize photonic quantum computation. Based on the proposal, an algorithm to perform the machine learning tasks is developed, in which the essential nonlinearities come from the appropriately designed operations.
Haar integrals over the unitary group contain subleading terms that are needed for unitarity. We study analogous effects in the time evolution operators of JT gravity and Brownian SYK. In JT gravity with bulk matter we find an explanation for the fir
st subleading terms, and in Brownian SYK we find configurations that can explain the full series. An important role is played by slightly off-shell modes that are exponentially amplified by chaos.
We show theoretically that the characteristic modes of dielectric resonator antennas (DRAs) must be capacitive in the low frequency limit, and show that as a consequence of this constraint and the Poincar{e} Separation Theorem, the modes of any DRA c
onsisting of partial elements of an encompassing super-structure cannot resonate at a frequency that is lower than that of the encompassing structure. Thus, design techniques relying on complex sub-structures to miniaturize the antenna, including topology optimization and meandered windings, cannot apply to DRAs. Due to the capacitive nature of the DRA modes, it is also shown that the Q factor of any DRA sub-structure will be bounded from below by that of the super-structure at frequencies below the first self-resonance of the super-structure. We demonstrate these bounding relations with numerical examples.
To resolve the long-standing discrepancy between the precision measurement of bottom quark forward-backward asymmetry at LEP/SLC and the Standard Model prediction, we propose a novel method to probe the $Zbbar{b}$ coupling by measuring the single-spi
n asymmetry $A_e^b$ of the polarized lepton cross section in neutral current DIS processes with a $b$-tagged jet at HERA and EIC. Depending on the tagging efficiency of the final state $b$-jet, the measurement of $A_e^b$ at HERA can already partially break the degeneracy found in the anomalous $Zbbar{b}$ coupling, as implied by the LEP and SLC precision electroweak data. In the first year run of the EIC, the measurement of $A_e^b$ can already break the degeneracy, due to its much larger luminosity and higher electron beam polarization. With enough integrated luminosity collected at the EIC, it is possible to either verify or exclude the LEP data and resolve the $A_{rm FB}^b$ puzzle. We also discuss the complementary roles between the proposed $A_e^b$ measurement at EIC and the measurement of $gg to Zh$ cross section at the HL-LHC in constraining the anomalous $Zbbar{b}$ coupling.
Path representations are critical in a variety of transportation applications, such as estimating path ranking in path recommendation systems and estimating path travel time in navigation systems. Existing studies often learn task-specific path repre
sentations in a supervised manner, which require a large amount of labeled training data and generalize poorly to other tasks. We propose an unsupervised learning framework Path InfoMax (PIM) to learn generic path representations that work for different downstream tasks. We first propose a curriculum negative sampling method, for each input path, to generate a small amount of negative paths, by following the principles of curriculum learning. Next, emph{PIM} employs mutual information maximization to learn path representations from both a global and a local view. In the global view, PIM distinguishes the representations of the input paths from those of the negative paths. In the local view, emph{PIM} distinguishes the input path representations from the representations of the nodes that appear only in the negative paths. This enables the learned path representations to encode both global and local information at different scales. Extensive experiments on two downstream tasks, ranking score estimation and travel time estimation, using two road network datasets suggest that PIM significantly outperforms other unsupervised methods and is also able to be used as a pre-training method to enhance supervised path representation learning.
In this paper, we study an adaptive planewave method for multiple eigenvalues of second-order elliptic partial equations. Inspired by the technique for the adaptive finite element analysis, we prove that the adaptive planewave method has the linear convergence rate and optimal complexity.
We present the design of the prototype telescope and spectrograph system for the Affordable Multiple Aperture Spectroscopy Explorer (AMASE) project. AMASE is a planned project that will pair 100 identical multi-fiber spectrographs with a large array
of telephoto lenses to achieve a large area integral field spectroscopy survey of the sky at the spatial resolution of half an arcminute and a spectral resolution of R=15,000, covering important emission lines in the optical for studying the ionized gas in the Milky Way and beyond. The project will be enabled by a significant reduction in the cost of each spectrograph unit, which is achieved by reducing the beam width and the use of small-pixel CMOS detectors, 50um-core optical fibers, and commercial photographic lenses in the spectrograph. Although constrained by the challenging high spectral resolution requirement, we realize a 40% reduction in cost per fiber at constant etendue relative to, e.g., DESI. As the reduction of cost is much more significant than the reduction in the amount of light received per fiber, replicating such a system many times is more cost effective than building a single large spectrograph that achieves the same survey speed. We present the design of the prototype telescope and instrument system and the study of its cost effectiveness.
